Adaptive Market Neutral Strategy Amid COVID-19 Regime-shifting Times, a Reinforcement Learning Approach
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Pairs trading is the foundation of market neutral strategy, which is one of the most sought-after quantitative trading strategies because it does not profit from market directions, but from the relative returns between a pair of assets, avoiding systematic risk and the Random Walk complexity. The profitability of market neutral strategies lie within the assumed underlying relationship between pairs of assets, however, when such relationship no longer withhold, often during volatile regime-shifting times such as this year with COVID-19, returns generally diminishes for such strategies. In fact, according to HFR (Hedge Fund Research, Inc.), the HFRX Equity Hedge Index, by the end of July, 2020, reported a YTD return of -9.74% ; its close relative, the HFRX Relative Value Arbitrage Index, reported a YTD return of -0.85%. There is no secret that for market neutral quants, or perhaps any quants, the challenge is not just to find profitable signals, but more in how to quickly detect and adapt complex trading signals during regime-shifting times. Within the field of market neutral trading, most research have been focusing on uncovering correlations and refining signals, often using proprietary alternative data purchased at high costs to find an edge. However, optimization of capital allocation at trade size and portfolio level is often neglected. We found that lots of pair trading signals, though complex, still utilizes fixed entry thresholds and linear allocations. With the recent advancement of complex models and learning algorithms such as Deep Reinforcement Learning (RL), these class of algorithm is yearning for innovation with non-linear optimization.
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