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This dissertation, Modelling and Forecasting the General Financial Performance of Listed Construction Firms in Hong Kong by Yick-tat, Tsang, 曾億達, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: It is well recognised that construction firms encounter risk and are sensitive to trends and volatility in the business environment. Measuring the financial performance of a firm serves as the basis of monitoring and evaluating its management competence, resource allocation and corporate strategy in response to environmental change. Forecasting is paramount in responding to potential problems and perpetuating positive developments that result in sustainable competitiveness. Thus, an enriched understanding and prediction of the financial performance of construction firms are desirable for decision makers and other industry stakeholders. Notwithstanding that, little research attention has been paid to this premise conceptually and empirically. Thus, the overall aim of this study was to model and forecast the general financial performance of Hong Kong construction firms under the dynamic influence of the business environment. This study involved the application of quantitative modelling using various statistical and econometric techniques. Multidimensional firm financial performance was first approximated using factor analysis based on the financial data of local publicly listed construction firms from 1992 to 2010. The factor model uncovers five common financial factors: liquidity, asset, leverage, profitability and activity. The time trends of these factors display diverse and cyclical patterns with irregular cycle periods. Autoregressive integrated moving average (ARIMA) models were then constructed based on the Box-Jenkins approach, which provided univariate forecasts of the financial factors. The results reaffirmed that ARIMA models were highly effective in forecasting. In conjunction with cross-correlation analysis, multiple linear regression (MLR) models were next used to explore the influence of environmental determinants on firm financial performance. The findings identified different sets of significant leading determinants for different financial factors. They further justified the dominance of sectoral factors in the determination of firm performance. Supported by empirical verification, a theoretical framework depicting the relationships between business environment and firm performance was proposed. In conjunction with cross-correlation analysis, multiple linear regression (MLR) models were next used to explore the influence of environmental determinants on firm financial performance. The findings identified different sets of significant leading determinants for different financial factors. They further justified the dominance of sectoral factors in the determination of firm performance. Supported by empirical verification, a theoretical framework depicting the relationships between business environment and firm performance was proposed. This study is among the first to apply advanced econometric techniques to develop reliable performance measurement and forecasting models. The results improve the theoretical framework by explaining the dynamic relationships between the financial performance and business environment of construction firms. The empirical findings of the quantitative analysis offer new implications for firms' financial performance and the significant leading determinants in a local context. The outcomes of this study make seminal contributions to current knowledge and practice. DOI: 10.5353/th_b52

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