When Security Analysts Talk, Who Listens?

2007 ◽  
Vol 82 (5) ◽  
pp. 1227-1253 ◽  
Author(s):  
Michael B. Mikhail ◽  
Beverly R. Walther ◽  
Richard H. Willis
Keyword(s):  
Investment Decisions ◽  
Earnings Forecast ◽  
Security Analysts ◽  
Stock Recommendations ◽  
Security Analyst ◽  
Small Investors ◽  
Forecast Revision ◽  
Recommendation Revisions ◽  
Small Traders

Regulators' interest in analyst reports stems from the belief that small investors are unaware of the conflicts sell-side analysts face and may, as a consequence, be misled into making suboptimal investment decisions. We examine who trades on security analyst stock recommendations by extending prior research to focus on investor-specific responses to revisions. We find that both large and small traders react to analyst reports; however, large investors appear to trade more than small traders in response to the information conveyed by the analyst's recommendation and earnings forecast revision (proxied by the magnitudes of the recommendation change and the earnings forecast revision, respectively). We also find that small investors do not fully account for the effects of analysts' incentives on the credibility of analyst reports, as captured by the type of recommendation (i.e., upgrade versus downgrade or buy versus sell). In particular, small investors not only trade more than large investors following upgrade and buy recommendations, but also trade more following upgrade and buy recommendations than they do following downgrade and hold/sell recommendations. Furthermore, we observe that, on average, small traders are net purchasers following recommendation revisions regardless of the type of the recommendation; large traders tend to be net sellers following downgrades and sells. Consequently, large traders generate statistically positive returns from their trading, while small traders generate statistically negative returns from their trading. These findings are consistent with large investors being more sophisticated processors of information, and provide some support for regulators' concerns that analysts may more easily mislead small investors.

Does Forecast Accuracy Matter to Security Analysts?

1999 ◽  
Vol 74 (2) ◽  
pp. 185-200 ◽  
Author(s):  
Michael B. Mikhail ◽  
Beverly R. Walther ◽  
Richard H. Willis
Keyword(s):  
Forecast Accuracy ◽  
Earnings Forecast ◽  
Statistical Relation ◽  
Security Analysts ◽  
Forecast Horizon ◽  
Stock Recommendations ◽  
Period Effects ◽  
Time Period ◽  
Turn Over ◽  
Relative Profitability

We investigate if earnings forecast accuracy matters to security analysts by examining its association with analyst turnover. Controlling for firm- and time-period effects, forecast horizon and industry forecasting experience, we find that an analyst is more likely to turn over if his forecast accuracy is lower than his peers. We find no association between an analyst's probability of turnover and his absolute forecast accuracy. We also investigate another observable measure of the analyst's performance, the profitability of his stock recommendations. There is no statistical relation between the absolute or relative profitability of an analyst's stock recommendations and his probability of turnover. We interpret our findings as indicating that forecast accuracy is important to analysts.

Discussion: “Dispersion in Analysts' Earnings Forecasts as a Measure of Uncertainty”

1998 ◽  
Vol 13 (3) ◽  
pp. 271-274 ◽  
Author(s):  
Lawrence D. Brown
Keyword(s):  
Stock Price ◽  
Annual Report ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Data Set ◽  
Forecast Dispersion ◽  
Forecast Revision ◽  
Analyst Earnings ◽  
The Impact

This paper tackles an interesting question; namely, whether dispersion in analysts' earnings forecasts reflects uncertainty about firms' future economic performance. It improves on the extant literature in three ways. First, it uses detailed analyst earnings forecast data to estimate analyst forecast dispersion and revision. The contrasting evidence of Morse, Stephan, and Stice (1991) and Brown and Han (1992), who respectively used consensus and detailed analyst data to examine the impact of earnings announcements on forecast dispersion, suggest that detailed data are preferable for determining the data set on which analysts' forecasts are conditioned. Second, it relates forecast dispersion to both analyst earnings forecast revision and stock price reaction to the subsequent earnings announcement. Previous studies related forecast dispersion to either analyst forecast revision (e.g., Stickel 1989) or to subsequent stock price movements (e.g., Daley et al. [1988]), but not to both revision and returns. Third, it includes the interim quarters along with the annual report. In contrast, previous research focused on the annual report, ignoring the interims (Daley et al. [1988]).

Cyber Situation Awareness through Instance-Based Learning

2012 ◽  
pp. 125-140 ◽  
Author(s):  
Varun Dutt ◽  
Cleotilde Gonzalez
Keyword(s):  
Situation Awareness ◽  
Risk Tolerance ◽  
Decision Support Tools ◽  
Cyber Attack ◽  
Security Analysts ◽  
Instance Based Learning ◽  
Security Analyst ◽  
Support Tools ◽  
Attack Scenario ◽  
Past Experiences

In a corporate network, the situation awareness (SA) of a security analyst is of particular interest. The current work describes a cognitive Instance-Based Learning (IBL) model of an analyst’s recognition and comprehension processes in a cyber-attack scenario. The IBL model first recognizes network events based upon events’ situation attributes and their similarity to past experiences (instances) stored in the model’s memory. Then, the model comprehends a sequence of observed events as being a cyber-attack or not, based upon instances retrieved from its memory, similarity mechanism used, and the model’s risk-tolerance. The execution of the model generates predictions about the recognition and comprehension processes of an analyst in a cyber-attack. A security analyst’s decisions in the model are evaluated based upon two cyber-SA metrics of accuracy and timeliness. The chapter highlights the potential of this research for design of training and decision support tools for security analysts.

From K Street to Wall Street: Political Connections and Stock Recommendations

2016 ◽  
Vol 92 (3) ◽  
pp. 87-112 ◽  
Author(s):  
Dane M. Christensen ◽  
Michael B. Mikhail ◽  
Beverly R. Walther ◽  
Laura A. Wellman
Keyword(s):  
Affordable Care Act ◽  
Private Information ◽  
Relevant Information ◽  
Political Connections ◽  
Omitted Variables ◽  
Political Contributions ◽  
Security Analysts ◽  
Wall Street ◽  
Stock Recommendations ◽  
Gain Access

ABSTRACTIn this study, we examine whether sell-side security analysts gain access to value-relevant information through political connections. We measure analysts' political connections based on political contributions at the brokerage-house level. We argue that if brokerages are able to obtain private information through their political connections, then analysts at politically connected brokerages should issue more profitable stock recommendations, and this increased profitability should be more pronounced for politically sensitive stocks. Our evidence is consistent with these predictions. Analyses of recommendations issued surrounding the Affordable Care Act further support our main inferences. Moreover, our findings hold after we employ numerous tests to address correlated omitted variables and endogeneity. Collectively, these results suggest that brokerages obtain value-relevant, nonpublic information from their political connections.JEL Classifications: G24; G38; G14.

Making investment decisions using XBRL filing data

2019 ◽  
Vol 32 (4) ◽  
pp. 587-609
Author(s):  
Rimona Palas ◽  
Amos Baranes
Keyword(s):  
New York ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Investment Strategy ◽  
New York Stock Exchange ◽  
Content Type ◽  
York Stock Exchange ◽  
Directional Movement ◽  
Small Investors ◽  
Industry Membership

Purpose The Securities Exchange Commission mandated eXtensible Business Reporting Language (XBRL) filing data provide immediate availability and easy accessibility for both academics and practitioners. To be useful, this data should provide information for decisions, specifically, investment decisions. The purpose of this study is to examine whether the XBRL database can be used with models, developed in previous studies, predicting the directional movement of earnings. The study does not attempt to examine the validity of these models, but only the ability to use the data in the analysis of financial statements based on these models. Design/methodology/approach The study analyzes New York Stock Exchange companies’ XBRL data using a two-step logistic regression model. The model is then used to arrive at the directional movement of earnings between current and subsequent quarters. Additional models are created by dividing the sample into industry membership. Findings The results classified companies as realizing an increase or a decrease in earnings. The final model indicated a significant ability to predict earnings changes, on average about 65 per cent of the time, for the entire model, and 71 per cent, for the industry-based models (higher than those of previous studies based on COMPUSTAT). The investment strategy created average quarterly return between 2.8 and 10.7 per cent. Originality/value The originality of this study is in the way it examines the quality of XBRL data, by examining whether findings from prior research which relied on traditional databases (such as COMPUSTAT) still hold using XBRL data. The use of XBRL allows not only easier and less-costly access to the data but also the ability to adjust the models almost immediately as current information is posted, thus providing a much more relevant tool for investors, especially small investors.

Recommendation-Forecast Consistency and Earnings Forecast Quality

2013 ◽  
Vol 27 (3) ◽  
pp. 451-467 ◽  
Author(s):  
Lawrence D. Brown ◽  
Kelly Huang
Keyword(s):  
Earnings Forecasts ◽  
Firm Valuation ◽  
Data Availability ◽  
Earnings Forecast ◽  
Market Reactions ◽  
Stock Recommendations ◽  
Ex Ante ◽  
Forecast Quality

SYNOPSIS: We investigate the implications of recommendation-forecast consistency for the informativeness of stock recommendations and earnings forecasts and the quality of analysts' earnings forecasts. Stock recommendations and earnings forecasts are often issued simultaneously and evaluated jointly by investors. However, the two signals are often inconsistent with each other. Defining a recommendation-forecast pair as consistent if both of them are above or below their existing consensus, we find that 58.3 percent of recommendation-forecast pairs are consistent in our sample. We document that consistent pairs result in much stronger market reactions than inconsistent pairs. We show that analysts making consistent recommendation forecasts make more accurate and timelier forecasts than do analysts making inconsistent recommendation forecasts, suggesting that consistent analysts make higher-quality earnings forecasts. We extend the literature on informativeness of analyst research by showing that recommendation-forecast consistency is an important ex ante signal regarding both firm valuation and earnings forecast quality. Investors and researchers can use consistency as a salient, ex ante signal to identify more informative analyst research and superior earnings forecasts. Data Availability: All data are available from public sources.

scholarly journals Examine and Visualising Packet Capture Files

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3930-3933
Keyword(s):  
Big Data ◽  
Security Analysts ◽  
Amount Of Information ◽  
Visualisation Tool ◽  
Log Files ◽  
Packet Capture ◽  
Security Analyst ◽  
Enormous Amount ◽  
Graphical View ◽  
Readable Format

The field of security Visualisation is an interesting and tough field of research. Enormous amount of (big) data is involved in the networking of devices. In order to analyse and get data for solving the problem, visualisation can be very helpful. Combination of security world as well as the network world is discussed in this paper. Identifying various visualisation techniques for security log data and executing workflow based composition of multiple analytic components will be identified. Interactive modes of the techniques will be discussed. Making the security files to be readable and the format for analysing are identified. More network visualisation tool allows the security analysts to quickly examine the large amount of information by rendering a millions of events and log entries in a single graphical view. Extracting files from full packet captures can save security analyst a great deal of time. There are tools available for capturing PCAP(Packet Capture) files. This PCAP files will be analysed for further details. In the proposed solution, the PCAP files will be generated with the help of Wireshark and it will be processed with the help of Apache drill for converting it into a readable format and the Visualisation can be done with R Studio. Various Visualisation tools in R will be used to visualise the PCAP files. This in order will thoroughly give some insight on the log files for any detection and prediction of malicious data.

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