Friendly Investing and Information Sharing in the Asset Management Industry
(with Antonino Emanuele Rizzo and Rafael Zambrana)
Journal of Financial and Quantitative Analysis, accepted for publication
ABSTRACT: Do asset managers engage in friendly investing to obtain privileged investment information? We test this hypothesis in the context of mutual fund connections to financial groups. Using brokers as the source of connections, we show that (a) funds overweight and are reluctant to sell the stock of connected financial groups, and (b) funds tend to cast their vote with the management of connected financial groups. The extent of friendly investing drives funds to trade similarly, and it is positively associated with a fund's performance. The lending channel confirms the information flow from financial companies to connected funds.
Horizon Bias and the Term Structure of Equity Returns
(with Stefano Cassella, Huseyin Gulen, and Peter Kelly)
The Review of Financial Studies (2023)
Data and Code
ABSTRACT: We label the degree to which individuals are more optimistic at long horizons relative to short horizons the horizon bias. We examine whether time-series variation in the horizon bias can explain the time-series variation in the equity term structure. We use analyst earnings forecasts to measure the degree of the horizon bias in the stock market. Consistent with the intuition from a stylized present value model, we find that periods of above-average horizon bias are associated with negative term premia, whereas periods of below-average horizon bias are associated with positive term premia.
Disagreement in the Equity Options Market and Stock Returns
(with Ruslan Goyenko)
The Review of Financial Studies (2022)
for data, see https://ruslangoyenko.com/research/
ABSTRACT: We estimate investor disagreement from synthetic long and short stock trades in the equity options market. We show that high disagreement predicts low stock returns after positive earnings surprises and high stock returns after negative earnings surprises. The negative effect is stronger for high-beta stocks and stocks that are more difficult to sell short. In the cross-section of all stocks and the subset of the 500 largest companies, high disagreement robustly predicts low monthly and weekly stock returns.
Financial Market Misconduct and Public Enforcement: The Case of Libor Manipulation
(with Priyank Gandhi, Jens Jackwerth and Alberto Plazzi)
Management Science (2019)
ABSTRACT: Using comprehensive data on London Interbank Offer Rate (Libor) submissions from 2001 through 2012, we provide evidence consistent with banks manipulating Libor to profit from Libor-related positions and to signal their creditworthiness during distressed times. Evidence of manipulation is stronger for banks that were eventually sanctioned by regulators and disappears for all banks in the aftermath of the Libor investigations that began in 2010. Our findings suggest that the threat of large penalties and the loss of reputation that accompany public enforcement can be effective in deterring financial market misconduct.
ABSTRACT: We combine annual stock market data for the most important equity markets of the last four centuries: the Netherlands and UK (1629–1812), UK (1813–1870), and US (1871–2015). We show that dividend yields are stationary and consistently forecast returns. The documented predictability holds for annual and multi-annual horizons and works both in- and out-of-sample, providing strong evidence that expected returns in stock markets are time- varying. In part, this variation is related to the business cycle, with expected returns increasing in recessions. We also find that, except for the period after 1945, dividend yields predict dividend growth rates.
ABSTRACT: Fund managers are double agents; they serve both fund investors and owners of management firms. This conflict of interest may result in trading to support securities prices. Tests of this hypothesis in the Spanish mutual fund industry indicate that bank-affiliated mutual funds systematically increase their holdings in the controlling bank stock around seasoned equity issues, at the time of bad news about the controlling bank, before anticipated price drops, and after non-anticipated price drops. The results seem mainly driven by bank managers’ incentives. Ownership of asset management companies thus matters and can distort capital allocation and asset prices.
ABSTRACT: The dividend-price ratio is a noisy proxy for expected returns when expected dividend growth is time-varying. This paper uses a new and forward-looking measure of dividend growth extracted from S&P 500 futures and options to correct the dividend-price ratio for changes in expected dividend growth. Over January 1994 through June 2011, dividend growth implied by derivative markets reliably forecasts future dividend growth, and the corrected dividend-price ratio predicts S&P500 returns substantially better than the standard dividend-price ratio, in-sample and out-of-sample. Time-varying expected dividend growth is important to explain price movements, especially because it is highly correlated with expected returns.
ABSTRACT: We show that Standard & Poor’s (S&P) 500 futures are pulled toward the at-the-money strike price on days when serial options on the S&P 500 futures expire (pinning) and are pushed away from the cost-of-carry adjusted at-the-money strike price right before the expiration of options on the S&P 500 index (anti-cross-pinning). These effects are driven by the interplay of market makers’ rebalancing of delta hedges due to the time decay of those hedges as well as in response to reselling (and early exercise) of in-the-money options by individual investors. The associated shift in notional futures value is at least $115 million per expiration day.
Holding Period Effects in Dividend Strip Returns
(with Jens Jackwerth)
Revise and Resubmit at the Review of Financial Studies
ABSTRACT: We estimate short-term dividend strip prices from 26 years of S&P 500 index options data (1996-2021). We endogenize interest rates when estimating strip prices and use longer holding period returns to mitigate the effect of measurement error. We find that Sharpe ratios for short-term strips are sizeable and substantially higher than Sharpe ratios for the market. Short-term strips also have a low market beta and a high alpha. Over the business cycle, realized term premia (i.e., the difference between market and strip returns) and the term structure of Sharpe ratios move countercyclically, whereas the term structure of alphas moves procyclically.
Equity Duration and Predictability
(with Peter Koudijs)
Revise and Resubmit at the Journal of Financial Economics
last version: June 2023
AFA 2021, EFA 2020
ABSTRACT: One of the most puzzling findings in asset pricing is that expected returns dominate the variation in equity price movements, leaving little room for expected dividends to have an impact. Even more puzzling is that this dominance only emerged after 1945. We argue that an increase in equity duration can explain these findings. We provide empirical support across three datasets: dividend strips, the long time series for the market, and the cross-section of stocks. We develop and calibrate a simple present value model that incorporates the effect of duration. Around 50% of the duration effect comes from expected returns being more persistent than dividend growth rates; 46% comes from the variance of shocks to expected returns increasing with duration, and the remaining 4% is due to the interaction between both effects.
Equity Term Structure Response to FOMC Announcements
(with Ben Matthies)
first version: December 2020
last version: April 2023
ABSTRACT: We study the response of the equity term structure to FOMC announcements using a high frequency event study approach. We find that monetary policy surprises have opposite effects on short-term dividend strips and the long-term equity market. Following an unanticipated cut in the target rate, short-term dividend strip prices decrease while long-term equity prices increase on average. Furthermore, the short-term dividend strip return in the 30-minute window around each FOMC announcement positively predicts macroeconomic growth up to one-year ahead. We present a stylized framework which shows this pattern can arise when policy decisions signal information about current economic conditions.
ABSTRACT: Is news reported more favorably in companies’ domestic newspapers than abroad? Do cross-country differences in media reporting reflect differences in investor sentiment? We test these hypotheses for the case of the automotive industry across the U.S., Germany, and Japan. Using comprehensive hand-coded news data, we show that companies obtain substantially more media coverage and are presented with a significantly more positive news tone in their home countries than abroad. The media slant increases during crisis periods and it predicts stock price deviations of cross-listed stocks. The predictive relation is strongest for news reported by journalists who have native-sounding names.
Motivated Beliefs in Macroeconomic Expectations
(with Stefano Cassella, Huseyin Gulen, and Peter Kelly)
last version: May 2022
Quadrant Behavioral Finance Conference 2019, TAU Finance Conference 2019, Colorado Finance Summit 2019, ASU Sonoran Winter Finance Conference 2020
ABSTRACT: Motivated beliefs are an important framework for understanding macroeconomic expectations. We extend popular motivated belief models and show that they predict an upward sloping term structure of optimism (horizon bias). Analyzing professional forecasts over the past fifty years, we find that forecast errors exhibit both optimism bias and horizon bias. Further, consistent with the conceptual framework of Bénabou (2015), we show that time-series variation in the optimism bias is related to theory-based drivers of motivated beliefs like uncertainty and anxiety. Finally, we find that forecasters react more strongly to good than bad news, consistent with motivated beliefs.
(with Rasa Karapandza and Fredrik Wisser)
Lst version: June 2023
ABSTRACT: We introduce a novel method for training computer algorithms to measure news sentiment. Our approach leverages human-coded sentiment scores from over 200,000 newspaper articles to teach the computer to select words, word combinations, and their linear weights. In an out-of-sample test, examining newspaper articles about US companies, we show that: (i) our news sentiment metric displays a bimodal distribution similar to that observed in the human-coded sentiment scores, (ii) our news metric outperforms the widely-used bag-of-words approach and recent machine learning models in explaining human-coded news sentiment, and (iii) our news sentiment metric serves as a robust predictor for daily stock returns.