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
Second Quarter (2020)
Introduction to the Special Issue on Machine Learning
Volume 18, No. 2, 2020 Charles Elkan View PDF… Read more
Practitioner’s Digest
Volume 18, No. 2, 2020 Practitioner’s Digest View PDF… Read more
Case Study – Collective Defined Contribution Plans
Volume 18, No. 2, 2020 Case Study – Collective Defined Contribution Plans Seoyoung Kim View PDF… Read more
Book Review: Smart(er) Investing – How Academic Insights Propel the Savvy Investor
Volume 18, No. 2, 2020 Book Review: Smart(er) Investing – How Academic Insights Propel the Savvy Investor by Elisabetta Basilico and Tommi Johnsen (Reviewed by Zachary Simon) View PDF… 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
On the Stability of Machine Learning Models: Measuring Model and Outcome Variance
Volume 18, No. 2, 2020 Vasant Dhar and Haoyuan Yu How do you know how much you should trust a model that is learned from data? We propose that a central criterion in measuring trust is the decision-making variance of a model. We call this “model variance.” Conceptually, it refers to the inherent instability machine… Read more