Research Review | 17 February 2023 | Risk Analysis

Submergence = Drawdown Plus Recovery
Dane Rook (Stanford University), et al.
February 2023
Drawdowns and recoveries are often analyzed separately – yet doing so can leave investors with a distorted view of risk. Indeed, this problem is so commonplace that there’s no consistently-used term for the joint event of a drawdown plus its subsequent recovery. We propose the term ‘submergence’ for such events, and present a new risk metric to help investors analyze them: submergence density. Submergence density overcomes pitfalls of existing metrics, and also allows investors to inject elements of their own risk tolerances, thereby ‘personalizing’ it to their own contexts. Submergence density also offers an alternative method for risk-adjusting returns (with multiple advantages over current methods, such as Sharpe ratios). We use our new risk-adjustment approach to study key markets, and show how it leads to novel diversification strategies. We compare these strategies with other defenses against submergence risk, and conclude that submergence-based diversification is likely the best way for most investors to handle the threat of drawdowns.

A Century of Asset Allocation Crash Risk
Mikhail Samonov (Two Centuries Investments) and N. Sorokina (Penn. State U.)
January 2023
We extend proxies of the main asset allocation approaches back to 1926 using long-run return data for a variety of sub-asset classes and factors and test the long-term performance of U.S. and Global 60/40, Diversified Multi-Asset, Risk Parity, Endowment, Factor-Based and Dynamic Asset Allocation portfolios. While Factor-Based portfolios exhibit best traditionally measured risk-adjusted returns in the long run, the Dynamic Asset Allocation reduces the abandonment risk due to its lower expected drawdown. Across all strategies, risk-tolerant investors that rely on the longer history for setting their expectations, experience significantly better outcomes, particularly if their investment horizon includes times of crisis.

A New Factor Model for REIT Returns
Jie Cao (The Hong Kong Polytechnic University), et al.
January 2023
We propose a new conditional factor model to explain the cross-section of REIT returns. Using the instrumented principal component analysis (IPCA) approach, we extract five latent factors and form a conditional factor model, which outperforms traditional factor models in explaining the cross-section of REIT returns. We further map the latent factors with REIT characteristics and identify firm size, operating cash flows, earnings-to-price ratio, dividend yield, momentum, and REIT-type dummies as the most important contributors. Lastly, we provide economic rationales for the latent factors.

A Five-Factor Asset Pricing Model with Enhanced Factors
Manuel Ammann (University of St. Gallen), et al.
January 2023
A simple manipulation of the dividend discount model establishes that firms’ book-to-market, profitability, and investment are related to their expected returns. This insight motivates the value, profitability, and investment factors in the Fama-French (2015) five-factor model. Yet, variation in book-to-market, profitability, or investment stems not only from differences in expected returns. In this study, we narrow down the variation in these variables that is actually informative about expected returns to construct enhanced versions of the value, profitability, and investment factors. Our enhanced factors exhibit considerably higher Sharpe ratios than the standard factors. Importantly, a five-factor model using our enhanced factors exhibits a much better pricing performance and generates a more upward sloping multivariate security market line than the standard five-factor model. Moreover, we show that our approach either complements or outperforms other recently proposed approaches to improve the Fama-French (2015) factors.

Why Stock Returns Are Different Across Countries: Risks or Risk Premia?
Weige Huang (Zhongnan University of Economics and Law)
November 2022
A stock’s return comes from two components, i.e., risk and risk premium of the stock. Therefore, differences in stock returns are due to differences in risks or risk premia or both. The paper addresses to the question why stock returns are different across countries using Blinder-Oaxaca decomposition method. We show that cross-country differences in stock returns are mostly due to differences in risk premia (especially in market’s risk premia) across countries and the contributions of differences in risks are relatively small. We also find that differences in returns tend to change over time and the shares of the contributions seem to vary after 2008 financial crisis, implying that risk premia and risks are time-varying. The results are robust to changes in reference structure used, time periods, data frequency and ways of sorting portfolios.

On the Anomaly Tilts of Factor Funds
Markus S. Broman (Ohio U.) and Fabio Moneta (U. of Ottawa)
February 2023
By analyzing portfolio holdings, we find that a significant subset of Hedged Mutual Funds (HMFs) and smart-beta Exchange-Traded Funds (ETFs) tilt their portfolios towards well-known anomaly characteristics and that such tilts are highly persistent. Short positions of HMFs amplify their factor tilts. Most single-factor ETFs target multiple factors, while many also exhibit offsetting tilts to other factors. HMFs with large factor tilts outperform corresponding ETFs, which is driven by short positions and higher factor-related returns. Overall, we show the superior factor replication ability of HMFs over ETFs, and that HMFs achieve similar (or better) performance as the academic factors.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return

By James Picerno

One thought on “Research Review | 17 February 2023 | Risk Analysis

  1. Pingback: Quantocracy's Daily Wrap for 02/17/2023 - Quantocracy

Comments are closed.