Kent Daniel

Bio: Kent Daniel is an academic researcher from Columbia University. The author has contributed to research in topics: Capital asset pricing model & Overconfidence effect. The author has an hindex of 36, co-authored 70 publications receiving 19855 citations. Previous affiliations of Kent Daniel include National Bureau of Economic Research & Northwestern University.

Investor Psychology and Security Market Under- and Overreactions

Abstract: We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors’ confidence as a function of their investment outcomes. We show that overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations ~“momentum”!, short-run earnings “drift,” but negative correlation between future returns and long-term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy. IN RECENT YEARS A BODY OF evidence on security returns has presented a sharp challenge to the traditional view that securities are rationally priced to ref lect all publicly available information. Some of the more pervasive anomalies can be classified as follows ~Appendix A cites the relevant literature!: 1. Event-based return predictability ~public-event-date average stock returns of the same sign as average subsequent long-run abnormal performance! 2. Short-term momentum ~positive short-term autocorrelation of stock returns, for individual stocks and the market as a whole!

Presentation Slides for 'Investor Psychology and Security Market Under and Overreactions'

Abstract: We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. We show that overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations (momentum), short-run earnings drift, but negative correlation between future returns and long-term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy. Prepublication version available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2017

Measuring mutual fund performance with characteristic-based benchmarks

Abstract: This article develops and applies new measures of portfolio performance which use benchmarks based on the characteristics of stocks held by the portfolios that are evaluated. Specifically, the benchmarks are constructed from the returns of 125 passive portfolios that are matched with stocks held in the evaluated portfolio on the basis of the market capitalization, book-to-market, and prior-year return characteristics of those stocks. Based on these benchmarks, "Characteristic Timing" and "Characteristic Selectivity" measures are developed that detect, respectively, whether portfolio managers successfully time their portfolio weightings on these characteristics and whether managers can select stocks that outperform the average stock having the same characteristics. We apply these measures to a new database of mutual fund holdings covering over 2500 equity funds from 1975 to 1994. Our results show that mutual funds, particularly aggressive-growth funds, exhibit some selectivity ability, but that funds exhibit no characteristic timing ability. CURRENTLY, OVER ONE TRILLION dollars are invested in actively managed equity mutual funds. Assuming that the fees and expenses of these funds average about one percent of assets-a conservative estimate that ignores the expenses that funds generate from buying and selling stocks-the total costs generated by this industry exceed $10 billion per year. Although mutual funds provide a number of services, such as check-writing and bookkeeping services, more than half of the expenses of mutual funds arise because of their stock-selection efforts.I This article examines whether mutual funds can systematically pick stocks that allow them to earn back a significant fraction of the fees and expenses that they generate. This question has been asked a number of times before, and has generated a great deal of controversy. Beginning with Jensen (1968),

Evidence on the Characteristics of Cross Sectional Variation in Stock Returns

Abstract: Firm size and book-to-market ratios are both highly correlated with the returns of common stocks. Fama and French (1993) have argued that the association between these firm characteristics and their stock returns arises because size and book-to-market ratios are proxies for non-diversifiable factor risk. In contrast, the evidence in this paper indicates that the return premia on small capitalization and high book-to-market stocks does not arise because of the co-movements of these stocks with pervasive factors. It is the firm characteristics and not the covariance structure of returns that explain the cross-sectional variation in stock returns.

Evidence on the Characteristics of Cross Sectional Variation in Stock Returns

Abstract: Firm sizes and book-to-market ratios are both highly correlated with the average returns of common stocks. Fama and French (1993) argue that the association between these characteristics and returns arise because the characteristics are proxies for nondiversifiable factor risk. In contrast, the evidence in this article indicates that the return premia on small capitalization and high book-to-market stocks does not arise because of the comovements of these stocks with pervasive factors. It is the characteristics rather than the covariance structure of returns that appear to explain the cross-sectional variation in stock returns. THERE IS NOW CONSIDERABLE evidence that the cross-sectional pattern of stock returns can be explained by characteristics such as size, leverage, past returns, dividend-yield, earnings-to-price ratios, and book-to-market ratios.' Fama and French (1992, 1996) examine all of these variables simultaneously and conclude that, with the exception of the momentum strategy described by Jegadeesh and Titman (1993), the cross-sectional variation in expected returns can be explained by only two of these characteristics, size and book-to-market. Beta, the traditional Capital Asset Pricing Model (CAPM) measure of risk, explains almost none of the cross-sectional dispersion in expected returns once size is taken into account.2

On Persistence in Mutual Fund Performance

Abstract: Using a sample free of survivor bias, I demonstrate that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual funds' mean and risk-adjusted returns Hendricks, Patel and Zeckhauser's (1993) "hot hands" result is mostly driven by the one-year momentum effect of Jegadeesh and Titman (1993), but individual funds do not earn higher returns from following the momentum strategy in stocks The only significant persistence not explained is concentrated in strong underperformance by the worst-return mutual funds The results do not support the existence of skilled or informed mutual fund portfolio managers PERSISTENCE IN MUTUAL FUND performance does not reflect superior stock-picking skill Rather, common factors in stock returns and persistent differences in mutual fund expenses and transaction costs explain almost all of the predictability in mutual fund returns Only the strong, persistent underperformance by the worst-return mutual funds remains anomalous Mutual fund persistence is well documented in the finance literature, but not well explained Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Wermers (1996) find evidence of persistence in mutual fund performance over short-term horizons of one to three years, and attribute the persistence to "hot hands" or common investment strategies Grinblatt and Titman (1992), Elton, Gruber, Das, and Hlavka (1993), and Elton, Gruber, Das, and Blake (1996) document mutual fund return predictability over longer horizons of five to ten years, and attribute this to manager differential information or stock-picking talent Contrary evidence comes from Jensen (1969), who does not find that good subsequent performance follows good past performance Carhart (1992) shows that persistence in expense ratios drives much of the long-term persistence in mutual fund performance My analysis indicates that Jegadeesh and Titman's (1993) one-year momentum in stock returns accounts for Hendricks, Patel, and Zeckhauser's (1993) hot hands effect in mutual fund performance However, funds that earn higher

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

Abstract: In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.

The Cross-section of Expected Stock Returns

Abstract: This paper studies the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions. These forecasts mimic how an investor could, in real time, combine many firm characteristics to obtain a composite estimate of a stock’s expected return. Empirically, the forecasts vary substantially across stocks and have strong predictive power for actual returns. For example, using ten-year rolling estimates of Fama- MacBeth slopes and a cross-sectional model with 15 firm characteristics (all based on low-frequency data), the expected-return estimates have a cross-sectional standard deviation of 0.87% monthly and a predictive slope for future monthly returns of 0.74, with a standard error of 0.07.

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior

Abstract: We present a consumption†based model that explains a wide variety of dynamic asset pricing phenomena, including the procyclical variation of stock prices, the long†horizon predictability of excess stock returns, and the countercyclical variation of stock market volatility. The model captures much of the history of stock prices from consumption data. It explains the short†and long†run equity premium puzzles despite a low and constant risk†free rate. The results are essentially the same whether we model stocks as a claim to the consumption stream or as a claim to volatile dividends poorly corelated with consumption. The model is driven by an independently and identically distributed consumption growth process and adds a slow †moving external habit to the standard power utility function. These features generate slow countercyclical variation in risk premia. The model posits a fundamentally novel description of risk premia. Investors fear stocks primarily because they do poorly in recessions unrelated to the risks of long†run average consumption growth.