DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation

TL;DR

This paper presents three adaptive methods for time series modeling—model selection, ensembling, and asset allocation—applied to financial data, demonstrating superior performance in forecasting and portfolio strategies during market fluctuations.

Contribution

It introduces novel adaptive learning methods for financial time series, integrating model selection, ensembling, and dynamic asset allocation, with empirical validation on US market indices.

Findings

Outperform long-only benchmarks in testing period

Effective during 2020 market crash

Improved forecasting accuracy

Abstract

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.