Forecasting stock market trend: a comparison of machine learning algorithms

Forecasting the direction of stocks markets has become a popular research topic in recent years. Different approaches have been applied by researchers to address the prediction of market trends by considering technical indicators and chart patterns from technical analysis. This paper compares the performance of four machine learning algorithms to validate the forecasting ability of popular technical indicators in the technological NASDAQ index. Since the mathematical formulas used in the calculation of technical indicators comprise historical prices they will be related to the past trend of the market. We assume that forecasting performance increases when the trend is computed on a longer time horizon. Our results suggest that the random forest outperforms the other machine learning algorithms considered in our research, being able to forecast the 10-days ahead market trend, with an average accuracy of 80%.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.