Stock Analysts Research Articles

10‐K complexity, analysts' forecasts, and price discovery in capital markets

AbstractThis paper examines the behaviour of stock prices and analysts' earnings forecasts following firms' increasingly complex 10‐K filings. Our evidence suggests that greater information uncertainty outweighs greater analyst ability, and the net effect drives greater underreaction in analysts' earnings forecasts following the release of more complex 10‐Ks. Further analysis suggests that analyst underreaction mitigates market overreaction to the information in low‐complexity 10‐K filings, and overwhelms market overreaction to the information in high‐complexity 10‐K filings thus compromising price discovery of the earnings implications of more complex 10‐Ks.

How do analyst recommendations on banks respond to monetary policy news? An application to the Eurozone

ABSTRACT We assess the extent to which stock analysts consider European Central Bank monetary policy decisions when formulating their investment recommendations on European banks from 2002 to 2020. To identify monetary policy shocks, we extract monetary policy factors from high-frequency rate movements surrounding monetary policy announcements. Our findings indicate that stock analysts are more concerned with non-standard (or unconventional) monetary policy measures than standard policy measures. We also demonstrate that analysts interpret restrictive (accommodative) monetary surprises as positive (negative) news for bank financial health, particularly during periods characterized by high levels of financial uncertainty. Finally, we emphasize that the reaction of stock recommendations to rate- and forward guidance-related decisions is primarily influenced by the implicit disclosure of information on the economic outlook by the ECB along with its monetary policy decision.

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.

Why stock analysts may make wrong predictions?

Investors and financial analysts employ various mathematical and statistical methods to forecast stock prices. An investor or a stock analyst with a superior forecasting method can harvest huge and almost riskless profits. This is the reason why so many people are investing time and money enthusiastically in developing methods to correctly predict future stock prices. In this note, we demonstrate that even if a sophisticated investor or a stock analyst has superior (but not perfect) forecasting methods, she/he can suffer from a large false positive error. We provide a simple formula for the probability of the rate of False Positives, and analyze its properties.

Extreme Weather and Firm Market Value: Roles of Corporate Social Responsibility and Stock Analyst Attention

ABSTRACT The outbreak of extreme weather events is increasing in frequency for the last decade, resulting in tremendous human and industrial losses worldwide. Evaluating and managing the effects of such events are highly urgent matters for firms, but the literature on this topic is limited. In this study, the effects of extreme weather events on the stock market performance of firms were estimated to provide a foundation for exploring how to manage such effects. From the perspective of the shareholder view, the heterogeneous effect of corporate social responsibility (CSR) on stock performance associated with extreme weather events were examined. Data on natural disasters that were caused by six types of extreme weather and occurred between 2001 and 2021 in China were used to construct a database of 2,718 Chinese listed manufacturing firms and 23,529 observations regarding the potential effects by extreme weather events. An event analysis indicated that the extreme weather events bring an average of 0.97% drop in the stock performance of potentially affected firms for the month that the event starts and the next month. A subsequent cross-sectional analysis revealed that a high CSR level is associated with low market value during extreme weather events, while a high level of stock analyst attention alleviates the negative effect of CSR on the stock performance of extreme weather events. This study enriches the existing literature and offers practical insights for managers in the fields of CSR and operations management, highlighting the complex dynamics between CSR activities, analyst attention, and stock market performance during extreme weather events.

Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements

Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements

How do investors perceive corporate general counsel? The role of monitoring and advising demand

We find that firms with a general counsel (GC) in top management (GC firms) have a higher ex-ante cost of equity implied in stock prices and analysts’ earnings forecasts. Lead-lag changes analysis shows that the cost of equity increases after, but not before, the appointment of GCs to top management. Cross-sectional analyses suggest that the cost of equity difference between GC and non-GC firms and the cost of equity increase following the appointment of GCs to top management are more pronounced for firms with greater monitoring demand and less pronounced for firms with greater advising demand. We also observe negative market reactions to proxy statements that reveal the appointment of a GC to top management. Finally, our falsification tests find no evidence that investors react to the addition of Chief Marketing Officers, Chief Operating Officers, or Chief Financial Officers to top management. Overall, our results suggest that investors’ perceptions of including GCs in top management depend on the firm’s demand for monitoring and advising.

Effects of Bad News on Stock Returns and Analysts’ Recommendations: The Influence of Executive Gender

Although women are more conservative and more ethical than men, the proportion of female executives is still lower than that of men. Both Chief Executive Officers (CEOs) and Chief Financial Officers (CFOs) have strong influences in making corporate financial decisions. However, most of the literature focuses only on CEOs and ignores CFOs. Therefore, when bad news happens, it is possible that a female CEO/CFO can better alleviate the negative impact. We thus employ 4,405 firm-year observations over the period of 1996–2018, of which 680 are unique companies listed on the S&P1500 index, to examine how CEO and CFO gender influences stock returns and analyst recommendations. Our ordinary least squares and logistic regression results show that investors are pessimistic about companies led by female CEOs/CFOs, especially female CEOs. That is, when something bad happens, stock returns and analyst recommendations are worse for firms with female CEOs. Overall, this study is first to use stock returns to observe market reactions to firms with female CEOs/CFOs. In other words, the corporate remains unfriendly toward women, even those who are qualified as CEOs/CFOs

CSR performance and firm idiosyncratic risk in a data-rich environment: The role of retail investor attention

In the Chinese stock market, there are many retail investors who focus on short-term profits and may consider corporate social responsibility (CSR) differently from institutional investors. We find that CSR significantly reduces firms’ idiosyncratic risk in the Chinese financial market. This result still holds after a series of robustness checks with potential endogeneity concerns. We further test the role of CSR as a nonfinancial informational supplement, the interplay of CSR with stock analyst forecasts and the effect of heterogeneity in corporate governance characteristics. Finally, we find that retail investors’ attention mediates the relationship between CSR and firms’ idiosyncratic risk. Our results have general implications for understanding the impact of CSR on retail investors.

Analyst coverage, executive compensation and corporate risk‐taking: Evidence from property–casualty insurance firms

AbstractUsing an exogenous drop in analyst coverage introduced by broker closures and mergers, we test for the causal impact of analyst coverage on corporate risk‐taking, in an opaque industry. We document an increase in risk using several book‐based and market‐based risk measures, including tail and default risk measures. Results are driven by firms with stronger managerial risk‐taking compensation incentives. The increase in risk is stronger in more opaque firms, and firms with weaker policyholder monitoring. Firm risk increases through at least one risk‐taking action, such as investing firm assets in higher‐risk bonds. Our study highlights the importance of stock analysts in affecting corporate risk‐taking, especially in the presence of stronger managerial, compensation risk‐taking incentives.

The effects of environmental information disclosure on stock price synchronicity in China

Environmental information disclosure has attracted the attention of the capital market because it can convey corporate characteristic information. But there needs to be direct evidence that environmental information disclosure can improve the market's overall efficiency. This study examines whether corporate environmental information disclosure can improve the information efficiency of the capital market. This study takes Chinese listed companies from 2008 to 2021 as samples, constructs a panel fixed effect model, and adopts multiple linear, instrumental variable method and Heckman sample selection model. We find that the disclosure of environmental information in the Chinese market reduces the information efficiency of the capital market represented by the synchronicity of stock prices. The reason is that the information after greenwashing by enterprises needs to be of better quality and more obscure, which disrupts market information. Environmental information disclosure by enterprises prone to greenwashing significantly affects stock price synchronicity, such as low institutional ownership, non-state-owned, growth, and manufacturing firms. Finally, this paper discusses the impact mechanism and confirms that stock liquidity and analyst coverage are the two paths through which environmental information disclosure impacts stock price synchronicity. This study is significant in encouraging the government to strengthen market supervision, promoting enterprises to disclose high-quality environmental information, and improving the pricing efficiency of the capital market.

How economic uncertainty influences the performance of investor perceptions and behavior

In the presence of high uncertainty and asymmetric information, firms might opportunistically shift earnings from uncertain to more certain times. Thus, this paper examines how investor perceptions and risk-taking behavior fluctuate under high economic uncertainty and how earnings quality influences stock return synchronicity and analyst forecast accuracy during periods of high uncertainty. Using a large sample obtained from 24 countries worldwide over the period from 1997 to 2017, we find that investors take a bearish attitude and stocks are more synchronized during years with high economic uncertainty. Furthermore, we find a negative association between analyst forecast accuracy and economic uncertainty. Second, a low level of investor protection indicates a low level of market sentiment. Third, stocks become more synchronous and analyst forecasts are more accurate during periods of high investor sentiment and in the presence of higher levels of earnings quality. Fourth, higher stock return synchronicity implies greater analyst forecast accuracy. Overall, we show that all these effects are more intense during periods of high economic uncertainty.

Research of Manipulation of Stock Assets on the Example of Shares of American High-Tech Companies of the Nasdaq Stock Exchange

The subject of the study is the processes of manipulation of stock asset prices on the example of shares of American high-tech companies of the NASDAQ exchange. The concept of manipulation of stock assets is formulated and the signs of manipulation of them are presented. The main purpose of the work is to identify signs of manipulation of the stock prices of Facebook, Nasdaq and Google companies from the moment of the initial public offering of the company's shares on the stock exchange (IPO) to the present. The methodology of the study includes statistical methods of data analysis: comparative analysis, correlation and regression analysis. Based on the annual quotes and financial reports of Facebook, Google and Nasdaq, a comparative and correlation analysis of the annual values of investment multipliers is carried out. Particular attention is paid to the comparative and statistical analysis of the data of these companies at the initial time stage after their IPO on the NASDAQ exchange. The result of the analysis showed a fairly strong correlation between them on this indicator. At the same time, a significant overvaluation of Facebook shares was established in the first years after its initial public offering on the stock exchange, in relation to the analog companies of Google and Nasdaq, as well as to the companies listed on the S&P500 stock market of the USA. The scientific novelty of the study lies in the fact that the presented methodology allows identifying signs of manipulation with the share prices of companies-analogues of any sufficiently liquid stock market. The topic of the article is of interest to stock market participants, stock analysts, potential investors and purchasers of companies, as well as to stock market regulators dealing with the reliability and security of its processes.

The Optimal Strategic Performance Positioning (OSPP) Matrix as an Evaluative Tool for Stock Analysts

The Optimal Strategic Performance Positioning (OSPP) Matrix as an Evaluative Tool for Stock Analysts

Amgen to buy Horizon for nearly $28 billion

Amgen has agreed to buy Horizon Therapeutics, a Dublin-based biotech specializing in medicines for rare, autoimmune, and severe inflammatory diseases, for about $27.8 billion in cash. It is the highest price tag of any merger in the pharmaceutical industry this year. Horizon had previously been in talks with Sanofi and Johnson & Johnson. “The acquisition builds on Amgen’s success in rare diseases and provides a strong platform for us to expand in this space in the future,” Amgen CEO Robert A. Bradway said on a Dec. 12 conference call with stock analysts. Amgen, which markets blockbuster medicines like Otezla for plaque psoriasis and Aimovig for migraines, will receive Horizon’s Tepezza and Uplizna, both monoclonal antibodies, and Krystexxa, an enzyme. Those three drugs, all for rare diseases, garnered $2 billion in sales in the first 9 months of 2022, Bradway said. Horizon also has several candidates in the clinic for various

The Nursing Labor Market’s Structural Shift

In a recent Wall Street Journal article, stock analysts reported that weekly pay for temporary nurses had dropped another 15% to the low $3,000s, helping to improve the bottom line at investor-owned hospital groups.1 While travel nurse contracts and wages have been decreasing concurrently with pandemic-related hospital admissions, many wonder when (if ever) the registered nurses (RNs) who for the first time in their career decided (for a myriad of reasons) to travel to pandemic hot spots will return to permanent positions.

Portfolio Analysis Using Malaysia Stock Market Data: Before and During COVID-19 Pandemic

This study attempts to evaluate Malaysia’s stock performance before and during COVID-19 across all sectors by using the Sharpe ratio and Sortino ratio with risk measured by standard deviation. We develop an algorithm for stock selection to construct the portfolio investment. In our study, we apply the Sharpe ratio, Treynor ratio and Jensen’s alpha to identify the optimal portfolio. The result shows that the portfolio with stock selection based on the Top 20 Sortino ratio from all stocks is superior to stock selection based on the Top 3 Sortino ratio for each sector. The daily adjusted closing stock prices are collected from March 1, 2019 to December 31, 2021. The result of this study indicates that several sectors are not affected during the COVID-19 pandemic, which are Technology, Industrial, Consumer Products and Services and Property. Hence, investors are suggested to form an optimal portfolio investment based on these sectors. Stock analysts are recommended to imply various risk-adjusted measures to evaluate the portfolio performance with a comprehensive perspective to support the outcome analysis.

INNOVATION AMBIDEXTERITY MODEL IN STOCK ANALYSIS SERVICES MICRO BUSINESS IN INDONESIA

One of a kind from the micro business sector in Indonesia is micro stock analysis services business thatmanaged by stock analysts who provide positive predictive recommendations to their clients. During the pandemic outbreak, the stock analysis service business was experiencing problems due to the decline in prices and affect its performance. This problem led researchers to help micro stock analysis services business with knowing the most influencing factors of the innovation ambidexterity model. Data collection was carried out by conducting an initial survey from Indonesia professional investment manager as expert. The collected data were analyzed using the partial least square (PLS) method to determine most influencing factors of the innovation ambidexterity model. As a result, investment capability is the most is the most influential variable on innovation ambidexterity.

Asset Management of Transportation Companies: Analysis and Methods

The article deals with gaining some ideas and skills that an investor might use to choose the best opportunities in the market from among the thousands of options concentrated in the transport industry. The authors are also trying to build up a standard set of tools that make up the core of every stock analyst’s tool box. From the practical perspective, the ideas and techniques in the discussion part will be useful for analysing just every possible investment. A special focus in the article has been made on the transportation companies struggling with the consequences of the corona-crisis of 2020.

Stock Prediction Using Machine Learning Algorithms

In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.