Stock Returns Following Profit Warnings: A Test of Models of Behavioural Finance.

Models in behavioural finance have been developed to explain apparent anomalies in stock returns. A property common to a number of these models is that agents under react in the short run to public signals about future earnings. This contrasts sharply with the popular informal belief that stock prices overreact to news. A behavioural model also predicts returns reversals over longer horizons. We examine stock returns following profit warnings to test which, if any, of these hypotheses stands up to scrutiny on a new data set which was generated by a process which corresponds closely to that assumed in the behavioural models.

Panel Data Unit Roots Tests: The Role of Serial Correlation and the Time Dimension

We investigate the influence of residual serial correlation and of the time dimension on statistical inference for a unit root in dynamic longitudinal data, known as panel data in econometrics. To this end, we introduce two test statistics based on method of moments estimators. The first is based on the generalised method of moments estimators, while the second is based on the instrumental variables estimator. Analytical results for the IV based test in a simplified setting show that (i) large time dimension panel unit root tests will suffer from serious size distortions in finite samples, even for samples that would normally be considered large in practice, and (ii) negative serial correlation in the error terms of the panel reduces the power of the unit root tests, possibly up to a point where the test becomes biased. However, near the unit root the test is shown to have power against a wide range of alternatives. These findings are confirmed in a more general set-up through a series of Monte Carlo experiments.Dynamic longitudinal (panel) data, Generalized method of moments, Instrumental variables, Unit roots, Moving average errors

Extreme downside risk and market turbulence

This is the author accepted manuscript. The final version is available from Taylor & Francis (Routledge) via the DOI in this record.We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali, Demirtas, and Levy (2009) with a Markov switching mechanism. We show that the risk-return relationship identified by Bali, Demirtas, and Levy (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the riskreturn relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship in all states of market volatility

Long memory conditional volatility and asset allocation

Pre-print version dated May 2011 issued as Discussion paper by University of Exeter. A definitive version was subsequently published in International Journal of Forecasting Volume 29, Issue 2, April–June 2013, Pages 258–273. Available online at http://www.sciencedirect.com/In this paper, we evaluate the economic benefits that arise from allowing for long memory when forecasting the covariance matrix of returns over both short and long horizons, using the asset allocation framework of Engle and Colacito (2006) In particular, we compare the statistical and economic performances of four multivariate long memory volatility models (the long memory EWMA, long memory EWMA–DCC, FIGARCH-DCC and component GARCH-DCC models) with those of two short memory models (the short memory EWMA and GARCH-DCC models). We report two main findings. First, for longer horizon forecasts, long memory models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory models. Second, the two parsimonious long memory EWMA models outperform the other models–both short and long memory–across most forecast horizons. These results apply to both low and high dimensional covariance matrices and both low and high correlation assets, and are robust to the choice of the estimation window

A momentum trading strategy based on the low frequency component of the exchange rate

Authors' draft published as working paper; version August 2008. Final version published in Journal of Banking & Finance. Available online at http://www.sciencedirect.com/In this paper, we develop a momentum trading strategy based on the low frequency trend component of the spot exchange rate. Using kernel regression and the high-pass filter of Hodrick and Prescott [Hodrick, R., Prescott, E., 1997. Post-war US business cycles: An empirical investigation. Journal of Money, Credit and Banking 29, 1-16], we recover the non-linear trend in the monthly exchange rate and use short-term momentum in this to generate buy and sell signals. The low frequency momentum trading strategy offers greater directional accuracy, higher returns and Sharpe ratios, lower maximum drawdown and less frequent trading than traditional moving average rules. Moreover, unlike traditional moving average rules, the performance of the low frequency momentum trading strategy is relatively robust across different time periods. The low frequency momentum trading strategy is also robust to the choice of smoothing parameter (in the case of the HP filter) and the distribution and bandwidth parameter (in the case of kernel regression) over a wide range of values. (C) 2009 Elsevier B.V. All rights reserved

Can behavioral biases explain the rejections of the expectation hypothesis of the term structure of interest rates?

We test whether the rejections of the expectations hypothesis can be explained by two behavioral biases: the law of small numbers and conservatism. We use the term structure to decompose excess bond returns into components related to expectation errors and expectation revisions, enabling a direct test of behavioral models using the expectations of market participants. We find systematic patterns in expectation errors, and expectation revisions, which are consistent with these two biases. We show that a trading strategy that exploits these biases delivers significant economic profits and that our results are unlikely to be driven by a time-varying risk premium

Rules versus Discretion in Committee Decision Making: An Application to the 2001 RAE for UK Economics Departments

The question of rules versus discretion has generated a great deal of debate in many areas of the social sciences. Recently, much of the discussion among academics and stakeholders about the assessment of research in UK higher education institutions has focused on the means that should be used to determine research quality. We present a model of committee decision-making when both rules and discretion are available. Some of the predictions of the model are tested empirically using the UK RAE 2001 results

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries