Equity Price Direction Prediction For Day Trading Ensemble Classification Using Technical Analysis Indicators With Interaction Effects Dirk Van den Poel, Céline Chesterman, Maxim Koppen Faculty of Economics and Business Administration Ghent University, Tweekerkenstraat 2 Ghent, B-9000, Belgium dirk.vandenpoel@UGent.be Michel Ballings Department of Business Analytics and Statistics University of Tennessee, Knoxville, TN 37996, USA Abstract—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. Keywords—day trading; equity price direction prediction; technical analysis; stock trading; ensemble classification; systematic trading; quantitative analysis; big data analytics. I. INTRODUCTION Stock-market predictability remains a widely debated topic. Reference [1] argued that any attempt to forecast future stock prices based on historic price information - technical analysis – may be unsuccessful. However, in the early 2000’s, many economists and statisticians came to believe that stock markets are at least somewhat predictable based on technical analysis as well as certain fundamental valuation metrics [2]. This finding gave a boost to the research stream aimed at exploring market predictability by means of automated trading systems. Recent studies provide evidence for stock-market predictability [3] [4] [5] [6]. Stock price prediction based on technical analysis has become increasingly popular. Most studies using technical analysis make use of continuous-valued technical indicators as inputs to predictor models. This causes prediction models to make a classification based on the continuous values of these indicators, depriving trend information inherent in technical indicators [6]. In order to benefit from this additional knowledge, technical trading rules can be added to these conventional predictors (i.e., by creating interaction effects or complex trading rules). Various studies support the predictive power of technical trading rules [7] [8]. Evidence of improved predictive performance when adding simple technical trading rules to past returns is provided by [9]. In extant literature, we only found two previous studies, that take into account such complex trading strategies. Reference [10] has extracted prediction strategies for future price level estimation by using the optimum combination of technical indicators and concludes that combining multiple technical indicators improves the predictive performance compared to using a single indicator. Reference [11] studies the profitability of technical trading rules based on nine popular technical indicators and establishes several trading models based on two-way or three-way combinations. Reference [11] does not employ any classification algorithm and does not combine technical and indicators and trading rules into one model. As a result, we strongly believe that this research stream on complex trading rules should be explored more deeply. Furthermore, we construct an integrated automated trading system that combines continuous-valued technical indicators, single technical trading rules as well as complex trading rules based on two-way and three-way combinations of individual indicators. Although most empirical studies are concerned with the statistical and economic performance of prediction models, we are also interested in whether a classification algorithm, such as Random Forest (RF), is able to better predict future stock price direction movements. While many studies try to accurately predict future price levels, we focus on forecasting stock price direction. We investigate whether our proposed two-layered model trained on an eight-month sample of minute-by-minute S&P 100 data is able to generate profits. We hypothesize that a model based on a combination of technical indicator values with single and complex trading rules trained on intra-day data is able to predict future stock price direction accurately. The remainder of the paper is structured as follows. In Section II, we explore related work in stock market prediction. Section III describes the methodology employed in this study. We describe our dataset and explain our different types of predictor variables