The latest Four@Four event was held in the Margery Fry Meeting Room at Birmingham Business School on 28 June 2017.
From Research plan for the application for the join research fund offered by FAPESP, University of Sao Paulo, University of Birmingham and University of Nottingham : “Mandatory adoption of International Financial Reporting Standards, accounting quality and investment decision for Latin American firms”
This project aims to investigate whether Latin American firms can benefit from the mandatory adoption of International Financial Reporting Standards (IFRS), by evaluating the impact of IFRS adoption, institutional settings and firm-level incentives on the accounting quality. It further investigates the effect of IFRS adoption on financial analysts’ forecasts, the information environment, debt financing and institutional investment decisions, which are the key components for the development of capital markets in Latin American countries. Apart from relying on standard event time approach to analyse the archival data obtained from the commercial sources, we will also collect primary data about the institutional settings from a questionnaire survey and interviews with regulators, academics and practitioners. This is essential to reflect the latest situation of implementing IFRS in Latin American countries and to explore the key factors required to be considered at IFRS adoption. In turn, this project directly addresses the concern of the International Accounting Standard Board (IASB, 2005) and regulators for research to be conducted on IFRS adoption in developing countries by demonstrating the impact of IFRS and the firm-level incentives under weak institutional settings, the changes in information environment and the shift in debt financing and institutional investment decisions. Besides to learn the experience of IFRS adoption in Latin American countries, this project further recommends the regulators about the improvements in institutional settings in order to further realise the benefits from the IFRS adoption.
“Characteristics, determinants and predictability of multi-asset return comovements”
The paper investigates the joint dependence structure of return comovements of stocks, bonds, gold, oil and real estate during economic expansion and contraction regimes. Our findings show that the return comovements exhibit significant regime-switching behaviour. We find that non macroeconomic variables, in particular inflation uncertainty and liquidity play an important role in explaining the joint dependence structure of return comovements. A dynamic strategy incorporating regime switching framework outperforms the multivariate conditional covariance strategy in forecasting multi-asset return comovements. Finally, we show that investors with different risk-appetites are able to enhance their portfolio optimisation choices by utilising the analytical approach proposed in the study.
Keywords: Return comovements, determinants, forecasting, economic regimes, copula, asset allocation.
From “Risk-based supervision”
Textbooks and published research tends to analyse the structure and conduct of financial services regulation from the viewpoint of the small number of countries with large financial services sectors and multiple regulators.
This analysis often has a poor read across to the large number of countries with small financial services sectors and single regulators where head count in separate sectoral directorates can be in single digits.
This case study-based research reports on a change in approach by the regulator in each of two European countries with small financial services sectors.
The case study concerns a shift from a relatively resource intensive compliance approach focused on past behaviour and developments, to a quantitative and qualitative forward-looking approach based on early indicators of evolving risk.
The outcome has been the identification of new and different perceptions of locations of risk.
From “Prediction of Consumer Spending from Social Media Data"
Consumer spending is an important indicator of the health of an economy. In this talk we present a new method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis or data on search engine queries, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the regression models, in comparison to models that used only autoregressive predictors.