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The paper aims to investigate differences in non-professional and professional stock investors’ trust in and tendency to follow financial analysts’ buy and sell recommendations.

Online experiment conducted in Sweden in March 2022 comparing non-professional private investors (n = 80), professional investors (n = 33), and master students in finance (n = 28). Information was presented about four company stocks listed on the New York stock exchange. Two stocks were buy-recommended and two stocks sell-recommended by financial analysts. For one stock of each type, the recommendation was presented to participants. Dependent variables were predictions of the stock price after three months, ratings of confidence in the predictions and choices of holding, buying or selling the stock. Ratings were also made of the importance of presented stock-related information as well as trust in analysts’ skill and integrity.

More positive return predictions were made of buy-recommended than sell-recommended stocks. Non-professionals and to some degree finance students tended to trust financial analysts more than professional investors did and they were more influenced by the presentation of the buy recommendations. All groups made too optimistic return predictions, but the professionals were less confident in their predictions, more likely to sell the stocks and lost less on their investments.

A new finding is that non-professional stock investors are more likely than professional stock investors to trust financial analysts and follow their recommendations. It suggests that financial analysts’ recommendations influence non-professional investors to take unmotivated investment risks. Non-professionals in the stock market should hence be advised to exercise more caution in following analysts’ recommendations.

In this study, we examine the influence of a firm’s environmental, social, and governance (ESG) performance on analysts’ stock recommendations and earnings forecast accuracy in the Chinese context.

We take a textual analysis approach to analyst research reports issued between 2010 and 2019, and differentiate between two distinct analyst categories: “sustainability analysts,” which refer to those more inclined to incorporate ESG information into their analyses, and “other analysts.”

Our evidence indicates that sustainability analysts tend to be significantly more likely than others to provide positive stock recommendations and demonstrate enhanced accuracy in forecasting earnings for companies with superior ESG performance. Our additional analyses reveal that this finding is particularly prominent for analysts who graduated from institutions emphasizing the protection of the environment, those recognized as star analysts, those affiliated with ESG-oriented brokerages, and forecasts made by analysts in the later part of the sample period. Our findings further indicate that sustainability analysts exhibit a more pronounced negative response when confronted with a negative ESG event.

In general, the evidence from this study reveals the interplay between ESG factors and analyst behavior, offering valuable implications for both financial analysts and sustainable investment strategies.

The aim of this paper is to document the effect of analyst coverage on the ex ante probability of stock price crash and the ex ante probability stock price jump.

This paper uses the data of non-financial firms from France to test the arguments presented in this paper during the period between 1997 and 2019. The paper also uses flexible quadrants copulas to compute the ex ante probabilities of crashes and jumps.

The results show that the extent of analyst coverage is positively associated with the ex ante probability of crash and negatively associated with the ex ante probability of jump. The results remain qualitatively the same after several sensitivity checks. The results also show that the relationship between the extent of analyst coverage and the probability of cash and the probability of jump holds when ex post probability of stock price crash and stock price jump is used.

Unlike most of the earlier papers on this topic, this paper uses the ex ante probability of crash and jump. This proxy is better suited than the ones used in the prior literature because it is a forward-looking measure.

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

This paper aims to examine whether firms meeting or just beating an earnings benchmark engage in tone management in earnings conference calls to complement earnings management in the UK context. It also investigates whether the audience tone in beating or just meeting earnings fails to predict future performance.

This study was performed using a sample of non-financial UK firms listed in the FTSE 350 index over the period 2010–2015.

The findings show that firms that exercise more earnings management to meet or just beat earnings are positively associated with the abnormal tone during earnings conference calls. The outcomes also reveal that the audience’s tone of firms meeting or just beating an earnings benchmark fails to predict future performance. This confirms the effectiveness of the tone management in managing the perception of audience.

This study highlights the need for increased accountability by firms on earnings conference call. It also supports academics and practitioners in understanding the management discretion used in reporting and communication during the earnings conference call. Overall, the results of this study are beneficial for regulators, policymakers and professionals, regarding confirming the need for the earnings conference calls to be regulated.

To the best of the authors’ knowledge, this is the first study that examines the association between earnings management and tone management in the UK earnings conference calls. It adds to the existing literature by examining the self-serving behaviour of managerial tone during earnings conference calls within a sitting in which meeting or just beating a benchmark is used. Unlike several studies that explain the behaviour of tone as a signalling strategy, this study reveals that the tendency of impression management behaviour can explain the tone management.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This study aims to examine the association between chief executive officers’ (CEOs) characteristics and sell-side analysts’ recommendations.

This study uses a sample of firms listed on the London Stock Exchange and uses two databases, Capital IQ and BoardEx to study the above relationship. A variety of regression analyses are used in the empirical models, including ordinary least squares, fixed effect, random effect, Tobit, Logit and generalized method of moments.

The authors find that firms with CEOs who had a wider network size and firms with foreign CEOs receive favorable investment recommendations. Further, firms with CEOs who have more time to retire are more likely to receive favorable investment recommendations. However, the authors find that firms with CEOs with more qualifications receive unfavorable recommendations and female CEOs are not affecting investment recommendations.

Ultimately, this study demonstrates the importance of CEO characteristics for sell-side analysts who play an important role in the stock markets.

In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.

Considering the role of analysts in disseminating information, the paper explains the idiosyncratic volatility puzzle of China's stock market. As the largest developing country, China's research can provide meaningful reference for the research of financial markets in other new countries.

From the perspective of behavior, establishing a direct link between individual investor attention and stock price overvaluation.

The authors find that there is a significant idiosyncratic volatility puzzle in China's stock market. Due to the role of mispricing, individual investor attention significantly enhances the idiosyncratic volatility effect, that is, as individual investor attention increases, the greater the idiosyncratic volatility, the lower the expected return. Attention can explain the idiosyncratic volatility puzzle in China's stock market. In addition, due to the role of information production and dissemination, securities analysts can reduce the degree of market information asymmetry and enhance the transparency of market information.

China is the second largest economy in the world, and few scholars analyze it from the perspective of investors' attention. The authors believe this paper has the potential in contributing to the academia.

This paper aims to conduct a comparative analysis of the impact of market uncertainty on the degree of accuracy and bias of analysts' earnings forecasts versus four model-based earnings forecasts.

The study employs panel regression analysis on a sample of Egyptian listed companies from 2005 to 2022 to examine the impact of market uncertainty on the accuracy and bias of each type of earnings forecast.

The empirical analysis reveals that market uncertainty significantly affects analysts’ earnings forecast accuracy and bias, while model-based earnings forecasts are less affected. Furthermore, the Earnings Persistence and Residual Income model-based earnings were found to be superior in terms of exhibiting the least susceptibility to the impact of market uncertainty on their forecast accuracy and biasness levels, respectively.

The findings have important implications for stakeholders within the financial realm, including investors, financial analysts, corporate executives and portfolio managers. They emphasize the importance of considering market uncertainty when formulating earnings forecasts, while concurrently highlighting the potential benefits of using alternative forecasting methods.

To our knowledge, the influence of market uncertainty on analysts' earnings forecast accuracy and bias in the MENA region, particularly in the Egyptian market, remains unexplored in existing research. Additionally, this paper contributes to the existing literature by pinpointing the forecasting method, specifically distinguishing between analysts-based and model-based approaches, whose predictive quality is less adversely impacted by market uncertainty in an emerging market.