Enhancing Trading Strategies Using Model Uncertainty

Most trading systems focus on algorithms for generating entry and exit signals. When the performance deteriorates, developers often try to introduce additional filters and/or modify system parameters.

Reference [1] applied a novel technique, called Dynamic Model Averaging (DMA), to improve model performance. Basically, DMA estimates model uncertainty, and a trade is executed when signals are generated and the model uncertainty is low.

DMA, widely applied in forecasting inflation, S&P 500 volatilities, and exchange rates, dynamically assigns a model probability to each candidate model, enabling time-varying parameters. It aggregates forecasts from all models, using Kalman filtering for estimation and updating model probabilities based on historical forecast accuracy, yielding robust out-of-sample predictions.

The authors pointed out,

We have proposed augmented trading strategies by incorporating considerations of market entry timing. Leveraging estimations from the DMA approach, two criteria are employed to determine optimal market entry times: (1) low uncertainty regarding the model used to forecast trading returns, and (2) positive forecasted trading returns. Subsequently, spanning from April 4th, 2001, to December 31st, 2023, we collect daily data from the Chinese stock market to empirically examine our augmented trading strategies. Utilizing lagged trading excess returns and nine higher-order moments of market performance as market indicators, we forecast future excess returns in both momentum and reversal trading.

Results affirm our augmented strategies yield significant positive returns, surpassing canonical momentum and reversal trading. Canonical strategies mostly saw negative average returns over the period, except 1-day momentum. Conversely, augmented strategies reliably produced positive returns, transaction costs notwithstanding, with most showcasing over 7 % average annual absolute returns. Implementation of our criteria didn’t notably diminish trading chances, selected entry days constituting over 12 % of total. Selected entry days were evenly spread, indicating brief waiting periods for trading.

In short, by applying the DMA approach to estimate model uncertainty and taking signals when the uncertainty is low, the authors managed to greatly improve the performance of momentum and reversal trading strategies.

This is an innovative technique in trading system design. Let us know what you think in the comments below or in the discussion forum.

References

[1] Wenhao Wang, Qingyi Zhang, Pengda An, Feifei Cai, Momentum and reversal strategies with low uncertainty, Finance Research Letters Volume 68, October 2024, 105970