Californias anti terrorism laws after september 911 Essay

info tech essays

information tech expositions Topology implies how a system is set up. Token Ring topology is one of them. It implies that few PCs are associated in a ring. At the point when one PC needs to send information to another, it needs to get a free token which gives the authorization to send information. As before long as one specific machine gets the token, it has the command over the entire system which implies no other PC can send information. At that point it puts its goal address and the information on the token, and sends the token to the machine thatâ ¡s close to it. In the event that the following machine isn't the goal, it will pass the information endlessly until it gets to the location. At the point when the tended to station gets the information, it can not keep the message and discharge the token. It duplicates the information, at that point it sends the message back to the initiator with the affirmation to state that it has gotten the information. After the initiator gets the message and peruses the affirmation, the free token can be discharged onto the ring. There are likenesses and contrasts between Token Ring topology and Bus topology. In a Bus topology (Ethernet) all gadgets are associated with a focal link, called the transport or backbone.(ce.org/networkguide/netstructure/net4.asp) Ring topology is like Bus topology in that the two systems have numerous entrance so that the messages will got to each station on the system. Be that as it may, they are extraordinary. Initial, a Token Ring system has a ring which associates the system rather than a link. Second, for Ethernet, when PCs get messages which don't have a place with them, they won't keep them and pass them on. Token Ring topology is unique. Each machine on the system will continue giving the information until it gets to its goal. Third, a PC on a Bus organize needs to trust that the system will be clear so as to send information bundles. A Ring system has a token, so when a machine ca ... <!

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Game Obermeyer Ltd. To: Wally Obermeyer From: 341 Consulting Group Date: Re: Production Process Problem Sport Obermeyer Ltd has many issues right now that come from an absence of smoothing out of its tasks. One of the issues that the skiwear producer right now faces is request vulnerability and how to gauge it considering the divergent conjectures the purchasing panel individuals have thought of. Likewise, the long lead-times for the creation procedure makes determining significantly increasingly troublesome. The second issue for Sport Obermeyer is deciding how to distribute creation between the manufacturing plants in Hong Kong and China. End and Recommendation Inaccurate anticipating is a significant issue confronting Sport Obermeyer, which has been tended to in Appendices An and B. Informative supplement A shows what amount ought to be delivered in Hong Kong given the suspicion that there is no restriction to the limit; nonetheless, we have been approached to remark just on the underlying request amount, and not the reorder amount. With regards to this requirement, and utilizing the estimates given by all the advisory group individuals, we accept that Appendix B is an exact portrayal of the measure of each style that ought to be created in Hong Kong. It ought to be noticed this is just a transient answer for the issue and the model itself must be taken a gander at. For increasingly reliable, precise estimates, a weighted normal strategy ought to be utilized to give those board individuals who have been generally exact in the past more significance, rather than utilizing a basic moving normal. As long lead times add to the trouble in estimating request, Sport Obermeyer should endeavor to lessen lead times for its creation. One of the root issues causing the length of lead time is the quantity of SKUs, just as the assortment of parts utilized in their creation. Streamlining the product offering would include halting creation of those items with the least interest, diminishing providers just as modifying plans so they share however many of indistinguishable segments from could be expected under the circumstances. Utilizing the interest figure for the 10 women’s parkas in Appendix B, there are three items, which have estimated requests of under 357, after which the interest shoots up. By expelling the bottommost items, Stephanie, Teri, Isis would be erased, which would take into consideration a shorter lead time. Game Obermeyer ought to likewise present an incorporated electronic framework associating all the distinctive gracefully chain joins. This would help diminish the time spent handling requests and utilize the crude materials. A blend of activities referenced above can help roll out a noteworthy improvement in the anticipating procedure and will permit Sport Obermeyer to utilize its current limit. The organization intends to source items generally half from China and the other half from Hong Kong industrial facilities this year is feasible. In any case, our suggestion is to allocate items to Hong Kong and China processing plants dependent on quality and structure. As per Appendix C, the cost contrast when looking at 19 parkas created at the two production lines isn't noteworthy; yet the Hong Kong industrial facility is substantially more effective. The items in Hong Kong are of a higher caliber, require lower fix rates, and are delivered twice as quick as those created in the Chinese industrial facility. Another favorable position to the industrial facility in Hong Kong is the base request amount of 600 units, which furnishes the administration with greater adaptability as far as deciding their underlying creation demands. Lower quality items ought to be delivered in the Chinese manufacturing plant to exploit their low work costs. Assessment Criteria The principle objective of the suggestions is to smooth out Sport Obermeyer’s business forms. One of the most significant standards is the speed of the arranging and creation cycle. Any elective that can diminish this time, from its present edge of roughly two years, ought to be firmly thought of. With respect to piece of the overall industry, Columbia Sportswear is picking up piece of the pie by giving lower-evaluated, higher-volume-per-style items. Game Obermeyer needs to attempt to reduce expenses, and smooth out its number of SKUs to accomplish showcase predominance. It positions itself as a center to top of the line maker, and the quality level should keep on being thought about when hoping to reduce expenses. Options concerning the wrong gauges, Obermeyer could investigate the interest for its items dependent on a propelled appearing preceding the one in Vegas and contrast it and real buys. While this option can conceivably be executed, its adequacy and dependability would be obscure. Rearranging the product offerings would have a more straightforward and quick effect on the lead times. Another conceivable option is begin delivering the items with the most unsurprising interest ahead of time. Be that as it may, before executing this change, Sport Obermeyer should concentrate on making generally exact interest figures dependent on the recently suggested weighted normal technique. Usage. Our usage procedure will start quickly with Wendy Hemphill exploring the particulars for a coordinated automated framework that coordinate the flexibly chain structure of the organization. In view of the mind boggling nature of such an undertaking, Sport Obermeyer would need to put resources into such a venture in November 1992, to start use in February of the time of fulfillment. In January 1993, the Buying Committee should execute the weighted-normal determining strategy to break down item request. Since this is certainly not an enormous change structure the basic moving normal, it ought to be utilized to discover the determined interest for 1993. The next month, the SKUs ought to be investigated dependent on the figure made to consider which product offerings to drop. This will be a progressive procedure, starting with the cancellation of a base number of items. In May 1993, the Buying Committee ought to apportion diverse item styles between Hong Kong and China dependent on quality. This can be found in Appendix D.

Theory Term Paper Example | Topics and Well Written Essays - 1000 words

Hypothesis - Term Paper Example In considering each guarantee it is critical to investigate what majority rules system really implies. This paper investigates vote based system and its cases from both Marxism and Leninism points of view. Down to earth utilization of majority rules system will frame the middle stage in expounding the importance of vote based system and whether it is an end in itself or an unfortunate obligation. Majority rule government exacting significance is People’s Rule implying that the masses should bear a nation’s power. This definition is an incredible inverse of absolutism and government which mean types of tyranny propagated by one individual or various individuals separately. Accordingly, majority rules system is where individuals practice political force through picking pioneers or taking an interest in administration. The last proclamation has little quality yet its case is unquestionable as an inalienable characteristic of a fair foundation. These definitions raise vario us contentions one being that some administration frameworks are more vote based than others (Lenin 177-215). A flat out majority rules system will accordingly request that people’s wishes are fused in the administration framework. Wishes of the individuals even in the cutting edge law based foundations are essentially accomplished through decisions. For this situation people’s agents are chosen for different degrees of government where they are required to speak to the perspectives and wishes of their electorates. Utilitarian scholars believe that majority rules system offers a channel for people’s will to be finished. Decisions for the situation are an operator of people’s will since voters pick the individuals who are nearest to their standards. In spite of the fact that this is certainly not a solid avocation with regards to the foundation of majority rules system, it goes further to expand the portrayal of the desire of the individuals in administrat ion. This utilitarian way to deal with majority rules system and general will of the individuals is anyway not exact. It is fundamental actuality that a general public is included individuals who hold various perspectives on issues. Subsequently, it is difficult for all to concur on one thing particularly on issues relating to open arrangement. This contention of races as an image of popular government doesn't really bear people’s will (Loo and Peter 45-80). Majoritarianism is utilized broadly in decisions banter since the individuals who end up in government are the most mainstream competitors and this converts into numbness of the minority’s interests. This proviso in popular governments has been overwhelmed by setting up frameworks that encourage relative portrayal despite the fact that their reasonability has demonstrated sketchy as groups are compelled to unite to gain significant larger part. England is a model where oppression of the minority is experienced. Thi s has been the pattern after the second World War where just two gatherings have ruled the political field. In winning races these gatherings need to strive to charm voters who are hesitant. In this manner the gatherings end up disregarding their ideologically based supporters so as to speak to the couple of unsure ones. This situation is more clear in the wake of investigating majority rule government as an unfortunate obligation. Majority rule government portrays a circumstance where individuals are deciding themselves implying that a state is unequipped for constraining an individual resident (Lenin 277-313). Opportunity is thusly a column expected to be maintained by a law based framework more than in others. The level of uniformity among individuals likewise frames the

Learn About Guardianship or Conservatorship

Learn About Guardianship or Conservatorship Guardianship and conservatorship are two legal terms that many people confuse.Whereas some take them to mean the same thing, others don’t even know what they mean. Although you may instinctively know what guardianship is all about, based on the word “guardian,” do you know what conservatorship is?Below you will read about these two legal concepts and understand when each one applies. You will also learn how a guardian and conservator are appointed as well as their responsibilities.Since these two are not the only solutions available for the situations they seek to handle, we will also tell you about alternatives that can be implemented. These help prevent the tedious work and long process of appointing especially a conservator.But first, let’s distinguish between these two terms.Who is a guardian?A guardian is a person appointed by a court to take care of the health and general well-being of another person. The person being taken care of is called a “ward.” Legally, he is referred to as a “ward of the court.”In most cases, a ward is a minor who cannot make some decisions for himself because he has not attained the legal age for making such decisions. The ward may however be an adult.In this case, he will be having a guardian appointed over him since he is incapable of making appropriate decisions. Being incapable in this case means mental incapacity.This might be the result of any of various causes like accidents or old age.Accidents which result in injuries to the brain or spine often have an impact on mental capabilities. One may end up with some memory loss or suffer an inability to properly coordinate body movement. As a result, taking care of his health, taking medication or even deciding on things like where to live, can become difficult.This is where a guardian comes in. A petition will be filed in a court and the court, upon being convinced of the situation, goes ahead and appoints a guardian. Guardians will typically only handle health matters and the day to day life situations of the ward.Who is a conservator?A conservator on the other hand is mainly tasked with taking care of the financial assets of the person he is appointed over. The person getting conservator services is called a “conservatee.”Watch the below video for some more information on conservatorship. Although orphaned children whose parents left them some wealth will benefit from conservatorships, most beneficiaries are the aged people. These are those who have been physically weakened by diseases like Alzheimer’s and thus need someone to help them with their financial duties.Such people may be having difficulty remembering to pay their bills on time, pay relevant fees like insurance or maybe they are just falling prey to fraudsters. Whichever the case, the court can rule that they need a conservator to assist them handle their finances.The major difference between a guardian and a conservator is that the guardian handles health and general li fe issues while the conservator handles finances.APPOINTING A GUARDIAN OR CONSERVATORThe process of appointing either a guardian or conservator is a legal one. There must be an application for the same in a court. A petition will be filed describing the situation and showing the need for either of these two people.The petition can be filed by any person of interest including family members, friends, relatives or even professional advisers. The intention here is to bring to the court’s attention the fact that someone is not able to care for himself thus the need for the court to intervene.The appointing process normally starts with the court appointing either a person or committee to investigate the matter. This person or committee will be tasked with carrying out an impartial investigation into the health status of the person for whom the service is being sought.The investigation process may require interviews with the allegedly incapacitated person, family members and relatives. Other people may also be interviewed to find out the true health standing. These may include friends and neighbors.Several meetings are bound to take place between the incapacitated person and the investigating team. As necessary, medical tests will be run to determine the extent of incapacity suffered by the person.Since the person himself will be involved in the process as he is interviewed, he must be able to understand what he is being asked or told. In the event that it is determined that he cannot, then an attorney will represent him.The investigating team will then prepare and present a report of their findings to the court for the judge to review. Depending on how the team operated, every member of the team may have to present his own report of what he observed and concluded.During the hearing of the petition, the judge may ask the team some questions so as to establish the condition of the person. Just like a normal court hearing, the court will listen to arguments and coun terarguments concerning the matter.The interested parties will normally be present. The judge will proceed to make a ruling on whether he deems the allegedly incapacitated person to be truly incapacitated. Mental incapacity normally comes in different degrees as medical professionals may diagnose it.Generally speaking tough, the court will either rule that the person is either partially or completely incapacitated. This ruling is what will determine whether a guardian and conservator will be appointed and for what they will be responsible.RESPONSIBILITIES OF A GUARDIANThe guardian is primarily responsible for making decisions touching on the health of the incapacitated person. This can happen in many ways. Some are listed below:Make appropriate plans for relevant services â€" these may cover the provision of food, home cleaning services, laundry services etc. The guardian must ensure that these services are not just planned for but are actually provided.Make the right decisions on b ehalf of the ward â€" decisions made must be able to keep the ward safe and benefit him accordingly. This is achieved by understanding the needs and, in some cases, the preferences, of the ward.Ensuring that the necessary life activities such as recreation and education are not excluded from the ward’s life.Change the ward’s residence â€" this can happen if it is determined to benefit the ward. This and other major decisions may require the court’s approval before being implemented.Organize for medical procedures where necessary.Keep the court updated with reports of the guardianship.The court may put in place some restrictions or give complete leeway to the guardian in regards to the amount of authority the guardian may exercise. This is especially important in cases where there may be emergency medical services required.RESPONSIBILITIES OF A CONSERVATORThe conservator is mainly responsible for matters to do with assets and other financial materials. Depending on the exact ar rangement and the ruling given by the court, conservators generally perform the below duties:Manage the finances of the conservateeSecure and protect the known assets of the conservatee â€" this could mean locating the assets and controlling their usage.Make wise investments on behalf of the conservatee.Collect income, debt and claims for the conservateeSettle bills and make necessary payments where the conservatee is bound to do so.Sell or transfer the conservatee’s property either for profit or to avoid destruction.Vote on behalf of the conservatee in meetings.The lists of responsibilities presented here are not exhaustive. Neither are they applicable across all the states. The laws of specific states may give more leeway or restrictions on the part of the guardian or conservator.All in all, the court will be working to ensure the incapacitated person is taken care of.Compensation of the guardian or conservatorConsidering the amount of work done by the guardian or conservator, a re they compensated and by who?There are basically two ways through which conservators are compensated.From the conservatee’s estate â€" this is the usual source of compensation and always the first option to be considered. This is because it is considered that the conservatee is receiving a service for which he should pay. The amount paid as compensation can be very high, especially if the person appointed is a professional conservator. This adds considerably to the cost of the whole process before the actual appointment.By the state â€" this option is usually reserved for those conservatees who do not have the funds to pay for the service. In this case, the court will appoint a public conservator or guardian. His compensation will therefore be done by the state.ALTERNATIVES TO GUARDIANSHIP AND CONSERVATORSHIPThe process of appointing a conservator is normally long and costly. Many people are involved and fees, especially for attorneys, can be high.Some alternatives exist and the y are worth considering because of the benefits of saving time and money. One thing to note however, is that these alternatives must be implemented early. They have to be done before the person involved becomes incapacitated.The reason for this is obvious and simple. If these options are attempted at a time when the mental capacity of the person is already in question, then the whole process will be questionable. It may be necessary to investigate the intentions of those making the petition or those suggested as the conservators.Such cases have been widely witnessed when a wealthy person is said to be incapable of making decisions for himself. Suddenly, a family member makes arrangements for the wealth to be transferred to him so that he takes care of the suffering person.To avoid this, the conservatee must make the decision as to which options he wants to go with. Below are some of the options available. You should check with the specific laws of your state to ensure you are operat ing within the law.1. Power of attorneyThis is one of the most common alternatives that people implement.A power of attorney is a legal document which gives a person (referred to as agent or attorney-in-fact) the power to act on behalf of someone else (the principal).These documents do not necessarily come into force when someone is declared incapable of running his own life. Rather, they are in effect from the time they are signed.Watch the below video to understand more about a power of attorney and what dangers could result from it. They can be general in nature, granting authority to the agent to engage in a wide range of actions on behalf of the principal. When the power of attorney is termed as limited, it means that the principal has granted general authority but with certain restrictions.The power of attorney could also be specific. In this case, the authorization given is tied to a particular situation.These documents can also be distinguished as being either regular or dur able powers of attorney.Regular power of attorney â€" this is a power of attorney which is in effect as long as the principal is able to make his own decisions. The principal remains to be the overall authority and can make contrary decisions.Durable power of attorney â€" this power of attorney is “durable” in the sense that it remains in effect even after the principal is not able to make decisions by himself. In other words, it serves its purpose when the principal becomes incapacitated.When considering an alternative to conservatorship, a durable power of attorney is what you should go for. This covers you even when you cannot communicate and prevents the need for costly court processes.2. TrustsTrusts are agreements entered into between a trustor and a trustee, for the benefit of a third party called a beneficiary.In most cases, trusts are built around assets and the title handed over to the trustee by the trustor. The specific agreement depends on the parties involved. Howe ver, usually, the trustee will hold the title and hand it over to the beneficiary at a later date.This can be when the trustor is no longer alive or just when he is no longer able to make decisions for himself. In this case, it is not just physical assets that are involved but also money. This could be in the form of regular earnings from different sources of income held by the trustor.Trusts can be used as great alternatives to conservatorship. They will also save you the hassle and costs involved in taking the probate route. They mainly fall under the below categories:Living trust â€" this is a trust in which the assets are specified as being for the use of the trustor in the course of his life. It is upon death that the assets are transferred to his beneficiaries.Testamentary trust â€" this trust is usually part of a will. The testamentary trust basically contains instructions on how to distribute the assets of the trustor to his beneficiaries.Revocable trust â€" this is a trust which can be altered by the trustor in the course of his life. If he determines so, the trustor can also terminate the trust altogether.Irrevocable trust â€" this is a trust that cannot be altered at all. Once it is made, it remains as it is and will be implemented accordingly once the trustor dies.3. Representative payeeA representative payee is someone or an organization which has been appointed to receive social security benefits on behalf of someone else. The person on whose behalf the benefits are being paid, is one deemed unable to either manage or direct the management of his benefits.The representative payee will usually be restricted only to the social security benefits and does not have any legal authority over the beneficiary’s other forms of income. It should also be noted that representative payees are not allowed to charge fees for being a payee.4. Healthcare proxyThis is a document which gives a medical patient the ability to appoint an agent who will be legally all owed to make decisions on his behalf. These decisions are purely for the healthcare of the patient.Once effected, a healthcare proxy does not limit the patient, called a primary individual, to make decisions for himself. It is only when the patient has been confirmed to be incapable of making such decisions by himself that the agent takes over decision making.5. Informal arrangementsThere is also an option of setting up informal agreements. In fact, many of these exist. In most cases, it is the potential disagreements which may arise that cause people to prefer implementing legal options.Informal agreements are easy to reach and can also be legally binding. You can involve an attorney who will draft a simple document detailing how the person involved will be cared for.The document can include as much details as possible and when it’s finished, the attorney should maintain a copy of it.Situations where informal agreements are chosen normally have the care of the incapacitated perso n handled by family members. In their absence, close relatives or trusted friends can also be in charge.CONCLUSIONThe process of appointing a guardian or conservator can be draining. It is best to plan ahead and choose an alternative to this process. One of the options described above can certainly save you time and money. Since old age is coming and you won’t escape the effects, why not plan ahead?

Foreign Exchange Practices and Hedging Tools in the Software Industry - Free Essay Example

â€Å"FOREIGN EXCHANGE PRACTICES AND HEDGING TOOLS USED BY THE SOFTWARE INDUSTRY† This report is submitted as a part of the requirements of the MBA Program of Bangalore University. This research has been undertaken by RAJEEV SAMUEL JAYAMANOHAR Reg. No: 04VWCM6068 With the guidance and support of Prof. RATHNAKAR ACHARYA Faculty, ABA [pic] ALLIANCE BUSINESS ACADEMY BANGALORE – 560 076 Batch: 2004-2006 ACKNOWLEDGEMENT I would like to express my heartfelt gratitude to our President Prof. Sudhir Angur and Director Dr. B. V. Krishnamurthy for having granted me an opportunity to conduct this study. My sincere thanks to Prof. Ratnakar Acharya for guiding me through the project. My special thanks to the respondents of the study who are been instrumental in the successful completion of this work are Finance Managers, Forex Managers of various software companies at Bangalore City, whom I approached for information and were kind enough to spend time with me irrespective of their busy schedule. Finally I thank my friends who have been with me at every stage of the project in the form of constant support and encouragement without whom, completing this research work would have remained an unfulfilled dream. RAJEEV SAMUEL CERTIFICATE BY THE GUIDE This is to certify that the dessertation entitled â€Å"Foreign Exchange Practices And Hedging Tools Used By Software Industry† by Rajeev Samuel bearing Reg. No 04VWCM6068 has been prepared under my guidance and supervision. The work has been satisfactory and is recommended for consideration towards partial fulfillment of requirement for the M. B. A degree of Bangalore University. Date: Place: Bangalore Signature (Prof. Ratnakar Acharya) DECLARATION This is to state that the dessertation titled â€Å"Foreign Exchange Practices And Hedging Tools By Software Industry† is based on the original work carried out by me under the supervision of Prof. Ratnakar Acharya towards the partial fulfillment of req uirements for the degree of Master of Business Administration of Bangalore University during the IV semester. This has not been submitted in part or full towards any other degree or diploma. Register No: 04VWCM6068 Date: Place: Bangalore. Signature (Rajeev Samuel) EXECUTIVE SUMMARY The subject title for the study was â€Å" Foreign Exchange Practices and Hedging Tools used by Software Industry† Like any other business organisation, software compnies too face risks inherent to the company and the industry in which they exist. In what way companies face foreign exchange risks? What can companies do when they face Forex risk? Which are the best hedging tools to adopt? On the same lines we can come up with so many other questions for which managers have to take a decision. Since , no formal study has been done to understand all this, the author took the opportunity to conduct this study. The methodology adopted for the study is based on the simplest research methods  œ descriptive research, which is a fact –finding investigation with adequate interpretation. It is focused and aimed on Foreign Exchange practices prevailing in the Indian software industry. The research methodology used for the study was the survey method. Around 10 companies from Bangalore were taken for the survey. An interview with the finance managers of these companies was taken to collect the primary data. Foreign exchange transactions include a substantial amount of risk due to fluctuations in the exchange rates. Hence, corporates are continuously striving to minimize this risk exposure by the use of various hedging tools like Forward contracts, Options, Swaps, Netting etc. Foreign exchange risk may also be linked to other types of market risks, such as interest rate risk. Interest rates and exchange rates often move simultaneously. So, a bank’s interest rate position indirectly affects overall foreign exchange exposure. It is essential for the corporates to study the Forex market, its market potential and the benefit one gets in dealing in Forex trading. It is true that Forex trading is expensive. It is basically because of few traders who trade in large volumes, which affects the small traders. That’s why the market has become so big. Nowadays, corporates take the help of consultants like Thomas Cook, for their Forex trading. Today,the forex market has grown to more than $ 1. 5 trillion per day. 51% is in spot forex transactions, followed by 32% in currency swap transactions, and forward outright forex transactions represents another 5% of this daily turnover. The general public market is an enormous potential of customers who want to speculate in the largest and most efficient market in the world. Three years ago it totaled 15 billion USD daily turn over. The surveyed software companies show that 25% of them perceive that they do not face foreign exchange risk. But, in actual practice, they do face cash balance risk by keeping their balances in their foreign accounts. It has also been found that 45% of the software companies face Exchange rate risk. Companies have rated the Transaction and Operating Exposures as high. Thus, this all shows that software companies do face risk. But, most of the companies don’t adopt any hedging strategy. They maintain foreign accounts and only 35% of the companies use Forward contracts of 3-6 months. Thus, corporates play a major role in the Forex market. They should know the various risks to which they are directly or indirectly exposed. They should also analyse the best strategy which suits best their organisation. Since, the organization is not found to be using any trading software’s which the author has recommended. The most reliable trading system was found to be Live Voice Dealing(LVD). Thus, the companies can be benifited by using these softwares. It will take time for the companies to change their trading system. Companies have become de pendent on their Banks and Consultants for their forex trading. So, any organisation whether large or small needs some sort of change in their trading system. Some of the companies are facing some risk but since they are not aware of these practices are not using them. The corporates should identify the exposure, quantify the exposure, and then monitor the performance. Some of the corporates do have their own philosophy for exposure management and they follow it. Thus, the author has suggested improving the Forex risk management into their organisation. TABLE OF CONTENTS | | | | |CHAPTER |PARTICULARS |PG. NO | | | | | |I |INTRODUCTION | | |1. |History of Forex |1 | |1. 2 |Definition and types of Foreign Exchange |3 | |1. 3 |Why do we need Foreign Exchange? |4 | |1. 4 |Forex characteristics |4 | |1. 5 |Foreign Exchange Market and its types |5 | |1. |Role of the exchange rate |7 | |1. 7 |Forex and Stock Market |8 | |1. 8 |Foreign Exchange Reserves in India |9 | |1. 9 |What is Forei gn Exchange Risk |10 | |1. 10 |Sources of Foreign Exchange Risks |10 | |1. 1 |Types of Foreign Exchange Transaction Risks |11 | |1. 12 |Measures Of Foreign Exchange Risk |14 | |1. 13 |Why should Firms manage Foreign exchange risk |15 | |1. 14 |What are Foreign Exchange Exposures |17 | |1. 15 |Types of Foreign Exchange Exposures |17 | |1. 6 |Methods Of Hedging |19 | | | | | |II |INDUSTRY PROFILE | | |2. 1 |Introduction to Indian software industry |21 | |2. 2 |Indian software industry-Advantages |21 | |2. |Classification of Software companies |22 | |2. 4 |Value chain of software industry |23 | |2. 5 |Critical Success Factors |24 | |2. 6 |SWOT Analysis of Software industry |25 | |2. 7 |Introduction to Indian software companies |26 | |1. HCL Perot Systems |26 | | |2. Honeywell |26 | | |3. IBM |27 | | |4. i-Flex |29 | | |5. Infosys Technologies |29 | | |6. M-Phasis BFL Software |30 | | |7. Oracle |30 | | |8 . Satyam |30 | | |9 . Texas Instruments |31 | | |10. Wipro |31 | | | | | |I II |DESIGN OF THE STUDY | | |3. 1 |Title |33 | |3. |Background of the study |33 | |3. 3 |Statement of the Problem |33 | |3. 4 |Scope of the Study |34 | |3. 5 |Objectives of the Study |34 | |3. 6 |Research Design and Methodology |34 | |3. |Limitation of the Study |35 | | | | | |IV |ANALYSIS AND INTERPRETATION |37 | | | | | |V |FINDINGS AND CONCLUSIONS |53 | | | | | |VI |RECOMMENDATIONS |58 | | | | | |VII |BIBLIOGRAPHY | | | | | | |VIII |ANNEXURES | | | | 1) Questionnaire | | | |Operational definition of concepts | | LIST OF TABLES |NO |PARTICULARS |PAGE NO | |1 |The daily net Foreign Exchange Market turnover in various countries |5 | |2 |Foreign Exchange eserves in India |9 | |3 |Percentage of Software companies facing Foreign Exchange risk |37 | |4 |Percentage showing type of Forex risk faced by software companies |38 | |5 |Percentage showing types of exposure |40 | |6 |Percentage of companies use various source of information |41 | |7 |Percentage showing the type of hedging tool ad opted by software companies |42 | |8 |Percentage of companies showing normal time for forward contract |43 | |9 |Percentage of companies considers various factors while formulating hedging strategy |44 | |10 |Percentage of companies showing the nature of usage of hurdle rate |45 | |11 |Percentage showing satisfaction level of companies |46 | |12 |Percentage showing satisfaction level of companies |47 | |13 |Percentage of companies hedge its imports an Exports |48 | |14 |Percentage of banks trade through Indian/foreign banks |49 | |15 |Percentage showing companies trade through different types of banks |50 | |16 |Percentage of companies exposed to various currencies |51 | LIST OF GRAPHS |NO. |PARTICULARS |PAGE NO | |1 |Foreign Exchange reserves in India |10 | |2 |Companies facing isk |38 | |3 |Forex risk faced by software companies |39 | |4 |Risks of companies |40 | |5 |Types of exposure |41 | |6 |Percentage of companies use various source of information |42 | |7 |Type of hedging too l adopted by software companies |43 | |8 |Percentage of companies showing normal time for forward contract |44 | |9 |companies considers various factors while formulating hedging strategy |45 | |10 |Percentage of companies showing the nature of usage of hurdle rate |46 | |11 |Percentage showing satisfaction level of companies |47 | |12 |Percentage showing satisfaction level of companies |48 | |13 |Percentage of companies hedge its imports an Exports |48 | |14 |Percentage of banks trade through Indian/foreign banks |49 | |15 |Percentage showing companies trade through different types of banks |50 | |16 |percentage of companies is exposed to various currencies |51 | 1. FOREX-AN OVERVIEW Since the demise of fixed foreign currency exchange rates in the early 1970’s, the world economy has undergone sweeping changes. The collapse of the Breton Woods Agreement in 1971 signaled an increase in currency market volatility and trading opportunities. What is the lure of the Foreign Exchan ge markets? What is its power? How does it grow to be the most important market in the World? How can you benefit from it? The foreign exchange market dwarfs the combined operations of the New York, London, Tokyo futures and stock exchanges, the daily turnover is approximately 1. 5 Trillion (U. S) dollars per day. The fascination of this market lies in its sheer size, its complexity and almost limitless reach. During the past decade, the foreign exchange market has been the invisible hand guiding the purchase and sale of goods, services and raw materials in every corner of the globe. The foreign exchange market directly affects every country’s bonds, equities, private property, manufacturing and all assets that are accessible to foreign investors. Foreign exchange rates play a major role in determining who finances government deficits, who buys equity in companies, who owns real-estate, who hires and fires employees and who owns the bank at which to maintain your corpor ate or personal account(s). There is little doubt that this market affects every aspect of our daily personal and corporate financial lives and influences the economic and political destiny of every nation. The foreign exchange market, then, is the one stabilizing factor in the world’s system of monetary exchange. This market was created not by design but necessity. Traders, bankers, investors, importers and exporters recognized the benefits of hedging risk, or speculating for profit. The currency in your pocket is literally your stock in your country, like stock, its value fluctuates on the international market providing substantial opportunities for profit or loss. The market has its own momentum; it follows its own imperatives, and arrives at its own conclusions. Since the conclusions of value, fortunately or unfortunately affect the value of all assets it is crucial that every individual or institutional investor have an understanding of the foreign exchange markets an d the forces behind this ultimate free-market system. There is approximately one and half trillion-dollar worth of average daily 24-hour turnover in the global foreign exchange market. 51% is in spot forex transactions, followed by 32% in currency swap transactions, and forward outright forex transactions represent another 5% of this daily turnover. Spot transactions and forward outright Forex transaction all take place in the inter-bank market with options on inter-bank Forex Transactions making up another 8%, the inter-bank market accounts for 96% of the global foreign exchange market, the remaining 4% is divided among all the global futures exchanges. Inter-bank currency contracts and options, unlike futures contracts, are not traded on exchanges and are not standardized: rather banks and dealers act principles in these markets, negotiating each transaction on an individual basis. Forward â€Å"cash† or â€Å"spot† trading in currencies is substantially unregulat ed; there are no limitations on daily price movements and speculative positions limits are not applicable. During problems of liquidity dealers can place trades through a larger number of market participants for better execution. Cash markets are the primary markets and futures are the secondary markets. The cash currency market represents 24 times the volume of currency futures. Cash trading deals in â€Å"Real† instruments with volume exceeding one trillion U. S. dollars worldwide daily. Cash markets provide better liquidity, execution and trading hours. 1. 2 DEFINITION OF FOREIGN EXCHANGE: â€Å"The means and methods by which rights to wealth expressed in terms of currency of one country are converted into rights to wealth in terms of currency of another country are known as ‘Foreign Exchange. The term cover the method by which the currency of one country is exchanged for that of another, the causes, which render such exchanges necessary, the forms in which su ch changes are conducted and the ratio or equivalent values at which they are reflected. † A foreign exchange transaction is still a shift of funds, or short-term financial claims, from one country and currency to another. â€Å" Foreign exchange refers to money denominated in the currency of another nation or group of nations. Any person who exchanges money denominated in his own nation’s currency for money denominated in another nation’s currency acquires foreign exchange. † Federal Reserve Foreign Exchange Regulations Act, 1973 (FERA) defines Foreign Exchange as â€Å"Foreign currency and any drafts, travelers cheques, letter of credit and bill of exchange, expressed and drawn in Indian currency but payable in any foreign currency. † â€Å"Foreign exchange† refers to money denominated in the currency of another nation or group of nations. Any person who exchanges money denominated in his own nation’s currency for money denominate d in another nation’s currency acquires foreign exchange. That holds true whether the person involved is a tourist cashing a traveler’s check in a restaurant abroad or an investor exchanging hundreds of millions of dollars for the acquisition of a foreign company; and whether the form of money being acquired is foreign currency notes, freeing currency-denominated bank deposits, or other short-term claims denominated in foreign currency. A foreign exchange transaction is still a shift of funds, or short-term financial claims, from one country and currency to another. There are three main types of foreign exchange system: a) The gold standard in its various forms; b) Freely fluctuating exchange rates; and c) The several varieties of exchanges control. The fact that each country has its own monetary system is one of the principle complications of international trade and balances of payments. 1. 3 WHY WE NEED FOREIGN EXCHANGE Almost every nation has its own national cur rency or monetary unit-its dollar, its peso, its rupee-used for making and receiving payments within its own borders. But foreign currencies are usually needed for payments across national borders. Thus, in any nation whose residents conduct business abroad or engage in financial transaction with person in other countries, there must be a mechanism for providing access to foreign currencies, so that payments can be made in a form acceptable to foreigners. In other words, there is a need for â€Å"foreign exchange† transactions-exchanges of one currency for another. 1. 4 FOREX CHARACTERISTIES †¢ Size of the global Forex market: 1500 Billion $ per day. For comparison: Bond/Treasury US Market: 300 Billion $ per day. Stock Exchanges Markets: 30 Billion $ per day (estimated). The market does not have a precise location, and the transactions are done via telephone, facsimile, and recently via Internet, this situation facilitates the activity of the traders. †¢ The pric es of the market are established electronically by more than 500 international banks, which carry out exchanges between the market companies and governments. These banks constantly issue their prices, and the last quotation issued is considered as the price of the market. †¢ Forex is opened 24/24, five days a week, therefore, the players have the possibility of an immediate reaction. †¢ Leverage on deposit is possible due top the small consecutive change in price. †¢ Forex is characterized by the fact that is cannot be high or low. The potential of profit exists in one direction as in the other. Table 1:The daily net Foreign Exchange Market turnover in various countries |AVERAGE DAILY NET FOREIGN | |EXCHANGE MARKET TURNOVER IN THE MAIN CENTRES (In US$ billions) | |United Kingdom |4645 | |United States |2444 | |Japan |1613 | |Singapore |1054 | Hong Kong |902 | |Switzerland |865 | |Germany |762 | |France |58 | |Australia |395 | |Denmark |305 | |Canada |298 | |Sweden |281 | 5. FOREIGN EXCHANGE MARKET AND ITS TYPES The foreign exchange market is the market in which currencies are bought and sold against each other. Today, it is the largest market in the world with a turnover of about $1. 5 trillion approximately every day. The reason is the organizations like International banks, multi-national corporations, and large brokerage houses trade in huge volumes of currencies. The major currencies traded in this market are the US dollar, Deutschemark (DM), yen, Pound Sterling, Swiss franc, Canadian dollar, Dutch guilder, Italian Lira and the Belgian franc. The foreign exchange market is a cash inter-bank or inter-dealer market. It is called as ‘over the counter market’. This means that there is no single market place or an organized exchange (like a stock exchange) where traders meet and exchange currencies. The traders sit in the offices (Foreign exchange dealing rooms) of major commercial banks around the world and communicate over the telephone and through computer terminals at thousand of locations worldwide. Geographically, the markets span all the times ones from New Zealand to the west cost of the United States. The time New York is staring to wind down at 3. 00 p. m. , it is noon in Los Angeles. By the time it is 3. 00 p. m. , in Los Angeles it is 9. 00 a. m. or the next day in Sydney. Thus the market functions virtually 24-hours enabling a trader to offset a position created in one market using another market. . Five widely used international markets are: 1. Foreign Exchange market The foreign exchange market allows currencies to be exchanged in order to buy the products or invest in securities denominated in foreign currency. 2. Eurocurrency Market The Eurodollar market, which is now referred as Eurocurrency market was created as corporations in the U. S deposited U. S dollar in European banks. These European banks were willing to accept dollar deposit, since they could then lend dollars to corpora te customers based in Europe. Because the U. S dollar deposited is placed in banks located in Europe and other continents became know a Eurodollars. 3. Euro credit Market Loans of one year or longer extended by Euro banks are commonly called Euro credits or Euro credit loans. Such loans in the Euro credit Market have become popular since corporations and government agencies often desire to borrow for a term exceeding one year, and a common maturity for Euro credit loans in five years. 4. Eurobond market While the Euro currency and Euro credit loans help to accommodate short and medium-term borrowers, they do not accommodate the long-term borrower. To fill this gap, the Euro bond market was created. This market facilitates the transfer of long-term funds form surplus units to deficit units around the world. 5. International Stock Market When MNC’s issue stock, they often consider placing some in foreign stock markets to increase the probability that investors will absorb the entire issue. MNC’s with access to foreign stock markets may be able to issue stock at a higher price, which reflect a lower cost of capital. 1. 6 ROLE OF THE EXCHANGE RATE The exchange rate is price-the number of units of one nation’s currency that must be surrendered in order to acquire one unit of another nation’s currency. There are also various â€Å"trade-weighted† or† effective† rates designed to show a currency’s movements against an average of various other currencies. Quite apart from the spot rates, there are additional exchange rates for other delivery dates, in the forward markets. A market price is determined by the inter-action of buyers and sellers in that market and a market exchange rate between two currencies are determined by the interaction of the official and private participants in the foreign exchange rate market. For a currency with an exchange rate that is fixed, or set by the monetary authorities, the central bank or another official body is a key participant in the market, standing ready to buy or sell the currency as necessary to maintain the authorized pegged rate or range. The participants in the foreign exchange market are thus a heterogeneous group. But, whether official or private, or the motive being investing, hedging, speculating, arbitraging, paying for imports, or seeking to influence the trade, they are all part of the aggregate demand for and supply of the currencies involved, and they all play a role in determining the market exchange rate at that instant. Given the diverse views, interests, and time frames of the participants, predicting the future course of exchange rates is a particularly complex and uncertain business. At the same time, since the exchange rate influences such a vast array of participants and business decisions, it is a pervasive and singularly important price in an open economy, influencing consumer prices, investment decisions, interest rat es, economic growth, the location of industry, and must else. The role of the foreign exchange market in the determination of that price is critically important. 1. 7 FOREX AND THE STOCK MARKET ? Forex works 24 hours a day. The stock market in India works only from 12 to 3 p. m. ? There are always the same 5 major currencies traded on the Forex market, whereas in the stock market there are thousands of securities to trade, and it is hard to understand why each particular stock will go up or down today. Choosing the right stocks from thousands to make a portfolio is not an easy thing either. ? The minimum amount needed in order to open a trading account on the Forex market-$1000-2000. This relatively small amount of money gives you an opportunity to ear $300-800 per day or even greater. To have an opportunity to ear $300-800 per day on the stock market, you have to put up $15000-20000 for your account. Certainly you can lose on both markets, but on forex you can win using a muc h smaller amount of trading capital. ? It is more difficult to predict the stock market because of the millions of inexperienced investors making the movements chaotic ? There is no â€Å"bull† or â€Å"bear† market on Forex. On the other hand in the stock market, you can earn money mostly during a period of booming economy. But economy development is cyclical-and periods of growth will eventually be replaced by periods of recession. And in this case, when the stock market is going down, you cannot win as a day trader. On the forex market you have a unique feature- a so-called† demo account† or simulated account, which allows you to participate in trading using real-time prices on the deal station with the same interface and functions as on real trading, using the same news and technical analysis tools to predict market, movements, from the comfort of your home and via the internet. Now you can understand why more and more people go for forex trading. It is convenient and inexpensive. It gives you the opportunity and the time to develop your personal trading system. The most important participants in the market are banks. Foreign Exchange is traded â€Å"over the counter† via telephone and computer communications among banks, and not in organized exchanges such as stock exchanges. 8. FOREIGN EXCHANGE RESERVES IN INDIA Table 2: Foreign Exchange Reserves in India |YEAR |US $ MN. |1995 |17000 | |1996 |19500 | |1997 |25300 | |1998 |25300 | |1999 |32000 | |2000 |42500 | |2001 |48500 | |2002 |53400 | Source: International Financial Statistics, IMF. Graph 1:Foreign Exchange Reserves in India . FOREIGN EXCHANGE RISK Michael Adler and Bernard Dumas define foreign exchange risk in terms of the variance of unanticipated change in exchange rates. That is, they define exchange rate risk in terms of the unpredictability of exchange rates as reflected by the variance. From this, it is clear that unpredictability is paramount in the measuremen t of exchange-rate risk. Thus, the author defines foreign exchange risk as follows: â€Å"Foreign Exchange risk is measured by the variance of the domestic-currency value of an asset, liability income that is attributable to unanticipated changes in exchange rates. 1. 10 SOURCES OF FOREIGN EXCHANGE RISK Foreign exchange rate fluctuation affect banks both directly and indirectly. The direct effect comes from banks’ holdings of assets (or liabilities) with net payment streams denominated in a foreign currency. Foreign exchange rate fluctuations alter the domestic currency of such assets. This explicit source of foreign exchange risk is the easiest to identify, and it is the most easily hedged. The indirect sources of risk are subtler but just as important. A bank without foreign assets or liabilities can be currency risk because the exchange rate can be affecting the profitability of its domestic banking operations. For example consider the value of a banks’ loan to an U. S. exporter. An appreciation of the dollar might make it more difficult for the U. S. exporter to compete against foreign firms. If the appreciation thereby diminishes the exporter’s profitability, it also diminishes the probability of timely loan repayment and, correspondingly, the profitability of the bank, in this case, the bank is exposed to foreign exchange risk; a stronger dollar decreases its profitability. In essence, the bank is† short† dollars against foreign currency. Any time the value of the exchange rate is linked to foreign competition, to the demand for loans, or to other aspects of banking conditions, it will affect even â€Å"domestic† banks. Foreign exchange risk also may be linked to other types of market risk, such as interest rate risk. Interest rates and exchange rates often move simultaneously. So, a bank’s interest relates position indirectly affects its overall foreign exchange exposure. The foreign exchange rate sensitivity of a bank with an open interest rate position typically will differ from that of a bank with no interest rate exposure, even if the two banks have the same actual holding of assets denominated in foreign currencies. Against, the vulnerability of the bank as a whole to foreign exchange fluctuations depends on more than just is its holdings of foreign exchange. 1. 11 TYPES OF FOREIGN EXCHANGE TRANSACTION RISKS Foreign exchange transactions include substantial amount of risk due to fluctuations in exchange rates. Hence corporates are continuously striving to minimize their risk exposure by the use of various hedging tools like forward contract, options, swaps, Off Balance Sheet netting etc. The various risks of Foreign Exchange Transactions are: 1. Open Position or Exchange Rate Risk (Risk from market movement) It is the risk of change in exchange rates, which affects imports/ exports; this risk prevails from the date of order till the date of payment. 2. Cash Balance R isk The balances maintained in the foreign accounts (EEFC) at the end of each day are referred to as cash balances. The balances in the EEFC account do not earn any interest. 3. Maturity Mismatch/Liquidity/Gap/Interest Rate Risk (Risk due to improper transaction) The risk arises out of the fact that maturity period of purchase and sale of foreign currency in case of imports and exports don’t match. Liquidity risk is the risk that bank will be unable to meet its funding requirements or execute a transaction at a reasonable price. Market liquidity risk is the risk that bank not being able to exit or offset positions quickly at a reasonable price. 4. Credit or counter party Risk (Risk from customers) This is a risk due to inability or unwillingness of the counterpart to meet its obligations. Over this kind of risk bank has got proper control but bank tries to avoid or minimize the risk by taking following actions: . By fixing counterpart limits .By appropriate measurements of exposure Credit evaluation and monitoring .By following sound operating procedure This risk can be classified into two ways: a) Pre-Settlement Risk Pre settlement risk is the risk of loss due to counter party defaulting on a contract during the life of a transaction. This exposure is also referred to as the replacement cost. A key tool for effective management of this risk is the fixation of exposure limits on counter parties. b) Settlement Risk Settlement risk is the risk arising when a bank performs its obligation under a contract prior to the counter party doing so. This risk frequently arises in international transactions because of the time zone differences. The credit risk can also be classified in to: a) Contract Risk If before the performance of the contract, the counter party fails the contract has to be canceled. In the mean time if rate has moved against it, then the loss is to be born by the bank as the contract is to be closed at the on going market rates. b) Clean risk In an exchange contract the currencies are to be exchanged on the value dates. The time zone difference between various center sometimes results in situations when one bank has already paid the amount of currency to be given before receiving the amount the currency to be received the counter party fails, it may result in total loss. c) Sovereign risk If the counter party bank is situated in different country then there is a possibility of having sovereign risk. Also because of the political and economic factors in that country. If a country suspends the foreign currency payments the bank may stand to lose, although the counter party have performed its part of the contract in local currency. The bank while fixing counter party limits for the overseas bank has to give due weight age to the political stability, health of the economy, availability of financial infrastructure, and expected state interference in financial transactions, particularly foreign exchange transactions. d ) Country Risk This risk related to the ability and willingness of a country to service its external liabilities. It is also known as ‘sovereign risk’ or ‘transfer risk’. e) Overtrading Risk Risk of Overtrading arises when the volume of transactions by the dealer or the bank is beyond its administrative and financial facility. In the anxiety to earn huge profits, the dealer or the bank may take up large deals, which a normal prudent bank would have avoided. f) Fraud Risk Dealers or operational staff may indulge in frauds for personal gains or to conceal a genuine mistake committed earlier. g) Legal risk In addition to the foregoing risk there is a legal risk, which exists in all kinds of financial markets. It its probably more so in foreign exchange and interest rates given that inherent volatility. It is therefore extremely important the banks as also the corporate dealing in such products take such steps as would sufficiently protect them from the l egal standpoint. 1. 12 MEASURES OF FOREIGN EXCHANGE RISK The direct source of foreign exchange risk can be gauged by tallying the net positions on a bank’s assets and liabilities that are denominated in foreign currencies. The example of the bank’s loan to the exporter shows the limitations of the narrow, standardized method most clearly. While the exporter’s loan by itself leaves the bank short in dollars, the standardized method captures none of this indirect exposure. Further, if the bank were to use the foreign currency market to hedge the short dollar position, then the standardized method, having messed the original exposure, would mistakenly treat the hedge as if it added to exposure. In general, if a bank chooses its foreign exchange holdings as though they contribute to risk as the standardized approach does it inappropriate. Use of the latter option, know as the â€Å"internal models† approach, is subject to several requirements for prudence, transparency and consistency. When used appropriately, it can provide a significant improvement over the standardized method. The internal model approach enables banks to take a broader view of their foreign exchange risk than does the standardized method. This year, the internal model approach focuses on evaluating the risks arising from banks’ trading activities. The approach is well suited to incorporating the correlation between, say, the value of interest rate instruments and the value of foreign exchange. In principle, the internal model approach allows each bank to gauge its exposure carefully enough to incorporate the relationships among even its non-trading operations. However, even at is its, best, the internal models approach is limit din its range of coverage. An even broader approach to assessing banks’ foreign exchange risks can be obtained from an analysis of a bank’s equity returns. Equity returns reflect changes in the value of the firm as a whole. So, if the value of a bank as a whole is sensitive to changes in the exchange rate, the bank’s equity returns will mirror that sensitivity. Whether from direct or indirect sources, foreign exchange exposure will be reflected in the behavior of returns. Thus, the exchange rate sensitivity of a bank’s equity return provides a comprehensive e measure of its foreign exchange exposure. One drawback of this equity approach is that it is not useful for evaluating the risk level of a particular action. The approach is not linked to an explicitly model of the determinants of foreign exchange exposure, so it cannot be used to trace out the implications of specific decision. However, the approach is useful for bankers and regulators as a tool to evaluate the success of past management of foreign exchange risk. It is especially uitable for comparing the exposure of an assortment of a bank because it can be applied consistently across banks and because it does not requir e access to their detailed internal models. More over, its comprehensiveness makes it a good benchmark for evaluating other gauges of exposure. 1. 13 WHY SHOULD FIRMS MANAGE FOREIGN EXCHANGE RISK? Many firms refrain from active management of their foreign exchange exposure, even though they understand the exchange rate fluctuations can affect their earnings and value. They make this decision for a number of reasons. First, management does not understand it. They consider any use of risk management tools, such as forwards, futures and options, as speculative. Or they argue that such financial manipulations lie outside the firm’s field of expertise. We are in the business of manufacturing slot machines, and we should not be gambling on currencies. â€Å" Perhaps they are right to fear the use of hedging techniques, but refusing to use forwards and other instruments may expose the firm to substantial speculative risks. Second, they claim that exposure cannot be measured. They a re right currency exposure is complex and can seldom be gauged with precision. But as in many business situations, imprecision should not be taken as an excuse for indecision. Third, they say that the firm is hedged. All transactions such as imports or exports are covered, and foreign subsidiaries finance in local currencies. This ignores the fact that the bulk of the firm’s value comes from transactions not yet completed, so that transaction hedging is a very incomplete strategy. Fourth, they say that the firm does not have any exchange risk because it does all its business in dollars (or yen, or whatever the home currency is). But a moment’s thought will make it evident that even if you invoice German customers in dollars, when the mark drops your prices will have to adjust or you’ll be undercut by local competitors. So revenues are influenced by currency change. Finally, they say that the balance sheet is hedged on an accounting basis—especially whe n the â€Å"functional currency† is held to be the dollar. The misleading signals that balance sheet exposure measure can give are documented in later sections. Modern principles of the theory of finance suggest prima facie that the management of corporate foreign exchange exposure may neither be an important nor a legitimate concern. It has been argued, in the tradition of the Modigliani-Miller Theorem, that the firm con not improve shareholder value by financial manipulations: specifically, investors themselves can hedge corporate exchange exposure by taking out forward contracts in accordance with their ownership in a firm. Managers do not serve them by second-guessing what risk shareholders want to hedge. One counter-argument is that transaction costs are typically greater for individual investors than firms. Yet there are deeper reasons why foreign exchange risk should be managed at the firm level. Operating managers can make such estimates with much more precision than shareholders who typically lack the detailed knowledge of competition, markets, and the relevant technologies. Furthermore, in all but the most perfect financial markets, the firm has considerable advantages over investors in obtaining relatively inexpensive debt at home and abroad, taking maximum advantage of interest subsidies and minimizing the effect of taxes and political risk. Another line of reasoning suggests that foreign exchange risk management does not matter because of certain equilibrium conditions in international markets for both financial and real assets. These conditions include the relationship between price of goods in different markets, better known as Purchasing Power Parity (PPP), and between interest rates and exchange rates, usually referred to as the International Fisher Effect. 1. 14 FOREIGN EXCHANGE EXPOSURE Foreign exchange exposure is the sensitivity of changes in the real domestic currency value of assets, liabilities, of operating incomes to u nanticipated changes in exchange rates. Several features of this definition are: First, it is exposure of the sensitivity of domestic currency values i. e. , it is a description of the extent or degree to which the home currency value of something is changed by exchange rate changes. Second, it is concerned with real domestic- currency values,. By this we mean that this adjusts inflation to changes in exchange rates. Third, it has existed on assets and liabilities or on operating incomes of firms. Since the values of operating income are so much per period of time, we see that exposure exists on stocks and flows. Fourth, it has not been qualified in the list of exposed items by describing them as being foreign assets, and so on. This is because, unanticipated changes in exchange rates can affect domestic as well as foreign assets, liabilities, and operating incomes. Finally, it has been noticed that the definition refers only to unanticipated changes in exchange rates. This is be cause markets compensate for changes in exchange rates that are anticipated. Consequently, it is only to the extent that exchange rates change by more or less than had been expected that there will be gain or loss on assets, liabilities or operating incomes. 1. 15 TYPES OF EXPOSURE Whether the exposure is accounting-based or economic, the evidence indicates very clearly that changes in foreign exchange rates can change the real cash flows of the firm and thus can have a significant negative impact on a firm’s ability to compete. There are three types of foreign exchange exposure that impact the operation and performance of multinational companies; translation and transaction that are accounting based, and economic which is operational or real exposure. Foreign exchange exposure can be classified into three broad categories. ? Transaction exposure ? Translation exposure ? Economic exposure First and the third together are known as ‘cash flow exposures’ while the second is referred to as ‘accounting exposure’ or ‘balance sheet exposure’. †¢ Transaction exposure: A transaction exposure exists when a change in one of the financial prices will change the amount of a receipt or expense. The amount of a transaction ( a receipt or expense) would be determined by the price per unit and the number of units sold or purchased. Transaction exposures typically focus on only the direct effect of a price change-the impact of price changes on quantity is ignored. A transaction exposure will often lead to trouble when there is a mismatch in receipts and expenses. Eg. If an Indian exporter has a receivable of $200,000 due three months, hence and if in the meanwhile the dollar depreciates relative to the rupee a cash loss occurs, conversely if the dollar appreciates relative to the rupee cash gain occurs. Conversely reverse will take place in case of imports. †¢ Translation exposure: A parallel exposure-one that als o focuses only on the direct effects of a price change-that would be reflected in the firm’s balance sheet is referred to as a translation exposure. A translation exposure reflects the change in the value of the firm as foreign assets are converted to home currency. Most of the firms make a point of noting that they do not manage translation exposures. Economic exposure: Moving beyond the strike accounting-based exposures, firms have begun to consider their firm’s economic, or real, exposure-also referred to as competitive exposures. Changes in foreign exchange rates will change the firm’s receipts or expenditures not only because of the direct price change but also because the price change will change the amount that the firm buys or sells. This view of finical price risk recognizes changes in foreign exchange rates on the firm’s sales and market share and then on the firm’s net profits (net cash flows). 16. METHODS OF HEDGING Methods of hedging c an be classified as a. Internal methods b. External methods a. Internal methods: Internal methods include: i. Invoicing: In invoicing the corporate shifts the entire exchange risk to the other party by insisting that all its imports and exports be invoiced in its home currency. ii. Netting/matching of cash flows: In this method of hedging if a firm has receivables and payables corresponding to the same periods then even if no other action is taken it will be able to match these exposures and make payments out of the payments received, since it will not have to buy or sell currencies in respect of these matched receipts and payments, there is no forex exposure risk involved. This is also called as ‘natural hedging’. iii. Leading and Lagging: The expression leading mean s paying before the due date and lagging means postponing the receipt of funds beyond the date on which they are due. The general rule is to lead i. e. advance payables and lag i. e. postpone receiva bles in strong currencies and conversely lead receivables and lag payables in weak currencies. b. External methods: External method includes: i. Forward contract: Forward contract is a firm and binding contract entered into by the bank and its customer for the purchase of specified amount of foreign currency at an agreed rate of exchange for delivery and payment at a future date or period agree upon at the time of entering into foreword deal. The bank on its part will cover itself in the inter-bank market or by matching a contract to sell with a contract to buy. ii. Option contracts: These are contracts in which the rate of exchange between the two currencies is fixed at the time the contract is entered into as in a standard forward, but the delivery date is not a fixed date. The corporate (customer) can at its option, take or make delivery on any day between the fixed dates. The internal between the two dates is the option period. Options are financial instruments that confer up on the holder the right to do something without the obligation to do so. More specifically, the option is an asset on or up on a specified date if he chooses to do so. The option buyer can simply let his right lapse by not exercising his option on the other hand; the seller of the option has an obligation to take the other side of the transaction if the buyer wishes to exercise his option. For this privilege the option buyer has to pay the seller a fee. iii. Financial Swaps: Swaps is an arrangement whereby a firm borrows in the currency in which it has advantage and exchanges the liability with another firm for an equivalent liability in another currency. Under the same currency, the relative strengths of the firms may be with regard to the payment of interest. One firm may have advantage in borrowing at fixed rate of interest while the other in floating rate. Therefore, the swap many involve borrowing at floating rate and exchanging the liabi8lity for payment of interest with another firm borrowing at fixed interest rate. It is also possible that both currency and interest factors affect the choice for going for a swap. iv. Futures: Currency futures are standardized contracts that trade like conventional commodity futures on the floor of a futures exchange. A standardized forward contract is a future contr4act (quantity, date and delivery conditions are standardized). They are traded on organized exchanges. In future contract a margin is required, future contracts are ‘marketed to market’ on a daily basis i. e. rofits and losses arising on future contracts are settled daily. 2. 1 INTRODUCTION TO THE INDIAN SOFTWARE INDUSTRY India accounted for about 0. 4% of the global software industry with a turnover of approximately Rs. 120 bn, during 1998-99. Of the total turnover, about Rs. 63 bn. is derived from the global markets, while the remaining is from domestic market. India’s global software revenues are from the US (56. 3%) and Europe (24%) which together contributes about 80% of the export revenues. The Indian software industry is fragmented with over 600 players. However, the major 11 companies accounted for approximately 32 % of the total exports during 1998-99. All the software companies have to follow the rules and regulations mention by the Software Technology Pact. The Indian software industry has grown rapidly at a compounded annual rate of 50% from US$ million eight years ago to a US$ 3. 9 billion in 1998-99 according to National Association of software and service Companies (NASSCOME); a faster rate than the US grew in the same stage of this life cycle. Fuelled by domestic deregulation, entrepreneurial flair and the soaring global demand for low-cast, high-quality software and services India is becoming one of the world’s main centers for offshore software work. Among the Fortune 500 companies, 203 outsource software development to India. 2. 2 INDIAN SOFTWARE INDUSTRY ADVANTAGES Quality |131 c ompanies have ISO9000 certificate | |Reliability |Ultimate adherence to delivery schedules | | |Customer satisfaction by using state-of-the-art technology | |Cost and time savings |High speed data communications 64 kbps+easing off-shore communications | | |24 hour virtual offices | |Large manpower pool people |115,000 engineers graduate every year | | |Second largest IT professional source in the world | |Year 200 |Fuller range of cost effective solutions | 2. 3 CLASSIFICATION OF SOFTWARE COMPANIES Software companies can be classified based on numerous parameters. The following bases can be there for their classification: 1. Based on software revenue stream consideration the companies can be classified in the following categories. †¢ Core software companies (says revenues from software greater the 50% of the total revenues). †¢ Diversified software companies –those in the area other than software including the sales and service of computer hardware. 2. Based on t he work platform employed: the companies may be either or some fo all of the available platforms-say RS 600, LAN, SUN, DEC, AS/400,PS/2, IBM/Mainframe, UNIX/Variants, PC, etc. 3. Based on area of work: ? Software products: the company can be dealing in the software products and packages. ? Software services: the companies can provide software services ins the form of maintenance of the system, up gradation of the software packages, data entry, solutions to Y2K problems, internet needs, etc. ? On-site service company: these are the companies that work on the client premises and have minimum capital expenditure at own office. ? Offshore service company: these offshore development centers works on the project with regular interface with the client via data communication lines. The solutions are development in-house and transmitted/installed in the client premises with the help of data communication. 4. Based on the area of application: the companies can target any of the sectors as their customer and focus to provide products or services for that sector. Generally the big companies undertake software work in-house to cater to their specific needs rather than outsourcing the same. The sector of focus can be agriculture, banking, communication, telecommunication, finance, manufacturing, etc. 5. On the basis of growth achieved: ? The companies can have an established reputation in the market with solid base and some assurance of business. They enjoy high growth and are experiencing movement up the value chain. ? The companies can otherwise be startup concerns. These are typically promoted by some technocrats and are vying to making a placer for themselves in the market. They are generally at the low level in the value chain. Survival is the main concern. 6. On the basis of ownership: ? The companies can be multinational’s subsidiaries that are doing job exclusively for the parent company. The parent MNC typically supports them financially and otherwise. There is assurance of business so the risks are minimal. ? The captive software division or separate company owned by Indian Company for their captive use. The Indian companies that are in the fray to provide solutions on competitive basis as any other vendor in the open market. They do not have captive clients. ? The joint ventures between the foreign company and Indian company to provide in-house support as well as to have commercial operations as any other company in the open market. 2. 4 VALUE CHAIN OF SOFTWARE INDUSTRY The value chain of the software industry is very difficult to conceive. These are rather two sub-segments of the industry having their separate value chains-Software products and software services. While the product is a height-risk strategy as the investment has to be made upfront and the revenue generation is later, the margins are therefore higher to compensate the risk therein. The services are having a low element of risk in them. The investment and the revenue generation are simultaneous. The margins are therefore low as compared to the products. The company can adopt either of the strategy depending on its risk profile and preference. While Infosys has been persistently moving in the product direction, the Wipro strategy is of service. The movement up the value chain is usually accompanied with the following effects. |FROM |TO | |Manpower multiplication game |An intellectual roperty business | |Suppliers of cheap labor |Providers of value added service | |Pay for effort |Pay for solutions | |Focus on the lower costs |Focus is more on quality | |Slow growth, low margins, and manpower Dependant Company. |Faster growth, higher margins and fewer person-dependants | |The value that customer obtains will be measured in terms of |company. | |man-hours spent. |The value that customer derives shall be measured in term of | | |function points delivered or maintained, and in terms of business| | |leverage that the customer obtains from the product | |

A Study Of The Indian Stock Market - Free Essay Example

Sample details Pages: 18 Words: 5476 Downloads: 7 Date added: 2017/06/26 Category Statistics Essay Did you like this example? 1.0 Introduction Seasonal variations in production and sales are a well known fact in business. Seasonality refers to regular and repetitive fluctuation in a time series which occurs periodically over a span of less than a year. The main cause of seasonal variations in time series data is the change in climate. Don’t waste time! Our writers will create an original "A Study Of The Indian Stock Market" essay for you Create order For example, sales of woolen clothes generally increase in winter season. Besides this, customs and tradition also affect economic variables for instance sales of gold increase during marriage seasons. Similarly, stock returns exhibits systematic patterns at certain times of the day, week or month. The most common of these are monthly patterns; certain months provide better returns as compared to others i.e. the month of the year effect. Similarly, some days of the week provides lower returns as compared to other trading days i.e. days of the week effect. The existence of seasonality in stock returns however violates an important hypothesis in finance that is efficient market hypothesis. The efficient market hypothesis is a central paradigm in finance. The EMH relates to how quickly and accurately the market reacts to new information. New data are constantly entering the market place via economic reports, company announcements, political statements, or public surveys. If the market is informationally efficient then security prices adjust rapidly and accurately to new information. According to this hypothesis, security prices reflect fully all the information that is available in the market. Since all the information is already incorporated in prices, a trader is not able to make any excess returns. Thus, EMH proposes that it is not possible to outperform the market through market timing or stock selection. However, in the context of financial markets and particularly in the case of equity market seasonal component have been recorded. They are called calendar anomalies (effects) in literature. The presence of seasonality in stock returns violates the weak form of market efficiency because equity prices are no longer random and can be predicted based on past pattern. This facilitates market participants to devise trading strategy which could fetch abnormal profits on the basis of past pattern. For instance, if there are evidences of à ¢Ã¢â€š ¬Ã‹Å"day of the week effectà ¢Ã¢â€š ¬Ã¢â€ž ¢, investors may devise a trading strategy of selling securities on Fridays and buying on Mondays in order to make excess profits. Aggarwal and Tandon (1994) and Mills and Coutts (1995) pointed out that mean stock returns were unusually high on Fridays and low on Mondays. One of the explanation put forward for the existence of seasonality in stock returns is the à ¢Ã¢â€š ¬Ã‹Å"tax-loss-selling hypothesis. In the USA, December is the tax month. Thus, the financial houses sell shares whose values have fallen to book losses to reduce their taxes. As of result of this selling, stock prices declin e. However, as soon as the December ends, people start acquiring shares and as a result stock prices bounce back. This lead to higher returns in the beginning of the year, that is, January month. This is called à ¢Ã¢â€š ¬Ã‹Å"January effectà ¢Ã¢â€š ¬Ã¢â€ž ¢. In India, March is the tax month, it would be interesting to find à ¢Ã¢â€š ¬Ã‹Å"April Effectà ¢Ã¢â€š ¬Ã¢â€ž ¢. 2.0 Theoretical Background The term à ¢Ã¢â€š ¬Ã‹Å"efficient marketà ¢Ã¢â€š ¬Ã¢â€ž ¢ refers to a market that adjusts rapidly to new information. Fama (1970) stated , à ¢Ã¢â€š ¬Ã‹Å" A market in which prices always fully reflect available information is called efficient.à ¢Ã¢â€š ¬Ã¢â€ž ¢ If capital markets are efficient, investors cannot expect to achieve superior profits by adopting a certain trading strategy. This is popularly called as the efficient market hypothesis. The origins of the EMH can be traced back to Bachelierà ¢Ã¢â€š ¬Ã¢â€ž ¢s doctoral thesis à ¢Ã¢â€š ¬Ã‹Å"Theory of Speculationà ¢Ã¢â€š ¬Ã¢â€ž ¢ in 1900 and seminal paper titled à ¢Ã¢â€š ¬Ã‹Å"Proof That Properly Anticipated Prices Fluctuate Randomlyà ¢Ã¢â€š ¬Ã¢â€ž ¢ by Nobel Laureate Paul Samuelson in 1965. But it was Eugane Famaà ¢Ã¢â€š ¬Ã¢â€ž ¢s work (1970) à ¢Ã¢â€š ¬Ã‹Å"Efficient Capital Marketsà ¢Ã¢â€š ¬Ã¢â€ž ¢ who coined the term EMH and advocated that in efficient market securities prices fully reflect all the information. It is important to note that efficiency here does not refer to the organisational or operational efficiency but informational efficiency of the market. Informational efficiency of the market takes three forms depending upon the information reflected by securities prices. First, EMH in its weak form states that all information impounded in the past price of a stock is fully reflected in current price of the stock. Therefore, information about recent or past trend in stock prices is of no use in forecasting future price. Clearly, it rules out the use of technical analysis in predicting future prices of securities. The semi-strong form takes the information set one step further and includes all publically available information. There is plethora of information of potential interest to investors. Besides past stock prices, such things as economic reports, brokerage firm recommendations, and investment advisory letters. However, the semi-strong form of the EMH states that current market p rices reflect all publically available information. So, analysing annual reports or other published data with a view to make profit in excess is not possible because market prices had already adjusted to any good or bad news contained in such reports as soon as they were revealed. The EMH in its strong form states that current market price reflect all à ¢Ã¢â€š ¬Ã¢â‚¬Å"both public and private information and even insiders would find it impossible to earn abnormal returns in the stock market. However, there is the notion that some stocks are priced more efficiently than others which is enshrined in the concept of semi-efficient market hypothesis. Thus, practitioners support the thesis that the market has several tiers or that a pecking order exist. The first tier contains well-known stocks such as Reliance Industries and Sail which are priced more efficiently than other lesser-known stocks such as UCO Bank. However, instead of considering stocks, we analyzed this phenomenon using Nif ty Junior index which is an index of next most liquid stocks after SP Nifty. 3.0 Review of Literature Seasonality or calendar anomalies such as month of the year and day of the week effects has remained a topic of interest for research since long time in developed as well as developing countries. Watchel (1942) reported seasonality in stock returns for the first time. Rozeff and Kinney (1976) documented the January effect in New York Exchange stocks for the period 1904 to 1974. They found that average return for the month of January was higher than other months implying pattern in stock returns. Keim (1983) along with seasonality also studied size effects in stock returns. He found that returns of small firms were significantly higher than large firms in January month and attributed this finding to tax-loss-selling and information hypothesis. A similar conclusion was found by Reinganum (1983), however, he was of the view that the entire seasonality in stock returns cannot be explained by tax-loss-selling hypothesis. Gultekin and Gultekin (1983) examined the presence of stock market seasonality in sixteen industrial countries. Their evidence shows strong seasonalities in the stock market due to January returns, which is exceptionally large in fifteen of sixteen countries. Brown et al. (1985) studied the Australian stock market seasonality and found the evidence of December-January and July-August seasonal effects, with the latter due to a June-July tax year. However, Raj and Thurston (1994) found that the January and April effects are not statistically significant in the NZ stock market. Mill and Coutts (1995) studied calendar effect in FTSE 100, Mid 250 and 350 indices for the period 1986 and 1992. They found calendar effect in FTSE 100. Ramcharan (1997), however, didnà ¢Ã¢â€š ¬Ã¢â€ž ¢t find seasonal effect in stock retruns of Jamaica. Choudhary (2001) reported January effect on the UK and US returns but not in German returns. Fountas and Segredakis (2002) studied 18 markets and reported seasonal patterns in returns. The reasons for the January effect in stock returns in most of the developed countries such as US, and UK attributed to the tax loss selling hypothesis, settlement procedures, insider trading information. Another effect is window dressing which is related to institutional trading. To avoid reporting to many losers in their portfolios at the end of year, institutional investors tend to sell losers in Decembers. They buy these stocks after the reporting date in January to hold their desired portfolio structure again. Researchers have also reported half- month effect in literature. Various studies have reported that daily stock returns in first half of month are relatively higher than last half of the month. Ariel (1987) conducted a study using US market indices from 1963 to 1981 to show this effect. Aggarwal and Tandon (1994) found in their study such effect in other international markets. Ziemba (1991) found that returns were consistently higher on first and last four days of the month. The holiday effect refers to higher returns around holidays, mainly in the pre-holiday period as compared to returns of the normal trading days. Lakonishok and Smidt (1988) studied Dow Jones Industrial Average and reported that half of the positive returns occur during the 10 pre-holiday trading days in each year. Ariel (1990) showed using US stock market that more than one-third positive returns each year registered in the 8 trading days prior to a market-closed holiday. Similar conclusion were brought by Cadsby and Ratner (1992) which documented significant pre-holiday effects for a number of stock markets. However, he didnà ¢Ã¢â€š ¬Ã¢â€ž ¢t find such effect in the European stock markets. Husain (1998) studied Ramadhan effect in Pakistan stock market. He found significant decline in stock returns volatility in this month although the mean return indicates no significant change. There are also evidences of day of the week effect in stock market returns. The Monday effect was identified as early as the 1920s. Kelly (1930) based on three years data of the US market found Monday to be the worse day to buy stocks. Hirsch (1968) reported negative returns in his study. Cross (1973) found the mean returns of the SP 500 for the period 1953 and 1970 on Friday was higher than mean return on Monday. Gibbons and Hess (1981) also studied the day of the week effect in US stock returns of SP 500 and CRSP indices using a sample from 1962 to 1978. Gibbons and Hess reported negative returns on Monday and higher returns on Friday. Smirlock and Starks (1986) reported similar results. Jaffe and Westerfield (1989) studied day of the week effect on four international stock markets viz. U.K., Japan, Canada and Australia. They found that lowest returns occurred on Monday in the UK and Canada. However, in Japanese and Australian market, they found lowest return occurred on Tuesday. B rooks and Persand (2001) studied the five southeast Asian stock markets namely Taiwan, South Korea, The Philippines, Malaysia and Thailand. The sample period was from 1989 to 1996. They found that neither South Korea nor the Philippines has significant calendar effects. However, Malaysia and Thailand showed significant positive return on Monday and significant negative return on Tuesday. Ajayi al. (2004) examined eleven major stock market indices on Eastern Europe using data from 1990 to 2002. They found negative return on Monday in six stock markets and positive return on Monday in rest of them. Pandey (2002) reported the existence of seasonal effect in monthly stock returns of BSE Sensex in India and confirmed the January effect. Bodla and Jindal (2006) studied Indian and US market and found evidence of seasonality. Kumari and Mahendra (2006) studied the day of the week effect using data from 1979 to 1998 on BSE and NSE. They reported negative returns on Tuesday in the Indian stoc k market. Moreover, they found returns on Monday were higher compared to the returns of other days in BSE and NSE. Choudhary and Choudhary (2008) studied 20 stock markets of the world using parametric as well as non-parametric tests. He reported that out of twenty, eighteen markets showed significant positive return on various day other than Monday. The scope of the study is restricted to days-of-the week effect, weekend effect and monthly effect in stock returns of SP CNX Nifty and select firms. The half month effect and holiday effect are not studied here. 4.0 Objective The objective of the study are as follows: To examine days of the week effect in the returns of SP CNX Nifty To examine weekend effect in SP CNX Nifty returns. To examine the seasonality in monthly returns of the BSE Sensex. 5.0 Hypotheses a) Our first hypothesis is that returns on all the days of weeks are equal. Symbolically, H 0 : ÃŽÂ ²1 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²2 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²3 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²4 H1 : at least one ÃŽÂ ²i is different b) Our second hypothesis is as follows: H 0 : ÃŽÂ ²1 à ¯Ã¢â€š ¬Ã‚ ½ 0 H1 : ÃŽÂ ²1 à ¢Ã¢â‚¬ °Ã‚  0 c) Our third hypothesis is: H 0 : ÃŽÂ ²1 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²2 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²3 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²4 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²5 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²6 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²7 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²8 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²9 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²10 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²11 H1 : at least one ÃŽÂ ² is different 6.0 Data and its Sources The monthly data on SP Nifty for the period April 1997 to March 2009 obtained from the Handbook of Statistics on Indian Economy published by the Reserve Bank of India. We also collected daily data on SP Nifty from 1st January 2005 to 31st December 2008 from www. nseindia.com for studying the above objectives. 7.0 Research Methodology To examine the stock market seasonality in India, first we measure stock return of Nifty as given below: Rt à ¯Ã¢â€š ¬Ã‚ ½ (ln Pt à ¢Ã‹â€ ln Pt à ¢Ã‹â€ 1 ) *100 (1) where Rt is the return in period t, Pt and Pt-1 are the monthly (daily) closing prices of the Nifty at time t and t-1 respectively. It is also important to test stationarity of a series lest OLS regression results will be spurious. Therefore, we will first test whether Nifty return is stationary by AR(1) model. We also use DF and ADF tests which are considered more formal tests of stationarity. For testing stationarity, let us consider an AR(1) model yt à ¯Ã¢â€š ¬Ã‚ ½ à ?1 yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « et (2) The simple AR(1) model represented in equation (2) is called a random walk model. In this AR(1) model if | à ?1 |à ¯Ã¢â€š ¬Ã‚ ¼1, then the series is I(0) i.e. stationary in level, but if à ?1 à ¯Ã¢â€š ¬Ã‚ ½1 then there exist what is called unit root problem. In other words, series is non-stationary. Most economists think that differencing is warranted if estimated à ? à ¯Ã¢â€š ¬Ã‚ ¾ 0.9 ; some would difference when estimated à ? à ¯Ã¢â€š ¬Ã‚ ¾ 0.8 . Besides this, there are some formal ways of testing for stationarity of a series. . Dickey-Fuller test involve estimating regression equation and carrying out the hypothesis test The simplest approach to testing for a unit root is with an AR(1) model:. Let us consider an AR(1) process: yt à ¯Ã¢â€š ¬Ã‚ ½ c à ¯Ã¢â€š ¬Ã‚ « à ? yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ µt (3) where c and à ? are parameters and is assumed to be white noise. If à ¢Ã‹â€ 1 p à ? p1, then y is a stationary series while if à ? à ¯Ã¢â€š ¬Ã‚ ½1 , y is a non-stationary series. If the absolute value of à ? is greater than one, the series is explosive. Therefore, the hypothesis of a stationary series is involves whether the absolute value of à ? is strictly less than one. The test is carried out by estimating an equation with yt à ¢Ã‹â€ 1 subtracted from both sides of the equation: à ¢Ã‹â€ Ã¢â‚¬  yt à ¯Ã¢â€š ¬Ã‚ ½ c à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ³ yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ µt (4) where ÃŽÂ ³ à ¯Ã¢â€š ¬Ã‚ ½ à ? à ¢Ã‹â€ 1 , and the null and alternative hypotheses are H0 : ÃŽÂ ³ à ¯Ã¢â€š ¬Ã‚ ½ 0 H1 : ÃŽÂ ³ p0 The DF test is valid only if the series is an AR(1) process. If the series is correlated at higher order lags, the assumption of white noise disturbances is violated. The ADF controls for higher-order correlation by adding lagged difference terms of the dependent variable to the right-hand side of the regression: à ¢Ã‹â€ Ã¢â‚¬   yt à ¯Ã¢â€š ¬Ã‚ ½ c à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ³ yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ´ 1 à ¢Ã‹â€ Ã¢â‚¬   yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ´ 2 à ¢Ã‹â€ Ã¢â‚¬   yt à ¢Ã‹â€  2 à ¯Ã¢â€š ¬Ã‚ « . à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ´ p à ¢Ã‹â€ Ã¢â‚¬   yt à ¢Ã‹â€  p à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ µt (5) This augmented specification is then tested for H0 : ÃŽÂ ³ à ¯Ã¢â€š ¬Ã‚ ½ 0 H1 : ÃŽÂ ³ p0 in this regression. Next, to test the presence of seasonality in stock returns of Nifty, we have used one technique called dummy variable regression model. This technique is used to quantity qualitative aspects such as race, gender, religion and after that one can include as an another explanatory variable in the regression model. The variable which takes only two values is called dummy variable. They are also called categorical, indicator or binary variables in literature. While 1 indicates the presence of an attribute and 0 indicates absence of an attribute. There are mainly two types of model namely ANOVA and ANCOVA. This study uses ANOVA model. Analysis of variance (ANOVA) model is that model where the dependent variable is quantitative in nature and all the independent variables are categorical in nature. To examine the weekend effect and days of the week effect, the following dummy variable regression model is specified as follows: Nifty returns à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ± à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²1Monday à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²2Tuesday à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²3 wednesday à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²4thrusday à ¯Ã¢â€š ¬Ã‚ « à ¯Ã¢â‚¬Å¡Ã‚ µ (6) The variables Monday, Tuesday, Wednesday and Thursday are defined as: Monday = 1 if trading day is Monday; 0 otherwise Tuesday = 1 if trading day is Tuesday; 0 otherwise, Wednesday = 1 if the trading day is Wednesday; 0 otherwise Thursday = 1 if the trading day is Thursday; 0 otherwise ÃŽÂ ± represents the return of the benchmark category which is Friday in our study. Similarly, to find whether there are monthly effects in Nifty returns, we used ANOVA model specified below as: Nifty returns à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ± à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²1 DJune à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²2 DJuly à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²3 DAug à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²4 Dsep à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²5 DOct à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²6 DNov à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²7 DDec à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²8 DJan à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²9 DFeb à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²10 DMar à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²11 DApril à ¯Ã¢â€š ¬Ã‚ « à ¯Ã¢â‚¬Å¡Ã‚ µ (7) where Y = Monthly returns of Nifty D1= 1 if the month is June; 0 otherwise D2 = 1 if the month is July; 0 otherwise D3 = 1 if the month is August; 0 otherwise D4 = 1 if the month is September; 0 otherwise D5 = 1 if the month is October; 0 otherwise D6 = 1 if the month is November; 0 otherwise D7 = 1 if the month is December; 0 otherwise D8 = 1if the month is January; 0 otherwise D9 = 1 if the month is February; 0 otherwise D10 = 1 if the month is March; 0 otherwise D11 = 1 if the month is April; 0 otherwise ÃŽÂ ± represents the mean return on the May month where as ÃŽÂ ²1 to ÃŽÂ ²11 indicate the shift in mean returns across months. Statistically significant values of ÃŽÂ ²Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢s imply significant shifts in mean monthly returns, thus confirming the existence of the month of the year effect. The problem with this approach is that disturbance error term may have autocorrelation. Besides this, residual may contain ARCH effect. Therefore, we will test autocorrelation and ARCH effect in residual and improve our (6) and (7) model accordingly. 8.0 Results At the outset, we plotted the trend of SP CNX Nifty in Fig.1 which shows the movement of index over the sample period. For a long time hovering between 1000 and 2000, Nifty crossed the 2000 mark November 2005. Since then the one can see rising trend in Nifty till September 2008. After September 2008, we witnessed a stock market crash in the backdrop of mortgage crisis in the US followed by economic slowdown round the world which is quite visible in the movement of Nifty also. Fig. 1 Next, we computed descriptive statistics of returns of Nifty and Junior Nifty. The results are reported in Table 1 which show the mean returns of Nifty and Junior Nifty for the period April 1997 and March 2009 are 0.93 and 1.38 percent respectively. Junior Nifty provided higher mean return than the Nifty over the sample period. As the Nifty and Junior Nifty returns are not normally distributed evident from coefficient of skewness and kurtosis, one can use median return instead of mean to represent returns of Nifty and Junior Nifty which are 1.58 and 2.38 percent respectively. Thus, it is clear that Junior Nifty yielded better returns over the sample period. Table 1: Descriptive Statistics (%) Summary Statistics Nifty Junior Nifty Mean 0.93 1.38 Median 1.58 2.38 Standard Deviation 6.71 9.75 Minimum -23.71 -27.66 Maximum 17.01 32.09 Skewness -0.6029 -0.44 Kurtosis 0.5049 0.97 The variability in returns as measured by standard deviation which is the square root of variance The standard deviation is a conventional measure of volatility. Volatility as measured by standard deviations of returns of the sample period for Nifty and Junior Nifty are 6.71 and 9.75 percent respectively. Thus, it is evident that Junior Nifty is more volatile than the Nifty implying investment in Junior Nifty is more riskier. Table 2: AR(1) Model Monthly Series Level Series Return Series Niftyt = 35.0224 + 0.989 Niftyt à ¢Ã‹â€ 1 Niftyt = 0.58 + 0.2686 Niftyt à ¢Ã‹â€ 1 (1.21) (83.725) (0.9) (3.29) NJuniort = 35.0224 + 0.989 NJuniort à ¢Ã‹â€ 1 NJuniort = 35.0224 + 0.989 NJuniort à ¢Ã‹â€ 1 (1.21) (83.725) (0.74) (4.11) Daily Series Level Series Return Series Niftyt = 11.87 + 0.9969 Niftyt à ¢Ã‹â€ 1 Niftyt = 0.79 + 0.07 Niftyt à ¢Ã‹â€ 1 (1.46) (466.11) (0.33) (2.25) NJuniort = 20.01 + 0.997 NJuniort à ¢Ã‹â€ 1 NJuniort = 0.0154 + 0.1624 NJuniort à ¢Ã‹â€ 1 (1.17) (409.28) (0.00) (5.18) In time series econometrics, it is now customary to check stationarity of a series before using it in regression analysis in order to avoid spurious regression. We tested the stationarity of Nifty, Junior Nifty by AR(1) model and augmented Dickey-Fuller Test; while the former is an informal test, the later is a formal test of stationarity. The results of AR(1) model and ADF are reported in Table 2 and Table 3. The results of AR(1) model show that monthly and daily Nifty and Nifty Junior series are not stationary in their level form. However, AR(1) model fitted to Nifty and Nifty Junior return series are stationary. Table 3: Results of ADF Test Series Original Series Return Series Monthly Nifty -1.1851 -4.59* Monthly Junior Nifty -1.564 -4.2 Daily Nifty -1.48 -15.15 Daily Junior Nifty -1.32 -15.46 * MacKinnon critical values for rejection of hypothesis of a unit root at 1%, 5% and 10% are -3.4786, 2.8824 and -2.5778 respectively. The results of augmented Dickey-Fuller test is very much in consistent with AR(1) model. Table 3 shows that both monthly and daily Nifty and Nifty Junior are non-stationary in their level form. However, return series of Nifty and Nifty Junior are stationary as the null of unit root can be rejected at conventional level of 1%, 5% and 10%. Thus, analysis of stock market seasonality is based on return series of Nifty and Nifty Junior as they are stationary. Next, we estimated model (6) to study days of the week effects in daily Nifty and Nifty Junior returns. The results for Nifty are reported in Table 4. The benchmark day in the model is Friday represented by the intercept which provided a return of 0.08 percent on an average of the sample period. Table 4. Results of Equation (6) for Nifty Variables Coefficients t-statistic P-Value Intercept 0.0836 0.624 0.53 Monday -0.0875 -0.46 0.64 Tuesday -0.0405 -0.21 0.83 Wednesday -0.0432 -0.22 0.82 Thursday -0.0784 -0.41 0.68 R2 =0.0002 F Statistic = 0.06( 0.99) Ljung-Box Q(2) = 0.7045 (0.40) D-W Statistic = 1.86 ARCH LM Test(1): F- stat = 54.31 (0.00) Note: Figures in () are p-values Returns of Monday, Tuesday, Wednesday and Thursday can be found out by deducting the coefficients of these days from the benchmark day, that is, Friday which were 0.1711, 0.1241, 0.1268 and 0.162 respectively. The coefficient of Monday is not significant at 5 percent level which indicates that there is no weekend effect in Nifty returns. Further, none of the coefficients are significant at conventional levels of significance indicating that there is no days of the week effects in the Nifty returns. R2 is 0.0002 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.86 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation upto order of 2 is 0.7045 with an insignificant p-value of 0.40 which indicates that we have autocorrelation problem of order one. However, return series exhibits autoregressive conditional heteroskedasticity (ARCH) effects. We corrected the results for autocorrelation of order one by including an AR(1) term on the right hand side of the dummy regression model and ARCH effect is taken care of by fitting a benchmark GARCH (1,1) model. Table 5: Results of Equation (6) for Nifty corrected for autocorrelation and ARCH Effect Mean Equation Variables Coefficients t-statistic P-Value Intercept 0.2368 2.53 0.01 Monday -0.0838 -0.72 0.46 Tuesday -0.1362 -1.018 0.30 Wednesday -0.0912 -0.70 0.47 Thursday -0.0164 -0.13 0.89 AR(1) 0.0767 2.03 0.04 Variance Equation C 0.09 4.94 0.00 ARCH(1) 0.1674 8.45 0.00 GARCH(1) 0.8086 40.53 0.00 Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q (5) = 5.33 (0.25) ARCH LM Test(1): F- stat = 0.1645(0.68) Table 5 shows that after correcting for serial autocorrelation and ARCH effect, we found Friday effect in Nifty returns. However, our analysis do not find weekend effect. The Ljung-Box Q statistic shows that there is no pattern in residual. ARCH LM test also indicate that there is no ARCH effect in residual now. We also examined the presence of seasonality in Nifty Junior. The results are given in Table 6 which shows that there is neither weekend effect or days of the week effects in Nifty Junior. Table 6. Results of Equation (6) for Nifty Junior Variables Coefficients t-statistic P-Value Intercept 0.1824 1.20 0.22 Monday -0.2988 -1.40 0.16 Tuesday -0.0766 -0.35 0.72 Wednesday -0.2191 -1.024 0.30 Thursday -0.3149 -1.46 0.14 R2 =0.003 F Statistic = 0.84 (0.49) Ljung-Box Q(5) = 26.55 (0.00) D-W Statistic = 1.70 ARCH LM Test(1): F- stat = 145.54 (0.00) Note: Figures in () are p-values. The coefficient of Monday is not significant at 5 percent level which indicates that there is no weekend effect in Nifty Junior returns. None of the coefficients are significant at conventional levels of significance implying that there are no days of the week effects in the Nifty Junior returns. R2 is 0.003 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.7 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation upto order of 5 is 26.55 with a significant p-value of 0.00 which indicates that we have autocorrelation problem of higher order. Nifty Junior series also exhibits autoregressive conditional heteroskedasticity (ARCH) effects. We corrected the results for autocorrelation of order one by including an AR(1) term on the right hand side of the dummy regression model and ARCH effect is taken care of by fitting a benchmark GARCH (1,1) mod el. Autoregressive conditional heteroskedasticity (ARCH) model was first introduced by Engle (1982), which does not assume variance of error to be constant. In ARCHGARCH models, the conditional mean equation is specified, in the baseline scenario, by an AR(p) process i.e. is regressed on its own past values. Let the conditional mean under the ARCH model may be represented as: y à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ± à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ « à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ «Ãƒ ¯Ã¢â€š ¬Ã‚  Ãƒ ¯Ã¢â‚¬Å¡Ã‚ µ and à ¯Ã¢â‚¬Å¡Ã‚ µ ~ (N ,0, à Ã†â€™ 2 ) (8) t 1 1 2 2 3 3 n n t t t In equation (8), the dependent variable yt varies over time. Similarly, conditional variance of à ¯Ã¢â‚¬Å¡Ã‚ µt may be denoted as à Ã†â€™t2 , which can be represented as: à Ã†â€™t2 à ¯Ã¢â€š ¬Ã‚ ½ var(ut | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 ..) à ¯Ã¢â€š ¬Ã‚ ½ E[(ut à ¢Ã‹â€  E(ut )2 | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 .)] It is usually assumed that E(à ¯Ã¢â‚¬Å¡Ã‚ µt ) à ¯Ã¢â€š ¬Ã‚ ½ 0 , so: à Ã†â€™t2 à ¯Ã¢â€š ¬Ã‚ ½ var(ut | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 .) à ¯Ã¢â€š ¬Ã‚ ½ E(ut2 | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 ,.) (9) Equation (9) states that the conditional variance of a zero mean is normally distributed random variable ut is equal to the conditional expected value of the square of ut . In ARCH model, à ¢Ã¢â€š ¬Ã‹Å"autocorrelation in volatilityà ¢Ã¢â€š ¬Ã¢â€ž ¢ is modeled by allowing the conditional variance of the error term, à Ã†â€™t2 , to depend immediately previous value of the squared error. This may be represented as: à Ã†â€™t2 à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ±0 à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ ±1ut2à ¢Ã‹â€ 1 (10) The above model is ARCH (1) where, the conditional variance is regressed on constant and lagged values of the squared error term obtained from the mean equation. In equation (5.12), conditional variance must be strictly positive. To ensure that these always result in positive conditional variance, all coefficients in the conditional variance are usually required to be non- negative. In other words, this model make sense if ÃŽÂ ±0 à ¯Ã¢â€š ¬Ã‚ ¾ 0 and ÃŽÂ ±1 à ¢Ã¢â‚¬ °Ã‚ ¥ 0 . However, if ÃŽÂ ±1 à ¯Ã¢â€š ¬Ã‚ ½ 0 , there are no dynamics in the variance equation. An ARCH (p) can be specified as: ht à ¯Ã¢â€š ¬Ã‚ ½ à Ã¢â‚¬ ° à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ± 1ÃŽÂ µ t2à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ± 2 ÃŽÂ µ t2à ¢Ã‹â€  2 à ¯Ã¢â€š ¬Ã‚ « .. à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ ± p ÃŽÂ µt2à ¢Ã‹â€ p (11) This ARCH model might call for a long-lag structure to model the underlying volatility. A more parsimonious model was developed by Bollerslev (1986) leading to generalized ARCH class of models called GARCH in which, the conditional variance depends not only on the squared residuals of the mean equation but also on its own past values. The simplest GARCH (1, 1) is: à Ã†â€™ 2 à ¯Ã¢â€š ¬Ã‚ ½ à Ã¢â‚¬ ° à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ± ÃŽÂ µ 2 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²Ãƒ Ã†â€™ 2 (12) t 1 t à ¢Ã‹â€ 1 1 t à ¢Ã‹â€ 1 The conditional volatility as defined in the above equation is determined by three effects namely, the intercept term given by w , the ARCH term expressed by ÃŽÂ ± ÃŽÂ µ2 and the forecasted 1 t à ¢Ã‹â€ 1 volatility from the previous period called GARCH component expressed by ÃŽÂ ²Ãƒ Ã†â€™1 t2à ¢Ã‹â€ 1 . Parameters w and ÃŽÂ ± should be higher than 0 and ÃŽÂ ² should be positive in order to ensure conditional variance à Ã†â€™2 to be nonnegative. Besides this, it is necessary thatÃŽÂ ±1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²1 p1 . This condition secures covariance stationarity of the conditional variance. A straightforward interpretation of the estimated coefficients in (12) is that the constant term à Ã¢â‚¬ ° is the long-term average volatility, i.e. conditional variance, whereas ÃŽÂ ± and ÃŽÂ ² represent how volatility is affected by current and past information, respectively. Table 7: Results of Equation (6) for Nifty Junior corrected for autocorrelation and ARCH Effect Mean Equation Variables Coefficients t-statistic P-Value Intercept 0.3572 3.74 0.001 Monday -0.2962 -2.47 0.01 Tuesday -0.2183 -1.53 0.12 Wednesday -0.2849 -2.1 0.03 Thursday -0.1672 -1.27 0.2 AR(1) 0.1667 4.74 0.00 Variance Equation C 0.1387 4.78 0.00 ARCH(1) 0.1833 9.41 0.00 GARCH(1) 0.789 41.99 0.00 F-stat = 2.28 (0.02) Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q (5) =7.12(0.12) ARCH LM Test(1): F- stat = 1.37 (0.24) Table 7 shows that after correcting for serial autocorrelation and ARCH effect, we found weekend effect in Nifty Junior returns. Our study also found significant seasonality in Nifty Junior returns across the days. Returns of Monday, Wednesday and Friday are significantly different from each other. The F-statistic shows that at least one beta coefficient is different from zero. The Ljung-Box Q statistic shows that there is no pattern in residual. ARCH LM test also indicate that there is no ARCH effect in residual now. We also examined seasonality of Nifty and Nifty Junior return using monthly data. We estimated equation (7). The results for Nifty are reported in Table 8. The benchmark month in the model is May represented by the intercept which provided negative return of -0.7132 percent on an average over the sample period. None of the coefficients are significant except December month which indicate the presence of December effect in Nifty monthly returns. Table 8: Results of Equation (7) for Nifty Variables Coefficients t-statistic P-Value Intercept -0.7132 -0.35 0.71 June -0.8535 -0.30 0.76 July 3.1781 1.13 0.25 August 1.5309 0.54 0.58 September 2.1704 0.77 0.44 October -0.2136 -0.07 0.93 November 1.8055 0.64 0.52 December 5.047 1.79 0.07 January 3.4969 1.24 0.21 February 1.1607 0.41 0.67 March -0.2425 -0.08 0.93 April -0.2809 -0.09 0.92 R2 =0.06 F Statistic = 0.84( 0.59) Ljung-Box Q(5) = 11.85(0.03) D-W Statistic = 1.46 ARCH LM Test(1): F- stat = 0.8851 (0.34) Note: Figures in () are p-values R2 is 0.06 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.46 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation up to order of 5 is 11.85 with a significant p-value of 0.03 which indicates that we have autocorrelation problem of higher order. However, monthly Nifty returns do not exhibits autoregressive conditional heteroskedasticity (ARCH) effects. Therefore, we augmented the model specified in equation (7) with autoregressive order of 5 and moving average order of 1 and 5 on a trial and error basis. The results are reported in Table 9 which shows the presence of seasonality in monthly returns of Nifty. The coefficients of July, September and January are statistically significant at 5 percent level. The coefficient of December month is statistically highly significant at 1 percent level of significance. The augmented model has R-squ are of 0.22 which shows that 22 percent of the variations are explained by these months. F-statistic is 2.62 with significant p-value of 0.002 implying that the null of all slope coefficients is rejected at 1 percent level of significance. Table 9: Results of Equation (7) for Nifty Variables Coefficients t-statistic P-Value Intercept -1.6045 -1.03 0.30 June -0.13 -0.06 0.94 July 4.3899 1.97 0.05 August 2.2566 0.91 0.36 September 3.9858 1.86 0.06 October -0.0504 -0.02 0.98 November 3.1714 1.54 0.12 December 5.8317 2.52 0.01 January 4.8644 2.08 0.03 February 2.5038 1.07 0.28 March 0.1636 0.07 0.94 April 0.7953 0.39 0.69 AR(5) 0.6094 6.77 0.00 MA(1) 0.3559 453.72 0.00 MA(5) 0.689 -9.89 0.00 R2 =0.22 F Statistic = 2.62( 0.002) Ljung-Box Q(5) = 1.73 (0.42) D-W Statistic = 1.96 Note: Figures in () are p-values Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q statistic of augmented model of order up to 5 is 1.73 with insignificant p value of 0.42 which implies that there is no pattern left in residual. This is also evident from D-W statistics of 1.96 which is very close to 2. Table 10: Results of Equation (7) for Nifty Junior Variables Coefficients t-statistic P-Value Intercept -0.0106 -0.0037 0.99 June -3.1408 -0.79 0.42 July 2.5269 0.63 0.52 August 2.78 0.70 0.48 September 1.6919 0.42 0.67 October -2.1813 -0.55 0.58 November 1.6522 0.41 0.67 December 7.2491 1.82 0.06 January 4.0079 1.01 0.31 February 0.131 0.03 0.97 March -3.3807 -0.85 0.39 April -0.3954 -0.09 0.92 R2 =0.09 F Statistic = 1.20( 0.28) Ljung-Box Q(5) = 19.31(0.00) D-W Statistic = 1.33 ARCH LM Test(1): F- stat = 12.36 (0.00) Note: Figures in () are p-values Finally, we examined the seasonality of monthly Nifty Junior returns. We estimated the model specified in equation (7) for Nifty Junior. The results are reported in Table 10 which shows that December effect is present in Nifty Junior returns. Besides this, the coefficient of June month is found to be statistically significant at 5 percent level indicating the presence of seasonality in the returns of Nifty Junior. In this regression model, R2 is 0.09 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.33 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation up to order of 5 is 19.31 with a significant p-value of 0.00 which indicates that we have autocorrelation problem of higher order. However, unlike Nifty monthly Nifty Junior returns exhibits autoregressive conditional heteroskedasticity (ARCH) effects. Table 11: Results of Equation (7) for Nifty Junior corrected for autocorrelation and ARCH Effect Mean Equation Variables Coefficients t-statistic P-Value Intercept 1.9045 0.85 0.39 June -4.67 -1.93 0.05 July 2.3638 0.48 0.62 August 0.6749 0.17 0.86 September 0.253 0.06 0.94 October -2.9230 -0.80 0.42 November 0.038 0.01 0.99 December 5.86 1.69 0.08 January 2.7228 0.70 0.47 February -1.2328 -0.33 0.74 March -2.7668 -1.01 0.31 April -0.7839 -0.29 0.76 AR(1) 0.364 4.08 0.00 Variance Equation C 8.13 0.11 ARCH(1) 0.1648 0.10 GARCH(1) 0.00 0.7393 F-stat = 1.73(0.04) Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q (5) = 2.07 (0.72) ARCH LM Test(1): F- stat = 0.0142 (0.9051) Note: Figures in () are p-values Table 11 shows that after correcting for serial autocorrelation and ARCH effect, we found June and December effect in monthly Nifty Junior returns because the coefficient of these dummy variables are found statistically significant at 5 and 10 percent respectively. The F-statistic shows that at least one beta coefficient is different from zero. The Ljung-Box Q statistic shows that there is no pattern in residual. ARCH LM test also indicate that there is no ARCH effect in residual now. 9.0 Conclusion In this study, we tried to examine the seasonality of stock market in India. We considered the SP CNX Nifty as the representative of stock market in India and tested whether seasonality are present in Nifty and Nifty Junior returns using daily and monthly data sets. The study found that daily and monthly seasonality are present in Nifty and Nifty Junior returns. The analysis of stock market seasonality using daily data, we found Friday Effect in Nifty returns while Nifty Junior returns were statistically significant on Friday, Monday and Wednesday. In case of monthly analysis of returns, the study found that Nifty returns were statistically significant in July, September, December and January. In case of Nifty Junior, June and December months were statistically significant. The results established that the Indian stock market is not efficient and investors can improve their returns by timing their investment.