Investor Psychology and Security Market Under- and Overreactions

https://w4.stern.nyu.edu/finance/docs/pdfs/Seminars/081w-whitelaw.pdf

Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns

International Momentum Strategies

User-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume.The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns.A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on.

Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance

We study the after-trading-cost performance of anomalies and the effectiveness of transaction cost mitigation techniques Introducing a buy/hold spread, with more stringent requirements for establishing positions than for maintaining them, is the most effective cost mitigation technique Most anomalies with less than 50% turnover per month generate significant net spreads when designed to mitigate transaction costs; few with higher turnover do The extent to which new capital reduces strategy profitability is inversely related to turnover, and strategies based on size, value, and profitability have the greatest capacity to support new capital Transaction costs always reduce strategy profitability, increasing data-snooping concerns

/pdf/a-taxonomy-of-anomalies-and-their-trading-costs-12tizg5lc4.pdf

A Taxonomy of Anomalies and Their Trading Costs

The empirical evidence on the cross-sectional relation between idiosyncratic risk and expected stock returns is mixed. We demonstrate that the omission of the previous month's stock returns can lead to a negatively biased estimate of the relation. The magnitude of the omitted variable bias depends on the approach to estimating the conditional idiosyncratic volatility. Although a negative relation exists when the estimate is based on daily returns, it disappears after return reversals are controlled for. Return reversals can explain both the negative relation between value-weighted portfolio returns and idiosyncratic volatility and the insignificant relation between equal-weighted portfolio returns and idiosyncratic volatility. In contrast, there is a significantly positive relation between the conditional idiosyncratic volatility estimated from monthly data and expected returns. This relation remains robust after controlling for return reversals.

Return Reversals, Idiosyncratic Risk, and Expected Returns

The empirical evidence on the cross-sectional relation between idiosyncratic risk and expected stock returns is mixed. We demonstrate that the omission of the previous month's stock returns can lead to a negatively biased estimate of the relation. The magnitude of the omitted variable bias depends on the approach to estimating the conditional idiosyncratic volatility. Although a negative relation exists when the estimate is based on daily returns, it disappears after return reversals are controlled for. Return reversals can explain both the negative relation between value-weighted portfolio returns and idiosyncratic volatility and the insignificant relation between equal-weighted portfolio returns and idiosyncratic volatility. In contrast, there is a significantly positive relation between the conditional idiosyncratic volatility estimated from monthly data and expected returns. This relation remains robust after controlling for return reversals. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

/pdf/return-reversals-idiosyncratic-risk-and-expected-returns-30dgnt5ew3.pdf

Stock returns unexplained by “fundamentals,” such as cash flow news, are more likely to reverse in the short run than those linked to fundamental news. Making novel use of analyst forecast revisions to measure cash flow news, a simple enhanced reversal strategy generates a risk-adjusted return four times the size of the standard reversal strategy. Importantly, isolating the component of past returns not driven by fundamentals provides a cleaner setting for testing existing theories of short-term reversals. Using this approach, we find that both liquidity shocks and investor sentiment contribute to the observed short-term reversal, but in different ways: Specifically, the reversal profit is attributable to liquidity shocks on the long side because fire sales more likely demand liquidity, and it is attributable to investor sentiment on the short side because short-sale constraints prevent the immediate elimination of overvaluation.

This paper was accepted by Brad Barber, finance.

A Closer Look at the Short-Term Return Reversal

We study the 52-week high momentum strategy in international stock markets proposed by George and Hwang (2004). This strategy produces profits in 18 of the 20 markets studied, and the profits are significant in 10 markets. The 52-week high momentum profits still exist conditional on past individual and industry returns, and are independent from the profits from the Jegadeesh and Titman (1993) momentum strategy. These profits are robust when we control for the Fama-French three factors and they do not show reversals in the long run. We find that the 52-week high is a better predictor of future returns than macroeconomic risk factors or the acquisition price. The individualism index has no explanatory power to the variations of the 52-week high momentum profits across different markets. However, the profits are no longer significant in most markets once transaction costs are taken into account.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1647872_code578408.pdf?abstractid=1364566&mirid=1

Qianqiu Liu

Papers

Return Reversals, Idiosyncratic Risk, and Expected Returns

Return Reversals, Idiosyncratic Risk, and Expected Returns

A Closer Look at the Short-Term Return Reversal

The 52-Week High Momentum Strategy in International Stock Markets

The 52-week high momentum strategy in international stock markets