用CNN直接处理原始股市数据，预测标普500股票走势
S&P 500 Stock's Movement Prediction using CNN
- 将原始多维股市数据视为图像输入CNN模型
- 无需人工构造金融特征，直接预测个股或行业走势
- 适合量化交易与投资组合分析的研究者参考
本文旨在预测标普500指数成分股的股价变动。以往研究多采用传统数学方法，虽在算法交易和阿尔法生成系统中应用广泛，但大多依赖工程化金融特征。近年来深度学习兴起，为该领域带来新机遇。然而多数工作仅使用单维数据，未充分挖掘真实市场数据的复杂性。本文创新性地直接利用包含除权除息事件的多变量原始数据，不进行特征工程，将历史数据矩阵化为类似图像的形式，输入卷积神经网络（CNN）进行建模。该方法可实现个股、行业或投资组合级别的预测，取得了有前景的结果。
原文摘要 · Abstract (English)
This paper is about predicting the movement of stock consist of S&P 500 index. Historically there are many approaches have been tried using various methods to predict the stock movement and being used in the market currently for algorithm trading and alpha generating systems using traditional mathematical approaches [1, 2]. The success of artificial neural network recently created a lot of interest and paved the way to enable prediction using cutting-edge research in the machine learning and deep learning. Some of these papers have done a great job in implementing and explaining benefits of these new technologies. Although most these papers do not go into the complexity of the financial data and mostly utilize single dimension data, still most of these papers were successful in creating the ground for future research in this comparatively new phenomenon. In this paper, I am trying to use multivariate raw data including stock split/dividend events (as-is) present in real-world market data instead of engineered financial data. Convolution Neural Network (CNN), the best-known tool so far for image classification, is used on the multi-dimensional stock numbers taken from the market mimicking them as a vector of historical data matrices (read images) and the model achieves promising results. The predictions can be made stock by stock, i.e., a single stock, sector-wise or for the portfolio of stocks.
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