Improving S&P stock prediction with time series stock similarity
This work addresses stock prediction for traders by improving accuracy and profit, but it is incremental as it builds on existing forecasting methods with data enrichment.
The paper tackled stock market prediction by enriching stock data with related stocks using similarity functions, finding co-integration similarity best improved models, resulting in a mean accuracy of 0.55 and profit of 19.782 compared to SOTA's 0.52 accuracy and 6.6 profit.
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.