Prediction of stock price movement based on daily high prices

Prediction of stock close price movements has attracted a lot of research interest. Using machine learning techniques, especially statistical classifiers, for day ahead forecasting of the movement of daily close prices of a broad range of several hundreds of liquid stocks is generally not very successful. We suspect that one of the reasons for failure is the relatively high volatility of prices in the last minutes before the market closes. There have been some attempts to use less volatile daily high prices instead, but the studies concentrated only on a specific non-statistical machine learning approach on a small number of specific securities. We show that incorporating statistical classifiers for day ahead daily high price movement predictions in to some simple portfolio management techniques significantly increases their performance. Tests performed on S&P 500 stocks show that such a strategy is robust, i.e. the difference in reliability for different stocks does not vary significantly, and that such a strategy greatly outperforms the S&P 500 index and several other benchmarks while increasing the risk only by a small amount.