Analyst Ratings Research Articles

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

The Effect of Analysts’ Reports on Stock Liquidity: The Interaction of Ratings and Qualitative Indicators

We utilized a Python program to collect analysts’ reports on the constituents of the CSI 300 (China Securities Index). From these reports, we extracted analyst ratings, readability, and visualization using text analysis techniques. Additionally, a FinBERT model was employed to evaluate the tone and disagreement among analyst reports. Subsequently, the FGLS model was developed to explore the influence of analysts’ reports on stock liquidity. The analysis revealed a positive impact of analyst ratings on stock liquidity, with all qualitative indicators playing significant moderating roles. Specifically, there was a more pronounced response in stock liquidity to ratings when accompanied by higher readability, richer visualization, a more positive tone in the report, and lower disagreement. Our study highlights that investors consider both ratings and qualitative information from analyst reports, with the latter guiding investors on how to interpret ratings effectively. These findings have implications for investors, analysts, and regulators.

The cross section of information transmission in news media and stock returns

This paper examines the correlation between the information dissemination cross-section of news media and stock returns by analyzing monthly news coverage data from well-known Chinese newspapers. We show that firms that receive more media attention in the current month experience higher long-term stock returns in the following months, spanning a year, compared to firms with less media coverage. This suggests that media coverage has a significant and positive impact on sustainable stock returns, especially in markets that are largely driven by individuals or inexperienced investors. Further empirical research has found that analyst ratings have an important positive moderating effect in the Chinese stock market.

Analyst coverage and innovation information manipulation: evidence from listed companies in China

ABSTRACT This study examines the impact of analyst coverage on the strategic innovation information disclosure behaviour of Chinese A-share listed companies from 2011 to 2022. The findings suggest that firms with higher analysts coverage are more likely to manipulate their innovation information, especially positive innovation information manipulation (IIM), during the annual report disclosure process. Heterogeneity analysis suggests that the promotion effect is stronger in firms with high analyst ratings.

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.

A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application

The present paper develops a Bayesian Belief Network (BBN) for quantification of aggravating actions, as outcomes of inappropriate decisions, to be integrated in probabilistic safety assessment (PSA) models (i.e., the so-called errors of commission, EOCs). The BBN connects analyst ratings on influencing factors to the error forcing impact of a specific scenario, supporting the CESA-Q method (the Quantification module of the Commission Error Search and Assessment method). While contributing to the quantification of EOCs, this paper presents a novel process for the quantification of the BBN parameters (the Conditional Probability Distributions, CPDs), striving for traceable integration of expert knowledge and (scarce) data, in the form of retrospective analyses of operational events involving EOCs. The process combines the functional interpolation method for populating CPDs and Bayesian updates to adjust the BBN response to the available evidence. A first, prior BBN is developed, then sequentially updated to adjust to two data sets. This allows some intermediate validation and puts forwards the steps for future BBN updates as new EOC events (or new analyst assessments) become available.

Analyst Ratings and MOAT Ratings Sensitivities to ESG Risk and Fund Charges

Investment risk has many dimensions beyond the macroeconomic, industry, and firm gradients. This article reports on a study of investor response to various levels of corporate exposure to environmental, societal, and governance risk, which is frequently referred to as sustainability. The article also studies the correlation between sustainability and economic moats. Investor response is measured using Morningstar’s Analyst ratings and Quantitative ratings. The extent to which loads and annual expenses impact analyst and MOAT ratings is also explored. We document the surprising negative relationship between mutual fund sustainability and Morningstar’s Analyst ratings and Quantitative ratings. By comparison, there is an anticipated positive relationship between fund Sustainability ratings and Morningstar Economic Moat ratings (MOAT ratings). Although Analyst ratings are swayed by loads and expense ratios, MOAT ratings are insensitive. Fortunately, paying loads and higher expense ratios does not result in higher MOAT ratings.

Practical Applications of Importance of Costs in Target Date Fund Selection Using Three Morningstar Ratings

In <b><i>Importance of Costs in Target Date Fund Selection Using Three Morningstar Ratings</i></b>, from the Spring 2021 issue of <b><i>The Journal of Retirement</i></b>, authors <b>C. Edward Chang</b> (of <b>Missouri State University</b>), <b>Thomas M. Krueger</b> (of <b>Texas A&amp;M University-Kingsville</b>), and <b>H. Doug Witte</b> (of <b>Missouri State University</b>) studied the factors influencing the rating scales that people use to pick investments. Target-date funds (TDFs) are popular because they automatically diversify investments and reallocate to conservative assets as retirement approaches. Investors need tools to pick good TDFs from the thousands available. The study focused on Morningstar’s different rating systems: 1) star ratings, 2) Analyst Ratings (ARs), and 3) the new computer-generated Quantitative Ratings (QRs). It found that TDFs with low expense ratios receive higher ratings across the board. However, human analysts give higher ARs to larger and newer TDFs and do not penalize for poor past performance, while the computer does penalize for poor performance and does not favor funds by age. Therefore, ARs and QRs are not comparable, but TDFs with any higher ratings have lower costs and are positioned to outperform.

Enhancing Equity Index Mutual Fund Returns Using Cost and Morningstar Ratings Information

Although equity index mutual funds (EIMFs) are often viewed as a commodity, we analyze the impact that differences in expenses and other fund characteristics have on both past returns and forward-looking ratings. Morningstar evaluates past performance with their “star” rating and evaluates future fund prospects using analyst ratings (ARs) and quantitative ratings (QRs). Funds with low expense ratios and no loads earn higher returns without adding proportionally higher risk. Morningstar analysts carefully consider fund fees in their evaluations and assign much higher ARs to lower-cost funds. Interestingly, although loads initially appear to negatively impact these ratings, once expense ratios are controlled for, analysts seem to be favorably disposed to funds with loads. Regression analysis further suggests that ARs are also positively influenced by Morningstar ratings and fund size. QRs are positively influenced by past performance, but negatively correlated with loads and expense ratios. EIMF age has little influence on AR and QR levels, though the likelihood of an AR rating increases with time since inception. Similar results occur in both the US and foreign equity markets. Expense ratios, loads, past performance, and size can all be used as fund selection tools that aid in maximizing investor wealth.

Socially Responsible Investing in Good and Bad Times

AbstractWe investigate the time variability of abnormal returns from socially responsible investing (SRI). Using portfolio regressions and event studies on multiple data sources, including analyst ratings, firm announcements, and realized incidents, we find that highly rated SRI stocks outperform lowly rated SRI stocks during good economic times, for example, periods with high market valuations or aggregate consumption, but underperform during bad times, such as recessions. This variation in abnormal returns of high-SR stocks vis-à-vis low SR stocks is consistent with a wealth-dependent investor preference for SR stocks that leads to an increased (decreased) demand for SRI during good (bad) times.

Importance of Costs in Target Date Fund Selection Using Three Morningstar Ratings

Although target date mutual funds (TDFs) have only been around since the mid-1990s, they are now common vehicles for retirement investing. Sixty percent of US companies now automatically funnel employees’ non-directed retirement funds into TDFs, which account for nearly one-quarter of all savings US workers have in 401(k)s. Helping investors pick among TDFs, Morningstar rates past risk-adjusted performance using a star system and provides forward-looking evaluations of more than 2,500 TDFs using either Analyst Ratings or a newly implemented machine-learning-based Quantitative Ratings. Morningstar assigns Analyst Ratings to a smaller subset of TDFs that tend to have been in existence the longest and have the largest size. We find that TDFs with lower fees have significantly higher star ratings than their higher expense counterparts. No-load TDFs have significantly higher star ratings than their load-charging counterparts. In assessing future fund prospects, TDFs with low expense ratios are favored by both analysts and artificial intelligence. TDFs without load charges have significantly better Quantitative Ratings than their load charging counterparts. TDFs with Quantitative Ratings tend to be smaller, younger, and have poorer prior performance than TDFs with Analyst Ratings. <b>TOPICS:</b>Long-term/retirement investing, mutual funds/passive investing/indexing, information providers/credit ratings <b>Key Findings</b> ▪ Target date funds (TDFs) with low expenses and without load charges have significantly higher Morningstar star ratings of past risk-adjusted performance. These same low-cost funds have higher Morningstar Analyst ratings and Quantitative ratings, which estimate future fund prospects of performance. ▪ Regression results suggest Morningstar’s two forward-looking fund ratings are not perfect substitutes. Analyst ratings weight fund expense and fund age more heavily (both negatively) while the computer-generated Quantitative ratings are more positively influenced by past fund performance. ▪ Morningstar’s star ratings, Analyst ratings, and Quantitative ratings all suggest that retirement investing will result in a larger nest egg when attention is restricted to no-load, low-expense TDFs. Additionally, the ratings suggest investors should favor larger but relatively younger funds.

Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating

Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&amp;P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance—rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG (Facebook, Amazon, Apple, Netflix &amp; Google) equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.

Retail investor attention and firms' idiosyncratic risk: Evidence from China

This paper investigates retail investor attention to firms' idiosyncratic risk in China. We use the Baidu search index as a proxy for attention and test its effect on Chinese firms' idiosyncratic risk from 2011 to 2017. Our empirical results suggest investor attention has a positive impact on firms' idiosyncratic risk. This effect is robust to possible endogeneity issues and alternative channels of effects and is stronger for small firms. Additional analysis finds that the effect of our proxy for attention on firms' idiosyncratic risk is stronger than investor sentiment and traditional attention proxies, including announcements, media news, analyst ratings, and brokerage reports. These findings provide evidence of retail investor attention could increase firms' contemporaneous idiosyncratic risk, and decrease firms' subsequent period risk.

Rating a Robo-Rater

Since 2011, Morningstar has issued Morningstar Analyst Ratings on many of the largest mutual funds in the United States. In June 2017, Morningstar launched the Morningstar Quantitative Rating™ to provide a forward‐looking rating on all mutual funds. Morningstar uses a “robo‐rater” machine‐learning model to assign Morningstar Quantitative Ratings. However, the “robo‐rater” cannot utilize the complete set of information available to Morningstar's analyst as it cannot process “soft information.” The purpose of this study is to evaluate if and how this “robo‐rater” is conducive to mutual fund selection. I find no evidence that the “robo‐rater” offers value to investors beyond its assessment of mutual fund expenses and I find that its inability to process “soft information” makes the Morningstar Quantitative Rating™ much less useful than the Morningstar Analyst Rating™. I also examine the relationship between Morningstar Quantitative Rating™ and mutual fund flows and find that the “robo‐rater” has little to no influence on investors' choice of mutual funds. Paper is available through the Wiley Online Library at https://onlinelibrary.wiley.com/doi/full/10.1002/cfp2.1090

Introduction to a Special Issue of the Financial Planning Review on financial technology

Introduction to a <i>Special Issue</i> of the <i>Financial Planning Review</i> on financial technology

Rating a robo‐rater

AbstractSince 2011, Morningstar has issued Morningstar Analyst Ratings on many of the largest mutual funds in the United States. In June 2017, Morningstar launched the Morningstar Quantitative Rating™ to provide a forward‐looking rating on all mutual funds. Morningstar uses a “robo‐rater” machine‐learning model to assign Morningstar Quantitative Ratings. However, the “robo‐rater” cannot utilize the complete set of information available to Morningstar's analyst as it cannot process “soft information.” The purpose of this study is to evaluate if and how this “robo‐rater” is conducive to mutual fund selection. I find no evidence that the “robo‐rater” offers value to investors beyond its assessment of mutual fund expenses and I find that its inability to process “soft information” makes the Morningstar Quantitative Rating™ much less useful than the Morningstar Analyst Rating™. I also examine the relationship between Morningstar Quantitative Rating™ and mutual fund flows and find that the “robo‐rater” has little to no influence on investors' choice of mutual funds.

Relative Importance of Sustainability Measures and Costs in Mutual Fund Selection

Impact investing has become a mainstream investment practice, especially for millennial investors. More financially savvy than their Generation X predecessors and with an increased concern for environmental and social justice issues, Millennials are transforming socially responsible investing from unconventional to commonplace. This article examines the relationship between sustainability and mutual fund returns (both historical and anticipated) to identify beneficial information that socially responsible investors can use in making investment decisions. Using Morningstar sustainability ratings, star ratings, and analyst ratings, the authors find a very low correlation between sustainability and fund returns. Sustainability ratings do not vary across Morningstar star ratings or analyst ratings; nor do Morningstar star or analyst ratings vary within high or low sustainability groupings. The practical relevance of these findings is that socially responsible investors do not have to sacrifice financially when investing with a positive social inclination. However, the authors document the ability to enhance sustainable investment returns by limiting fund costs in the form of load charges and expense ratios. Low costs are associated with higher Morningstar star ratings and forward-looking analyst ratings. Hence, it is possible to “do well” financially by selecting less expensive funds with high sustainability ratings. TOPICS:ESG investing, mutual fund performance Key Findings • The practical relevance of these findings is that socially responsible investors do not have to sacrifice financially when investing with a positive social inclination. • The authors document the ability to enhance sustainable investment returns by limiting fund costs in the form of load charges and expense ratios. • Hence, it is possible to “do well” financially by selecting less expensive funds with high sustainability ratings.

Expected Mutual Fund Performance

We recover a forward-looking distribution of expected abnormal returns (alphas) for active equity mutual funds from analyst ratings. Professional analysts believe that alphas are dispersed, that the average fund will underperform, and that the largest funds will outperform. We estimate a rational expectations learning model of fund performance and confront the model-implied expectations based on fund size, perceived skill, and fees with analysts' expectations. Analysts and the rational learner respond similarly to changes in perceived skill and fees, but in contrast to the rational learner, analysts do not believe in a negative impact of fund size on fund returns. The absence of such decreasing returns to scale in analysts' expectations and the presence thereof in actual fund returns make it difficult to reconcile analysts' expectations with rational expectations, but can help explain the size of the industry together with its poor performance.

Influences on mutual fund performance: comparing US and Europe using qualitative comparative analysis

This study examines the conditions that lead mutual funds to underperform or outperform competitors. Using fuzzy-set qualitative comparative analysis (fsQCA), we draw upon extensive research on fund returns to affirm and extend earlier discoveries. Fund performance (Morningstar ratings), features of the funds themselves, and characteristics of the fund managers are considered. Positive Morningstar star and analyst ratings are necessary conditions for funds to generate value (measured by Jensen’s alpha). Funds with low management fees and low ongoing fees have attractive Sharpe ratios and high returns. Likewise, large funds with good Morningstar ratings have good Sharpe ratios and returns, often when fund managers have short tenures.

Alerts for community pharmacist-provided medication therapy management: recommendations from a heuristic evaluation

BackgroundMedication therapy management (MTM) is a service, most commonly provided by pharmacists, intended to identify and resolve medication therapy problems (MTPs) to enhance patient care. MTM is typically documented by the community pharmacist in an MTM vendor’s web-based platform. These platforms often include integrated alerts to assist the pharmacist with assessing MTPs. In order to maximize the usability and usefulness of alerts to the end users (e.g., community pharmacists), MTM alert design should follow principles from human factors science. Therefore, the objectives of this study were to 1) evaluate the extent to which alerts for community pharmacist-delivered MTM align with established human factors principles, and 2) identify areas of opportunity and recommendations to improve MTM alert design.MethodsFive categories of MTM alerts submitted by community pharmacists were evaluated: 1) indication, 2) effectiveness; 3) safety; 4) adherence; and 5) cost-containment. This heuristic evaluation was guided by the Instrument for Evaluating Human-Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA) which we adapted and contained 32 heuristics. For each MTM alert, four analysts’ individual ratings were summed and a mean score on the modified I-MeDeSA computed. For each heuristic, we also computed the percent of analyst ratings indicating alignment with the heuristic. We did this for all alerts evaluated to produce an “overall” summary of analysts’ ratings for a given heuristic, and we also computed this separately for each alert category. Our results focus on heuristics where ≤50% of analysts’ ratings indicated the alerts aligned with the heuristic.ResultsI-MeDeSA scores across the five alert categories were similar. Heuristics pertaining to visibility and color were generally met. Opportunities for improvement across all MTM alert categories pertained to the principles of alert prioritization; text-based information; alarm philosophy; and corrective actions.ConclusionsMTM alerts have several opportunities for improvement related to human factors principles, resulting in MTM alert design recommendations. Enhancements to MTM alert design may increase the effectiveness of MTM delivery by community pharmacists and result in improved patient outcomes.