Aurora Borealis
1 November 2017 CFA Institute Journal Review

The Role of Speculative Trade in Market Efficiency: Evidence from a Betting Exchange (Digest Summary)

  1. Stuart Fujiyama, CFA
Examining horserace betting in the United Kingdom, the authors find evidence that speculative trading reduces mispricing and promotes market efficiency.

How Is This Research Useful to Practitioners?

Betting-exchange trading is similar to speculative trading in equity markets, but instead of trading stocks and options, participants trade bets. In horseracing, a bet is a wager for (or against) a horse in the place market or the win market during either the pre-race or the “in-running” trading period. The authors liken the place market to the stock market and the win market—with its need for more precise predictions—to an options market. They liken the pre-race trading period to trading before earnings announcements and in-running trading to trading immediately after such announcements.

Controlling for the favorite–long shot bias—the tendency of returns from betting on favorites to exceed those from betting on long shots—the authors find that during in-running trading, high-volume bets are 9.3% more likely to win than their low-volume equivalents in the win market and 13.3% more likely to win in the place market. They conclude that as information is arriving, trading is highly predictive of fundamentals, which is truest within the (stock-market-like) place market.

For the in-running trading period, the authors find that at the start of the period in the win market, bets that subsequently experience high trading volume win 19.7% more often than similarly priced bets that subsequently experience low trading volume; this difference shrinks to the previously mentioned 9.3% during the trading period. At the start of the period in the place market, bets that subsequently experience high trading volume win 49.9% more often than similarly priced low-volume bets; this difference shrinks to 13.3% during the trading period. The authors conclude that as information is arriving, trading promotes market efficiency by driving prices toward fundamental values.

How Did the Authors Conduct This Research?

The authors obtain second-by-second data on 9,562 UK horseraces between March 2013 and March 2014 from Fracsoft, a third-party provider of Betfair betting-exchange limit-order data. The data include both the win and the place markets and both pre-race and in-running trading. For pre-race trading, the authors concentrate on the last 20 minutes before each race. They merge these data with Betfair data, which identify horses that win and place in each race, resulting in a dataset representing 60 million separate trades.

For each race, the authors categorize bets along two dimensions: market (win or place) and period (pre-race or in-running). Within each of the four resulting subsets, the authors compute the trading volume (in British pounds) for each bet and compare that amount with the median amount wagered on bets within that subset. Bets above the median are deemed high-volume bets, and bets below the median are deemed low-volume bets.

The authors assess the impact of high trading volume versus low trading volume on the win frequency of a bet. They use linear probability models in their analysis, controlling for any favorite–long shot bias by using nearest-neighbor matching methods to restrict comparisons to bets with similar implied win probabilities. They set significance at the 10% level, although all their in-running results are significant at the 0.1% level.

Abstractor’s Viewpoint

From their examination of betting-exchange trading, the authors glean several insights into speculative trading in equity markets. Their findings help explain how speculators—despite their “propensity for overconfidence, erroneous beliefs, and herd behavior”—can nevertheless make markets more efficient and why any existing inefficiencies tend to be short-lived and difficult to profit from.

The authors suggest that this research should be of interest to regulators and other market participants contemplating measures to curtail speculative trading. But they also seem to imply that their results may be of limited value to those seeking a profitable investment strategy, because they find that predictive trading occurs primarily during the brief in-running period, which they liken to trading immediately after earnings announcements.

In betting-exchange trading, seconds can elapse between the observation of trading volume differentials and the dissemination of fundamental information. In equity market trading, the window of opportunity is probably even smaller, given the presence of high-frequency-trading algorithms.

We’re using cookies, but you can turn them off in Privacy Settings.  Otherwise, you are agreeing to our use of cookies.  Accepting cookies does not mean that we are collecting personal data. Learn more in our Privacy Policy.