Wednesday, 1 February 2023
Dr. Samuel Rosen is an Assistant Professor in the Department of Finance. He received his PhD in finance from the University of North Carolina at Chapel Hill and a BA in economics from Cornell University. Prior to his PhD program, he worked in the Financial Stability division at the Federal Reserve Board.
Rosen’s research focuses on the intersection between macroeconomics and finance.
Talk: Investor Experience Matters: Evidence from Generative Art Collections on the Blockchain
Abstract: In the market for non-fungible tokens (NFTs) on the blockchain, experienced investors systematically outperform inexperienced investors. Controlling for holding period, experienced investors make 8.6 percentage points more per trade on average. This outperformance is mostly explained by experienced investors’ greater participation in primary market sales of NFT collections, which produced significantly higher average returns during our sample period. Our results shed light on the frictions present in NFT markets, and have implications for the design of NFT investment strategies.
Wednesday, 8 February 2023
Marc-Oliver Pohle is a visiting professor of econometrics at the Faculty of Economics and Business at Goethe University Frankfurt, where he completed his PhD in 2021. He holds bachelor’s degrees in economics and mathematics and a M.Sc. in Quantitative Economics from Goethe University. His fields of interest are statistics and econometrics and his research currently focuses on forecast and model evaluation, probabilistic forecasting and dependence measures.
Talk: Generalised Correlation
Abstract: We introduce and examine new dependence measures. Generalised covariance and correlation allow to measure dependence between two random variables X and Y around arbitrary statistical functionals just as covariance and Pearson correlation measures dependence around their means. The key idea behind this is to replace the error or deviation from the mean showing up in Pearson correlation by a suitable measure for the deviation from a general statistical functional, where identification functions provide us with such a generalized error. Generalised correlation has favourable theoretical properties and a multitude of practically relevant measures arise from this class, for example quantile correlation. Quantile correlation is akin to the extension of least squares to quantile regression. Quantile and the related threshold correlation make it possible to measure dependence locally, for example to analyse tail dependence. When choosing distribution functions as functionals we arrive at distributional correlations, which are two-dimensional functions lying between -1 and 1. They uncover the full dependence structure between X and Y and are closely related as well as natural complements in statistical analysis to the joint CDF and the copula. To condense the full dependence structure into a single number we finally introduce summary correlations as appropriately normalized integrals over distributional covariances with respect to arbitrary measures. Interesting new measures arise, but also Spearman's rho and an improved version of Pearson correlation as canonical special cases.
Wednesday, 15 February 2023
Grigory Vilkov (personal page) is Professor of Finance at Frankfurt School of Finance and Management. After his undergraduate studies with concentration in world economy, he continued to get his MBA from the University of Rochester, M.Sc. and Ph.D. in Management from INSEAD, and then habilitation from Goethe University Frankfurt . He was an assistant professor at Goethe University and visiting professor at the University of Mannheim. His work on various asset pricing topics has been published in top finance and economics journals such as the Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Journal of Monetary Economics, Review of Finance, and Management Science. His latest research interests lie in the area of measuring climate exposure and quantifying its effects in financial markets.
Talk: Media Narratives and Price Informativeness
Abstract: We theoretically and empirically show that stock return exposure to media narratives' attention, measured with standard methods for extracting topic attention from news text, is linked to lower stock price informativeness about future fundamentals. In the model, narrative exposure proxies for media bias-driven return volatility and is inversely related to price informativeness. Empirically, narrative exposure significantly decreases price informativeness and explains over 82% of idiosyncratic variance in the cross-section. Consequently, idiosyncratic variance and variance related to public information decrease stock price informativeness. Moreover, stocks affected by large average narrative shocks demonstrate elevated trading volume.
Wednesday, 22 February 2023
Title: Forecasting Option Returns with News
Abstract: This paper investigates whether text data contain useful information about the cross-section of expected equity option returns. We apply machine-learning approaches to extract qualitative signals from over five million news articles. The machine-learning textual indicators significantly predict future delta-hedged option returns and generate sizable profits. Our results are robust after controlling for known option return predictors and various underlying stock characteristics. An analysis of the keywords identified by machine-learning methods suggests that more than half of the important features come from sentiment-related dictionaries. Moreover, machine-learning approaches can capture information beyond the sentiment scope, such as information related to the implied volatility. Our work highlights the importance of analyzing unstructured data like texts for pricing derivatives and provides new evidence for machine-learning approaches’ superiority in extracting information from unstructured data.
Professor Jie (Jay) Cao is currently a full professor of finance at the School of Accounting and Finance, Hong Kong Polytechnic University (PolyU). He also serves as an Advisory Council member for Monetary Research at Hong Kong Institute for Monetary and Financial Research (HKIMR), an Academic and Accreditation Advisory Committee member for The Securities and Futures Commission (SFC) of Hong Kong, a member of the Board of Directors for Chicago Quantitative Alliance Asia (CQAsia), and an associate editor of Financial Management. Before joining PolyU, he served as a tenured associate professor of finance at the Chinese University of Hong Kong (CUHK) Business School and had worked there for 13 years.
His research interests center on empirical asset pricing, derivatives, and sustainable finance. His papers are published or forthcoming in top finance and management journals such as Journal of Financial Economics, Review of Financial Studies, Journal of Financial and Quantitative Analysis, and Management Science. He is the Principal Investigator of several Hong Kong competitive RGC grants and many other research grants from both academic and industry sponsors such as The Canadian Derivatives Institute (CDI) and Geneva Institute for Wealth Management. He has received various research awards such as AAM–CAMRI Prize in Asset Management by Asia Asset Management and NUS, the ETF Research Academy Award by the Paris–Dauphine House of Finance and Lyxor Asset Management, Chicago Quantitative Alliance (CQA) Academic Competition Award, and the best paper awards at several academic conferences such as the 28th Australian Finance & Banking Conference, 2020 FMA Consortium on Asset Management, 2020 Northern Finance Association Annual Conference, etc.
Wednesday, 8 March 2023
Bing Han is a Professor of Finance and the TMX Chair in Capital Markets at Rotman. His research focuses on Behavioral Finance, Investments, and Asset Pricing. He has published in top finance and economics journals as well as practitioner oriented journals. His research has been presented at many international and national conferences, and featured in mainstream media. He has taught both undergraduate and graduate courses at the University of Chicago, Ohio State University and University of Texas at Austin.
Talk: Idiosyncratic Volatility and the ICAPM
Abstract: We show that idiosyncratic volatility under CAPM contains useful information about the risk-return trade-off under the ICAPM. Motivated by the ICAPM pricing relations, we propose new methods to measure the ICAPM covariance risk (covariance of the market and the unobserved hedge portfolio) as well as individual stock exposure to the hedge portfolio using cross-sectional weighted average idiosyncratic volatilities. Our results support the ICAPM predictions about the time series and cross-sectional variations in risk premia. We find that the estimated covariance risk via average idiosyncratic volatilities is a robust time-series predictor of stock market returns. Moreover, stock’s beta with respect to the hedge portfolio is a significant determinant of the cross-section of expected stock returns. Finally, the ICAPM covariance risk closely tracks the tail index of Kelly and Jiang (2014) and explains its predictive power for stock market return.
Wednesday, 22 March 2023
Carole Bernard is currently professor of finance at Grenoble Ecole de Management in France. She graduated from Ecole Normale Supérieure de Cachan (France) in 2003 and obtained her Ph.D. in Finance from the Institute of Financial and Actuarial Sciences in Lyon (France) in. She has then been working at the University of Waterloo in Canada from 2006 to December 2014.
Her research interests are in finance, behavioural modelling, insurance and theoretical economics. Carole has published articles in leading international journals, such as Management Science, Journal of Risk and Insurance, Journal of Banking and Finance and Mathematical Finance among others. Some of her papers have received awards such as the 2006 NAAJ best paper, the 2011 EGRIE Young Economist Best Paper, the 2012 Johan de Witt prize from the Dutch Actuarial Society, the 2014 PRMIA award for Frontiers in Risk Management and the 2018 ARIA R.C. Witt award. She is on the editorial board of SIAM Journal on Financial Mathematics, Journal of Risk and Insurance and Journal of Banking and Finance.
Talk: Option-Implied Dependence and Correlation Risk Premium
Abstract: We propose a novel model-free approach to obtain the joint risk-neutral distribution among several assets that is consistent with market prices of options on these assets and their weighted index. In an empirical application, we use options on the S&P 500 index and its nine industry sectors. The results of our analysis reveal that the option-implied dependence for the nine sectors is highly nonnormal, asymmetric, and time-varying. We then study two conditional correlations: when the market moves down or up. The risk premium for the down correlation is strongly negative, while the opposite is true for the up correlation. These findings are consistent with the economic intuition that investors dislike the loss of diversification when markets fall, but they actually prefer high correlation when markets rally.
Wednesday, 29 March 2023
Raman Uppal is a Professor of Finance at EDHEC Business School. He holds a bachelors degree in Economics (Honors) from St. Stephen’s College (Delhi University), and M.A., M.B.A and Ph.D. degrees from The Wharton School of the University of Pennsylvania. Prior to working at Edhec Business School, he worked at London Business School and UBC Sauder School of Business. He has held visiting positions at Catholic University (Leuven), MIT Sloan School of Management, and London School of Economics and Political Science, and has served as Co-Director of the Financial Economics Programme of the Centre for Economic Policy Research (CEPR).
Talk: What is Missing in Asset-Pricing Factor Models
Abstract: Our objective is to price the cross section of asset returns. Despite considering hundreds of systematic risk factors (``factor zoo''), factor models still have a sizable pricing error. A limitation of these models is that returns compensate only for systematic risk. We allow compensation also for unsystematic risk. The resulting stochastic discount factor (SDF) prices the cross section of stock returns exactly, resolving the factor zoo. Empirically, more than half the variation of this SDF is explained by its unsystematic risk component, which is correlated with strategies reflecting market frictions and behavioral biases.
Quantitative FinTech Research Group, University of Sussex Business School, 2023