JOIM: 2023

Practitioner’s Digest • Vol. 21, No. 1

The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.

Leveraging Text Mining to Extract Insights from Earnings Call Transcripts

by: Andrew Chin and Yuyu Fan

We apply text-mining techniquesin earnings call transcriptsto extract meaningful features that capture management and investment community signals. Using a corpus of transcripts of earnings calls for global companies from 2010 to 2021, we create fundamentally driven features spanning document attributes, readability, and sentiment on different sections of the transcripts. We test the efficacy of these features in predicting the future stock returns of companies and find that there are opportunities for investors to use these signals in stock selection. Specifically, we find that readability and sentiment-based techniques can enhance an investor’s ability to differentiate amongst outperformers and underperformers and these results are robust across market capitalization as well as investment universes (US Large Cap, US Small Cap, World ex-US, and Emerging Markets). We also introduce methods to create more robust sentiment features for active and systematic investors. By analyzing the performance patterns of the various call participants, we find evidence that the analyst questions may contain more information than the executive sections. Finally, we observe that sentiment features derived from context-driven deep learning language models like BERT are promising and may have more efficacy than bag-of-words approaches.

How Inefficient is the 1/N Strategy for a Factor Investor?

by: Kevin Khang, Antonio Picca, Shaojun Zhang and Minzhi Zhu

The last decade’s dramatic democratization of factor investing has broadened its investor base to individual investors and their advisors. This paper studies the performance of classic allocation strategies—1/N, mean–variance, and minimum-variance—from these investors’perspective. Specifically, we curate commonly available long-only factor funds, adjust their premia fortransaction costs, impose sensible concentration limits, and explicitly focus on active risk-and-return properties. Block bootstrap-based simulation shows that no alternative optimization strategy consistently dominates the simple 1/N strategy in active returns and information ratios. 1/N allocation appears a sensible strategic allocation for most factor investors without an edge in predicting factor premium.

The Role of Options in Goals-Based Wealth Management

by: Sanjiv R. Das and Greg Ross

We develop a methodology using dynamic programming for goals-based wealth management over long horizons where portfolio rebalancing uses the standard securities and also derivative securities. A kernel density estimation approach is developed to accommodate derivative assets, solving a high-dimensional problem with fast computation. The approach accommodates skewed and fat-tailed distributions. Portfolio performance is better with the use of options, especially for investors with aggressive goals. The improved performance arises because options unlock additional leverage, which is useful for reaching upside goals. Calls are preferred to puts unless upside goals are modest. The framework is extensible with periodic withdrawals and multiple goals, while being cognizant of downside risk.

Financing Fusion Energy

by: Abdullah Alhamdan, Zachery M. Halem, Irene Hernandez, Andrew W. Lo, Manish Singh and Dennis Whyte

The case for investing in fusion energy has never been greater, given increasing global energy demand, high annual carbon dioxide output, and technological limitations for wind and solar power. Nevertheless, financing for fusion companies through traditional means has proven challenging. While fusion startups have an unparalleled upside, their high upfront costs, lengthy delay in payoff, and high risk of commercial failure have historically restricted funding interest to a niche set of investors. Drawing on insights from investor interviews and case studies of public–private partnerships, we propose a megafund structure in which a large number of projects are securitized into a single holding company funded through various debt and equity tranches, with first loss capital guarantees from governments and philanthropic partners. The mega fund exploits many of the core properties of the fusion industry: the diversity of approaches to engender fusion reactions, the ability to create revenue-generating divestitures in related fields, and the breadth of auxiliary technologies needed to support a functioning power plant. The model expands the pool of available capital by creating tranches with different risk–return tradeoffs and providing a diversified “fusion index” that can be viewed as a long hedge against fossil fuels. Simulations of a fusion mega fund demonstrate positive returns on equity (ROE) and low default rates for the capital raised using debt.

Prediction Revisited: The Importance of Observation, 1st Edition

by: Mark P. Kritzman, David Turkington and Megan Czasonis • reviewed by: Kenneth Winston

Prediction Revisited is a delightfully nonlinear romp through the basics of linear statistical inference. Its stated goal is to reframe the subject with a relevance-weighted approach. But there’s a second goal: to emulate the singer Mark Anthony. Mark Anthony, of course, is the envy of crossover aspirants everywhere, moving effortlessly back and forth between Spanish hits and English hits. Czasonis, Kritzman and Turkington (CKT hereafter) also seek a crossover hit, moving back and forth between mathematical exposition and conceptual English-based exposition.