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.

股市表现预测一直是热门的研究主题，并且由于其产生财务利润的强大潜力而具有吸引力。能够以相对准确的方式预测未来股价构成了股票市场分析的一项重要任务。从基本面分析到统计模型的不同机制已被用来研究股票市场表现，从基本面因素，技术因素到市场情绪的各种因素也被纳入了股价预测任务中。然而，由于接近随机波动的股市表现混乱，并且难以辨别影响因素，因此预测股价面临许多挑战。近年来，机器学习等领域的快速发展提供了解决此任务的新方法。在本文中，我们采用极限学习机（ELM）算法，它是对传统的具有单个隐藏层的前馈神经网络的最新改进，其基于扎实的数学背景极大地提高了学习速度，并且还增强了解决诸如局部极小值等问题的能力。 ELM组合模型可研究股票市场表现并预测股票价格。进行了预测输出与实际数据之间的比较，以检验将ELM模型应用于股票市场分析的可行性。结果表明，ELM模型对于预测股票价格的变化趋势是理想的，而峰值预测中存在一些误差，这可能需要进一步的模型修改。总体而言，通过应用机器学习模型ELM预测股票价格并产生理想的结果，