The bookkeeping procedures have been described as an information process of data collection, classification, tabulation, summarization, and presentation. The accounting classification and measurement function tends to be overlooked due to the clerical work involved in it. The financial state of the firm is continuously changing, of course, because of the occurrence of economic events. It is the accountant's function to recognize their effect on the financial state. A general accounting process may be described as a means of achieving the decision-making end of an accounting information user. This paper centers on the first part of the process-the information formation process, a process by which the accounting classification and measurement function determines the effect of an economic event on a firm's financial state. A communication channel model is used to describe the accounting classification function as the link between the various economic events of a firm and its financial state. The accounting channel of classification may be deterministic, lossless, or noisy as defined in information theory.

We present evidence on inter-firm differences in the predictive ability of quarterly earnings data for a sample of 109 New York Stock Exchange firms. The sample consisted of large, medium, and small firms after deletion of nonseasonal and volatile growth and inconsistent strata membership firms. Although the structure of the best fitting time-series models was constant across firm-size strata, we did find significant differences in the autoregressive parameters of the Foster and Brown and Rozeff ARIMA models across firm-size strata. One-step-ahead quarterly earnings forecasts were generated by a set of best fitting time-series models. A repeated measure multivariate analysis of variance design indicated that predictive ability differed on the basis of size at the .012 level. Tests also indicated that large-and medium-size firms generated one-step-ahead forecasts that were significantly more accurate than smaller firms at the .05 level. We obtained similar predictive findings on the significance of the size-effect in a supplementary analysis of the nonseasonal and volatile growth and inconsistent strata membership firms.

We use trade-level data to examine the role of actively managed funds (AMFs) in earnings news dissemination. We find that AMFs are drawn to, and participate disproportionately more in, earnings announcements (EAs) that include bundled managerial guidance. When the two pieces of news are directionally inconsistent, AMFs trade in the direction of future guidance rather than current earnings. AMFs exhibit an ability to discern, and adapt their trading to, the bias in bundled guidance. While AMF trades at EAs are generally more profitable than their non-EA trades, this result reverses when guidance bias is extreme. Overall, we find that increased AMF trading during EAs leads to faster price adjustment. Collectively, these findings suggest that AMFs are sophisticated processors of bundled earnings news, and their trading generally improves market price discovery.

This study examines how an increase in tick size affects algorithmic trading (AT), fundamental information acquisition (FIA), and the price discovery process around earnings announcements (EAs). Leveraging the SEC's randomized Tick Size Pilot experiment, we show that a tick size increase results in a decline in AT and a sharp drop in absolute cumulative abnormal returns and volume around EAs. More importantly, we find increased FIA in the preannouncement period. Specifically, we show: (1) treatment firms' pre-announcement returns better anticipate next quarter's standardized unexpected earnings; (2) these firms experience an increase in EDGAR web traffic prior to EAs; and (3) they exhibit a drop in price synchronicity with index returns. Taken together, our evidence suggests that while an increase in tick size reduces AT and abnormal market reaction after EAs, it also increases FIA activities prior to EAs.

We document several factors that help explain cross-sectional variations in the post-revision price drift associated with analyst forecast revisions. First, the market does not make a sufficient distinction between revisions that provide new information (“high-innovation” revisions) and revisions that merely move toward the consensus (“low-innovation” revisions). Second, the price adjustment process is faster and more complete for “celebrity” analysts (Institutional Investor All-Stars) than for more obscure yet highly accurate analysts (Wall Street Journal Earnings-Estimators). Third, controlling for other factors, the price adjustment process is faster and more complete for firms with greater analyst coverage. Finally, a substantial portion of the delayed price adjustment occurs around subsequent earnings-announcement and forecast-revision dates. Collectively, these findings show that more subtle aspects of an earnings revision signal can hinder the efficacy of market price discovery, particularly in firms with relatively low analyst coverage, and that subsequent earnings-related news events serve as catalysts in the price discovery process.

We develop and evaluate an accounting-based Loan Portfolio Risk (LPR) variable that captures time-varying contagion effects in default risk for a portfolio of bank loans. Our results show that an Equity-to-LPR ratio (ELPR) is additive in predicting bank failure up to five years in advance, after controlling for all the capital adequacy, asset quality, management experience, earnings, liquidity, and sensitivity to market risks (CAMELS) variables as well as other fundamental-based bank risk measures from prior studies. Further, we find that publicly listed banks with higher ELPR have lower market-implied costs of capital, especially under market stress conditions. We conclude that ELPR captures key aspects of bank risk that are missing in current Basel Committee risk-weighted-asset calculations.

Local-thinking bias, wherein agents overweight information that comes readily to mind, is a prominent finding in cognitive psychology. In this study, we investigate local-thinking bias in the context of sell-side analysts and measure each analyst’s “local” information as news stemming from their individual coverage portfolio. Tests examining multiple analysts forecasting on the same focal firm at the same time find that individual analysts overweight idiosyncratic local news and underweight news from economically linked firms that are not in their coverage portfolios. Market prices track the analyst bias from local news, leading to predictable and economically significant return reversal patterns in the future. A trading strategy that adjusts for analysts’ biases earns meaningful abnormal returns. We discuss the implications of these findings for three literatures: (1) cognitive psychology, (2) analyst behavior, and (3) behavioral asset pricing.

We examine the financial health and performance of reverse mergers (RMs) that became active on U.S. stock markets between 2001 and 2010, particularly those from China (around 85 percent of all foreign RMs). As a group, RMs are early-stage companies that typically trade over the counter. However, Chinese RMs (CRMs) tend to be more mature and less speculative than either their U.S. counterparts or a group of exchange-industry-size-matched firms. As a group, CRMs outperformed their matched peers from inception through the end of 2013, even after including most of the firms accused of accounting fraud. CRMs that receive private investment in public equity (PIPE) financing from sophisticated investors perform particularly well. Overall, despite the negative publicity, we find little evidence that CRMs are inherently toxic investments. Our results shed light on the risk-performance trade-off for CRMs, as well as the delicate balance between credibility and access in well-functioning markets.