Stock market performance prediction has always been a hit research topic and is attractive due to its strong potential to generate financial profit. Being able to predict future stock price in a relatively accurate way forms a significant task of stock market analysis. Different mechanisms from fundamental analysis to statistical modeling have been deployed to study stock market performance and various factors from fundamental factors, technical factors to market sentiments are also incorporated in the stock price prediction task. However, due to the chaotic stock market performance, which is close to random walk, and the difficulty in discerning influential factors, predicting stock price faces a lot of challenges. In recent years, fast development in fields such as machine learning has offered new ways to look at this task. In this paper, we employ Extreme Learning Machine (ELM) algorithm, a recent modification of traditional feedforward neural network with single hidden layer, whose learning speed is greatly improved based on solid mathematical background and capability to circumvent problems such as local minimum is also enhanced, to construct an ELM combination model to study stock market performance and predict stock price. A comparison between the predicted output and the real data is carried out to test the feasibility of applying ELM model to stock market analysis. The result indicates that ELM model is desirable for predicting stock price variation trend while some inaccuracy exists in the prediction of peak values, which may require further model modification. Overall, by applying the machine learning model ELM to predict stock price and generating desirable outcome, this paper both contributes to offering a new way to investigate stock market performance and enlarging the field deployment of ELM model as well.
Development and implementation of a predictive method for the stock market analysis, using the long short-term memory machine learning method
