Stock market trend prediction using a functional time series approach

Thanks to advanced technologies, ultra-high-frequency limit order book (LOB) data are now available to data analysts. An LOB contains comprehensive information on all transactions in a market. We use LOB data to investigate the high-frequency dynamics of market supply and demand (S–D) and inspect their impacts on intra-daily market trends. The intra-daily S–D curves are fitted with B-spline basis functions. Technique of multi-resolution is introduced to capture inhomogeneous curvature of the S–D curves and a lasso-type criterion is employed to select a common basis set. Based on empirical evidence, we model the time varying coefficients in the B-spline interpolation by vector autoregressive models of order $p (\geq ~1) $p(≥ 1). The Xgboost algorithm is employed to extract information from the areas under the S–D curves to predict the intra-daily market trends. In the empirical study, we analyze the LOB data from LOBSTER (https://lobsterdata.com/). The results show that the proposed approach is able to recover the S–D curves and has satisfactory performance on both curve and market trend predictions.