Shan Hua

Dr Shan Hua


Lecturer in Accounting
PhD

Academic and research departments

Department of Finance and Accounting.

Biography

University roles and responsibilities

  • Program director MSc Accounting and Finance

    My qualifications

    Fellowship in HEA

    My publications

    Publications

    Christina Dargenidou, Alan Gregory, Shan Hua (2016)How far does financial reporting allow us to judge whether M&A activity is successful?, In: Accounting and Business Research46(5)pp. 467-499 Taylor & Francis

    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.

    Alan Gregory, Shan Hua, Rajesh Tharyan (2018)In search of beta, In: The British Accounting Review50(4)pp. 425-441 Elsevier

    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.

    Yi Cao, Xiaoquan Liu, Jia Zhai, Shan Hua (2020)A two‐stage Bayesian network model for corporate bankruptcy prediction, In: International Journal of Finance and Economics

    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.