Tail Risk Hedging: Contrasting Put and Trend Strategies

We summarize key research findings on risk-mitigating strategies and offer an overview of the strengths and weaknesses of regular index put buying (“Put”) and multi-asset trend following (“Trend”) as tail hedges. The two biggest questions we address are: (1) What is the long-term average return or cost, and (2) How reliable and efficient is the hedge in equity market tail events? We present empirical answers and discuss the economic rationale for each question. The common view that Put costs more but is a more effective tail hedge contains a kernel of truth but does not capture the full story. We will give a more nuanced picture, including practicality for investors, but in the end show the cost advantage favors Trend over Put.

Algorithmic Models of Investor Behavior

We propose a heuristic approach to modeling investor behavior by simulating combinations of simpler systematic investment strategies associated with well-known behavioral biases—in functional forms motivated by an extensive review of the behavioral finance literature—using parameters calibrated from historical data. We compute the investment performance of these heuristics individually and in pairwise combinations using both simulated and historical asset-class returns. The mean-reversion or momentum nature of a heuristic can often explain its effect on performance, depending on whether asset returns are consistent with such dynamics. These algorithms show that seemingly irrational investor behavior may, in fact, have been shaped by evolutionary forces and can be effective in certain environments and maladaptive in others.

Foreword - Why Is Systematic Investing Important?

In this era of inexpensive computation and vast data, systematic, or algorithmically driven, investment is increasingly popular. Systematic strategies appear in stand-alone products as well in tail-hedging and defensive-overlay strategies. Indeed, given the enormous growth in data, it is becoming infeasible to process these data without the assistance of systematic tools. The key advantage of the systematic approach is the discipline it imposes—for example, machines are not plagued by behavioral issues such as disposition bias, and in a time of crisis, a systematic strategy keeps a “cool head.” Systematic approaches also pose many challenges. Systematic strategies may not quickly adapt to structural changes in the market. They also present the risk of “tech-washing” whereby an investment product claims to use “the latest machine-learning tools,” but the tools are misapplied or play a minimal role. Importantly, when systematic tools are applied by an inexperienced researcher, the backtests are often overfit, leading to disappointing performance in live trading.

Alternative Risk Premia Timing: A Point-in-Time Macro, Sentiment, Valuation Analysis

We investigate the question of dynamic allocation across a diversified range of alternative risk premia. By using a set of point-in-time indicators across macro, sentiment and valuation dimensions, we find that a majority of indicators deliver a positive information ratio for a majority of alternative risk premia over the period 2005–2020. In our empirical simulations, the macro dimension seems to have worked well, notably during recession periods. Sentiment (based on market stress and momentum) struggled during recovery periods, but added value elsewhere. Valuation has worked well from 2005 to 2013 and lost part of its appeal since then. The combination of indicators allows to deliver a higher information ratio thanks to the low correlation among them. Our research also finds that point-in-time macroeconomic variables (“nowcasters”) can add value over traditional indicators, while this improvement is not significant in the case of the market stress indicator.

The Friend of Your Trend

Standard performance attribution to beta and alpha is not simple without full transparency into the investment process. This article develops an analytical framework to shed light on ex-ante stylized characteristics of a simple trend following strategy. Our analytical results show that rewards from the trend following strategy embed different degrees of underlying asset beta, which are determined by the asset’s return-to-volatility ratio, in addition to the trending behaviors that the strategy is built to harvest. We compare the results to ex-post realized returns-based style analysis of a CTA index. We discuss practical implications of our results with respect to fees and allocations to trend following strategies.

Here in the Real World: The Performance of Alternative Beta

We examine the realized performance of alternative beta strategies using a database of returns since 2008. Despite diversified portfolios of risk premia strategies offered by global investment banks achieving satisfactory Sharpe ratios of 0.80–1.07 during the decade to 2017, up to two thirds of the performance can be explained by exposure to traditional benchmarks. Furthermore, the outcomes are very sensitive to the estimated all-in fees incurred by investors. We find no evidence of positive alpha in the aggregate industry returns, and document a pattern of time-varying, asymmetric, and statistically significant betas to global equities and bonds. Our results suggest that the poor performance of the strategies in 2018–20 was not an aberration, but rather a continuation of patterns already present in earlier data. The findings are representative of the wider risk premia industry, as returns of managed alternative risk premia funds and those of diversified investment bank strategy portfolios appear closely aligned.