Efficiency analysis and prediction in the financial services industry

点击翻译按钮获取中文摘要

The financial services industry plays a vital role in the economic development and growth of a country. Its economic significance as well as the highly competitive market structure motivates research evaluating the industry's performance and monitoring its financial condition. Data Envelopment Analysis (DEA) is a very appropriate approach when considering the complex nature of the companies in the financial services industry since it directly incorporates multiple inputs and outputs, which means that the results will be explicitly sensitive to the complexity and mix of inputs and outputs. In addition, DEA can identify best practices and inefficient units by comparing their actual operating results without the need for any pre-defined production function.  Thus, DEA has won wide acceptance in the financial services industry. However, there are some limitations that have to be considered. Firstly, DEA can not deal with missing data. Secondly, the DEA frontier is very sensitive to the presence of the outliers and statistical noise and the measured efficiency scores can be contaminated by the observations on the frontier due to data errors or statistical noise. This motivates the further investigation of optimizing the frontier. Thirdly, DEA can hardly be used to predict the performance of other Decision Making Units (DMUs). This research will propose to integrate two methods, DEA and Neural Networks (NNs) to solve the above three issues. Furthermore, the proposed research will integrate fuzzy logic to the DEA formulation to address the cross-system comparison with multiple quantitative and/or qualitative environmental variables. As a result, the proposed research provides an innovative and unique approach to address key issues in DEA literature. It will open up new possibilities for performance analysis in the financial services industry. It is also the target of this research to gain further insight of the financial services industry as a whole in terms of its sustainable growth rate and potential.