Evaluating the information content of earnings forecasts

This study develops a framework to compare the ability of alternative earnings forecast approaches to capture the market expectation of future earnings. Given prior evidence of analysts’ systematic optimistic bias, we decompose earnings surprises into analysts’ earnings surprises and adjustments based on alternative forecasting models. An equal market response to these two components indicates that the associated earnings forecast is a sufficient estimate of the market expectation of future earnings. To apply our framework, we examine four recent regression-based earnings forecasting models, alongside a simple earnings-based random walk model and analysts’ forecasts. Using the earnings forecasts of the model that satisfies our sufficiency condition, we identify a set of stocks for which the market is unduly pessimistic about future earnings. The investment strategy of buying and holding these stocks generates statistically signi?cant abnormal returns. We offer an explanation as to why this and similar strategies might be successful.     

A new perspective on post‐earnings‐announcement‐drift: Using a relative drift measure

Prior research finds that there is a delayed reaction to both analyst‐based earnings surprises and random‐walk‐based earnings surprises. Focusing on the market reaction from the post‐announcement window, prior studies show that analyst‐based drift is larger than random walk‐based drift. This finding is counter‐intuitive if we believe large, sophisticated investors tend to trade on analysts’ forecast earnings news and thus react faster and more completely than smaller and less sophisticated investors react to random walk earnings news. In this study, we construct a relative measure of post‐earnings‐announcement drift (PEAD) (i.e., drift as a proportion of total market reaction to earnings news) which we refer to as the ‘drift ratio’, and we provide evidence, consistent with our intuition, that analyst‐based drift ratio is smaller (not greater) than random‐walk‐based drift ratio. We find that this difference is more pronounced in more recent periods and for firms with more sophisticated investors. Our approach to measure the PEAD is more intuitive than that in traditional PEAD literature. Our results thus complement existing research findings by utilizing the drift ratio measure to generate new insights about the drift phenomenon.     

Run length and the predictability of stock price reversals

Survival analysis is used to estimate time‐varying probabilities of price reversals using daily data for the Australian All Ordinaries Price Index. Lagged price changes lead to persistence (shortening) in a price run if they are of the same (opposite) sign as the run. An increase in the number of runs observed in the previous 30 days also increases the probability of price reversal. The predictive accuracy of the models is assessed using a probability scoring rule. Consistent with market efficiency, the estimated models are less accurate than the random walk model in predicting the length of individual price runs out‐of‐sample.     

An empirical assessment of the Feltham-Ohlson models considering the sign of abnormal earnings

This paper provides an empirical assessment of the Feltham-Ohlson models, distinguishing between firms with positive and negative abnormal earnings. Abnormal earnings persistence and conservatism parameters differ for these two groups; this implies different earnings prediction models and valuation functions for both profit-making and loss-making firms. The analysis refers to the period 1991-1999 and uses a sample of Spanish firms quoted on the Madrid S.E. The results suggest that our contextual approach is more useful than the non-contextual one to predict future abnormal earnings and explain current prices. Although the Ohlson (1995) model is accurate in forecasting future abnormal earnings and stock prices, the results improve when firms with negative abnormal earnings are valued using a temporary model and firms with positive abnormal earnings using a more permanent one. The Feltham and Ohlson (1995) model generates the lowest forecast errors in the prediction of positive abnormal earnings, but it produces the least accurate results in forecasting prices.     

Dynamic price discovery

This article analyzes the dynamic process of price discovery in a competitive securities market where investors are equally informed about the fundamental determinants of an asset's end-of-period value but, because they do not know each other's wealth positions, do not know the equilibrium price of shares at the start of a current trading session. Because a large number of participants is assumed, issues concerning market impact and market manipulation are avoided. As trading progresses, participants update their expectations of an asset's equilibrium value. As they do so, price can either converge to a new level or, following a run, revert back to a previous level. This implies that, in clusters of adjacent prices, price changes are more apt to be predominantly of like sign (positive or negative) than would be the case under random walk with a bid-ask spread. Moreover, reversals, when they do occur, should be larger than continuations. An examination of 1988 transactions data for the 30 Dow Jones Industrial stocks shows that this is indeed the case. With the effect of the bid-ask spread removed, first-order autocorrelation coefficients are found to be positive.     

Indexing and Stock Price Efficiency

Indexing has experienced substantial growth over the last two decades because it is an effective way of holding a diversified portfolio while minimizing trading costs and taxes. In this article, we focus on one negative externality of indexing: the effect on the efficiency of stock prices. Based on a sample of large and liquid US stocks, we find that greater indexing leads to less efficient stock prices, as indicated by stronger post‐earnings‐announcement drift and greater deviations of stock prices from the random walk. We conjecture that reduced incentives for information acquisition and arbitrage induced by indexing and passive trading are probably the main causes for degradation in price efficiency.     

Price Discovery,Causality and Forecasting in the Freight Futures Market

This paper investigates the causal relationship between futures and spot prices in the freight futures market. Being a thinly traded market whose underlying asset is a service, sets it apart from other markets investigated so far in the literature. Causality tests, generalised impulse response analysis and forecasting performance evaluation indicate that futures prices tend to discover new information more rapidly than spot prices. Revisions in the composition of the underlying index to make it more homogeneous, have strengthened the price discovery role of futures prices. The information incorporated in futures prices, when formulated as a VECM, produces more accurate forecasts of spot prices than the VAR, ARIMA and random-walk models, over several steps ahead. This revised version was published online in June 2006 with corrections to the Cover Date.     

Lead indicator models and UK analysts' earnings forecasts

This study examines the predictive ability of models which adjust random walk forecasts of corporate earnings, to incorporate past changes in economic lead indicators. The results suggest that changes in the broad money supply measure M4 contain predictive ability, beyond equivalent changes in other lead indicators or an individual firm's earnings. When forecasts from the broad-money model are compared with forecasts generated by financial analysts a size effect is evident: the superiority of analysts' forecasts is apparent much earlier for large firms than for small firms. This result is consistent with studies suggesting a size-related differential in the collection and dissemination of information by market participants.     

The generation of stock market cycles

This paper demonstrates that the relation between stock market and business cycle dynamics can be conceptualized using a dividend discount model. The interaction of changes in earnings and interest rates throughout the economic cycle are shown to cause changes in the level of stock prices. This implies that monitoring and forecasting these factors can help explain and possibly predict stock price behavior over time.     

Economic policy uncertainty,investor attention and post-earnings announcement drift

How the market incorporates information into stock price is a core issue in finance. This study focuses on the impact of economic policy uncertainty (EPU) on the stock prices information efficiency of China's A-share market and underlying role of investors' attention allocation mechanism. This study analyzes the information efficiency of stock prices using the sensitivity of stock cumulative abnormal return to earnings information across different windows following earnings announcement. Based on the earnings announcement events of listed companies in China's A-share market, this study presents an empirical study of the aforementioned issues using event study and regression analysis methods. The following results are seen: (1) EPU aggravates the underreaction of stock price earnings information and the post-earnings announcement drift in the A-share market. (2) Under highly uncertain economic policies, investors show a limited attention allocation pattern of devoting increasing attention to macroeconomic policies and decreasing attention to earnings information, which leads to a decrease in the information efficiency of stock price. This study also analyzes the heterogeneity of the influence of EPU on stock price information efficiency using the institutional shareholding ratio. The results show that increasing institutional shareholding does not reduce the adverse effects of EPU on the information efficiency of stock prices. This study not only provides empirical evidence for Brunnermeier, Sockin, and Xiong (2022) and rational inattention theory, but also reveals that institutional investors show similar behavioral characteristics to retail investors in China's stock market. The results of this study have policy significance for improving the information efficiency of stock market.     

Understanding Stock Price Volatility: The Role of Earnings

In an efficient capital market, asset prices vary when investors change their expectations about cash flows, discount rates, or both. Using dividends to measure cash flows, previous research shows that the aggregate dividend‐price ratio varies due to changes in expected discount rates (returns) rather than expected cash flows. In contrast, using accounting earnings instead of dividends as a measure of cash flows, this paper shows that as much as 70% of the variation in the dividend‐price ratio can be explained by changes in expected earnings. Moreover, the paper documents a significant negative correlation between expected returns and expected earnings, suggesting that variations in a common factor to both may generate significant price volatility. The results are consistent with the dividend‐policy irrelevance hypothesis.     

Stock exchange seats as capital assets

Stock exchange seats are important assets for securities brokers since they provide access to centralized secondary trading markets for corporate securities at a reduced cost. This paper provides empirical evidence on the dynamic behavior of monthly New York and American Stock Exchange seat prices over the 1926–1972 period. Specifically, evidence is presented which: (a) is consistent with a multiplicative random walk model for seat prices; (b) indicates that unexpected changes in the prices of exchange-listed stocks or in the volume of shares traded on the exchange are important new information about the value of seats in each month; and (c) indicates that the market for seats is efficient in assimilating new information and quickly incorporates new information into the prices of seats. In addition, we examine the effect of the infrequent trading of seats on the statistical properties of the models.     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

An information-based model of target stock price runup in the market for corporate control

The stock price runup of target firms in the market for corporate control has been anecdotally attributed to inside trading. Moreover, the empirical merger and acquisitions literature documents a time-varying level and duration of the stock price runup of target firms. Using a market microstructure approach, we model stock price runup as a stochastic process that shifts between a random walk without drift and a predictable process dependent on a parsimonious set of state variables. Consistent with the market microstructure literature, predictability in prices can be exploited only by the informed trader. The model is capable of explaining the complex stylized facts observed in stock price runup. It is also consistent with the merger wave literature, as we find that capital liquidity, economic growth, and market valuations drive the complex dynamics of stock price runup.     

Model-based earnings forecasts vs. financial analysts' earnings forecasts

Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.     

Price reversal and drift following earnings announcements

Systematic patterns in returns following earnings announcements are difficult to interpret. This study provides additional insights into the observation of price reversal and drift by examining the effects of both the method used to identify winners and losers and also the length of the subsequent period analyzed. The results show that both drift and reversal can be observed for the same sample and event. This evidence indicates that security price behavior following earnings announcements, especially in the short-term, depends not only on the earnings information, as in the drift studies, but also on the price reaction to the earnings information.     

Price Reactions to Dividend Initiations and Omissions: Overreaction or Drift?

This article investigates market reactions to initiations and omissions of cash dividend payments. Consistent with prior literature we find that the magnitude of short-run price reactions to omissions are greater than for initiations. In the year following the announcements, prices continue to drift in the same direction, though the drift following omissions is stronger and more robust. This post-dividend initiation/omission price drift is distinct from and more pronounced than that following earnings surprises. A trading rule employing both samples earns positive returns in 22 out of 25 years. We find little evidence for clientele shifts in either sample.     

An Event Option Pricing Model with Scheduled and Unscheduled Announcement Effects

There is considerable evidence supporting the time-varying distribution of asset returns. There is also ample evidence that scheduled announcement events such as money supply announcements (in the case of foreign exchange), earnings announcements (in the case of stocks), and crop reports (in the case of commodities), as well as random unscheduled events, can affect the level and volatility of asset returns. This study provides an Event Model for European call options which explicitly addresses effects of these two classes of events. This specification requires estimation of more parameters, but it could provide a more accurate basis for pricing options than previous Poisson jump-diffusion models. Parametric analysis shows that the standard models under price the options relative to the Event Model. The Event Model may be particularly useful in pricing short-term deep out-of-the-money options when scheduled events are present in the market.     

Tests of Analysts' Overreaction/Underreaction to Earnings Information as an Explanation for Anomalous Stock Price Behavior

This study examines whether security analysts underreact or overreact to prior earnings information, and whether any such behavior could explain previously documented anomalous stock price movements. We present evidence that analysts' forecasts underreact to recent earnings. This feature of the forecasts is consistent with certain properties of the naive seasonal random walk forecast that Bernard and Thomas (1990) hypothesize underlie the well-known anomalous post-earnings-announcement drift. However, the underreactions in analysts' forecasts are at most only about half as large as necessary to explain the magnitude of the drift. We also document that the “extreme” analysts' forecasts studied by DeBondt and Thaler (1990) cannot be viewed as overreactions to earnings, and are not clearly linked to the stock price overreactions discussed in DeBondt and Thaler ( 1985 , 1987 ) and Chopra, Lakonishok, and Ritter (Forthcoming). We conclude that security analysts' behavior is at best only a partial explanation for stock price underreaction to earnings, and may be unrelated to stock price overreactions.     

Manager-analyst conversations in earnings conference calls

Prior research finds that intraday stock prices move considerably during the discussion period of earnings conference calls. In this study, we explore what features of the manager-analyst dialogue during the discussion drive these price movements. We textually analyze the tone of managers and analysts and find that intraday prices react significantly to analyst tone, but not to management tone, for the full duration of the discussion. This effect strengthens when analyst tone is relatively negative. We then present intraday visual evidence that analysts are more neutral than managers over the call and that the tones of both parties drift downward as the call progresses. Overall, our findings illustrate how manager-analyst information exchanges evolve on earnings calls and indicate that analysts are the participants on earnings calls whose comments move stock prices during the discussion.