توانایی پیش بینی بازار معاملات آتی مجازی مبتنی بر اینترنت

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. While the virtual futures market has received much attention, questions remain as to the ideal number of participants. Results of Granger causality tests and analysis of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of the underlying stock price. Moreover, the participants were not professional investors but merely undergraduate finance students with only a cursory introduction to futures trading. Our results provide additional evidence supporting the use of VFMs in forecasting and show that VFMs are powerful forecasting tools.

The use of Internet-based virtual futures markets is a powerful and previously unexplored approach that can be used to predict movements in the stock markets. It is well known in finance literature that futures prices are powerful forecasters of spot asset prices. In fact, many popular existing financial assets (e.g. stock market index mutual funds, currency, major government bonds, etc.) have active futures markets and their futures prices are used widely by financial forecasters as inputs to their forecasting models. However, in the real world, not all financial assets have active (bricks and mortar) futures markets. For example, only in the past 2 years has the London Stock Exchange (LSE) started trading futures contracts on individual stocks, and so far, they have been limited to contracts on a select group of well-established companies. Suppose that an investor is interested in investing money in Taiwan Semiconductors (TSM), for example. Currently, however, there is not an active futures market for TSM anywhere in the world. Is it possible to create in virtual space a feasible futures market for TSM? Would this futures market be informative like the futures prices generated by the virtual futures market? Would they, therefore, be useful in the forecasting of the underlying spot prices (in this case TSM stock prices)? Additionally, how small can the virtual futures market be without sacrificing the integrity of its information? Can an Internet-based virtual futures market with only 75 or so participants generate informative futures prices useful in predicting the underlying asset? What caliber must the futures market participants be? Do they have to be experienced futures traders? Or can they be ordinary college finance students with interest in the stock market? Also, what incentive structure should the virtual futures market use? Should it be based on real money as in the bricks and mortar futures markets where traders gain and lose real money depending on their futures positions? Or would a simple prize system of incentives suffice and produce the same results? Or can a system of punishments in a classroom setting (i.e. lower grades for poor performing students) be used and still generate informative futures prices? These questions merely provide a glimpse of the many interesting research questions that can be studied. This study should be considered preliminary and only as a first step. Further studies are planned to address related issues in more detail and with more robust research methodologies.

We constructed an Internet-based VFM to predict an underlying stock price. We have found that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of the underlying stock price. Moreover, the participants were not professional investors but merely undergraduate finance students with only a cursory introduction to futures trading. Our results provide additional evidence supporting the use of VFMs in forecasting and show that VFMs are powerful forecasting tools.