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This paper aims to investigate informational efficiency of stock, options and credit default swap (CDS) markets. Previous research suggests that informed traders prefer equity option and CDS markets over stock markets to exploit their informational advantage. As a result, equity and credit derivative markets contribute more to price discovery compared to stock markets.

In this study, the authors investigate the dynamics behind informed investors’ trading decisions in European stock, options and CDS markets. This allows to identify the predictive explanatory power of the unique information contained in each market with respect to future stock, CDS and option market movements.

A lead-lag relation is found between the CDS market and the other markets, in which changes in CDS spreads are able to consistently forecast changes in stock prices and equity options’ implied volatilities, indicating how the fast-growing CDS market seems to play a special role in the price discovery process. Moreover, in contrast to results of US studies, the stock market is found to forecast changes in the other two markets, suggesting that investors also prefer stock market involvement to exploit their information advantages before moving to CDS and option markets. Interestingly, these patterns have only emerged during the recent financial crisis, while before the crisis, the option market was found to be of major importance in the price discovery process.

The authors are the first to study the lead-lag relationship among European stock, option and CDS markets for a large sample period covering the financial crisis.

Employee Stock Options are the most widely used incentive compensation tool, and prior research has shown their advantages. However, research among different peer groups, different time frames, different research methodologies, and the constantly changing public opinion prevents unanimous agreements on the various benefits of Employee Stock Options. In this paper we apply a number of research hypotheses tested in recent US studies to a European sample of EuroStoxx 50 companies. Due to the globalisation, the similar accounting regulations and the IT and telecommunications revolu tions, Europe and the United States have grown closer together than ever before and are expected to display similar business practices. This assessment should be especially relevant for the large European companies, which mostly have a dual listing in the United States and are therefore essentially forced to manage according to American practices. How ever, the results differ significantly from the existing US research, providing insufficient grounds to accept previous findings for European companies.

The purpose of this paper is to investigate the impact of individual investor sentiment on the return process and conditional volatility of three main US market indices (Dow Jones Industrial Average, S&P500 and Nasdaq100). Individual investor sentiment is measured by aggregate money flows in and out of domestically oriented US mutual funds.

A generalised autoregressive conditional heteroscedasticity (GARCH)‐in‐mean specification is used, where our measure for individual sentiment enters the mean and conditional volatility equation.

For a sample period of six years (February 1998 until December 2004), we find that sentiment has a significant and asymmetric impact on volatility, increasing it more when sentiment is bearish. Using terminology of De Long et al., we find evidence for the “hold more” effect, which states that when noise traders hold more of the asset, they also see their returns increase, and the “create space” effect, which states that noise traders are rewarded for the additional risk they generate themselves.

In contrast to existing studies using explicit measures of market sentiment on low sampling frequencies, the use of daily mutual flow data offers a unique picture on investors' portfolio rebalancing and trading behavior. We propose an integrated framework that jointly tests for the effects of mutual fund flows on stock return and conditional volatility.