A volatility-driven stock trading framework based on automated, short-term recommendation method

In modern economy, investment in the stock market is a key strategy for growing wealth. With the high volatility in the stock market, there is a strong need for investment techniques to exploit the high volatility in the market. In this work, we propose a volatility-driven, medium-frequency stock trading framework. Our primary goal is to capitalize on little fluctuations that occur in individual stock prices in one day or within a span of few days. In our suggested framework, we perform a price-analysis in the morning, after the stock market opens and decide whether to purchase a given stock. Then, towards the end of the trading day, we perform another type of analysis to decide whether any specific stock form the portfolio should be sold. We have evaluated the effectiveness and performance of our proposed approach on all the 30 stocks of Dow Jones Industrial Average and on the portfolio of Warren Buffet, as of mid-2016. We compared the return obtained from our investment approach against the return obtained from the traditional long term buy-and-hold investment strategy. Our experimental data-set indicates that our approach produces 38% to 118% more profit, on an average. This proposed methodology is practical and extremely easy to use. Since it requires very little time and knowledge, this approach will be quite appealing and useful to the common investors.