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Precautionary Savings and the Stock-Bond Covariance
Toomas Laarits, New York University
Andrea Vedolin, Boston University and NBER

Laarits shows that the precautionary savings motive can account for high-frequency variation in the stock-bond covariance. An increase in the price of risk lowers risky asset prices on account of an increase in risk premia; it lowers bond yields on account of the precautionary savings component. Consequently, a price of risk shock moves risky and safe asset prices in the opposite direction. Times when the price of risk is volatile see a more negative stock-bond covariance. Laarits demonstrates that a model calibrated to match the equity risk premium fits well the recent evidence on stock-bond covariance. Empirically, he shows that stock-bond covariance co-moves with credit spreads and can predict excess returns on risky bonds and on bond-like stocks. The calibrated model underlines the first-order effect of risk compensation on safe rates.

Who Owns What? A Factor Model for Direct Stock Holding
Vimal Balasubramaniam, Queen Mary University of London
John Y. Campbell, Harvard University and NBER
Tarun Ramadorai, Imperial College London
Benjamin Ranish, Federal Reserve Board of Governors
Johannes Stroebel, New York University and NBER

Balasubramaniam, Campbell, Ramadorai, and Ranish build a cross-sectional factor model for investors' direct stock holdings, by analogy with standard time-series factor models for stock returns. The researchers estimate the model using data from almost 10 million retail accounts in the Indian stock market. Balasubramaniam, Campbell, Ramadorai, and Ranis find that stock characteristics such as firm age and share price have strong investor clienteles associated with them. Similarly, account attributes such as account age, account size, and extreme underdiversification (holding a single stock) are associated with particular characteristic preferences. Coheld stocks tend to have higher return covariance, suggestive of the importance of clientele effects in the stock market.

FinTech Lending and Cashless Payments
Pulak Ghosh, India Institute of Management Bangalore
Boris Vallee, Harvard University
Yao Zeng, University of Pennsylvania
Lin William Cong, Cornell University

This study provides a new perspective on the rise of FinTech lending by uncovering an informational synergy with cashless payments. Theoretically, FinTech lenders screen borrowers more efficiently when borrowers use cashless payments that produce transferable and verifiable information. In turn, because borrowers expect lenders to rely on such payment information to screen them, a strategic consideration to stand out from non-adopting borrowers pushes more borrowers to adopt cashless payments. Using novel loan application data from a leading Indian FinTech lender targeting small businesses and an instrumental variable based on the 2016 Indian Demonetization, we identify that a larger use of cashless payments predicts a higher likelihood of loan approval, a lower interest rate, and lower default. These relationships are stronger for firms of higher observable risk, and for firms of higher quality that can be only inferred from payment records. This synergy supports data sharing and open banking, and more broadly the development of an alternative banking model without a balance sheet or traditional banking relationships.

Predictable Price Pressure
Samuel M. Hartzmark, University of Chicago and NBER
David H. Solomon, Boston College
Dimitri Vayanos, London School of Economics and NBER

Hartzmark and Solomon demonstrate that predictable uninformed cash flows forecast market and individual stock returns. Buying pressure from dividend payments (announced weeks prior) predicts higher value weighted market returns, four times greater on the top quintile of payment days than the lowest. This effect holds internationally, varies with reinvestment intensity, and increases with high VIX. Selling pressure leads predictable high stock expense firms to have lower returns when selling restrictions lift, by 117 bp in four days. Hartzmark and Solomon estimate market-level price multipliers of 1.5 to 2.3. These results suggest price pressure is a widespread result of flows, rather than an anomaly.

Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic
Jinfei Sheng, University of California, Irvine
Zheng Sun, University of California, Irvine
Wanyi Wang, University of California, Irvine
Pietro Veronesi, University of Chicago and NBER

Using two proxies for investors’ political affiliation, Sheng, Sun, and Wang document sharp differences in stock returns between firms likely dominated by Democratic investors (blue stocks) and those dominated by Republican voters (red stocks) during the COVID pandemic. Red stocks have about 20 basis points higher risk-adjusted returns than blue stocks on COVID news days (Partisan Return Gap). Lockdown policies, COVID cases, industry and firm fundamentals only explain at most 15% of the return gap. The majority of the return gap is likely due to polarized political beliefs. Overall, their paper provides partisanship as a novel aspect in understanding abnormal stock returns during the pandemic.



Markus Baldauf, University of British Columbia
Carl Beck, NBER
Matteo Benetton, University of California at Berkeley
Ulrich Bindseil, European Central Bank
Fousseni Chabi-Yo, University of Massachusetts Amherst
Gregory Duffee, Johns Hopkins University
Anastassia Fedyk, University of California at Berkeley
Lorena Keller, University of Pennsylvania
Eben Lazarus, Massachusetts Institute of Technology
Alexander Michaelides, Imperial College London
Claudia Moise, Duke University
Claudia Robles Garcia, Stanford University
Gill Segal, University of North Carolina at Chapel Hill
Andrea Tamoni, Rutgers University
Quentin Vandeweyer, University of Chicago



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