Leilei Tang

Exploring Decision Makers Use of Price Information in a Speculative Market

We explore the extent to which the decisions of participants in a speculative market effectively account for information contained in prices and price movements. The horse race betting market is an ideal environment to explore these issues. A conditional logit model is constructed to determine winning probabilities based on bookmakers closing prices and the time-indexed movement of prices to the market close. We incorporate a technique for extracting predictors from price (odds) curves using orthogonal polynomials. The results indicate that closing prices do not fully incorporate market price information, particularly information that is less readily discernable by market participants.

Time is money: Costing the impact of duration misperception in market prices

We explore whether, and to what extent, traders in a real world financial market, where participants’ judgements are reportedly well calibrated, are subject to duration misperception. To achieve this, we examine duration misperception in the horserace betting market. We develop a two-stage algorithm to predict horses’ winning probabilities that account for a duration-related factor that is known to affect horses’ winning prospects. The algorithm adapts survival analysis and combines it with the conditional logit model. Using a dataset of 4736 horseraces and the lifetime career statistics of the 53,295 horses running in these races, we demonstrate that prices fail to discount fully information related to duration since a horses last win. We show that this failure is extremely costly, since a betting strategy based on the predictions arising from the model shows substantial profits (932.5 percent and 16.27 percent, with and without reinvestment of winnings, respectively). We discuss the important implications of duration neglect in the wider economy.

Assessing the impact of derived behavior information on customer attrition in the financial service industry

The value of the customer has been widely recognized in terms of financial planning and efficient resource allocation including the financial service industry. Previous studies have shown that directly observable information can be used in order to make reasonable predictions of customer attrition probabilities. However, these studies do not take full account of customer behavior information. In this paper, we demonstrate that efficient use of information can add value to financial services industry and improve the prediction of customer attrition. To achieve this, we apply an orthogonal polynomial approximation analysis to derive unobservable information, which is then used as explanatory variables in a probit–hazard rate model. Our results show that derived information can help our understanding of customer attrition behavior and give better predictions. We conclude that both researchers and the financial service industry should gather and use derived financial information in addition to directly observable information.

The explanatory power of trading volume and insider activity in a pari-mutuel betting market

In this paper, we examine the role played by the holders of privileged information (insiders) in stimulating trading volume which adds explanatory power to a price-based model of returns in a market where the actions of insiders can be isolated – a pari-mutuel betting market. We conduct conditional logit analyses based on data relating to 19,164 horses running in 2078 races (49 racetracks) staged across the UK between 1 June and 31 August 1996. These analyses indicate that prices generally fail to incorporate fully information contained in trading volumes. However, the betting public fully discounts volume information in markets most commonly viewed as attracting bets from insiders. Isolation of the actual degree of insider activity sheds light on the variation in volume effects.