Below is a list of several new and recently updated working papers by the Yale SOM finance faculty on a variety of topics:
"Inside the Mind of a Stock Market Crash" by Stefano Giglio, Matteo Maggiori (Harvard University), Johannes Stroebel (New York University), and Stephen Utkus (Vanguard)
We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. Read more.
"Value and Interest Rates: Are Rates to Blame for Value’s Torments?" by Thomas Maloney (AQR Capital Management, LLC.) and Tobias J. Moskowitz
Value stocks sharply underperformed growth stocks from 2017 to early 2020, exacerbating a longer period of lackluster performance that dates back to the Global Financial Crisis for some value factors. Some have blamed the interest rate environment – the low level of interest rates, falling bond yields or the flattening yield curve. We examine these claims. Read more.
"Understanding Momentum and Reversals" by Bryan T. Kelly, Tobias J. Moskowitz & Seth Pruitt (ASU)
Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Read more.
Markets and firms offer contrasting methods to arrange production. In markets, contracts govern the purchase of parts and services that compose production. In firms, the shared values, customs, and norms coming from a corporate culture govern employees’ joint development of those parts and services. We argue for this distinction as a theory of the firm. Read more.
"Human Interactions and Financial Investment: A Video-Based Approach" by Allen Hu (Yale SOM PhD Candidate) and Song Ma
Economic decisions are often made after human interactions. This paper proposes an empirical approach to process and quantify features of micro-level human interactions, documents their connections to financial investment decisions, and investigates the underlying economic mechanisms. Using machine learning (ML)-based algorithms with videos as data input, we quantify human interactions in three-V dimensions--visual, vocal, and verbal--and construct interpretable metrics along these dimensions. Read more.
"Female Representation in the Academic Finance Profession" by Professor Mila Getmansky Sherman (University of Massachusetts at Amherst) and Heather Tookes
We present new data on female representation in the academic finance profession. In our sample of finance faculty from the top-100 U.S. business schools during 2009–2017, only 16.0% are women. The gender imbalance manifests itself in several ways. Read more.
"Bankrupt Innovative Firms" by Song Ma, (Joy) Tianjiao Tong (Duke University) and Wei Wang (Queen’s University)
We study how innovative firms manage their innovation portfolios after filing for Chapter 11 reorganization using three decades of data. We find that they sell off core (i.e., technologically critical and valuable), rather than peripheral, patents in bankruptcy. Read more.
"A Factor Model for Option Returns" by Matthias Büchner (University of Warwick) and Bryan T. Kelly
Due to their short lifespans and migrating moneyness, options are notoriously difficult to study with the factor models commonly used to analyze the risk-return tradeoff in other asset classes. Read more.
“The Structure of Economic News," by Leland Bybee (Yale SOM PhD candidate), Bryan T. Kelly, Asaf Manela (Washington University in St. Louis) and Dacheng Xiu (University of Chicago)
We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984–2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the proportion of news attention allocated to each theme at each point in time. Read more.
"Measuring Technological Innovation Over the Long Run" by Bryan T. Kelly, Dimitris Papanikolaou (Northwestern University), Amit Seru (Stanford University), and Matt Taddy (University of Chicago).
We use textual analysis of high-dimensional data from patent documents to create new indicators of technological innovation. Our technology indices capture the evolution of technological waves over a long time span (1840 to the present) and cover innovation by private and public firms, as well as non-profit organizations and the US government. Read more.