The stylized facts about the behavior of manufacturers' inventories and backorders over the business cycle: 1959–1980

Recent theoretical work has highlighted the role of inventories in propagating business cycles. This paper attempts to provide an empirical foundation for current and future research on the role inventories play in business cycles. The evidence presented here indicates that inventories and backorders are major variables in predicting output fluctuations. Further, the evidence is that disaggregating inventories by stage-of-fabrication is important in predicting output. In concert with previous findings, our results suggest that disaggregated inventories ought to be a component of models of the business cycle.     

Long-Term Earnings Forecasts in the Electric Utility Industry: Accuracy and Valuation Implications

This paper integrates research on the accuracy of alternative long-term earnings forecasts, the gain in accuracy achievable from combining various forecasts, and the power of different long-term earnings forecasts to explain stock prices. The tests are performed on 82 electric utility firms because of the relative homogeneity of accounting data in that industry and because of the importance of the findings for the determination of the cost of capital in a regulatory proceeding. The results are consistent with earlier research findings that analyst forecasts of long-term earnings growth are more accurate than forecasts from extrapolative models. Combined forecasts applied to out-of-sample data, however, did not result in markedly improved forecasting accuracy. Finally, valuation tests of alternative forecasting techniques offered strong evidence that investors place the greatest weight on forecasts from Value Line.     

The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.     

Contingent Commissions,Insurance Intermediaries,and Insurer Performance

This research investigates the relationship shared by contingent commission usage and insurer performance. We assess performance using both frontier efficiency and financial performance measures. Our findings reveal that the relationship is complex and varies across differing insurer business models. We find that nonusers of contingent commissions are more cost and revenue efficient than are users of contingents. However, among insurers that use contingents, relatively higher levels of use are associated with more efficient operations and also better financial performance. Additionally, these findings are conditioned on the type of distribution system the insurer employs.     

The real-time information content of macroeconomic news: implications for firm-level earnings expectations

This paper investigates the usefulness of the real-time macroeconomic news-flow as a leading indicator of firm-level end-of-quarter realized earnings. Using recent advances in macroeconomics, I develop a nowcasting model for quarterly earnings and provide two main findings. First, I show that my model provides out-of-sample expectations that are as accurate as analysts’ forecasts. Second, macroeconomic news embedded in my nowcasts is not fully incorporated into investors’ earnings expectations and predicts future abnormal returns around earnings announcements. These findings have three main implications for capital markets research. First, real-time macroeconomic news can be used to update earnings expectations in real-time. Second, there are economic benefits of doing so, as evidenced by the magnitude of risk-adjusted returns around earnings announcements. Third, after three decades of almost nonexistent research on time-series models for quarterly earnings, the door is open again for fruitful research in this area.     

小企业成功因素研究

对小企业的研究应该超越那种仅把其视为大企业"迷你版"的研究范式.基于不同研究视角,从小企业固有独特性基础上对小企业成功因素的研究形成了五种典型的研究模式,这包括单因素研究、分行业研究、成败企业对比性研究、利益相关者角度研究和综合分析性研究.     

Predicting the equity premium with dividend ratios: Reconciling the evidence

This paper evaluates the ability of dividend ratios to predict the equity premium. We conduct an in and out-of-sample comparative study and apply the Goyal and Welch (2003) graphical method to equity premia derived from the UK FTSE All-Share and the S&P 500 indices. Preliminary in-sample univariate regressions reveal that in both markets the equity premium contains an element of predictability. However, the considered out-of-sample models outperform the historical moving average only in the UK context. This is confirmed by the graphical diagnostic which further indicates that dividend ratios are useful predictors of UK excess returns. Our paper provides a possible explanation of why dividend ratios might be more informative in the UK market by linking these findings to the disappearing dividend phenomenon. Finally, Campbell and Shiller (1988) identities are employed to account for the time-varying properties of the dividend ratio and dividend growth processes. It is shown that by instrumenting the models with the identities, forecasting ability can be further improved.     

Determinants and consequences of tournament incentives: A survey of the literature in accounting and finance

This paper synthesizes the theoretical underpinnings of tournament models, reviews the extant empirical literature on the determinants and consequences of tournament incentives, critiques the findings and offers suggestions for future research. We synthesize findings from 63 empirical papers and find that several firm-level fundamental and corporate governance variables affect the structure of corporate tournaments. Our review of the consequences of tournament structure reveals that tournaments affect financial reporting and auditing as well as firm-level operational and capital market-based outcomes. This review reveals that the existing accounting and finance literature lacks a strong justification for why one theory rather than another is favored. Moreover, based on potential problems that may exist in empirical models, this review also offers some methodological implications for empirical tournament studies.     

<Emphasis Type="Italic">K</Emphasis>-fold cross validation performance comparisons of six naive portfolio selection rules: how naive can you be and still have successful out-of-sample portfolio performance?

Recent research reports that optimal portfolio selection models often perform worse than equal-weight naive diversification in out-of-sample testing. This paper extends this line of inquiry by comparing the out-of-sample performance of the equal-weight naive strategy to the out-of-sample performance of five alternative naive strategies, each of which derives from a simple heuristic that does not require any optimization. Out-of-sample portfolio performance is assessed by mean, standard deviation, skewness, and Sharpe ratio; k-fold cross validation is used as the out-of-sample testing mechanism. The results indicate that the proposed naive heuristic rules exhibit strong out-of-sample performance, in most cases superior to the equal-weight naive strategy. These findings are consequential for at least two reasons: first, if these simple heuristic-based rules outperform the equal-weight naive strategy, then by transitivity they can outperform the mean–variance- and shortfall-optimal portfolio rules that have been shown in the literature to be inferior to the equal-weight naive rule, which further emphasizes the out-of-sample fragility of “optimal” methods; and second, among naive diversification strategies, some appear more robust in out-of-sample testing than others, hence the proposed methods may be useful when forming mixed portfolio selection models wherein a naive strategy is combined with an optimal strategy to improve performance.     

A Comparative Analysis of Earnings Forecasts in Europe

This study examines the accuracy and bias of financial analysts' EPS forecasts in nine European countries during 1987 to 1995. There are significant differences between the countries which may be due to the differences in earnings behaviour, accounting practices, and the influence of securities markets. An optimistic bias is endemic in European forecasts, consistent with research from the US. Investors who incorporate earnings forecasts in their stock selection procedures may be able to improve returns by explicitly adjusting their models for observed regularities in earnings forecast errors. However, we have shown that these regularities differ in incidence and magnitude across the countries studied, and further research is needed to effectively model these differences.     

Does incorporating non-linearity into discretionary accrual models improve their performance?

Using a large sample of firms that restated earnings, this study investigates whether incorporating non-linearity (conditional conservatism) into discretionary accrual models improves their performance in detecting earnings management. The findings of this study are important because discretionary accrual models play a prominent role in several streams of accounting research and the models' ability to isolate the discretionary (managed) component from the non-discretionary (unmanaged) component of total accruals is critical. If the conventional linear discretionary accrual models are mis-specified, it is likely to result in misleading inferences about earnings management behavior. The findings indicate that the non-linear specification improves the performance of most linear models. The findings also indicate that a more sophisticated linear model that incorporates a performance measure and a future growth measure outperforms other simple models.     

Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks,discriminant analysis,and professional human judgment

This study compares the ability of discriminant analysis, neural networks, and professional human judgment methodologies in predicting commercial bank underperformance. Experience from the banking crisis of the 1980s and early 1990s suggest that improved prediction models are needed for helping prevent bank failures and promoting economic stability. Our research seeks to address this issue by exploring new prediction model techniques and comparing them to existing approaches. When comparing the predictive ability of all three models, the neural network model shows slightly better predictive ability than that of the regulators. Both the neural network model and regulators significantly outperform the benchmark discriminant analysis model's accuracy. These findings suggest that neural networks show promise as an off-site surveillance methodology. Factoring in the relative costs of the different types of misclassifications from each model also indicates that neural network models are better predictors, particularly when weighting Type I errors more heavily. Further research with neural networks in this field should yield workable models that greatly enhance the ability of regulators and bankers to identify and address weaknesses in banks before they approach failure. Copyright © 2001 John Wiley & Sons, Ltd.     

Ratings assignments: Lessons from international banks

This paper estimates ordered logit models for bank ratings which include a country index to capture country-specific variation. The empirical findings support the hypothesis that the individual international bank ratings assigned by Fitch Ratings are underpinned by fundamental quantitative financial analyses. Also, there is strong evidence of a country effect. Our model is shown to provide accurate predictions of bank ratings for the period prior to the 2007–2008 banking crisis based upon publicly available information. However, our results also suggest that quantitative models are unlikely to predict ratings with complete accuracy. Furthermore, we find that both quantitative models and rating agencies are likely to produce highly inaccurate predictions of ratings during periods of financial instability.     

Detecting earnings management using cross-sectional abnormal accruals models

This paper examines specification and power issues in relation to three models used to estimate abnormal accruals. In contrast to the majority of prior work evaluating models estimated in time-series, we examine the performance of cross-sectionally estimated models. In addition to testing the standard-Jones (Jones, 1991) and modified-Jones (Dechow et al., 1995) models, we also develop and test a new specification, labelled the ‘margin model’. Consistent with prior US research employing time-series specifications of the two Jones models, our findings suggest that each of the three cross-sectional models are well specified when applied to a random sample of firm-years. However, the margin model appears to generate relatively better specified estimates of abnormal accruals when cash flow performance is extreme. Analysis of the models' ability to detect artificially induced earnings management indicates that all three procedures are capable of generating relatively powerful tests for economically plausible levels of accruals management (e.g., less than 10% of lagged total assets). Regarding their relative performance, the standard-Jones and modified-Jones models are found to be more powerful for revenue and bad debt manipulations. In contrast, the margin appears to be more powerful at detecting non-bad debt expense manipulations.     

The failure of accounting research to improve accounting practice: a problem of theory and lack of communication

There is anecdotal evidence to support the assertion that accounting research, or what is alleged to be research, is of little or no value to the practice of accounting, nor to the development of accounting as an academic discipline. The problem is not that efforts have not been made to conduct research, but rather there is a fundamental flaw in the accounting research process itself.Tricker suggests that the research process can be understood using two models. One is a set of relationships which “feed-forward”. That is, a known theory suggests a hypothesis, which is tested through the accumulation of data. If the hypothesis is proven to be true, it is added to the body of knowledge, enhancing the legitimacy of the underlying theory. The second model is intended to provide “feed-back”. That is, the real world is observed and a model of it is proposed, based on known theory. Data is collected and processed, and the model is refined. When the model is consistent with the real world and known theory, it is added to the body of knowledge. These research models depend on the existence of known theory for their usefulness.The central problem of accounting research is that there is no known theory to use as a reference for creating hypotheses or models to be empirically researched. The absence of theory can be seen in education, practice, and the research literature itself. Practitioners, for example, because of their training and lack of experience with and interest in research tend not to look to research findings to meet their professional needs. Accounting researchers, on the other hand, have created what appears to be a highly advanced research context which, in effect, is an environment dominated by sophisticated methodology, rather than theory. The research basically emulates the hard sciences, which makes its pursuit academically acceptable, but it lacks substance. This explains the failure of accounting research to improve accounting practice.     

Textual sentiment in finance: A survey of methods and models

We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and market-level behavior and performance, and vice versa. We point to what is agreed and what remains controversial. Promising directions for future research are emerging from the availability of more accurate and efficient sentiment measures resulting from increasingly sophisticated textual content analysis coupled with more extensive field-specific dictionaries. This is enabling more wide-ranging studies that use increasingly sophisticated models to help us better understand behavioral finance patterns across individuals, institutions and markets.     

An Investigation of Framing and Firm Size on the Auditor's Going Concern Decision

This study investigated the effects of framed information and firm size on the auditor's going concern report modification decision. Framing has been shown to affect individual decision-making in a variety of contexts. Investigations of framing effects in an audit context have reported mixed results. The findings of this study indicate that auditors are susceptible to the effect of framed information. Previous research has reported that auditors from small firms may be less conservative in audit disclosure recommendations than auditors associated with larger firms. This study evaluated the going concern decisions of auditors from three firm sizes: local/regional, a large non-Big Six firm, and a Big Six audit firm. Results indicate that differences in the report issued do exist across firm size. Possible explanations for the reported results are discussed.     

Bank business models,failure risk and earnings opacity: A short- versus long-term perspective

Despite the ongoing bank regulatory reforms, relatively little research attention has been given to the effects of bank business models and opacity of bank balance sheet structure which may hinder regulation and market discipline. In this study, we explore the effects of business model strategies on banks' earnings opacity in the UK banking sector. Distinguishing between the short-term (within) and long-term (between) effects, our findings suggest that retail-oriented business models reduce the likelihood of earnings management practices in the short term but not over the long term. In contrast, wholesale-oriented business models increase the probability of earnings manipulation both in the short and long term. While bank business models characterised by a greater degree of functional diversification tend to lower earnings manipulation in the short term, the long-term incentives cannot be mitigated. Our findings also demonstrate that low failure risk (or greater solvency) represents an important channel in mitigating the effects of business models on earnings management practices both in the short and long term. Our results are robust to alternative proxies for earnings management and failure risk, and the use of alternative methods.     

Tracking bond indices in an integrated market and credit risk environment

Abstract

The management of credit risky assets requires simulation models that integrate the disparate sources of credit and market risk, and suitable optimization models for scenario analysis. In this paper we integrate Monte Carlo simulation models for credit risk with scenario optimization, and develop a methodology for tracking broadly defined corporate bond indices. Testing of the models shows that the integration of the multiple risk factors improves significantly the performance of tracking models. Good tracking performance can be achieved by optimizing strategic asset allocation among broad classes of corporate bonds. However, extra value is generated with a tactical model that optimizes bond picking decisions as well. It is also shown that adding small corporate bond holdings in portfolios that track government bond indices improves the risk/return characteristics of the portfolios. The empirical results to substantiate the findings of this study are obtained by backtesting the model over a recent 30 month period.