Forecasting a stock trend using genetic algorithm and random forest - Rebecca Abraham; Mahmoud El Samad; Amer M. Bakhach; Hani El-Chaarani; Ahmad Sardouk; Sam El Nemar; Dalia Jaber (Journal of Risk and Financial Management, 2022)
This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today's closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags' indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock's trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.
Keywords: 650; computational or mathematical finance; features selection; genetic algorithm; random forest; stock trend prediction