A multi-source heterogeneous data analytic method for future price fluctuation prediction

Most previous works on future market price forecasting only utilize the historical transaction data, while ignoring many other valuable factors. Recently, many research works propose multiple-source data -based predicting approaches in the stock market. Although the futures market and the stock market are very similar, the futures market still has its uniqueness. Most importantly, the subject matter of futures is usually commodity entities with prominent competing products or upstream, downstream industries, which can significantly influence the price. Therefore, it is essential to propose a future specific analysis framework by considering different factors. In this study, we constructed a Multi-source Heterogeneous Data Analysis (MHDA) method for future price prediction by integrating multiple-source information, i.e., trading data, news event data, and investor comments. Firstly, we first constructed a relation map to capture all related news events from upstream and downstream commodities and then built a future-specific sentiment dictionary to accurately quantify the sentiment impact of related news and investor comments during the feature extraction. Finally, we model the quantified multi-source heterogeneous information by an extended Hidden Markov Model to capture the underlying temporal dependency in the data. Evaluations on the data of palm oil futures from 2016.9 to 2017.9 show the effectiveness of our proposed framework. (c) 2020 Elsevier B.V. All rights reserved.