Grant: Discovery Grants Program - Individual
York University
$16,000.00
Efficiency analysis and prediction in the financial services industry
EF 2008-09
Résumé
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.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
261426-2006
Name nom
Yang, Zijiang
Department département
Information Technology, School of
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2008
Competition year année de concours
2006
Award amount
16000
Program id
RGPIN
Program nane en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
20
Committee name en
Industrial Engineering
Committee name fr
Génie industriel
Area of application code
900
Area of application group en
Commercial services
Area of application group fr
Services commerciaux
Area of application en
Commercial services
Area of application fr
Services commerciaux
Research subject code
1601
Research subject group en
Industrial engineering
Research subject group fr
Génie industriel
Research subject en
Operations research and management science
Research subject fr
Recherche opérationnelle et science de la gestion
Partie
3
Nb partie
5
Application title
Efficiency analysis and prediction in the financial services industry
Keyword
NA
Application summary
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.
