2021/10/21 by Parley Ruogu Yang, Yang, Parley Ruogu, Ryan Lucas +1
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Applications (stat.AP) #Econometrics (econ.EM) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Market Dynamics and Volatility #Other Statistics (stat.OT) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2110.11156
openalex publication_date 2021/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce three adaptive time series learning methods, called Dynamic Model Selection (DMS), Adaptive Ensemble (AE), and Dynamic Asset Allocation (DAA). The methods respectively handle model selection, ensembling, and contextual evaluation in financial time series. Empirically, we use the methods to forecast the returns of four key indices in the US market, incorporating information from the VIX and Yield curves. We present financial applications of the learning results, including fully-automated portfolios and dynamic hedging strategies. The strategies strongly outperform long-only benchmarks over our testing period, spanning from Q4 2015 to the end of 2021. The key outputs of the learning methods are interpreted during the 2020 market crash.