t+1, part of the costs, because of liquidating the risk free position at the end of day t, are at the expense of the profit at day t and part of the costs, because of initializing the long position at the beginning of day t+1 against the price at the end of day t, are at the expense of the profit at day t+1. In this chapter, 0, 0.10, 0.25, 0.50, 0.75 and 1% costs per trade are implemented. This wide range of transaction costs captures a range of different trader types. For example, floor traders and large investors, such as mutual funds, can trade against relatively low transaction costs in the range of 0.10 to 0.25%. Home investors face higher costs in the range of 0.25 to 0.75%, depending whether they trade through the internet, by telephone or through their personal account manager. Next, because of the bid-ask spread, extra costs over the transaction costs are faced. By examining a wide range of 0 to 1% costs per trade, we belief that we can capture most of the cost possibilities faced in reality by most of the traders.
3.5 Data snooping
Data snooping is the danger that the performance of the best forecasting model found in a given data set is just the result of chance instead of the result of truly superior forecasting power. The search over many different models should be taken into account before making inferences on the forecasting power of the best model. It is widely acknowledged by empirical researchers that data snooping is a dangerous practice to be avoided. Building on the work of Diebold and Mariano (1995) and West (1996), White (2000) developed a simple and straightforward procedure for testing the null hypothesis that the best model encountered in a specification search has no predictive superiority over a given benchmark model. This procedure is called White's Reality Check (RC) for data snooping. We briefly discuss the method hereafter.The performance of each technical trading strategy used in this chapter is compared to the benchmark of a buy-and-hold strategy. Predictions are made for M periods, indexed from J+1 through T=J+1+M, where the first J data points are used to initialize the K technical trading strategies, so that each technical trading strategy starts at least generating signals at time t=J+1. The performance of strategy k in excess of the buy-and-hold is defined as fk. The null hypothesis that the best strategy is not superior to the benchmark of buy-and-hold is given by
| H0: |
|
E(fk) ≤ 0, |