Information Ratio

QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE ArXiv ID: 2409.05144 “View on arXiv” Authors: Unknown Abstract Alpha factor mining aims to discover investment signals from the historical financial market data, which can be used to predict asset returns and gain excess profits. Powerful deep learning methods for alpha factor mining lack interpretability, making them unacceptable in the risk-sensitive real markets. Formulaic alpha factors are preferred for their interpretability, while the search space is complex and powerful explorative methods are urged. Recently, a promising framework is proposed for generating formulaic alpha factors using deep reinforcement learning, and quickly gained research focuses from both academia and industries. This paper first argues that the originally employed policy training method, i.e., Proximal Policy Optimization (PPO), faces several important issues in the context of alpha factors mining. Herein, a novel reinforcement learning algorithm based on the well-known REINFORCE algorithm is proposed. REINFORCE employs Monte Carlo sampling to estimate the policy gradient-yielding unbiased but high variance estimates. The minimal environmental variability inherent in the underlying state transition function, which adheres to the Dirac distribution, can help alleviate this high variance issue, making REINFORCE algorithm more appropriate than PPO. A new dedicated baseline is designed to theoretically reduce the commonly suffered high variance of REINFORCE. Moreover, the information ratio is introduced as a reward shaping mechanism to encourage the generation of steady alpha factors that can better adapt to changes in market volatility. Evaluations on real assets data indicate the proposed algorithm boosts correlation with returns by 3.83%, and a stronger ability to obtain excess returns compared to the latest alpha factors mining methods, which meets the theoretical results well. ...

Benchmarking M6 Competitors: An Analysis of Financial Metrics and Discussion of Incentives ArXiv ID: 2406.19105 “View on arXiv” Authors: Unknown Abstract The M6 Competition assessed the performance of competitors using a ranked probability score and an information ratio (IR). While these metrics do well at picking the winners in the competition, crucial questions remain for investors with longer-term incentives. To address these questions, we compare the competitors’ performance to a number of conventional (long-only) and alternative indices using standard industry metrics. We apply factor models to measure the competitors’ value-adds above industry-standard benchmarks and find that competitors with more extreme performance are less dependent on the benchmarks. We also uncover that most competitors could not generate significant out-performance compared to randomly selected long-only and long-short portfolios but did generate out-performance compared to short-only portfolios. We further introduce two new strategies by picking the competitors with the best (Superstars) and worst (Superlosers) recent performance and show that it is challenging to identify skill amongst investment managers. We also discuss the incentives of winning the competition compared to professional investors, where investors wish to maximize fees over an extended period of time. ...