Performance of Financial Holding Companies in Taiwan: An Application of Network Data Envelopment Analysis

By Lee, Yueh-Chiang; Yang, Yao-Hung | Global Journal of Business Research, May 1, 2014 | Go to article overview

Performance of Financial Holding Companies in Taiwan: An Application of Network Data Envelopment Analysis


Lee, Yueh-Chiang, Yang, Yao-Hung, Global Journal of Business Research


ABSTRACT

In this paper, we adopt the network data envelopment analysis model in lieu of the multi-stage data envelopment analysis model to evaluate the operational efficiency of financial holding companies and their subsidiaries; the advantage of the network data envelopment analysis model is that it fully captures the synergies of cross selling undertaken by subsidiaries. In this study, conducted in 2012, we find synergistic effects associated with the merger of four financial holding companies, Hua Nan, Cathay, Shin Kong and First, with operational efficiency significantly better than that of other financial holding companies. We also find that banking and securities companies of financial holding companies have superior operational efficiency to investment trust companies and insurance companies. This paper suggests that investment trust companies, insurance companies and securities companies within financial holding companies should decrease their use of relevant inputs to improve efficiency.

JEL: G2

KEYWORDS: Network Data Envelopment Analysis, Operational Efficiency, Financial Holding Companies, Cross Selling, Synergy

(ProQuest: ... denotes formulae omitted.)

INTRODUCTION

The "Financial Institutions Merger Act" was enacted in Taiwan on December 13, 2000. Subsequently, on June 28, 2001, referring to the American "Glass-Steagall Act (GLS)", the "Financial Holding Company Act" was passed, allowing for the establishment of Financial Holding Companies (FHCs). This act approved cross-sector business within the financial industry, with the goal of improved business integration and increased customer satisfaction through one-stop shopping that achieves economies of scope and synergy. In addition, through a distribution system based on network connections, low cost and multiple products can be enjoyed, facilitating the gradual development of large financial institutions in Taiwan (as "the big ones get bigger") and increasing the global competitiveness of the Taiwanese financial industry. FHCs have enabled increased integration of such industries as banking, insurance, and securities firms, in addition to other financial industries, through mergers and acquisitions (M&A), expanding the scale of these businesses and improving their competitiveness. Moreover, when there is external competition, FHC subsidiaries provide an advantage by satisfying customers' diverse demands through cross selling, thereby boosting the firm's overall effectiveness. In recent years, Taiwan's FHCs, led by banking, securities, and life insurance companies, have engaged in both vertical and horizontal diversification in Taiwan's financial market (Zhao & Luo, 2002). From 2001 to 2013, 16 FHCs have been established: Hua Nan, Fubon, Cathay, China Development, SinoPac, China Trust, First, E. Sun, Fuh Hwa, Mega, Taishin, Shin Kong, JihSun, Waterland, Taiwan Financial and Taiwan Cooperative.

Mergers of FHCs, however, are not easy. Integration following a merger first requires integration of organizational culture and new value creation. Previous studies investigating the operational efficiency of the financial industry have widely employed data envelopment analysis (DEA). In particular, many scholars have applied two-stage DEA to Taiwan's FHCs in studying the effects of industry diversification on profitability and efficiency (Lo & Lu, 2006; Sheu, Lo & Lin (2006). Chao, Yu and Chen (2010), however, argue that two-stage DEA is not appropriate and instead adopt a multi-activity data envelopment analysis (MDEA) to measure the performance of FHCs. In the above-cited literature, the conventional CCR or BCC model of DEA is utilized to obtain efficiency values for different firms. As such values turn out to be 1 for many decision making units (DMUs), such analyses provide no basis for further differentiation. Yen, Yang, Lin, and Lee (2012) use the super SBM efficiency model to resolve this problem and, employing a twostage DEA, provide management information during production. …

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