Vol. 18, No. 3, 2020 Miguel Palacios, Hayne Leland and Sasha Karimi Traditional corporate defined benefit (DB) plans provided retirees with constant retirement income, but DB plans have now all but disappeared. While defined contribution (DC) plans now permit low-fee wealth accumulation, the conversion of wealth to predictable nominal or real income during retirement remains… Read more
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Correlation Shrinkage: Implications For Risk Forecasting
Vol. 18, No. 3, 2020 Jose Menchero and Peng Li In this article, we study the impact of shrinking sample correlations toward zero. We find that while such shrinkage may be beneficial from a portfolio-construction perspective, there is virtually no benefit in terms of the accuracy of risk forecasts. In fact, we show that correlation… Read more
Is Sell-Side Research More Valuable in Bad Times?
Vol. 18, No. 3, 2020 Roger K. Loh and René M. Stulz Little is known about whether the value of sell-side research is different in bad times compared to good times. Because uncertainty is high in bad times, investors find it harder to assess firm prospects and hence should value analyst output more. However, higher… Read more
Attribution of Ex-Post Realized Sharpe Ratio to The Predictability Of The Ex-Ante Forecast Return and Risk.
Vol.18, No.3, 2020 Masahito Shimizu We propose to use an attribution formula that enables the ex-post realized Sharpe ratio to be decomposed into realized market conditions, ex-ante predictability of the returns, risk magnitude, and risk factors. We compare the predictability of the ex-ante return and exante risk directly, quantitatively identifying the main source of the… Read more
Comparing Anomalies Using Liquidity and Earnings
Vol. 18, No. 3, 2020 Robert Snigaroff, David Wroblewski, and Sean Sehyun Yoo We compare three factor models and their ability to explain a set of portfolio anomalies. Two of these models are based on market capitalization which most of the industry currently uses to characterize stocks. We replace this line of thinking by utilizing… Read more
Measuring Risk Preferences and Asset-Allocation Decisions: A Global Survey Analysis
Vol. 18, No. 3, 2020 Andrew W. Lo, Alexander Remorov, and Zied Ben Chaouch We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behaviors among these three groups of market… Read more
Local, Global, and International CAPM: For Which Countries Does Model Choice Matter?
Volume 18, No. 2, 2020 Demissew Ejara, Alain Krapl, Thomas J. O’Brien and Santiago Ruiz de Vargas For individual stocks of 46 countries, this study investigates empirical differences in discount rate estimates between three risk–return models of interest to practitioners who perform discounted cash-flow valuation analysis: (1) the traditional (local) CAPM; (2) the global CAPM… Read more
Using Machine Learning to Predict Realized Variance
Volume 18, No. 2, 2020 Peter Carr, Liuren Wu and Zhibai Zhang Volatility index is a portfolio of options and represents market expectation of the underlying security’s future realized volatility/variance. Traditionally the index weighting is based on a variance swap pricing formula. In this paper we propose a new method for building volatility index by… Read more
Dynamic Goals-Based Wealth Management Using Reinforcement Learning
Volume 18, No. 2, 2020 Sanjiv R. Das and Subir Varma We present a reinforcement learning (RL) algorithm to solve for a dynamically optimal goal-based portfolio. The solution converges to that obtained from dynamic programming. Our approach is model-free and generates a solution that is based on forward simulation, whereas dynamic programming depends on backward… Read more
Can Machines “Learn” Finance?
Volume 18, No. 2, 2020 Ronen Israel, Bryan Kelly and Tobias Moskowitz Machine learning for asset management faces a unique set of challenges that differ markedly from other domains where machine learning has excelled. Understanding these differences is critical for developing impactful approaches and realistic expectations for machine learning in asset management. We discuss a… Read more