Equity Price Direction Prediction For Day Trading Ensemble Classification Using Technical Analysis Indicators With Interaction Effects

We investigate the performance of complex trading rules in equity price direction prediction, over and above continuous-valued indicators and simple technical trading rules. Ten of the most popular technical analysis indicators are included in this research. We use Random Forest ensemble classifiers using minute-by-minute stock market data. Results show that our models have predictive power and yield better returns than the buy-and-hold strategy when disregarding transaction costs both in terms of number of stocks with profitable trades as well as overall returns. Moreover, our findings show that two-way and three-way combinations, i.e., complex trading rules, are important to "beat" the buy-and-hold strategy.