Managing Editor’s Letter

Abstract

Cathy Scott General Manager and Publisher Several articles, two of which were published in this journal, have shown how reinforcement learning can be used to take trading costs into account in hedging decisions. In the lead article of this issue, “Deep Hedging of Derivatives Using Reinforcement Learning,” Jay Cao, Jacky Chen, John Hull, and Zissis Poulos extend the standard reinforcement learning approach by utilizing multiple Q-functions for the purpose of increasing the range of objective functions that can be used and by using algorithms that allow the state space and action space to be continuous. The authors suggest an approach where a relatively simple valuation model is used in conjunction with more complex models for the evolution of the asset price. This allows good hedges to be developed for asset price processes that are not associated with analytic pricing models. Deep sequence models have been applied to predicting asset returns. These models are flexible enough to capture the high-dimensionality, nonlinear, interactive, low signal-to-noise, and dynamic nature of financial data. More specifically, these models can outperform the conventionally used models because of their ability to detect path-dependence patterns. Lin William Cong, Ke Tang, Jingyuan Wang, and Yang Zhang in their article “Deep Sequence Modeling: Development and Applications in Asset Pricing,” show how to predict asset returns and measure risk premiums by applying deep sequence modeling. They begin by providing an overview of the development of deep sequence models, introducing their applications in asset pricing, and discussing the advantages and limitations of deep sequence models. A comparative analysis of these methods using data on US equities is then provided in the second part of the article where the authors demonstrate how sequence modeling benefits investors in general by incorporating complex historical path dependence. They report that long short-term memory has the best performance in terms of out-of-sample predictive R-squared, and long short-term memory with an attention mechanism has the best portfolio performance when excluding microcap stocks. In the formulation of an investment process, it is critical to build a view of causal relations among economic entities. Because of the complex and opaque nature of many market interactions, this can be challenging. Various models of economic causality have been proposed to both explain the past and aide investors in the investment process such as causal networks. Such networks provide an efficient framework for assisting with investment decisions that are supported by both quantitative and qualitative evidence. When building causal networks, the addition of more causes adds to the issue of computational complexity because of the necessity to calculate the combined impact of larger and larger sets of causes. In “Causal Uncertainty in Capital Markets: A Robust Noisy-Or Framework for Portfolio Management,” Joseph Simonian argues that among the various approaches to causal networks, the “noisy-or model” offers the means to calculate the aggregate effect of causes in a linear manner assuming that the causal probability values used by model builders are completely reliable. To address the question of uncertainty, Simonian provides a robust, uncertainty-adjusted noisy-or framework that draws on evidence-based subjective logic (i.e., a many-valued logic explicitly designed to assess the reliability b y gu es t o n Fe br ua ry 9 , 2 02 1. C op yr ig ht 2 02 1 Pa ge an t M ed ia L td .