论文标题
通过时间序列相似性改善标准普尔股票预测
Improving S&P stock prediction with time series stock similarity
论文作者
论文摘要
如今，使用预测算法的股市预测是一个流行的话题，大多数预测算法仅根据在特定股票上收集的数据进行训练。在本文中，我们像专业交易者一样，用相关股票丰富了股票数据，以改善股票预测模型。我们测试了五个不同的相似性函数，并发现协整相似性在预测模型上具有最佳的改进。我们在五年内从各个行业的七个标准普尔股票上评估了这些模型。我们对类似股票进行培训的预测模型的结果明显更好，平均准确性为0.55，而19.782的利润与最先进的模型相比，准确性为0.52，利润为6.6。
Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models. We tested five different similarities functions and found co-integration similarity to have the best improvement on the prediction model. We evaluate the models on seven S&P stocks from various industries over five years period. The prediction model we trained on similar stocks had significantly better results with 0.55 mean accuracy, and 19.782 profit compare to the state of the art model with an accuracy of 0.52 and profit of 6.6.
