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The paper aims to provide the individual routes of the authors into behavioural finance in order to introduce the special issue.

The paper provides the background to the authors' personal route into behavioural finance.

The paper highlights general themes of development and influence of behavioural finance and relationships with practice and other areas of academic finance.

The paper offers the perspectives of the authors on how they feel the research area of behavioural finance will develop in the future.

The purpose of this paper is to explore the effect of leverage mimicking factor portfolios in explaining stock return variations. This paper broadens the focus of the current asset pricing literature by forming portfolios mimicking the leverage factor.

Following Fama and French's and Carhart's procedure in forming size, book‐to‐market and momentum mimicking portfolios, the authors of this paper form leverage mimicking factor portfolios to explain stock returns. A five factor model is constructed that explains the variations in stock returns better relative to the other asset pricing models including the Fama‐French‐Carhart four factor model.

The findings indicate that the leverage mimicking portfolio helps to explain stock return variations better relative to the other asset pricing models including the Fama‐French‐Carhart four factor model. Results are robust to other risk factors.

The results lead us to explore further avenues in using other risk factors in asset pricing such as future work to consider other cross‐sectional attributes such as the stochastic behaviour of earnings or profitability that might also produce common variation in stock returns. There may be other risk factors that carry a premium and thus can be used for asset pricing.

The paper's findings are important in fund management when selecting or evaluating portfolio performance. The authors introduce an additional factor that has a sound theoretical appeal and show that leverage mimicking factor portfolios provide additional information in pricing assets, both in the cross section of all shares and in different sectors.

To the best of the authors' knowledge this is the first study of the effect of leverage mimicking factor portfolios in explaining stock return variations.

The purpose of this paper is to reflect on the recent banking and financial crisis in the UK. It discusses the triggers of the crisis from a UK perspective and then examines the immediate reactions in the form of short‐term policies and concludes with a discussion on longer‐term policies.

This is a conceptual paper that argues that some of the triggers of the crisis are real and some are behavioural.

The crisis has its roots in the sub‐prime crisis of the USA with spillover effects for the UK due to its well‐developed and international financial sector. The systemic environment of high leverage in the financial, corporate and household sectors, the international nature of finance, and the opacity in banks' balance sheets are real triggers. In contrast, the underestimation of risks by almost all agents in the economy is behavioural.

The paper argues that some of the conditions that led to the crisis will not change and should now be incorporated in new banking regulations. This is particularly true in the case of behavioural factors. Optimism, greed, herding and underestimation of low‐probability high‐impact events, are all parts of human nature. Human nature will not change. Thus, it need better regulations.

Less privileged groups, such as the poor, the uneducated, and the elderly, need better regulation to make them less vulnerable not only to others' biases but also to their own biases.

The paper is original in discussing behavioural side of the crisis along with the real side of it.

The paper draws on the key themes raised at a Round Table discussion on behavioural finance attended by academics and practitioners. The paper provides a background to the key aims of behavioural finance research and the development of the discipline over time. The purpose of this paper is to indicate some future research issues on behavioural finance that emanate from the financial crisis and highlight areas of mutual benefit to both behavioural finance academics and the finance industry so as to encourage a creative cross‐fertilisation.

The paper draws on a Round Table discussion on behavioural finance that was organized by the Behavioural Finance Working Group, the Centre for the Study of Financial Innovation and Financial Services Knowledge Transfer Network.

The paper highlights numerous benefits that behavioural finance research can contribute to the financial industry, but at the same time there is an evident discrepancy between the academic and the professional world when it comes to utilising behavioural finance research.

The paper highlights several areas where behavioural finance can contribute significant benefits to a wide array of aspects of the finance industry.

The paper seeks to inform behavioural finance issues so as to encourage collaboration between the academic world and finance practitioners. In so doing, the paper aims to encourage a greater awareness of individual decision‐making frames and heuristics and how industry can apply these concepts to improve the allocation of finance products to society.

The paper brings together a wide array of finance professionals and academics to encourage greater collaboration and mutual respect of each others interest in and uses for behavioural finance.

The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings forecasts.

Two alternative measures of herding behaviour, on analysts' earnings forecasts are proposed. The first measure identifies herding as the tendency of analysts to forecast near the consensus. The second measure identifies herding as the tendency of analysts to follow the most accurate forecaster. This paper employs the method of The Generalised Method of Moments in order to relax any possible biases.

In both measures employed, a positive and significant relation is found between the accuracy of analysts' earnings forecasts and herding behaviour. According to the first measure analysts exhibit herding behaviour by forecasting close to the consensus estimates. According the second herding measure, it is found that analysts tend to herd towards the best forecaster at the time. Finally, it is concluded that the accuracy of analysts' forecasts increases as herding increases.

The present study triggers concerns for further research in the modelling of analysts' forecasting behaviour.

This paper proposes that a measure based on human biases is the best way to estimate and predict the analysts' earnings forecast future accuracy.