Impact of Fintech on Bank Risk-Taking: Evidence from China
Yüklə 1,3 Mb. Pdf görüntüsü
|
- Bu səhifədəki naviqasiya:
- Definition 1.
- Definition 2.
- Proposition 1.
- Proposition 2.
- Hypothesis 1.
- Hypothesis 2.
- Hypothesis 3.
| ψ
b and ψ s ( ψ s > ψ b ) (per active firm) to be active and the relationship cost (the cost spend to maintain customers) φ b and φ s ( φ s > φ b ) (per client) to work with a household. Asset managers charge a fee f ( W ) for their services. f i ( w ) =
φ i + uw , ( i = b , s ) (1) where
φ i is fixed cost and u is a markup. We suppose u < R i − r . In traditional asset management equilibrium (at this equilibrium, a household gets the same welfare whether it hires a traditional asset manager or not), we focus on a household’s decision whether to hire an asset manager or not. We find that when R i w − f i ( w ) > rw , they choose to hire an asset manager. Otherwise, they will invest by themselves. That is, when w >
i R i − r − u , (2) they prefer to an asset manager in different intermediaries. Let
w i ,0 = φ i R i − r − u , i = b , s . (3) Then, the net profit of any intermediary is π i ( M ) =
u M Z ∞ w i ,0 wdG
( w ) . (4)
For free and positive entry with equality, π ( M ) satisfies π i ( M ) ≥ ψ i , (5) Therefore, the conditions in the equilibrium with positive entry are as follows. M i
u ψ i Z ∞ w i ,0 wdG
( w ) , i = b , s , (6)
where w i ,0 satisfies formula (3). Welfare can be expressed as W i = Z w i ,0 0 rwdG ( w ) + Z ∞ w i ,0 ( R i − u ) w −
i dG
w ) , i = b , s . (7) Risks 2021 , 9 , 99 6 of 27
Now, we discuss that Robo-Advisors have access to the investment technology with return
R ( R < R s < R b ). Moreover, they have a higher fixed entry cost ψ >
s >
b but a
lower cost per client φ < φ b
φ s . To be similar to (2), when w =
R −
− u , (8) the households choose Robo-Advisors. Otherwise, they prefer to autarky. In other words, the first participation cutoff is w 1 = φ R − r − u . (9)
The second cutoff is between the Robo-Advisor and traditional manager in a big intermediaries as follows: ( R
u ) w b ,2 − φ = (
R b − u ) w b ,2 − φ b (10) That is, w b ,2 =
b −
R b − R . (11) Similarly, the second cutoff is between the Robo-Advisors and traditional manager in a middle or small intermediaries w s
= φ s − φ R s − R . (12) It is not difficult to find that the condition for profitable entry by the Robo-Advisors is w 1
w 2 . Therefore, the number of Robo-Advisors is M 0 = u
Z w
,2 w 1 wdG ( w ) , i = b , s , (13) and the number of traditional managers is M i ,1 = u ψ i Z ∞ w i ,2 wdG
( w ) , i = b , s . (14)
Moreover, welfare is W 0 i = R w 1 0 rwdG ( w ) + R w i ,2 w 1 (( R − u ) w −
) dG
w ) +
R ∞ w i ,2 (( R i − u ) w − φ i ) dG ( w ) , i = b , s . (15) If Robo-Advisors entry is profitable, i.e., W 1 < W i ,2 , it means some poor households can get asset management services. That is, more people can get asset management services in the Fintech equilibrium (it means household has the same welfare when facing investing by himself and choosing the robot advisors) than in the traditional equilibrium (which means household who is on this situation has the same welfare no matter he chose to invest by himself or chose the traditional invest advisors). When
w 1
w i
i.e., (
i −
)( r + u ) +
φ R i < φ i R , Robo-Advisors improves access to asset management services. Robo-Advisors compete with the traditional advisors. In other words, Fintech brings the traditional intermediaries competitive shocks. How big is the competitive shocks? Will the size of the competitive shock vary with the size of the intermediary? For discuss these problems, we introduce two indexes to measure the competitive shocks as follows. Definition 1. Num is defined Num =
− Advisors
the number o f traditional managers (16)
Risks 2021 , 9 , 99 7 of 27
The bigger the index is, Num, the bigger the competitive shock is, since the bigger Num means Robo-Advisors are more relative to the traditional advisors. Definition 2. Pro is defined pro =
− Adivisor
the total net pro f it o f traditional intermediary (17)
The bigger the index is, Pro, the bigger the competitive shock is, since the bigger Pro means the intermediary Robo-Advisors get more net profit relative to the traditional advisors. By the above indices, we compare the competitive shock for the big intermediary with one for the middle or small intermediary. Then, we can the key results as follows. Proposition 1. pro
b = R w b ,2 w 1 uwdG ( w ) R ∞ w b ,2 uwdG ( w ) < pro
s = R w s ,2 w 1 uwdG ( w ) R ∞ w s ,2 uwdG ( w ) . (18)
That is, the competitive shock for the big intermediary is less than one for the middle or small intermediary. Proof. With (18) and (12), R b
R s and φ b
φ s , one attains that w b ,2 = φ b − φ R b − R
w s
= φ s − φ R s − R . (19) Therefore, pro b
R w b ,2 w 1 uwdG ( w ) R ∞ w b ,2 uwdG ( w ) < R w s ,2 w 1 uwdG
( w ) R ∞ w b ,2 uwdG ( w ) < R w s ,2 w 1 uwdG
( w ) R ∞ w s ,2 uwdG ( w ) = pro
s . (20) Proposition 2. Num
b = u ψ R w b ,2 w 1 uwdG
( w ) u ψ i R ∞ w b ,2 uwdG
( w ) < Num
s = u ψ R w s ,2 w 1 uwdG
( w ) u ψ i R ∞ w s ,2 uwdG
( w ) . (21)
The results show that the competitive shock of small and medium-sized intermediaries is bigger than that of large-scale intermediaries. Noticing (2.19), it is easy to find that Num
b =
b
R wb ,2 w 1 uwdG ( w ) R ∞ wb ,2 uwdG ( w ) < ψ b
R ws
w 1 uwdG ( w ) R ∞ wb ,2 uwdG
( w ) < ψ b
R ws
w 1 uwdG ( w ) R ∞ ws ,2 uwdG
( w ) < ψ s
R ws
w 1 uwdG ( w ) R ∞ ws ,2 uwdG
( w ) = u
R ws
w 1 uwdG ( w ) u ψ i R ∞ ws ,2 uwdG ( w ) = Num s . (22) The result also proof that the competitive shock for big intermediary is less than one for the middle or small intermediary from the view of quantity shock. In summary, since banks account for a large share of the traditional investment advisory market, when Robo-Advisors enter the traditional investment advisory market as a product of Fintech development level, they will have a crowding out effect on the market share of traditional intermediary asset businesses. In the beginning, when Robo-Advisors have not entered the traditional investment advisory market, we can see from formula (4) that families only face two investment choices. Then when the robot investment adviser joins, its lower customer relationship cost (
b
φ s ) enables more families to benefit from the robot investment adviser service. Moreover, the Formula (15) shows that Fintech Risks 2021 , 9 , 99 8 of 27
has a crowding-out effect on traditional bank investment advisors, seizing part of the market profits, and having a competitive impact on traditional intermediaries in terms of market share. From “Definition 1”, we further established an index to measure the intensity of competition. Comparative analysis shows that, compared with larger-scale intermediaries, smaller-scale intermediaries are more subject to the competitive impact of financial technology, which limits the risk-taking behavior of banks. Please see Appendix B for the symbols and their meanings. 3.3. Research Hypotheses Based on previous research literature and the above theoretical analysis, this article proposes several research hypotheses to be verified:
The development of Fintech affects the risk behavior of banking groups, and has a greater impact on small and medium banks. Large banks have sufficient financial resources, strong technical strength and large customer groups, and they are more inclined to build their own teams, conduct independent research and development. Moreover, they are more likely to cooperate with the companies in the field of digital technology (like GAFA and BATX). Therefore, large banks are more actively developing Fintech from debt business, intermediary business, and asset business and realizing digital transformation. Large Banks are also likely to be subject to stricter supervision and their risk behaviors are more cautious ( Beltratti and Stulz 2009 ). In
addition, in China, the customer groups of large banks are mostly relatively high-quality large enterprises or state-owned enterprises, and they are more likely to receive implicit government guarantees, while small and medium-sized banks are mostly targeted at SMEs. As a result, Fintech has a bigger impact on smaller Banks than big ones. Hypothesis 2. Fintech affects the bank’s risk-taking by affecting the internal interest income and management costs of the bank. The rapid development of Fintech has two aspects on banks’ risk-taking behavior. On the one hand, Fintech gradually penetrates into the business areas of traditional banks. The increase of Banks’ capital cost, the weakening of their loan pricing ability and the acceleration of the frequency of interest rate fluctuations lead to the narrowing of the most traditional and major income source of banks’ deposit and loan spreads. All of these affect the banks’ operational stability. Compared with large banks and online banks, small and medium-sized banks have higher capital costs and a faster decline in net interest margin, which in turn increases the bank’s risk-taking. On the other hand, the spread of new technologies to banks will help improve efficiency and optimize governance, thereby reducing management costs. For example, big data technology can effectively process the bulk transaction data of traditional banks. Enable the bank to achieve precise marketing, low cost and centralized management. Moreover, it also can more effectively optimize the credit process, identify credit risks, standardize the behavior of senior executives, and reduce the risk-taking level of banks. The development of financial technology has promoted the transformation and upgrading of traditional banks to digitization, which has an impact on bank management capability and the operation stability of commercial banks. Considering the effects of both aspects, the impact of new technologies on bank risk-taking is uncertain and depends on the relative strength of the two effects ( Gu and Yang 2018 ).
Fintech influences Banks’ risk-taking by influencing market competition and residents’ willingness to save. On the one hand, Fintech promotes the marketization of interest rates in China and intensifies competition in the banking market. Moreover, the market space of banks will be greatly suppressed. China’s banking industry has experienced severe financial repression Risks 2021 , 9 , 99 9 of 27
for a long time ( Wang and Johansson 2013 ). It may not only reduce the bank franchise values through competition, thereby incentivizing them to take more risks ( Allen and Gale 2000 ), but also may break the monopoly of large banks and new technologies through competition, and improve bank efficiency through technology spillover effects ( Shen and Pin 2015 ). Meanwhile, Fintech has promoted the phenomenon of ‘financial disintermediation’, which means that with the development of direct financing, the supply of funds bypasses the intermediary system of commercial banks through some new institutions or new means, and is directly transported to the demand unit. Take Yu’eBao, launched by Alibaba, as an example. Its interest rate is significantly higher than the interest rate of commercial bank demand deposits, and even higher than the interest rate of commercial bank fixed deposits. At the same time, Yu’eBao also supports T + 0 deposit and withdrawal. The appearance of such internet financial products is obviously more attractive to customers than the traditional deposit products of commercial banks, and therefore may have an impact on residents’ willingness to save. To meet the challenge, banks will also attract savings back by raising deposit rates, but this will increase the cost of bank funds. As mentioned above, the impact of new technologies on banks’ risk-taking behavior depends on the relative strength of the two forces. Yüklə 1,3 Mb. Dostları ilə paylaş:
|