Justin Birru

The Impact of the Federal Reserve's Interest Rate Target Announcement on Stock Prices: A Closer Look at how the Market Impounds New Information

The Federal Reserve announces its new interest rate target while the stock market is open, at precisely 2:15 P.M. eight times a year. In the Efficient Markets model, information is impounded in prices immediately and accurately as soon as it becomes public knowledge and only the unanticipated portion moves prices. Responding accurately to news requires investors to judge how much other investors have been surprised and how their investment decisions will be affected, so how the market responds to the news generates additional information to be digested and acted upon. This suggests that the full process of returning to equilibrium can not be instantaneous. In this paper, we combine a non-model dependent procedure for extracting the markets risk neutralized probability density over future stock prices from a set of option prices, with a newly available real time options data set, in order to examine the informational microstructure of the stock market around Fed funds target announcements.

Psychological Barriers, Expectational Errors, and Underreaction to News

This paper provides evidence that the 52-week high serves as a psychological barrier, inducing expectational errors and underreaction to news. Two clear predictions emerge and are confirmed in the data. First, nearness to a 52-week high induces expectational errors; evidence from earnings surprises and analyst price targets indicate that investor and analyst expectations are biased in a downward direction for stocks near a 52-week high and biased in an upward direction for stocks trading far from a 52-week high. Second, nearness to a 52-week high induces underreaction to news. Among positive earnings surprise stocks, post-announcement drift exists only for those stocks near a 52-week high. The evidence suggests that in contrast to currently offered preference-based explanations, a belief-based explanation may better explain the previously documented 52-week high anomalies.

Capital Market Anomalies and Quantitative Research

Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. Quant-backed analysts exhibit more efficient forecasting behavior on anomaly predictors--stock recommendations and target prices issued on anomaly-longs (anomaly-shorts) are more (less) favorable. Investment value of such analysts research is higher and their research reports are more likely to discuss implications of quantitative modeling and market anomalies. Quant research facilitates smart money trades of institutional clients on anomaly stocks--Quant research is associated with an increased (decreased) likelihood of purchasing underpriced (overpriced) stocks. Market participants recognize Quants--thematic reports authored by Quants generate abnormal reactions for corresponding stocks. Finally, we provide evidence consistent with quantitative research increasing market efficiency by attenuating cross-sectional predictability of anomaly based long-short strategies.

Industry Familiarity and Trading: Evidence from the Personal Portfolios of Industry Insiders

We study whether industry familiarity is an advantage in stock trading by exploring the trading patterns of industry insiders in their own personal portfolios. To do so, we identify accounts of industry insiders in a large dataset provided by a retail discount broker. We find that insiders trade firms from their own industry more frequently. Furthermore, they earn abnormal returns exclusively when trading own-industry stocks, especially obscure stocks (small, low analyst coverage, high volatility). In a battery of tests, we find no evidence of the use of private information. The results are most consistent with the interpretation that industry familiarity is an advantage in stock trading. Itzhak Ben-David Department of Finance Fisher College of Business The Ohio State University 2100 Neil Avenue Columbus, OH 43210 and NBER bendavid@fisher.osu.edu Justin Birru Department of Finance Fisher College of Business The Ohio State University 2100 Neil Avenue Columbus, OH 43210 birru.2@fisher.osu.edu Andrea Rossi Department of Finance Fisher College of Business The Ohio State University 2100 Neil Avenue Columbus, OH 43210 andrearossi832@msn.com A online appendix is available at http://www.nber.org/data-appendix/w22115

Uninformative Feedback and Risk Taking: Evidence from Retail Forex Trading

We document evidence consistent with retail day traders in the Forex market attributing random success to their own skill and, as a consequence, increasing risk taking. Although past performance does not predict future success for these traders, traders increase trade sizes, trade size variability, and number of trades with gains, and less with losses. There is a large discontinuity in all of these trading variables around zero past week returns: e.g., traders increase their trade size dramatically following winning weeks, relative to losing weeks. The effects are stronger for novice traders, consistent with more intense “learning” in early trading periods.

The Nominal Price Premium

Motivated by the evidence that investors tend to be overly optimistic about low-priced stocks, we examine how nominal price affects the cross section of stock returns. To circumvent the mechanical inverse relationship between price and expected return, we construct a novel way of examining the effect of nominal price on the cross section of stock returns. In the cross-section, a portfolio exploiting this strategy generates a value-weighted (equal-weighted) four-factor alpha of 85 (88) basis points per month, while raw price does not predict return robustly. Consistent with a mispricing-based explanation, the results are stronger for hard-to-arbitrage stocks and following high sentiment periods, and strategy returns are highly correlated with contemporaneous changes in sentiment. Using stock splits as an exogenous change in price level, we find that the post-split return dynamics mimic those predicted by our hypothesis. Evidence from earnings surprises and analyst price target forecasts confirms that beliefs are overly optimistic for low-priced stocks. Providing further evidence that the results reflect a belief-based rather than purely a preference-based channel, we find that the effect is distinct from other gambling related proxies that have been used in the past such as extreme returns, idiosyncratic volatility, and skewness.