Alexander Nekrasov

Headline Salience, Managerial Opportunism, and Over- and Underreactions to Earnings

Limited attention theory predicts that higher salience of earnings news implies a stronger immediate market reaction to earnings news and a weaker post-earnings announcement drift (PEAD) or reversal (PEAR). Using a new measure, SALIENCE, defined as the number of quantitative items in an earnings press release headline, we find strong evidence consistent with salience effects. Higher SALIENCE is associated with stronger announcement reaction and subsequent PEAR. Managers are more likely to choose higher SALIENCE before selling shares in the post-announcement period and when earnings are high but less persistent, and to choose lower SALIENCE before stock option grants. The results are robust to using residual salience and an extended set of control variables. The findings are consistent with managers opportunistically headlining positive financial information in the earnings press release to incite overoptimism in investors with limited attention.

Tests of Long-Term Abnormal Performance: Analysis of Power

In tests of long-term performance, researchers are faced with several research design choices. For instance, when estimating abnormal returns, what specific firm characteristics should be used as matching criteria to select control firms? What weights should be placed on each characteristic? Should an event firm be matched with one control firm or multiple control firms or with a reference portfolio? Should we use the calendar-time portfolio approach? We provide guidance to researchers on these questions by evaluating the power of the test using simulation analyses. We find that the quality of matching when selecting control firms has little impact on the power of the test. Among the alternative approaches studied, the Fama-French calendar-time portfolio approach obtains the highest power in random samples. Interestingly, we find that the higher power of this approach is attributable to the return aggregation method rather than the use of multiple risk factors or the in-sample fit of the model. In most nonrandom samples, the Fama-French approach obtains the highest power – the one exception being samples with extreme event-time clustering. Overall, for a reasonable sample size, the power of the best available methodology to detect economically significant abnormal returns is quite low.

Target price forecasts: Fundamental and non-fundamental factors

This paper reveals both fundamental and non-fundamental factors play an important role in analysts’ target price formation. Analysts’ forecasts of short-term earnings and long-term growth are shown to be important explanatory variables for target prices; equally, the following salient non-fundamental factors are also shown to explain target price levels and especially target price biases: the 52-week high price and recent market sentiment. Here, increases in the 52-week high and market sentiment measures of one standard deviation correspond to increases in positive target price bias of 4.8% and 14.7%, respectively. Initially our analysis is constrained to analysts who provide long-term growth forecasts, however, our findings are robust to the removal of this constraint and the broader set of analysts. Our analysis reveals that analysts place greater weight on these non-fundamental factors in settings with greater task complexity and/or resource constraints, and when they rely on valuation heuristics as opposed to more rigorous valuation methodology, and that this greater weight is associated with increased optimistic bias. Finally, our results show that analysts’ target prices are useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies. However, in an internally consistent fashion, the informativeness of target prices for future returns is significantly reduced when greater weight is placed on either the 52-week high or recent market sentiment in the target price formation process.

Key Performance Indicators: The Incremental News in Their Disclosures and the Properties of Their Analyst Forecasts

The documented decline in the information content of earnings numbers has paralleled the emergence of disclosures, mostly voluntary, of industry-specific key performance indicators (KPIs). We find that the incremental information content conveyed by KPI news is significant for many KPIs, yet it is diminished when details about the computation of the KPI are absent or when the computation of the KPI changes over time. Consistent with analysts responding to investor information demand, we find that analysts are more likely to produce forecasts for a KPI when that KPI has more information content and when earnings are less informative. We also analyze the properties of analysts’ KPI forecasts, and we find that KPI forecasts are more accurate than mechanical forecasts, and their accuracy exceeds that of earnings forecasts. Our study contributes to the literature on the information content of KPIs and increases our understanding of the factors that affect this content. We provide evidence pertinent to the debate on whether and how to regulate KPI disclosures. This study further contributes to research on the properties of analysts’ forecasts.

Valuation Implications of Unconditional Accounting Conservatism: Evidence from Analysts' Target Prices: Valuation Implications of Unconditional Accounting Conservatism: Evidence from Analysts' Target Prices

We examine whether financial analysts understand the valuation implications of unconditional accounting conservatism when forecasting target prices. While accounting conservatism affects reported earnings, conservatism per se does not have an effect on the present value of future cash flows. We examine whether analysts adjust for the effect of conservatism included in their earnings forecasts when using these forecasts to estimate target prices. We find that signed target price errors (actual minus forecast) have a significant positive association with the degree of conservatism in forward earnings, suggesting that target prices are biased due to accounting conservatism. Cross-sectional analysis suggests that more sophisticated analysts and superior long-term forecasters adjust for conservatism to a greater extent than other analysts. In additional analyses, we explore the mechanism through which conservatism leads to bias in target prices. We first show that analysts’ earnings forecasts are negatively associated with the degree of conservatism, i.e., analysts include the effect of unconditional conservatism in their earnings forecasts. Based on alternative earnings-based valuation models that analysts may use, our evidence suggests that analysts fail to appropriately adjust their valuation multiple for the effect of conservatism included in their earnings forecasts when using these forecasts to derive target prices. As a consequence, we find that, for extreme changes in conservatism, the bias in analysts’ target prices due to conservatism leads to a distortion of market prices. The evidence highlights the concern that analysts may not appreciate the valuation implications of conservative accounting which could inhibit price discovery.