Prediction Markets in Currency Exchange Forecasts
SI 679 Research Paper By, Aditya Doshi School of Information, University of Michigan adityamd@umich.edu
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Introduction: The paper will give a brief description of the existing system and the need for a market for determining currency exchange rates for the near future. It will then explain the need and usefulness of a prediction market in this domain and propose a market design for such a prediction market based on certain criteria’s such as security design, the trading mechanisms, use of real money or play money, and interfaces. It then lightly discusses the strategic concerns of having such a market as well as some ethical issues. The paper ends with a comparison to other predicting mechanisms and recommendations to implement this market. Forecasting problem and scope: Currency values have come a long way from being determined by the actual gold reserves of a country to having currencies relative to the US$. Most markets today allow for trading on the currency futures. Predicting exchange rates is not easy as many factors work at determining these rates. A few important factors include: 1. Interest rate movements – the national interest rates have a significant effect on the exchange rate; as this determines the flow of money in a country, hence causing an appreciation of that currency and in turn the exchange rate. 2. Economic prospects – the economic prospects of an economy depends on the inflation rates and general economic situation of the country. This in turn affects interest rates and expectations towards devaluation of a currency. 3. Political policy and government control – policies and political conditions play an important role in determining the growth prospects of a country and its economy and affects currency exchange rates. It is very important for most people specially hedgers to have an accurate knowledge of this exchange rate movement in advance to cut risks in present international transactions. These
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people resort to currency futures markets and trade in currency future contracts1 and currency forward contracts2 to hedge against volatility in exchange rate prices. In this paper we shall study one of the many big organizations that gets affected by the change in currency rates over a period of time. The company under consideration is ‘Infosys’ which is India’s second largest software services company. In the summer of 2007 when the Rupee appreciated by more than 7% against the Dollar, Infosys made a strategic decision to cut its dependence on the US market from 63% to 50%; this was a risk mitigating effort. The company also participates in the recently opened3 currency futures market as a hedging mechanism4. Hence we herewith analyze the company’s use of a prediction market to make accurate buys/sells on the currency futures market. Use of prediction markets: Since the exchange rate varies according to various factors such as interest rates, political policies, growth prospects in industry and government control, there are many people possessing different tidbits of insider knowledge and publicly available knowledge. This knowledge can be categorized into three groups – firstly, the knowledge that everyone knows and gives one no competitive advantage, secondly, knowledge that gives no advantage but not knowing it can be potentially damaging, and thirdly, insider knowledge that is private and can be exploited to an advantage. A prediction market mainly provides incentive to seek this knowledge and aggregates a diverse opinion to project accurate forecasts. Hence prediction markets are also called as ‘strong form’ efficient markets, where the prices reflect all available information, including private information. Moreover, these markets are dynamic and factor in continuous time compared to having polls that are periodic and static. 1
Currency futures contract allow investors to hedge against foreign exchange risk. Investors can exit their obligation to buy or sell the currency prior to the contract’s delivery date. 2 Currency forward contracts obligate the contract holder to buy or sell the currency at a specified price, at a specified quantity and on a specified future date. These contracts cannot be transferred. 3 The currency futures market for trading on $ vs. Rs was opened to residents in India in August, 2008. 4 Infosys was assuming a rate of Rs 40.58 to a dollar in its forecast and has not factored any large deals. It has hedged $925 million at Rs 40.58. Mr. Balakrishnan said adding “if required we will increase the hedging”. – (2007, The Hindu Business)
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Hence we see that prediction markets provide investors with a hedging mechanism against future events, a sort of insurance against volatility that cannot be insured elsewhere; and is beneficial to exporters and importers who wish to hedge against volatility in currency rates. In our case study, it would be beneficial for Infosys to tap this diverse knowledge by means of a prediction market launched by the company. Currently, a set of experts (risk managers / analysts) make the decision of investing in currency future markets for the sole purpose of hedging. Having employees and stakeholders from across the world predict in this market will harness the ‘wisdom of crowds’ (James Surowiekci, 2004) as every trader will bring in new information both private and public regarding the various factors mentioned above. Additionally, the internal prediction markets of Infosys will also provide information regarding price ($) forecasts in the coming year and enable the company to make wise decisions regarding investments in research and development and its global expansion plans apart from helping the company to hedge against current investments/transactions5. Hanson et al. mention that “prediction markets can aid decision makers by gathering diffuse information and beliefs into easy to interpret price statistics” (Hanson et al., 2005, p. 2) Employee
Stakeholder
Speculator
Infosys prediction market
Currency futures contracts
Dynamic Pari-‐ mutuel Market
Currency forward contracts
NSE
FIGURE 1 – USE OF PREDICTION MARKET BY INFOSYS TO PREDICT CURRENCY FUTURES 5
“The sharp appreciation of the rupee against all major currencies impacted our operating margins,” said Mr V. Balakrishnan, Chief Financial Officer. (2007, reported in The Hindu Business)
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Market design: Security Design The event that would be traded on would have a question such as “What would the foreign exchange rate for $ vs. Rs be for the month of ‘X’?” where X would be a month in the next 12 months6. This means that the company would have 12 markets running at the same time, one for predicting the price for each month. The trade would be open 24x7 and last for a month or until the company has locked its futures contracts on the NSE (National Stock Exchange) for that month depending on the prediction in its own market. This monthly time frame allows for information gathering and trading in respectable volumes. The security traded on the Infosys prediction market will be a type of Index contract where the trader will get Rs. X in the future if the price of the Dollar is X. The trading price initially will be the current price of the $ equal to the Reference Rate for the day, as set by the Reserve Bank of India. In equilibrium, the market would converge at the best forecast of the future rate which can then be used to make a decision by the company to buy/sell on the futures exchange of currency futures. For example, if the current reference rate is 44.567 for the month of December and traders feel that the rate would be 45.567 in February, they would buy the contracts @ 44.567 (or at any amount till 45.566 rationally) and on maturity if the rate is more than the trading price today they stand to make a profit. Hence rational traders drive the current prediction market rate towards 45.567 and hence the company can trade on a futures market to buy contracts for 45.567. The size of each such security / contract shall be $100 and these contracts will be quoted and settled in Indian Rupees when the futures contract is settled in the respective month. The prices will reflect up to the fourth decimal digit of the rate (0.000X) and rounded off thereafter, this would reduce any exchange rate arbitrages. These contracts shall be traded on the Infosys prediction market, an internal exchange run by the company. 6
The Reserve Bank and SEBI have set guidelines for the trade on currency futures with maturity of contracts within 12 months. Hence only futures for within the next 12 months are traded upon.
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Trading Mechanism The Infosys prediction market should implement the Dynamic Pari-mutuel Market (DPM) which is a hybrid between a pari-mutuel and a Continuous Double Auction (CDA) market. Since the Infosys prediction market is a company market and would have lesser volumes being traded by lesser traders, it would be wise to implement a DPM which provides infinite buy-in liquidity and has zero risk for the market institution (Infosys). The pari-mutuel market overcomes the problem of having thin markets by allowing for buy-in trade continuously, thus encouraging traders to share their new information immediately. Moreover it incorporates the advantages of a CDA by allowing users to take out profit/loss before the event is resolved. This helps as most employees are marginal players and would like an option to limit losses. Additionally, as the prediction market is for the sole purpose of decision making by the company and not meant for making profits from it, the DPM is the correct choice. In DPM the market pays out exactly the amount taken in; that is the money is only redistributed amongst the traders. The market maker makes no profit or loss. Real money or Play money The prediction markets with real money and play money give similar accurate results of forecasts. This was proved by the experiments of Servan-Schreiber and Slamka et al. ServanSchreiber further mention that real money markets gave better incentive for information discovery. Since the currency futures depend on so many varied factors and the company would encourage the traders to discover information in each field to provide more than the public knowledge available to everyone, the use of real money is preferred in this market design. It also encourages speculators by providing them incentive to trade in the market and bringing in more traders thus reducing the problems of thin market. Moreover, the traders have incentive for truthful revelation of facts, as they stand to gain or lose real money and this can be a mechanism to handle the effects of information cascades and manipulation. Play money markets would be better if the company wanted to predict an internal forecast from employees only, for example the company sales or sentiments regarding an issue. But as the information required is at a global level and people outside the company, i.e. people from banks, government administration, US citizens, financial brokers and market watchers, take part in the prediction; real money would be a good choice for this market. Page 6 of 10
Specific Interface Design / Implementation Concerns Since the prediction market is used mostly in-house and by non-experts and experts alike, we would keep the interface simple with explanations and cause-effect panels. These panels would show the results of potentially choosing to buy or sell a certain amount of shares. Also would be included is an option to limit order. Since we are using a DPM which has a sort of automated market maker for buy-in, it would be wise to provide trader with a limit order option to check buy/sell prices. Also on the lines of limited trading the system would check a trader from not trading more than a certain amount (say $500) on a single day, this would be a good check for manipulation and reduce the risk of one trader having major influence on the market (considering smaller marginal employees are also trading on the same market). Moreover the system would maintain an order book and display the current holdings of a given trader in his screen to aid long term memory. Lastly, the most important display on the screen would be a dynamic price chart which would show the price fluctuations every 30 secs and would also have a sidebar showing digital price fluctuations (a combination of what is shown below).
FIGURE 2 – ELEMENTS OF SPECIFIC INTERFACE DESIGN FOR INFOSYS PREDICTION MARKET Page 7 of 10
Strategic concerns Hanson et al. mention that prediction markets are “susceptible to price manipulation by agents who wish to distort decision making”. Some individuals who wish to control the policy of a company indirectly can be willing to make some monetary loss for this purpose. Thus a company implementing prediction markets for decision making purposes needs to be aware of such manipulation activities. The experiment carried out by Hanson et al. showed that when the market agent suspects the presence or is aware of the presence of manipulators or the direction of manipulation, manipulation is ineffective. Strong mechanisms to check manipulation thus need to be in place. A step towards controlling this is to have a limit on each trader’s activity as mentioned above. This reduces the influence of a single trader in a single day and gives time for rational players in the market to drive the price back to rational equilibrium. Other steps such as checking for irrational behavior, out of bound predictions can also be implemented. Furthermore, the trading interface hides other trader’s detailed activities so as to prevent information cascade. Only the current price in digital form and an analog graph of past activity is shown. Policy and ethical concerns Since prediction markets are a recent domain it is not yet differentiated from gambling markets that are illegal in India. Many laws under the IPC prohibit lottery and any form of gambling. But taking cue from arguments for prediction markets elsewhere (Europe, USA) policy makers can allow for prediction markets for non-profit and decision making purposes. As in-company markets are generally small scale markets and have no clear speculative interests, soon a policy should be in place to make such markets legal. Arrow et al. argue in their paper that regulators should lower barriers to create small stake markets which help private firms manage economic risks. They also mention that these markets need to be separated from regulations that govern gambling markets. As on today, Infosys being a global company can start an online prediction market whose exchange is based offshore in Europe (or some parts of USA). This will allow for traders to legally trade in the market until Indian policies change. Moreover as they implement a DPM trading mechanism; wherein the exchange stands to make no profit from the transactions apart from having access to valuable information for decision making purpose; it would be regarded as a non-for profit market and bypass American gambling laws. Page 8 of 10
Strengths and weaknesses w.r.t alternative forecasting methods Currently decision making takes place with the consensus of a panel of experts also called risk managers within the company or by analysts in a professional risk management and consulting firm. They follow techniques such as Nominal Group technique or the Delphi process. These techniques specially the Delphi allow for accountability and justification of predictions which is not possible in prediction markets that are completely anonymous, hence there is chance of manipulation in prediction markets. It is also said that prediction markets work best by “deriving their power from aggregating the information of a diverse group of participants.” [Buckley, P. Managing prediction markets, in proceedings of MIS’s 47th conference, (2009), ACM, pg.217-220.] Hence when the advising body size is small or there is a thin market or few traders, there are chances of manipulation or polarization. One of the strengths of prediction markets is its capability to factor in new information almost instantaneously and at all times. Also in real money prediction markets there is incentive for truthful revelation of information and to search for newer information when the stakes are high enough. Hence, making the information and the process more credible. Overall recommendations / Conclusion Hence we see that under proper policies and by implementing a good market design for the prediction market, a company such as Infosys can be benefited by being able to aggregate diverse opinions of people, both within and outside the company, and this in turn would help them in decision making regarding hedging funds in the currency futures market as well as company policy formulation for the near future. A play money prediction market might also accurately enough help small exporters and importers to hedge against currency volatility. With better evaluation and trading mechanisms prediction markets would turn to be great information sources and decision-making systems in the near future.
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References 1. Prediction Markets, Wolfers and Zitzewitz, The Journal of Economic Perspectives 18(2), 2004 2. Comparing Face-‐to-‐face Meetings, Nominal Groups, Delphi and Prediction Markets on an Estimation Task, Graefe and Armstrong. 3. Information Markets: A New Way of Making Decisions -‐ Chapter 4: Deliberation and Information Markets, Cass R. Sunstein. 4.
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Buckley, P. Managing prediction markets, in proceedings of MIS’s 47 conference, (2009), ACM, pg.217-‐220. 5. Combinatorial Information Market Design, Hanson, Information System Frontiers (5), 2001. 6. Dynamic Parimutuel Markets, Pennock, ACM EC'04 7. Information Aggregation and Manipulation in an Experimental Market , Hanson, Oprea, and Porter, Journal of Economic Behavior and Organization 2006.
8. The Promise of Prediction Markets, Arrow et al, Science (320), May 2008. 9.
Prediction Markets as Decision Support Systems, Joyce E. Berg and Thomas A. Rietz. (2003)
10. Currency future. (2009, December 16). In Wikipedia, The Free Encyclopedia. Retrieved 19:23,
December 17, 2009, http://en.wikipedia.org/w/index.php?title=Currency_future&oldid=331967748 11. http://www.banknetindia.com/banking/80816.htm 12. https://dealbookweb.demo.gftforex.com/
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