Dr Shan Hua joined Surrey Business School as a lecturer in 2018. Prior to that, she worked as Post doc in University of Exeter, and as Lecturer in University of Reading.
Dr Hua holds a PhD in finance from the University of Exeter Business School and a MSc in Financial Mathematics. She has extensive teaching and supervising experience at undergraduate and postgraduate level. In addition, she holds Fellowship in HEA and also has oversea teaching experience.
Her current research covers empirical asset pricing, corporate governance, performance forecasting, the value premium, mergers and acquisitions and valuation effects of corporate social responsibility.
University roles and responsibilities
- Program director MSc Accounting and Finance
Evidence from share price returns suggests that acquisitions destroy value. On the other hand, evidence from accounting measures of performance suggests that acquisitions give rise to synergies and therefore potentially create value. In this paper, we first revisit the UK evidence using an updated sample, and confirm that these findings still hold, and importantly hold in the period following the introduction of FRS10. We then reconcile the (apparently conflicting) findings from these market-based and accounting-based approaches. Using accounting measures of performance, we confirm the presence of synergies developed during acquisitions. Finally we show that post-acquisition abnormal returns are associated with news of synergistic benefits conveyed in the financial statements.
Despite its limitations, the CAPM is a popular asset pricing model. However, the estimation of beta in the CAPM is affected by the choice of the returns frequency and firm characteristics. This study undertakes a detailed examination of the evidence for the UK and we find that the differences in beta computed from returns of various frequencies are related to size, liquidity, book-to-market and to some degree, opacity factors. One area where our conclusions might have important implications is in the regulatory use of the CAPM. Our results imply that low frequency beta estimates should, in most cases, be preferred to high frequency beta estimates.
We develop a Bayesian network (LASSO‐BN) model for firm bankruptcy prediction. We select financial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961–2018, show that the LASSO‐BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers.