Stock Recommendations Research Articles

Is the financial information still useful in issuing stock recommendations Evidence from the Tunisian financial analysts

Is the financial information still useful in issuing stock recommendations Evidence from the Tunisian financial analysts

Media Recommendation and Market Risk: Evidence from Chinese Market

This study analyzes 23,292 TikTok short-video stock recommendations from 2022 to explore their impact on stock market performance using the Difference-In-Differences Model. Key findings include: (1) short video recommendations provide valuable information, significantly boosting stock price discovery; (2) using a large language model (ChatGPT), it was found that the impact of clustered recommendations on stock price discovery is enhanced when considering recommendation strength. This pioneering research offers insights into the relationship between short-video stock recommendations and abnormal stock returns, benefiting researchers, investors, corporations, and social media analysts.

Is the financial information still useful in issuing stock recommendations Evidence from the Tunisian financial analysts

Is the financial information still useful in issuing stock recommendations Evidence from the Tunisian financial analysts

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

Business newspaper stock recommendations for intraday trading: empirical analysis from India

Business newspaper stock recommendations for intraday trading: empirical analysis from India

Automated Passive Income from Stock Market Using Machine Learning and Big Data Analytics with Security Aspects

With the evolution of Machine Learning, the Internet of Things (IoT), and Big Data Technologies, digital data has increased exponentially. To handle and process such a large bunch of data, high-performance computers are used across various fields. One of the fields has been the financial capital market of stocks, bonds, commodities, foreign exchange, and cryptocurrencies, where supercomputers essentially trade securities with high computational ability and intelligent algorithms. Large financial institutions with significant capital spend a lot of money on programmers and data analysts to develop the best accuracy trading algorithms to drive the overall market. A novice trader or investor with less to no experience in financial markets feels it difficult to search for good trades or stocks to invest their hard-earned money on a short to long-term time horizon. They rely on expert advice for stock recommendations and sometimes end up making significant losses. This paper focuses on developing a universal trend trading indicator that can analyze and predict the overall future trend of any stock, bond, commodity, forex, or cryptocurrency with the highest possible profitability. The historically traded extensive dataset of stock prices and investment reports of large financial institutions worldwide are gathered. Various machine learning and decision-making models are employed to perform technical and fundamental analysis across multiple securities. The output of the trend trading indicator is displayed on charting platforms, which can provide entry-exit levels at which even novice investors can decide where to invest their money. Multi timeframe analysis is deployed to predict short-term, mediumterm, and long-term overall trends, thus increasing the output accuracy. The indicator is helpful for all kinds of retail traders and investors worldwide who struggle to earn profits from financial markets. Our proposed system was able to achieve a profitability of 86.28% annual returns. The entire system, along with the trading orders, is automated so that anyone can earn extra passive income every month from the stock market.

Application of the AHP-TOPSIS Method to Support Stock Investment Decisions Based on Financial Ratio Analysis

Stock is one form of investment that is currently popular, both for young people, parents, and industry. This can be seen from the increasing number of investors and companies listed on the Indonesia Stock Exchange (IDX). This makes investors confused in determining the best stock choices. Analysis of stock selection needs to be done before someone invest so that the selected stock does not lose and can generate optimal profits. This study discusses the recommendation for alternative stocks from the IDX30 index. The parameters for selecting alternative stocks considered include the criteria of Earning Per Share (EPS), Return on Assets (ROA), Return on Equity (ROE), and Net Profit Margin (NPM). Then the weight of each criterion will be searched using the Analytical Hierarchy Process (AHP) method and sorted using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to produce stock recommendations from companies listed on the Indonesia Stock Exchange. The results showed that the Earning Per Share (EPS) criteria became the first priority with a weight of 0,2927, then Return on Equity (ROE) with a weight of 0,2728, followed by Net Profit Margin (NPM) with a weight of 0,2583, and Return on Assets (ROA) with a weight 0,1759. Then alternative results are obtained based on the preference value and ranking 14 companies that have a preference value above 0.5 and can be used as a consideration in making investment decisions, with BBCA as a priority alternative.

DR-GAT: Dynamic routing graph attention network for stock recommendation

Investors are increasingly interested in using financial technology to guide investment decisions. The phenomenon of “stock linkage” or “leading-lag” is often observed between related stocks on the stock market. Based on this feature of the stock market, complex relationships between stocks influence investment decisions. Existing methods use static prior relational data (e.g., industry relations and Wiki relations) to build corporate relation graph, while learning relational features using the same graph for all stocks. This description and use of corporate relationships ignored the dynamic nature of association relationships in time and space, limiting the ability to learn latent relationships between stocks. To address these issues, we propose a Dynamic Routing Graph Attention Network (DR-GAT) for stock recommendation. We propose a novel similarity measurement method called stock price trend similarity. To track the evolution of intercorporate relationships over time, we dynamically construct relation graph attention networks using prior knowledge and stock price trend similarity. Moreover, a newly designed relation graph router (RGR) can route each stock to an optimal relationship graph based on its volatility. Extensive experiments demonstrate the superiority of our DR-GAT method. It outperforms state-of-the-art methods achieving an average return ratio of 152% and 162% on NASDAQ and NYSE, respectively.

Washington Policy Analysts and the Propagation of Political Information

Washington policy research analysts (WAs) monitor political developments and produce research to interpret the impact of these events. We find institutional clients channel more commissions to brokerages providing policy research and commission-allocating institutional clients generate superior returns on their politically sensitive trades. We find that WA policy research reports are associated with significant price and volume reactions. Finally, we find sell-side analysts with access to WA issue superior stock recommendations on politically sensitive stocks. These effects are particularly acute during periods of high political uncertainty. Overall, we uncover a unique and an important conduit through which political information filters into asset prices. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4919 .

Do ties still bind? Analyst behaviour after financial restatements

AbstractWe find that, compared to non‐connected analysts, analysts with professional connections to a coverage firm (i.e., connected analysts) are more likely to continue covering the firm after it issues a restatement. Furthermore, connected analysts are more likely to issue pessimistic earnings forecasts and to downgrade stock recommendations for the firm after its financial restatement. Our results also reveal the costs and benefits associated with connected analysts' pessimism – a reduced market reaction to the analysts' pessimistic research on the restating firm, and a positive effect on the market's perception of the quality of the analysts' research on non‐restating firms.

Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits

Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits

Analysts’ Institutional Client Catering and Reputation Tradeoff: Strategic Timing of Recommendations

We examine whether sell-side analysts strategically time their favorable recommendations to cater to institutional investors while preserving analysts’ reputational capital. Although prior literature documents that analysts provide more positive recommendations for stocks that are part of their institutional clients’ (specifically, mutual funds’) portfolios, it does not explicitly address a reputation cost associated with such practice. Using a sample of analysts’ recommendations on U.S. firms for the 2002–2017 period, we document a pattern of analysts’ recommendations being more optimistic in the end month of a quarter and less optimistic in the beginning month of a quarter. This timing pattern ties to quarterly reporting periods of portfolio managers, with actively managed mutual funds’ holdings being affected the most. Analysts with Institutional Investor All-Star ranking do not engage in such stock recommendation timing practices. The market participants seem to believe rosy recommendations issued for stocks with more institutional holdings in the end month of a quarter with more positive cumulative abnormal returns to upgrade and downgrade recommendations. JEL classifications: G14, G24

Managerial ability and analysts’ stock recommendations

This study examines whether analysts consider managerial ability in forming their stock recommendations and, if so, whether it improves the quality of their recommendations. We find that sell-side analysts recommend firms with higher managerial ability more favorably, suggesting that analysts value the implications of managerial ability in assessing firm value. Moreover, the profitability of analyst recommendations increases when the recommendation takes managerial ability into account. Overall, our results suggest that managerial ability is a distinct and important attribute of firms in explaining analysts’ stock recommendations and the value of those recommendations to investors.

Analyst ability and research effort: non-EPS forecast provision as a research quality signal

The range of non-EPS forecast types provided by individual analysts to I/B/E/S has increased dramatically over time but varies considerably across firms. We propose that in providing a broader range of forecast types, analysts can signal superior research ability and research effort. Consistent with this hypothesis, we document positive associations between the number of forecast types (NFT) an analyst provides and common proxies for research quality, including earnings forecast accuracy, price target accuracy, stock recommendation profitability, market reactions to stock recommendation revisions, and analyst career outcomes. The effects of NFT are incremental to other quality proxies used in the literature and are distinct from the issuance of specific non-EPS forecast types studied previously (e.g., cash flow forecasts). We demonstrate the information value of NFT to investors by examining the out-of-sample performance of portfolios formed conditional on NFT and exploiting revisions in consensus earnings forecasts and individual analysts’ stock recommendations. We conclude that the number of forecast types an analyst provides for a firm is a readily available proxy for the quality of her research.

Interpreting institutional investment activity as a markov process: A stock recommender

Institutional investors like hedge funds are particularly resilient in times of volatility, primarily due to their access to a slew of complex trading strategies and extensive research capabilities; both of which are generally outside the reach of common investors. Fortunately, the U.S. Securities and Exchange Commission has mandated that large institutional investors publicly disclose the positions held by them at the end of a quarter, within the following 45 days. We then ask the question “Given our access to snapshots of positions held by large investors, can we extract alpha by approximating their aggregate behaviour?” In this paper, we introduce a stock recommendation model driven by the aggregate behaviour of institutional investors. We interpret stocks as the states of a Markov chain. The corresponding state transition matrix is defined based on the aggregate behaviour of investors. By designing the transition matrix to hold for aperiodicity and irreducibility, the steady state distribution of this chain is treated as a ranked list of stocks. We build a long only, equally weighted portfolio of stocks to trade using these recommendations. We observe that the returns on this portfolio beat existing models in the literature, and standard hedge fund indices both in terms of annualised returns and Sharpe ratio.

Financial analysts' bundling across firms: Target prices and stock recommendations

AbstractA rising trend is that analysts bundle earnings forecasts for multiple firms on the same day, and such forecast bundling is associated with low‐quality forecasts. We explore target price bundling and recommendation bundling. Factors driving bundling revisions of an output for multiple firms vary across three outputs: forecasts, target prices, and recommendations. Target price (recommendation) revisions bundled for firms are less informative than stand‐alone revisions. Although consistency in the direction of revisions between different outputs is typically associated with higher perceived quality, consistent revisions between outputs are not associated with higher informativeness of revisions bundled for multiple firms.

Throwing in the towel: What happens when analysts' recommendations go wrong?

AbstractEvery analyst will experience stock recommendation failures during their career. Unlike many other professions, these pivotal moments occur in the full glare of clients, colleagues, equity‐sales teams, and the media. This research explores the practices of analysts up to and beyond the point where, faced with a failing recommendation, they contemplate “throwing in the towel” on their recommendation. Based on empirical evidence gathered from interviews with sell‐side analysts and their key interlocutors—equity‐sales specialists, investors, and investor relations officers—this paper uncovers several new empirical insights into the recommendation practices of analysts. The main argument made in the paper is that capitulation practices emerge from the specific contextual framework of individual recommendations and the analyst's conduct as a knowledgeable, emotional human agent. We identify several contextual contingencies of stock recommendations that underpin how a capitulation episode unfolds, including the temporal proximity of the capitulation to the original recommendation; the importance and profile of the stock to the analyst's reputation (“franchise intensity”); the level of interest/reaction from clients, equity‐sales teams and corporates; the nature/cause of recommendation failure; and recommendation boldness. Our study provides evidence that what an analyst does when faced with a failing recommendation cannot be reduced to a predictable, rational process and informs our understanding of observed practices such as the reluctance of analysts to capitulate and why “recommendation paralysis” often follows a recommendation capitulation.

Flattering or Really Understanding? Research on Stock Recommendations by Sell-Side Analysts in China

This article investigates whether the business relationships between brokerage firms and listed companies influence sell-side analysts’ stock recommendation ratings. Using the recommendation data of A-share listed companies disclosed by China’s domestic brokerage firms from 2004 to 2019, this article finds that sell-side analysts of brokerage firms that have securities underwriting relationships with listed companies are inclined to give those companies investment evaluations that are more-positive than evaluations from non-affiliated analysts. After a series of robustness tests and alleviating endogenous problems, such as the self-selection of underwriting relationships and of the sell-side analysts’ coverage samples, the empirical result remains unchanged. Using an event study method to compare differences in the cumulative abnormal returns of stocks during the event window of the recommendation releases, the authors find that it is information advantage rather than collusion of interests that leads affiliated sell-side analysts to issue more aggressive investment recommendations. Further research shows that “client pressure” will enhance the investment rating enthusiasm of analysts of related brokerage firms, whereas “peer pressure” will not weaken the investment rating enthusiasm of them, thus verifying the robustness of causality. The findings in this article enlighten the regulatory authorities and the capital market about the characteristics of stock recommendations of related brokerage firms’ analysts and the motives behind these recommendations, which is of great significance in protecting the interests of investors.

The Value of Eliciting Information: Evidence from Sell-Side Analysts

ABSTRACT The ability to elicit information is a critical skill that many analysts and other information agents strive to master. This paper develops and validates a novel approach to measure analysts’ skill in eliciting information and studies its relation with analysts’ performance. The results suggest that analysts who are skilled in eliciting information issue more accurate forecasts and more informative stock recommendations. Further, skilled analysts’ recommendations are incrementally more informative for companies with more opaque information environments and with managers who may be delaying bad news. Finally, analysts skilled in eliciting information are more likely to be cited by journalists, recognized by the profession (Institutional Investor all-star status), and less likely to be demoted. These findings demonstrate the importance of elicitation as a distinct skill that influences analysts’ output quality, thereby extending previous research that generally focuses on the performance effects of general analyst characteristics rather than specific skills. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M41; G11; G14.

Market reaction to analyst forecasts by analysts with familyship: Evidence from South Korea

Using the surnames of financial analysts in Korea, this paper examines how investors react to forecasts of analysts with familyship. Since Korea has a strong family-centered culture of sharing the surname, it provides a suitable setting for examining whether investors incorporate analysts’ personal characteristics into their decision-making. This paper finds that stock recommendations by analysts with familyship elicit stronger market reactions. Additionally, the effect is only observed in the cases of upgrade, hold, and buy recommendations. Finally, the effect is observed only when the stock market was not in recession.