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Given the serious question raised by the subprime of the 2008 global financial crisis over the rising practices of excessive rewarding of executives in the USA and European firms, the executive pay-performance nexus has emerged as a popular topic of debate in the contemporary corporate finance research. Conducted mostly on the Anglo-Saxon contexts, research outcomes have been inconclusive and dichotomous. Considering this backdrop, this study aims to investigate the endogenous relationship between executive compensation and risk taking in the context of the USA.

Using a large sample of non-financial firms from 2010 to 2020 based on panel data and two-stage least square regression. In this study, the riskier corporate decision is measured as book leverage and ratio of R&D expense to total assets. Chief executive officers’ (CEO) experience and age are used as instrumental variables, and these are expected to influence compensation incentives and, hence, affect firm riskiness indirectly. Firm size, return on assets and CEO turnover are reported to affect compensation and corporate decisions, therefore, included as control variables. Given that higher executive compensation is related to riskier corporate decision in firms, this study incorporates total wealth (i.e. accumulated equity related compensation) as an additional proxy of compensation, and this selection is justifiable by the perfect contracting notion of the agency theory.

The results of this study show a significant positive and increasing nexus among compensation and riskier corporate decisions. Besides, the compensation level proxied through the percentage of each form of compensation in total compensation is very important as greater equity and greater salary diminishes risk taking.

The outcomes of this study have useful implications for firm stakeholders and policymakers.

The level of pay measured by the percentage of each type of compensation in total compensation is of utmost importance as it can increase or decrease risk taking in corporate decisions.

The purpose of this paper is to detect quantitatively the existence of anchoring bias among financial analysts on the Tunisian stock market. Both non-parametric and parametric methods are used.

Two studies have been conducted over the period 2010–2014. A first analysis is non-parametric, based on observations of the sign taking by the surprise of result announcement according to the evolution of earning per share (EPS). A second analysis uses simple and multiple linear regression methods to quantify the anchor bias.

Non-parametric results show that in the majority of cases, the earning per share variations are followed by unexpected earnings surprises of the same direction, which verify the hypothesis of an anchoring bias of financial analysts to the past benefits. Parametric results confirm these first findings by testing different psychological anchors’ variables. Financial analysts are found to remain anchored to the previous benefits and carry out insufficient adjustments following the announcement of the results by the companies. There is also a tendency for an over/under-reaction in changes in forecasts. Analysts’ behavior is asymmetrical depending on the sign of the forecast changes: an over-reaction for positive prediction changes and a negative reaction for negative prediction changes.

The evidence provided in this paper largely validates the assumptions derived from the behavioral theory particularly the lessons learned by Kaestner (2005) and Amir and Ganzach (1998). The authors conclude that financial analysts on the Tunisian stock market suffer from anchoring, optimism, over and under-reaction biases when announcing the earnings.

The current study aims to investigate the impacts of two behavioral biases, namely, loss aversion and overconfidence on the performance of US companies. First, the impact of loss aversion on the economic performance of companies was assessed. Second, the impact of overconfidence on market performance was discussed.

This study used around 6,777 quarterly observations on the population of US-insured industrial and services companies over the 2006-2016 period. Ordinary least squares (OLS) regression in two panel data models were used to test the hypotheses formulated for the study.

It was documented that the loss-aversion bias negatively affects the economic performance of companies and this is achieved for both sectors. In contrast, the findings suggest that overconfidence positively affects market performance of industrial firms but negatively affects market performance in service firms. Further robust evidence was found that overconfidence bias seems to be dominant, and hence, investors may tend to be more overconfident rather than more loss-averse.

This research can be extended by focusing on the following question: What is the impact of the contradictory (positive and negative) effects of an investor's loss aversion and overconfidence on the US company performance in case of realization of a stock market crisis or stock market crash?

The purpose of this paper is to examine the determinants of CEO compensation for sample of the US firms. It emphasizes the presence of executive compensation persistence and the importance of CEO power besides performance while setting CEO pay.

The empirical analysis is conducted on a large sample of US firms during the period 2006–2016. It is based on the generalized method of moments (GMM) models to assess the impact of numerous factors on CEO compensation.

The main findings reveal that firm performance proxied by accounting-based proxies, as well as market-based proxies, plays a significant role in explaining variations in levels of executive compensation. Moreover, there is a significant persistence in executive compensation among the US sample firms. The authors also document that poor governance conditions (managerial power hypothesis) lead to high compensation levels offered to CEO.

At the end, without a doubt, the analysis has some limitations that prompt the authors to consider future research directions. One future research avenue that can help better explain the effect of firm performance on the CEO compensation is to study this issue using an international sample to determine whether country-level characteristics (e.g. creditor rights, shareholder rights and the enforcement climate) can influence this relationship. Furthermore, it can be worthwhile to deepen the analysis of CEO power and its impact on CEO compensation. It will be interesting to emphasize how the CEO power interacts with the other governance characteristics and some CEO attributes as CEO gender.

The paper's findings have implications for practitioners, policymakers and regulatory authorities. First, the findings inform regulators that performance is not the only determinant of CEO pay level. This may warrant increased firm disclosure of the details of the pay structure. Second, the study offers insights to policymakers and members of boards of directors interested in enhancing the design of executive compensation and internal corporate governance, to better align managerial incentives to shareholder interests. Firms should strengthen the board independence and properly constitute the board committees (compensation, risk, nomination…).

This paper presents a comprehensive overview of the CEO compensation determinants. It supplements the classic pay-for-performance sensitivity predictions with insights gained from the dynamics of wage setting theory and managerial power theory. The authors develop a composite index to measure the CEO power in order to test the impact of CEO attributes on CEO pay. Additionally, it verifies whether the determinants of CEO pay depend on firm age and size.

This paper examines and forecasts correlations between cryptocurrencies and major fiat currencies using Generalized Autoregressive Score (GAS) time-varying copulas. The authors examine to which extent the multivariate GAS method captures the volatility persistence and the nonlinear interaction effects between cryptocurrencies and major fiat currencies.

The authors model tail dependence between conventional currencies and Bitcoin utilizing a Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedastic model (GJR-GARCH)-GAS copula specification, which allows detecting the leptokurtic feature and clustering effects of currency returns distribution.

The authors' results show evidence of multiple tail dependence regimes, implying the unsuitability of applying static models to entirely describe the extreme dependence between Bitcoin and fiat currencies. Compared to the most common constant copulas, the authors find that the multivariate GAS copulas better forecast the volatility and dependency between cryptocurrencies and foreign exchange markets. Furthermore, based on the value-at-risk (VaR) and expected shortfall (ES) analyses, the authors show that the multivariate GAS models produce accurate risk measures by adding cryptocurrencies to a portfolio of fiat currencies.

This paper has two main contributions to the existing literature on cryptocurrencies. First, the authors empirically examine the tail dependence structure between common conventional currencies and bitcoin using GJR-GARCH GAS copulas which consider the leptokurtic feature and clustering effects of currency returns distribution. Second, by modeling VaR and ES, the authors test the implication of using time-varying models on the performance of currency portfolios, including cryptocurrencies.

The purpose of this paper is to study a novel and direct measurement of investor sentiment index in the Tunisian stock market that overcomes the weaknesses of a well-known investor sentiment index by Baker and Wurgler (2006, 2007).

Based on the data of 43 firms of the Tunisian stock market index (Tunindex) over the period 2004–2016, the author constructs a monthly investor sentiment that reflects both the economic fundamentals and the investor sentiment components. Seven indirect indicators collected from investor sentiment literature and Tunisian stock exchange were analyzed. Specifically, after accounting to remove the sentiment component for macroeconomic factors, the author estimates each sentiment proxy with a number of controlling variables. The residual from the estimation is used to define the author’s measure of excessive investor sentiment. To determine the best timing of sentiment indicators, the author employs a factor sentiment series as the first principal component of these total seven sentiment proxies and their lags of a month. Furthermore, by capturing the highest saturations with the first factor analysis, the author regressed each selected indicator’s lead or one-month lag in a second linear principal component analysis to reach the author’s Tunisian market’s total sentiment index.

The results show that all employed indicators may reflect the investor sentiment on the Tunisian stock market. The findings also indicate significant evidence that the author’s sentiment index takes into consideration the political and economic events such as the Jasmine Revolution experienced by Tunisia during the period from January 2, 2004 to December 30, 2016. Moreover, investor sentiment index flow appears to be one leading mechanism for the performance of Tunindex.

Results found have clearly shown that the author’s seven indirect indicators can reflect investor sentiment in the Tunisian context. The various sentiment proxies are bullish indicators of investor sentiment. Brown and Cliff (2004) argue that the higher bull/bear ratio, the more investor sentiment is bullish. An important value of price–earnings ratio implies that the level of investor confidence as for change in market is also important. Liquidity measured by trading volume, market turnover ratio and liquidity ratio reflects individual investor sentiment. Otherwise, it seems that investors only invest when they are optimistic and reduce market liquidity once they became pessimistic. The monthly response rate to initial public offerings (IPOs) represents a bullish sentiment indicator. Indeed, the more optimistic investors are, the higher the response rate to IPOs. Investor satisfaction also reflects investor sentiment. In other words, a high level of satisfaction translates an important level of optimism. In addition, the author also recognizes that the authors’ Tunisian sentiment index follow general trend of stock market prices and appears to be an important determinant of Tunindex returns during the period of study, from January, 2004 to December, 2016. The author suggests investor sentiment can help predict Tunindex returns, distinguishing between turbulent and tranquil periods in the financial market. The graphical illustration of monthly investor sentiment index shows that it captures extreme events such as the Tunisian revolution of January, 2011, also known as the Jasmine revolution which marked the start of the Arab Spring and the consequences of economic and political turmoil in Tunisia that have disrupted economic activity in the next few years. Like all research work, the current research paper has certain limitations. The choice of control variables allowing the author to separate sentiment component of that fundamental might be criticized. Moreover, there is no unanimous number of control variables but they are chosen according to data availability. The author also believes that one of the study’s weaknesses is that the author has not examined the impact of investor sentiment on the Tunisian stock market. For future interesting avenues of research, the author proposes, first, to study the effect of investor sentiment on financial asset returns and check, second, if sentiment factor constitutes an additional source of business risk valued by the marketplace.

The purpose of this paper is to investigate the effect of forecast earnings’ revision on the evolution of securities prices in the Tunisian stock market.

A portfolio study of investor reaction and stock prices following revisions is first conducted to highlight the existence of abnormal return related to analysts’ earnings revisions. Analysis is then supplemented by a second empirical investigation based on the panel data to quantify the effect of revision on the abnormal profitability of securities.

The evidence found in this paper validates the fundamental theoretical hypothesis according to which the psychological bias resulting from the effect of the forecast earnings revision is related to the abnormal profitability of the securities. The authors conclude the importance of the revision impact on investors’ behavior on one hand, and the informational content of the analysts’ forecasts and the biases which they lead on the other hand.

Globally, the empirical illustrations largely validate the findings of behavioral models particularly that of Kormendi and Lippe (1987), Cornell and Letsman (1989), Beaver et al. (2008) which states that investors under psychological bias, react to the effect of forecast earnings revision by an abnormal variation in stock prices.

This paper aims to use three proxy variables – initial public offerings, trading volume and business confidence index (BCI) to construct an investor sentiment index both for the USA and China, taking into account the challenging periods of the COVID-19 pandemic and the Russo-Ukrainian conflict.

Based on monthly data for a period from January 2009 to June 2022, this paper uses data of BCI, consumer confidence index (CCI), gross domestic product, trading volume and Fama and French (1993) factor data; linear regression of single and multifactor model; and EGARCH-M model for analyzing the effect of investor sentiment on stock market return and volatility, both in the USA and China.

The empirical results indicate the suitability of BCI over CCI as a measure of investor sentiment, both in the USA and China. The results indicate that investor sentiment has a significant positive effect on the excess returns in the stock market in both countries. Moreover, the effect of investor sentiment is higher in China than it is in the USA. Such an effect of investor sentiment is significant and fluctuates asymmetrically in the short run but loses its significance in the long run. Optimistic investor sentiment has a larger effect on the stock market volatility in the USA, while the pessimistic investor sentiment has a larger effect in the Chinese stock market.

This paper focuses on finding a more suitable proxy for investor sentiment from BCI or CCI. This paper also contributes by including both optimism and pessimism in explaining the stock return and volatility in both markets. The overall findings are important for understanding investor behavior in different market conditions.

This chapter examines the existence of dynamic herding behavior by Tunisian investors in the Tunisia stock market during the revolution period of 2011–2013. The sample covers all Tunindex daily returns as a proxy for the Tunisia stock exchange index over the period 2007–2018. The author modifies the cross-sectional absolute deviation model to include all market conditions (bull and bear markets) and the geopolitical crisis effect corresponding to the Tunisian Jasmine revolution during 2011–2013, and show that herding is indeed not present in the Tunisia stock market including during its turmoil periods. These findings imply that the Tunisian emerging financial market became more vulnerable to adverse herding behavior after the revolution. There is also a clear implication for capitalist firms and angel investors in Tunisia that adverse herding behavior tends to exist on days of higher uncertainty and information asymmetry.

The aim of this paper is to analyze the impact of corporate governance (focused on some key mechanisms as board size, board independence, managerial ownership, institutional ownership, and chief executive officer duality) on financial analysts’ behavior in US. Results from panel data analysis for 294 US listed firms observed from 2007 to 2014 show that several attributes of the board of directors and audit committee have no effects on the number of analysts who are following the firm and the properties of analysts’ earnings forecasts. Findings also suggest that firms with independent and large boards and blockholders ownership benefit of more analyst following. In addition, it is proven that analysts’ earnings forecasts are optimistic and more accurate for companies where blockholder ownership, either by managers or external entities have larger quoted spreads but of lower quality for the ones which have greater independent board members and institutional investor’s holding.