Stock Ratings Research Articles

A comparative study on classification models for stock rating prediction

The digital transformation in the stockbroker industry has led to a significant increase in retail investors, who often lack the expertise to analyse stocks thoroughly. This research addresses the challenge by proposing a classification model to predict stock ratings such as "Reduce", "Hold", "Moderate Buy", and "Buy”, allowing retail investors to make informed decisions. The data analysed is collected from the S&P 500 index through web scraping using Beautiful Soup, resulting in a dataset used for training and testing the classification model. Popular stock indicators are used as attributes in predicting the rating of the stock, which includes the Exchange, Price, Volume, Market Cap, ROE, ROA, P/E Ratio, EPS, Annual Sales, Net Income, Net Margins, and PB Ratio of the stock. The models selected for classification include K-Nearest Neighbors (k-NN), Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest. GridSearch is employed to maximize each algorithm's parameters for optimal performance. Results indicate that the k-NN model outperforms others, achieving the highest accuracy (0.618644) and weighted F1-score (0.605011). However, all models exhibit relatively low accuracy, suggesting the complexity of predicting stock ratings due to external factors not considered in the study.

The Effect of Financial Performance on The Tobin's Q Value of Company Investment

This study aims to provide To explain the impact of financial performance, corporate social responsibility and good corporate governance on firm value. Companies that sell their shares to the public by going public must have a good picture. A good picture is necessary to gain the trust of investors. Companies in an effort to gain the trust of investors will provide reliable information. This information describes the character of the company, especially regarding financial performance. This study used a descriptive associative quantitative research method. To determine the effect of firm value used regression analysis techniques. From the research results there is a significant influence between financial performance on firm value, this occurs due to the Efficient Market Semi Strong Hypothesis. Based on the results of the discussion that has been put forward, the conclusion in this study is simultaneous that there is a significant influence between financial performance, corporate social responsibility, good corporate governance on firm value ratio expressed by the Tobin's Q, when investing in companies that are included in the LQ-45 stock indexer in Indonesia, list namely the best stock rating index in Indonesia. The limitation in this study is that the measurement for company value is to use Tobin's Q, which is calculated based on the market value/price of a company divided by the company's asset value. Measurement of company value is carried out for a years. The results of this research for the industrial world can be used as a source of information about efforts in implementing financial performance on firm value with CSR disclosure and good corporate governance (GCG) as intervening variables. And it is hoped that it can provide useful information for readers, especially investors, potential investors, and capital market authority bodies regarding the relevance of CSR information disclosure and good corporate governance in company annual reports with firm value and financial performance.

Ethnic Minority Analysts’ Participation in Public Earnings Conference Calls

ABSTRACTWe investigate ethnic minority and nonminority sell‐side analysts’ participation in public earnings conference calls. We find that minority analysts are underrepresented in conference call Q&A sessions, and minority analysts who do participate on the calls experience lower levels of prioritization than do nonminority analysts. Minority analysts’ lower participation rates are partially but not fully mediated by characteristics such as experience, work environment, and stock rating favorability. Additionally, firm and conference call fixed effects mediate approximately half the magnitude of lower minority participation rates. Extroverted minority analysts participate at higher rates, but the negative association between minority status and conference call participation is exacerbated when calls are more time constrained, when executive teams are less diverse, and when analysts are from less prestigious brokerage houses. Overall, we document the underrepresentation of minority analysts on earnings conference calls and provide evidence suggesting both analysts’ and managers’ choices influence minority analysts’ participation rates.

Mean reverting in stock ratings distribution

Mean reverting in stock ratings distribution

Identifying predictors of analyst rating quality: An ensemble feature selection approach

Forecasting the analyst rating quality (ARQ), defined as whether a stock rating provided by an analyst can correctly foretell the stock movement, is crucial to fully leveraging the value of this information resource. This study develops a two-phase method to identify key predictors for ARQ forecasting. In the first stage, we conduct a thorough literature review to obtain a comprehensive list of candidate features, and organise them under three categories: analyst-related, rating-related, and stock-related. In the second stage, we propose a heterogeneous community-based ensemble feature selection method (ComEFS), with the goal of identifying a subset of relevant predictors to be jointly used for ARQ forecasting. Thorough experiments are conducted on a real dataset to verify the effectiveness of our proposed method. The empirical results show that key predictors identified by ComEFS exhibit stronger predictive power compared to those identified by benchmark methods. This study provides insights about ARQ forecasting by selecting the right input. Selectively utilizing these predictive features can help improve the performance of downstream machine learning models and ultimately help investors avoid unreliable analyst ratings and financial loss.

Analysts’ stock ratings and the predictive value of news and Twitter sentiment

ABSTRACT This study investigates the relationship between the stock ratings of professional analysts and company-level sentiment. I find that investor sentiment expressed in tweets and news articles displays a positive relationship with professional analysts’ stock ratings and that the sentiment conveyed through tweets has a stronger effect than the sentiment from conventional news articles. Furthermore, the effect of sentiment derived from tweets and news articles on analysts’ ratings is stronger when the sentiment valence is the same (negative or positive) and weaker when the sentiment valence differs. The findings shed light on the link between investor sentiment and analysts’ stock ratings by demonstrating that analysts’ stock ratings are influenced by investor sentiment.

Trading Opportunities around Morningstar Stock Rating Changes

The authors examine Morningstar information changes stemming from changes in their 5-Star stock-rating system. Converse to many studies of other second-hand information effects, Morningstar rating changes lead to abnormal returns (ARs) that persist for 30 days subsequent to the information release. This information may be used to derive alpha-generating trading strategies. Statistically significant positive ARs persist for 30 days postannouncement for upgrades to 4 and 5 stars, while significant negative ARs persist for 30 days postannouncement for upgrades to 2 and 3 stars and for downgrades to 4, 3, 2, or 1 star. Traders can use this information to devise long, short, and long–short trading strategies. Morningstar’s analysis has high credibility, particularly with retail investors. Morningstar changes a stock’s rating when the price of the stock moves farther from the intrinsic value estimate derived by Morningstar’s model, suggesting that its analysis of the current stock price relative to intrinsic value and the timing of the rating change provide some relevant information to the market.

PEMANFAATAN TEKNIK WEB SCRAPING PYTHON UNTUK SISTEM PENCARIAN PRODUK DI TOKO ONLINE

Sales and purchase transactions utilizing electronic systems with the internet through computer networks has grown rapidly. Buying goods in online stores sometimes the items you are looking for are not what you want, especially in price and item details, it can even happen that the item you are looking for is not found. In searching for goods using search engines such as google, yahoo, it will generate information on goods in the form of a list of website addresses. So that the information obtained is not as desired, people have to visit the online shop address one by one, causing people to waste a lot of time. The results of the research on the product search system using web scraping techniques to several e-commerce, namely tokopedia, shopee, elevania and blibli, have succeeded in obtaining data in the form of product names, number of stock items, url, rating and product images. So that people can easily find the desired item along with the data

Employer Market Power in Silicon Valley

Adam Smith alleged that secret employer collusion to reduce labor earnings is common. This paper examines an important case of such behavior: no-poach agreements through which technology companies agreed not to compete for each other’s workers. Exploiting the plausibly exogenous timing of a US Department of Justice investigation, I estimate the effects of these agreements using a difference-in-differences design. Data from Glassdoor permit the inclusion of rich employer- and job-level controls. Estimates indicate each agreement cost affected workers approximately 2.5 percent of annual salary. Stock bonuses and ratings of job satisfaction were also negatively affected.

The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment

This paper investigates whether and how the research reports issued by securities companies affect stock returns from the perspective of investor sentiment in China. By collecting research reports and investor comments from a popular Chinese investor community, i.e., East Money, we derive two indices that represent the information contained in research reports: one is the attention of research reports and the other is the average stock rating given by research reports; then we develop an investor sentiment indicator using the machine learning method. Based on behavioral finance theory, we hypothesize that research reports have a significant effect on stock returns and investor sentiment plays a mediating role in it. The empirical analysis results confirm the above hypotheses. Specifically, the average stock rating given by research reports can better predict future stock returns, and investor sentiment plays a partial mediating role in the relationship between stock rating and stock returns.

Do Analysts’ Social Ties Affect the Textual Information in Their Reports? Evidence from China

ABSTRACT We develop insights into how analysts’ social ties affect their objectivity and the information content of reports, based on quantitative measures of social ties and textual information content. Using a sample ranges from January 2006 to December 2016 in China, we show that with wide social ties to fund managers, analysts’ ability to burnish their reputation with informative reports and to achieve career success with optimistic stock ratings is enhanced. Further evidence indicates that the trade-off between reputation and career success is dynamic. Specifically, after being elected as New Fortune stars, analysts attach more importance on textual information and are less subjective on stock ratings. These findings are robust across various robustness tests. Overall, our evidence highlights the role of social ties in the information-dissemination process in China A-share stock markets from the perspective of analysts and fund managers.

Evaluating equity analyst forecasts in South Africa

ABSTRACT Sell-side fundamental analyst reports are highly valued in the financial industry and include three main quantitative components: earnings forecasts, target prices, and buy/sell recommendations. This paper examines analyst accuracy by investigating forecasts made on South African equities over the period January 2004 to June 2018. We examine the statistical performance of 12-month target price forecasts, focusing on point, directional, and term accuracy. Further analysis is then done by grouping forecasts by stock rating, analyst firm, market sector, and market regime. We also conduct a study on the persistence of forecast accuracy across analysts and across stocks over the 15-year period. Finally, in order to determine their economic significance, we categorise the equity universe into quintiles based on various analyst forecast values and backtest a range of forecast-driven systematic strategies.

Information content of web-based stock ratings: the case of Motley fool CAPS data

PurposeThe purpose of this paper is to answer a fundamental question – are individual stock picks by a particular internet investment community informative enough to beat the market? The author observes that the stock picks by the CAPS community are reflective of existing information and portfolios based upon CAPS community stock rankings do not generate abnormal returns. The CAPS community is good at tracking existing performance but, it lacks predictive ability.Design/methodology/approachThe study uses a unique data set of stock ratings from Motley Fools CAPS community to determine the information content embedded in these ratings. Observing predictive ability of this web-based stock ratings forum will raise questions about the efficiency of the financial markets. The author forms stock portfolios based on stocks’ star ratings, and star rating changes, and test if the long-short portfolio strategy generates significant α after controlling for single, and multi-factor asset pricing models, such as Fama-French three-factor model and Carhart four-factor model.FindingsThe paper finds no evidence that the CAPS community ratings contain “information content,” which can be exploited to generate abnormal returns. CAPS community ratings are good at tracking existing stock performance, but cannot be used to make superior forecasts to generate abnormal returns. The findings are consistent with the efficient market hypothesis. Furthermore, the author provides evidence that CAPS community ratings are themselves determined by stock performance rather than the other way around.Originality/valueThe study employs a unique data set capturing the stock ratings of a very popular web-based investment community to evaluate its ability to make better than random forecasts. Besides applying well-accepted asset pricing models to generate α, the study conducts causality tests to discern a causal relation between stock ratings and stock performance.

Towards a Gradual Methodology for Sharia Stock Rating

Researchers are used to distinguishing between Shari’ah-compliant companies and non-compliant companies, and a number of terms have appeared in that regard such as “pure companies”,” mixed companies” and “prohibited companies”. This is because it is unimaginable that all companies would be of the same degree of Shari’ah quality. Therefore, there is an apparent need for Shari’ah stock rating similar to the credit ratings issued by the international rating agencies such as Moody's, Standard & Poor's and Fitch. Shari’ah rating differs from credit rating, which focuses on the company’s solvency and its ability to fulfill its obligations. Shari’ah rating is a complement of the latter. Investors in financial markets check credit ratings to avoid failed companies. Investors in the Islamic stock market would prefer the most Shari’ah-compliant companies; that is, those with the highest Shari’ah quality rating; and they would avoid the stocks of lower purity that is, those with the lowest Shari’ah quality rating. This study presents an appropriate methodology for rating of Shari’ah-compliant stocks.

Market reactions to stock rating and target price changes in analyst reports: Evidence from Japan

We aim to perform a comprehensive analysis of the effects of stock ratings and target prices published in the Japanese markets using a large number of analyst reports. Our study is the first to cover reports published by six domestic brokerage firms and ten foreign brokerage firms. In addition to the impact of rating changes on stock prices, we provide analyses on the combined effects of stock ratings and target prices. Furthermore, we find that market responses are affected by the information quality of securities companies issuing analyst reports and the number of reports issued on the same day.

Caring Capitalism: The Meaning and Measure of Social Value. By Emily Barman. New York: Cambridge University Press, 2016. Pp. 266. $99.99.

<i>Caring Capitalism: The Meaning and Measure of Social Value</i>. By Emily Barman. New York: Cambridge University Press, 2016. Pp. 266. $99.99.

Analyst’s ability, media selection and investor interests: evidence from China

PurposeThe purpose of this paper is to find out whether the election of star analysts leads to the conflict of interests between analysts\\institutional investors and individual investors. And then, further investigate how the election results to influence the individual investors’ decision making.Design/methodology/approachGiven the fact that earnings forecasts and stock ratings are the most important foundations for the investor’s investment decision, the authors investigate the relationship among the earnings forecasts, abnormal returns and the election of star analyst. This paper further analyzes the impact factors on investors’ decision. The data used in this paper for star analysts’ information, analysts’ forecast and recommendations, as well as stock performances-related data are from 2005 to 2012.FindingsThis paper finds that mass media cannot select analysts with high forecast accuracy, and then misleads investors. It demonstrates that the analysts with poorer forecast ability and more optimistic stock recommendations are more prone to be entitled as star analysts by mass media, and these titled star analysts tend to show a poorer performance. Therefore, the star analyst worsens investors’ cognition on analysts forecast ability and then misleads investors’ decision making.Social implicationsMedia plays a critical role in corporate governance, information collection and diffusion and reducing the information asymmetry, however, it is good to know the role of media in financial markets from a broader perspective. Because media may also bring negative factors to the financial markets such as misguiding the investors and intensify the conflict of interests between analyst and individual investors.Originality/valueThis paper supports a new perspective of the role of mass media in financial market, which is different from existing studies.

Closingbell Crowdsourced Stock Ratings

ClosingBell is an app-based collaborative stock trading platform which allows its users to connect their online brokerage accounts and share their trades and portfolios with one another. Users, who are retail traders, can also share their trade ideas, or ratings, with the ClosingBell community. The ClosingBell crowdsourced stock ratings dataset includes all such Buy and Sell ratings issued by the community’s members since 2014, currently over 47,000 ratings from 2,800 contributors. In this paper we examine the predictive power of the ClosingBell ratings, and find that the ratings are predictive of stock prices over a one to twenty trading day period. The returns of portfolios built using the ratings exhibit dollar neutral returns of 12% with a Sharpe ratio of 1.0. These returns are robust to trading costs and are not explained by common risk factors. The ClosingBell data represents a unique and powerful source of crowdsourced alpha which can improve systematic sentiment strategies.

Market Reactions to Stock Rating and Target Price Changes in Analyst Reports: Evidence from Japan

We aim to perform a comprehensive analysis of the effects of stock ratings and target prices published in the Japanese markets using a large number of analyst reports. Our study is the first to cover reports published by six domestic brokerage firms and ten foreign brokerage firms. In addition to the impact of rating changes on stock prices, we provide analyses on the combined effects of stock ratings and target prices. Furthermore, we find that market responses are affected by the information quality of securities companies issuing analyst reports and the number of reports issued on the same day.

A Fusion Model for Securities Analysts' Stock Rating Information Based on the Evidential Reasoning Algorithm under Two-dimensional Progressive Recognition Framework

Securities analysts' forecast information can effectively reduce the uncertainty of information in securities markets, and can also promote effective allocation in capital market. The personality difference of securities analysts will lead to different analysis results. In order to improve the utilization of analysts' forecast information, evidential reasoning algorithm under two-dimensional progressive framework and grouping method for combining evidence were used in this paper to fuse securities analysts' stock rating information. Based on the forecast earnings information and stock rating information of analysts, we constructed a two-dimensional progressive framework, and then fused stock rating information of multiple analysts into one piece of evidence information. Finally, we empirically verified the model in this paper by using Chinese analysts' forecast information. The analysis on the fusion results have shown that: compared to traditional statistic model, the accuracy, certainty and the discrimination of the fusion results in our model have been improved.