Distinguishing True and False Buy/Sell Triggers from Financial Technical Indicators

The objective of this study is to develop a method to distinguish True and False Buy/Sell recommendations. Various recommendation schemes, like 30/70 RSI (Relative Strength Index) scheme, are effectively used by many traders. However, the triggers produced by such recommendation schemes are found suspicious most of the time, and hence are non-actionable. In this study we develop a dynamic programming formulation to extract an optimal trade pattern from the price datasets. Such patterns are further augmented with several financial indicators to obtain binary classification model which is going to be consulted online. So, our system assists investors with removing uncertainties left from the primary recommenders. We show that our dynamic programming formulation runs efficiently in linear time. The approach is experimentally evaluated on BIST-100 stocks. The technical indicators used as predictor features are RSI, Trend Normalized RSI, Percentage Price Oscillator, Bollinger Band Percentage, Stochastic Oscillator, Rate of change (ROC), and Commodity Channel Index (CCI). We use Support Vector Machines as the binary classification algorithm. Up to 70% accuracies are obtained in this very hard binary classification task.