“Cross-Asset Information Synergy in Mutual Fund Families”
Prof. Jennie Bai (Georgetown University), and Prof. Jun Kyung Auh (Georgetown University)
Despite the conventional wisdom that the equity and bond markets are seg- mented, the organizational structure of mutual fund families facilitates infor- mation integration between shareholders and creditors, and thus oﬀsets fric- tions that cause cross-asset segmentation. We ﬁnd that actively managed eq- uity funds and corporate bond funds exhibit signiﬁcant comovement in the investment decisions of commonly held ﬁrms’ securities only when they are af- ﬁliated with the same family. This aﬃliation-induced comovement is caused by information spillovers across shareholders and creditors rather than common reactions to public information or non-information mechanisms. Synthesizing cross-asset information helps predict future equity returns on top of state-of- the-art risk factors, and thus creates proﬁts for fund families as a whole. Our ﬁndings accentuate the importance of collaboration between equity and bond investors, which is not widely recognized by market participants.
Auh, Jun Kyung and Bai, Jennie, Cross-Asset Information Synergy in Mutual Fund Families (November 4, 2020). Georgetown McDonough School of Business Research Paper No. 3163135, Available at SSRN: https://ssrn.com/abstract=3163135 or http://dx.doi.org/10.2139/ssrn.3163135
“Tradable Risk Factors for Institutional and Retail Investors”
Prof. Riccardo Sabbatucci (Stockholm School of Economics), and Prof. Andreas Johansson (Schockholm School of Economics), and Prof. Andrea Tamoni (Rutgers Business School)
We construct tradable, long-short risk factors using combinations of large and liquid mutual funds (long leg) and ETFs (long and short legs), based on their holdings, for both retail and institutional investors. Exploiting a novel dataset, our tradable factors explicitly take into account ETF shorting costs and the capacity constraints of factor strategies. The tradable risk factors constitute valid benchmarks to evaluate portfolio managers and trading strategies, and they perform differently from “on-paper” risk factors (2% to 5% per year) due to implementation frictions in the short leg.
Johansson, Andreas and Sabbatucci, Riccardo and Tamoni, Andrea, Tradable Risk Factors for Institutional and Retail Investors (May 5, 2020). Swedish House of Finance Research Paper No. 20-21, Available at SSRN: https://ssrn.com/abstract=3594064 or http://dx.doi.org/10.2139/ssrn.3594064
“Discretionary Information in ESG Investing: A Text Analysis of Mutual Fund Prospectuses”
Dr. Shema F. Mitali (University of Geneva), Prof. Philipp Krueger (University of Geneva), Prof. Angie Andrikogiannopoulou (King’s College London), and Prof. Filippos Papakonstantinou (King’s College London)
We construct novel measures of funds’ environmental, social, and governance (ESG) commitment by applying text analysis to the discretionary investment-strategy descriptions in their prospectuses. We find that investors respond strongly to text-based ESG measures. Using discrepancies between text- and fundamentals-based ESG measures, we identify greenwashing funds. We find greenwashing is more prevalent in the last five years and among funds with lower past flows and weaker oversight. Furthermore, greenwashing funds attract similar flows as funds that truthfully reveal their ESG commitment, suggesting that investors cannot distinguish between them. On the other hand, greenwashers have inferior performance than genuinely green funds.
Andrikogiannopoulou, Angie and Krueger, Philipp and Mitali, Shema Frédéric and Papakonstantinou, Filippos, Discretionary Information in ESG Investing: A Text Analysis of Mutual Fund Prospectuses (April 12, 2022), Available at SSRN: https://ssrn.com/abstract=4082263 or http://dx.doi.org/10.2139/ssrn.4082263
“ESG Preference and Market Efficiency”
Prof. Jie Jay Cao (The Chinese University of Hong Kong), Prof. Sheridan Titman (The University of Texas at Austin), Prof. Xintong Eunice Zhan (The Chinese University of Hong Kong), and Weiming Elaine Zhang (The Chinese University of Hong Kong)
We investigate how the trend towards socially responsible investing affects the informational efficiency of stock prices. Specifically, we study several questions – a) whether the return predictability of mispricing signals is stronger for firms held by more socially responsible institutions; b) is the inefficiency driven by the divergence of trading implications from ESG performance and mispricing signals, or other known limits to arbitrage; 3) why other market participants, like arbitragers, do not correct the mispricing; 4) As ESG investing become a greater part of money management industry, will this ESG preference become a new risk factor?
Cao, Jie and Titman, Sheridan and Zhan, Xintong and Zhang, Weiming Elaine, ESG Preference, Institutional Trading, and Stock Return Patterns (August 25, 2021). Journal of Financial and Quantitative Analysis, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3353623 or http://dx.doi.org/10.2139/ssrn.3353623
“The Rate of Return on Real Estate: Long-Run Micro-Level Evidence”
Prof. David Chambiers (University of Cambridge), Prof. Christophe Spaenjers (HEC Paris), and Prof. Eva Steiner (Cornell University)
We provide evidence that direct real estate investments are less profitable and more risky in the long run than previously thought. We hand-collect property-level data on realized income, expenses, and transaction prices from the archives of four large institutional investors in the U.K.—historically important Oxbridge colleges—for the period 1901–1970. Gross income yields mostly fluctuate around 5%, but trend to lower (higher) levels for agricultural and residential (commercial) real estate near the end of our sample period. Operating costs mean that net yields are about one third lower than gross yields on average. Long-term real income growth rates are between -1.0% and 0.0% for the three main property types. Together these findings imply limited long-run capital gains and real annualized net total returns of less than 4% across all property types. Moreover, we find substantial volatility in net income streams and variation in relative price levels across transacted properties, revealing the considerable idiosyncratic risks associated with real estate investments.
Chambers, David and Spaenjers, Christophe and Steiner, Eva Maria, The Rate of Return on Real Estate: Long-Run Micro-Level Evidence (January 5, 2021). Review of Financial Studies, Forhtcoming, Available at SSRN: https://ssrn.com/abstract=3407236 or http://dx.doi.org/10.2139/ssrn.3407236
“Tax-Efficient Asset Management”
Prof. Clemens Sialm (University of Texas), and Prof. Hanjiang Zhang (Washington State University)
We investigate the relation between tax burdens and mutual fund performance from both a theoretical and empirical perspective. The theoretical model introduces heterogeneous tax clienteles in an environment with decreasing returns to scale and shows that the equilibrium performance of mutual funds depends on the size of the tax clienteles. Our empirical results show that the performance of U.S. equity mutual funds is related to their tax burdens. We find that tax-efficient funds do not only exhibit superior after-tax performance, they also exhibit superior before-tax performance due to lower trading costs, favorable style exposures, and better selectivity.
Sialm, Clemens and Zhang, Hanjiang, Tax-Efficient Asset Management: Evidence from Equity Mutual Funds (August 31, 2019). Journal of Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2368625 or http://dx.doi.org/10.2139/ssrn.2368625
“Hedge Funds and Financial Intermediaries”
Prof. Magnus Dahlquist (Stockholm School of Economics), and Valeri Sokolovski (HEC Montréal)
In this project we will examine the link between hedge fund returns and financial intermediaries. There are at least two, non-mutually-exclusive, channels through which financial intermediaries, such as commercial and investment banks, impact hedge fund returns; direct and indirect. The first, direct, effect manifests itself in a hedge fund’s prime brokerage relationship. Prime brokers provide their clients with many services including clearing, securities lending and financing. The second, indirect, impact of financial intermediaries on hedge funds is through their effect on asset prices and risk premiums. Recent research finds that shocks to the intermediaries’ aggregate risk-bearing capacity can explain the cross-section of expected returns of multiple asset classes. Given that many hedge funds are essentially portfolios of such assets, one would expect them to have high exposure to the health of the intermediary sector. We will examine both effects. Methodologically, we will use standard approaches in the literature (such as panel regressions, portfolio sorts, and event studies) and recent techniques based on network theory. The structure of our enhanced hedge fund dataset allows us to identify the hedge-fund-broker relationships at each point in time, which makes it well-suited for network analyses.
Dahlquist, Magnus and Sokolovski, Valeri and Sverdrup, Erik, Hedge Funds and Financial Intermediaries (May 26, 2021). Swedish House of Finance Research Paper No. 19-8, Available at SSRN: https://ssrn.com/abstract=3396632 or http://dx.doi.org/10.2139/ssrn.3396632
“The Portfolios and Financial Decisions of High Net Worth U.S. Households”
Prof. Enrichetta Ravina (Kellogg School of Management)
The objective of our project is to describe the portfolio positions and transactions of a sample of high net worth US households over the period from 2005 to 2009. We will analyze the asset classes they invest in, their performance, the degree of diversification, the amount and timing of their rebalancing activity, and the characteristics, risk, liquidity, and tax status of their investments. We observe detailed transactions and portfolio positions data over time, and fees, interest, dividend income and capital gain for each security. While the strength of our data set is in the comprehensive view of the financial assets it offers, we also observe investments in real estate, and, in some cases, the value and characteristics of the business(es) the household owns directly. For each household, we also observe the different tax entities through which it invests, the different managers and advisors it employs, if any, as well as whether different households are part of the same family, which gives us a glimpse of the inter-family investment dynamics. Our time frame will also allow us to study in detail the behavior of these investors during the boom and the subsequent financial crisis.
Ravina, Enrichetta, Viceira, Luis, and Walter, Ingo, The Portfolios and Financial Decisions of High Net-Worth U.S. Households (under revision)
The research results featured in The Financial Times, Auther’s Note (read more), The Financial Times (read more), The WSJ – The Intelligent Investor (read more), WSJ. Money (read more), Il Sole 24 Ore (read more), Bloomberg (read more).
“Gaussian Process Methods for Asset Pricing and Dynamic Portfolio Choice”
Prof. Fabio Trojani (University of Geneva), and Prof. Simon Scheidegger (HEC Lausanne)
In this paper, we consider the portfolio optimization problem for a multiperiod investor who seeks to maximize her utility facing multiple risky assets and proportional transaction costs in the presence of return predictability. Due to the curse of dimensionality, this problem is challenging to solve, even numerically. To this end, we propose to embed Gaussian Process regression in combination the active subspace method and Bayesian active learning inside a parallelized dynamic programming algorithm. Preliminary results will show that with this generic setup, we push the boundary of the current state of the art in the literature along several dimensions. The said combination of tools allows us to study important open problems in this literature, including (i) the characterization of no-trade regions (potentially volume and welfare implications in economies with several assets), and (ii) the optimal portfolio behavior in economies with a stochastic opportunity set or stochastic frictions.
Trojani, Fabio, and Scheidegger, Simon, A General Machine Learning Approach for High-Dimensional Asset Pricing with Frictions, Geneva Finance Research Institute Research Paper (work in progress)
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