ASSESSING INEFFICIENCY IN THE STANDARD-AND-POOR-500 FUTURES MARKET

We analyse the price movement of the S&P 500 futures market for violat ions of the efficient market hypothesis on a short-term basis. To asse ss market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroskcedastic behaviour typica l of long series of financial data. To account for this non-linear beh aviour we scale the returns by a volatility factor which depends on th e daily high, low, and closing price. The rescaled series, which may b e interpreted as the trend-countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is a n ARMA(1, 1). The scale factors are assumed to form a time series and are modelled using a semi-non-parametric method which avoids the restr ictive assumptions of most ARCH or GARCH models. Using the combined mo del we perform 1000 simulations of market data, each simulation compri sing 250 days (approximately one year). We then formulate a naive trad ing strategy which is based on the ratio of the one-day-ahead expected return to its one-day-ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to m aximize profits for the simulation data. Next, we apply the trading st rategy to one year of recent out-of-sample data. Our conclusion is tha t the S&P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take a ccount of risk than the benchmark strategy of buy-and-hold. We have al so constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy-an d-hold. Finally, we have compared the profitability of the optimized t rading strategy, based on the non-linear model, to three ad hoc tradin g strategies using the out-of-sample data. The three ad hoc strategies are more profitable than the optimized strategy.