QRFE talk on Multi-(Horizon) Factor Investing with AI

Abstract

We provide a novel approach for multi-factor investing with big data by a multi-horizon investor who takes into consideration long-term versus short-term volatility, liquidity and trading costs trade-offs while maximizing expected portfolio returns.

Reinforcement learning (RL), which is generally used to solve problems with long- versus short-term reward trade-offs, allows explicitly incorporating long, ten-year investment horizon considerations during training. In out-of-sample, testing period we are the first to show the importance of investment horizon effect for portfolio performance. First, RL portfolio of long term investors with annual rebalancing performs competitively vis-à-vis their short-term peers with monthly rebalancing, and outperforms the latter due to lower portfolio rebalancing needs, turnover and trading costs. Second, when both, short and long-term investors are allowed to rebalance monthly, long-horizon portfolio outperforms by being more patient, with more strategic factor timing and turnover strategies spread over multiple months. Short horizon portfolio is less patient, has higher volatility and almost twice higher monthly turnover. Importantly, we identify different fundamental economic signals determining success of long vs. short-term strategies.