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Analysts' qualitative statements and the profitability of favorable investment recommendations

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Abstract

In this study, we examine the relation between sell-side analysts' justifications and favorable rating profitability. Using a novel text analysis methodology, we transform analysts' qualitative statements into a content-based text signal. Our results indicate that information contained in analysts' justifications is indeed associated with favorable recommendation profitability, controlling for information in the quantitative summary measures. We also develop trading strategies using our text signal and find that using the text signal generates economically significant returns. Importantly, to increase our understanding of factors associated with favorable rating quality, we disaggregate the text signal into five discrete information categories. Results show that references to historical financial and nonfinancial performance measures contain significant predictive power. Our findings have important implications for investors and financial analysts.

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... Sell-side analysts 1 (hereinafter called analysts) play a central role in equity capital markets by communicating their analyses and recommendations to investors via research reports. Their research reports typically include three 2 major (interrelated) parts: earnings forecast, target price, and stock recommendation (e.g., Bradshaw et al., 2004;Caylor et al., 2017). We can thus say that earnings forecasts, if simplified, are converted to a target price by using a valuation model. ...
... 5 Throughout the years, efforts have been made to improve the knowledge of equity markets by scrutinizing sell-side analysts' work by covering, e.g., their use of valuation models and the information utilized in valuations (see, e.g., Ramnath et al., 2008;Brown et al., 2015). However, the analysts' valuation task is still considered a kind of "black box" that needs further unpacking (Bradshaw, 2009;Caylor, 2017;Lo, 2012). Specifically, few studies have addressed analysts' choices of primary valuation models on which the target prices are based. ...
... By adopting a qualitative, multiple-case study approach about the valuation model choices of various analysts focusing on a particular company, we expect that valuation target-specific aspects of their choices could potentially be revealed. This study is also motivated by the calls of prior literature (e.g., Bradshaw, 2009;Caylor, 2017), which urged further research to unpack the analysts' valuation work. ...
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This study investigates the valuation model choices of sell-side analysts, an area largely overlooked by the prior literature. Specifically, we focus on the drivers that influence the choice of the primary valuation model that analysts use to derive a target price. The research was conducted as a multiple case study by drawing on interviews with sell-side analysts and their valuation reports of a major energy sector firm. We find that valuation target-specific aspects such as lack of peer comparability, lack of history, and extreme uncertainty about forecasting cash flows can play a dominant role in valuation model choices. Our research contributes to the equity valuation literature by providing a more nuanced picture of the drivers influencing the model choices of sell-side analysts, and specifically, it brings forth the need to pay sufficient a ention to valuation target-specific factors as the choice drivers.
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This paper performs a financial statement analysis that combines a large set of financial statement items into one summary measure which indicates the direction of one-year-ahead earnings changes. Positions are taken in stocks on the basis of this measure during the period 1973–1983, which involve canceling long and short positions with zero net investment. The two-year holding-period return to the long and short positions is in the order of 12.5%. After adjustment for ‘size effects’ the return is about 7.0%. These returns cannot be explained by nominated firm risk characteristics.
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Prior studies have identified systematic and time persistent differences in analysts’ earnings forecast accuracy, but have not explained why the differences exist. Using the I/B/E/S Detail History database, this study finds that forecast accuracy is positively associated with analysts’ experience (a surrogate for analyst ability and skill) and employer size (a surrogate for resources available), and negatively associated with the number of firms and industries followed by the analyst (measures of task complexity). The results suggest that analysts’ characteristics may be useful in predicting differences in forecasting performance, and that market expectations studies may be improved by modeling these characteristics.
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In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six "real world" medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for "single number" evaluation of machine learning algorithms. © 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.
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This paper provides a methodology for detection of management fraud utilizing basic financial data. The methodology is created using Support Vector Machines. An important component therein is a kernel which increases the power of the learning machine by allowing a mapping of linear attributes into nonlinear features. A kernel specific to the domain of finance is developed. This Financial Kernel constructs features which are shown in prior research to be helpful in detecting management fraud. An empirical dataset was collected which included quantitative financial attributes for fraudulent and nonfraudulent public companies. This dataset was analyzed with Support Vector Machines using the Financial Kernel. The results show that the Financial Kernel can discriminate between fraudulent and nonfraudulent companies using only externally available quantitative financial attributes. The results also show that the methodology has predictive value as it was able to distinguish fraudulent from nonfraudulent companies in subsequent years using historic data. The results support the Financial Kernel as a method for fraud detection. Furthermore, comparative tests show it dominates the alternative methods that use only externally available quantitative financial data. The potential users of this methodology include regulatory agencies, audit firms and investors. (Management Fraud, Classification, Support Vector Machines, Financial Event Detection)
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We develop a methodology for automatically analyzing text to aid in discriminating firms that encounter catastrophic financial events. The dictionaries we create from Management Discussion and Analysis Sections (MD&A) of 10-Ks discriminate fraudulent from non-fraudulent firms 75% of the time and bankrupt from nonbankrupt firms 80% of the time. Our results compare favorably with quantitative prediction methods. We further test for complementarities by merging quantitative data with text data. We achieve our best prediction results for both bankruptcy (83.87%) and fraud (81.97%) with the combined data, showing that that the text of the MD&A complements the quantitative financial information.
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The mixed results from earlier studies examining the links between nonfinancial measures and financial performance suggest that contextual variables may moderate these relationships. We use data from more than 800 stores of a retail chain to assess whether the relationship between nonfinancial measures, such as employee satisfaction and customer satisfaction, and financial performance is influenced by the competitive characteristics of retail store locations. We model and estimate the relationship between employee satisfaction, customer satisfaction, and earnings, controlling for past earnings to evaluate whether these nonfinancial measures are lead indicators of financial performance. In doing so, we examine the role of competition as a moderating variable in these relationships. We find that nonfinancial measures have significant incremental information content in predicting future store profitability only in the high-competition urban locations. In additional tests we estimate whether these nonfinancial measures are correlated with subjective information that senior management implicitly relies on when evaluating the performance of store managers and selecting stores for closure. The study underscores the importance of understanding the context when using nonfinancial measures as an integral part of the management control system. [ABSTRACT FROM AUTHOR] Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
When used in conjunction with traditional R&D expenditure information, scientific information on patent quality appear to give investors a more useful basis upon which to judge the economic merit of the firm's R&D effort. This complementary relation suggests that consistent disclosure of patent quality information would be helpful to investors in their ongoing assessment of firms in the high tech sector. Copyright 2001 by Kluwer Academic Publishers