结合公司财务报表数据的股票指数预测方法

股票市场参与者的所有市场活动综合影响着股票市场的变化，使股票市场的波动充满复杂性，也使得准确预测股票价格成为难题。在这些影响股市变化的活动中，财务披露是预测股票指数变化的一种吸引人的且具有潜在财务回报的手段。为了应对股票市场的复杂变化，提出一种结合公司披露的财务报表数据进行股票指数预测的方法。该方法首先对股票指数历史数据和公司财务报表数据进行预处理，主要是对公司财务报表数据生成的高维矩阵进行降维，然后用双通道的长短期记忆（LSTM）网络对归一化后的数据进行预测研究。在上证50指数和沪深300指数数据集上的实验结果表明，该方法的预测效果优于仅使用股票指数历史数据的预测效果。

Stock index forecasting method based on corporate financial statement data

All market activities of stock market participants combine to affect stock market changes， making stock market volatility fraught with complexity and making accurate prediction of stock prices a challenge. Among these activities that affect stock market changes， financial disclosure is an attractive and potentially financially rewarding means of predicting stock indexe changes. In order to deal with the complex changes in the stock market， a method of stock index prediction was proposed that incorporates data from financial statements disclosed by corporates. Firstly， the stock index historical data and corporate financial statement data were preprocessed， and the main task is dimension reduction of the high-dimensional matrix generated from corporate financial statement data， and then the dual-channel Long Short-Term Memory （LSTM） network was used to forecast and research the normalized data. Experimental results on SSE 50 and CSI 300 Index datasets show that the prediction effect of the proposed method is better than that using only historical data of stock indexes.