Exploring Candlesticks and Multi-Time Windows for Forecasting Stock-Index Movements

Abstract

Stock-index movement prediction is an important research topic in FinTech because the index indicates the economic status of a whole country. With a set of daily candlesticks of the stock-index, investors could gain a meaningful basis for the prediction of the next day's movement. This paper proposes a stock-index price-movement prediction model, Combined Time-View TabNet (CTV-TabNet), a novel approach that utilizes attributes of the candlesticks data with multi-time windows. Our model comprises three modules: TabNet encoder, gated recurrent unit with a sequence control, and multi-time combiner. They work together to forecast the movements based on the sequential attributes of the candlesticks. CTV-TabNet not only outperforms baseline models in prediction performance on 20 stock-indices of 14 different countries but also yields higher returns of index-futures trading simulations when compared to the baselines. Additionally, our model provides comprehensive interpretations of the stock-index related to its inherent properties in predictive performance.