Predicting stock trends through technical analysis and nearest neighbor classification

This paper presents the results of method designed to predict price trends in the stock market. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.