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China, the world’s second largest economy, has become the latest battlefield “fordotcom” companies with online financial exchange businesses booming. While, the traditional CAPM can’t price the online financial capital asset successfully, there is no theory of pricing online capital asset. In this paper we build an exchange online financial capital asset pricing model, which improves the performance of the CAPM in application. In our empirical results, the model ECAPM is more successful in pricing online financial capital asset rather than CAPM. Overall, our findings show some arguments of failure of online financial market in US, but of boom in China.

China, the world’s second largest economy, has become the latest battlefield “fordotcom” companies with online financial exchange businesses booming. In this paper we call the online financial businesses as exchange online financing, as it describes more accurately to its nature both in financial exchange performances and in online finance instead of internet finance or financing online, etc. The rapid growth of Chinese exchange online financing has been on a roll ever since Alipay, an affiliate of Alibaba Group, rolled out its Yu’E Bao, the country’s first ever wealth management product, on June 14, 2013. The potential of this market has not gone unnoticed by other Internet superstars like Tencent and Baidu. Both are now pushing similar online financial products.

The online financial exchange system is taking away the market share of traditional banks, and challenging how traditional banks carry out their businesses in China. The exchange online financial business also takes new booming in other countries, like Peer to Pear lending and crowdfunding in US. How to analyze the benefit rate of exchange online financing industry is key and essential for financial supervisors, investors and researchers at present.

There is a large literature argues the return of asset (Beta, β) or asset pricing model which Sharpe, William F. Sharpe (1964) introduces as the theory of market equilibrium under conditions of risk [

While the previous literatures have also analyzed the exchange online financial business, Christiansen H. (2001) defines an electronic financial transaction is a financial transaction that depends on the Internet or a similar network to which households or non-financial enterprises have access [

In this paper we develop a methodology to value expected returns of the market or beta in the condition of online financial exchange industry including online money market funds, which is developed by traditional CAPM. Here we call it as Exchange online Financial Capital Asset Pricing Model (ECAPM). The expected return of the market (Beta) is determined by exchange online financial betas, measures of risk with respect to the mean-variance-efficient portfolio in this model. This portfolio differs from the Markowitz market portfolio as it largely depends on the preferences of the saving rate and interbank offered rate, and sometimes deposit rate also takes function. The model premise is market interaction between information traders and exchange online financial traders.

The problem with the traditional approach is that CAPM has its empirical flaw^{ }and the existence of more modern approaches to asset pricing and portfolio selection. Another ICAPM is that it assumes that consumer expectations are homogenous, meaning that it cannot take into account individual risk preferences. Also it likes others such as, BAPM, BCAPM, CCAPM and HCAPM have discussed the pricing performances without considering online financial exchange business including money market funds.

We propose one method to derive and certify the beta by comparing different development of online financial exchange industry between China and USA. Some relative literatures are: Jiaqin Yang, Li Cheng and Xia Luo (2009) describes a comparative study about the issues in the current e-banking services among the young consumers between China and USA. They explore that different cultures and traditions will play a role in the development of e-banking industry among different nations [

To give an example of our findings using this approach: exchange online investment service, which is offered by Alipay, China’s leading online payment provider, and “Tian Hong” Asset Management, have attracted investment deposits of more than $65.96 billion by Feb 14, 2013. How to analyze the nature of benefit on investment deposits online is very helpful for us to verify the availability of ECAPM. The operation process of “Yu’E Bao” is shown in

substantially higher returns, effectively helping regulators in their deregulation efforts, though raising concerns at the nation’s banks.

The results provide direct evidence for Yu’E Bao allows customers to invest any balance on their account in a money market fund. On the other hand, it still has some potential risks. For example, the first comes from declining yields. Yields on all exchange online financing products mentioned above are declining, narrowing their advantages over wealth management products offered by banks. Secondly, tighten regulations. China’s securities regulator would work with other agencies to issue a set of rules to govern the burgeoning Exchange online Financing industry. Despite being “generally supportive” of exchange online financing, the China Securities Regulatory Commission thought the nascent sector still needs proper regulation and guidance. The final is capital safety. As Exchange online financing relies on the Web, online personal information and safety will always be a concern. Adding to this worry is the fact that people’s online accounts can be hacked via malicious viruses and their money stolen.

We believe that the main contribution of our paper is methodological analysis on expected return of market (Beta) in the exchange online financial industry. The ECAPM is an innovative model developed by traditional CAPM, ECAPM is efficient and effective due to its simplicity and utility in a situation of online money market funds.

Our results suggest that we can use ECAPM model to derive or predict the expected return of the market in exchange online financial industry including online money market funds. Online money market funds put pressure on the central bank’s ceiling on bank deposit rates, Central Bank governor should refine regulations covering online funds but do not necessary to have intention of cracking down on these competitors to the nation’s banks.

While the precise implementation details differ, the basic empirical strategy to incorporate exchange online financial industry into the CAPM, or ECAPM, is straightforward. Consider the return to interbank offered market, R t F , and the return to exchange online money market fund market, R t E F . Also, let the time-invariant, aggregate share of exchange online financial wealth in total return be denoted as ω . The return on the exchange online financial market portfolio, where we note R t M at the time t, is simply a weighted average of the return to interbank offered and exchange online money market fund market, R t M = ω R t E F + ( 1 − ω ) R t F .

The proxy for the return to exchange online money market funds market is the return on a broad online money market funds portfolio. But, for exchange online financial industry, the return to interbank offered market is far more difficult to observe. As a result, an assumption must be made about how to measure R t F .

We assume that the expected return to interbank offered market is constant. Under this assumption, the realized return is a linear function of the current interbank offered rate, b t . Finally, if one takes a stand on the fraction of return to interbank offered market in total return, 1 − ω , the return to the exchange online financial market portfolio, R t M , may be constructed and asset betas may be calculated. Alternatively, one can simply use both R t E F and R t F in a two factor expected return beta model. Using this approach the return on a particular exchange online financial asset i, R i t , can be expressed as a linear function of the exchange online financial industry’s “exchange online money market fund market” beta and “interbank offered market” beta as follows:

R i t − R f t = ( R i t F − R f t ) + ( R i t E F − R f t ) = β i F ( R t F − R f t ) + β i E F ( R t E F − R f t ) + ε i t (1)

Now take account on the return to interbank offered market, which is the R i t F in the form (1). Assume that an individual only views the outcome of any investment of interbank offered market that considers the preference in terms of the wealth which changes in the market. Also assume that measuring the return to traditional financial wealth with the simple interbank offered rate, does not hinge on the methodology used to measure the return to traditional financial wealth. In assessing the desirability of a particular investment, however, he is willing to act on the basis of only one parameter, his wealth marginal W t i in interbank offered market i at the time t. This can be represented by a total utility function of the form:

U = f ( W t ) (2)

where W t indicates wealth to interbank offered market at the time t.

The Euler equation is

U ′ ( W t ) = E { U ′ ( W t + 1 ) ∗ 1 + R i t + 1 F 1 + ρ } (3)

where R i t + 1 F , is the rate of return to interbank offered market (interbank rate) for any exchange online financial industry’s asset i at the time t + 1 .

We will derive an asset pricing equation resembling the CAPM from starting from the Euler Equation subject to some assumptions. The first assumption is that U ( W t ) = ln ( W t ) which implies that U ′ ( W t ) = 1 / W t . From (3) we then get

1 W t = E { 1 W t + 1 ∗ 1 + R i t + 1 F 1 + ρ } ⇔ E { W t W t + 1 ∗ 1 + R i t + 1 F 1 + ρ } = 1 (4)

Equation (4) holds for any asset, so in particular it holds for the safe asset giving,

E { W t W t + 1 ∗ 1 + R f t 1 + ρ } = 1 (5)

Now subtract (5) from (4) and we get,

E R i t + 1 F − R f t = E { W t + 1 − W t W t ( R i t + 1 F − R f t ) } (6)

Since W t + 1 − W t W t is the usual measure of the growth rate in investor’s wealth,

we prefer the form (6). Now we assume that the exchange online financial asset such that the return R t + 1 F is equal to interbank offered rate. Equation (6) will then have form

E R i t F − R f t = E { R t F ( R i t F − R f t ) } (7)

which can be transformed to

E R i t F − R f t = 1 1 − E R t F C o v ( R t F , R i t F − R f t ) (8)

Since (8) has to hold for any asset it also has to hold for i * . We then get

E R t F − R f t = 1 1 − E R t F V a r ( R t F ) (9)

Divide Equation (8) by Equation (9) and we get

E R i t F − R f t E R t F − R f t = C o v ( R t F , R i F − R f t ) V a r ( R t F ) (10)

If we denote the coefficient from a regression of R i F on the interbank offered rate by β i F we get

R i t F − R f t = β i F ∗ ( R t F − R f t ) + μ i t (11)

Form (11) generally identifies the structure of CAPM model, R i t F is the rate of return to interbank offered market for any exchange online financial industry’s asset i at the time t. R f t is the pure (riskless) interest rate at the time t, R t F is the interbank offered rate at the time t, β i F is the risk measure of interbank offered system, μ i t is the random error.

Implementing the dynamic ECAPM, however, is complicated by the fact that an explicit forecasting model needs to specified and estimated. In this case, the revisions to future interbank offered rates depend on the information set used to predict the future levels of return, which implies that the inference of asset pricing tests can hinge on the state variables included in this information set. Therefore, in this paper we focus on the static ECAPM. This paper reveals that the main conclusions drawn from measuring the return to traditional financial wealth with the simple rate of interbank offered, does not hinge on the methodology used to measure the return to traditional financial wealth.

Then move to focus on the return to exchange online money market fund market, as the R i t E F in the form (1). On exchange online financial market, we have an assumption that the return to exchange online financial asset depend on the return to exchange online money market fund market i and return to the online money market fund market portfolio EM. For any exchange online money market fund i, we have the expression CAPM,

R i t E F − R f t = β i E F ∗ ( R t E F − R f t ) + η i t (12)

where R i t E F , is the rate of return to online money market fund market for any exchange online financial industry’s asset i at the time t, R t E F is the rate return to the online money market fund market portfolio.

In conclusion, form (13) expresses the portfolio return rate of any exchange online financial asset i at the time t,

r H (13)

At the time t, the return on a particular exchange online financial industry’s asset i, R i t , can be expressed as a linear function of the exchange online financial industry’s “exchange Online Money market Fund Market” beta, β i E F and “interbank offered market” beta, β i F , where R t F is the rate of return to interbank offered market, which use the interbank offered rate in this paper. R t E F is the rate of return to online money market fund market portfolio. R f t is the pure (Riskless) interest rate.

The monthly rate of return to online money market fund market portfolio from respective 25 money market funds in China during the period from June 2013 through June 2014 were analyzed in the manner suggested by the theory.

Here choose the data that is the 25 money market funds which represents those top biggest scales on the Chinese money market funds market, and their average assets are more than about 150,000,000$. Take the F i as the scale of money market fund i on April 30, 2014; the weight of the rate to total scale of money market funds, that is θ i , we get

θ i = F i ∑ F j , for j = 1 , ⋯ , 25 (14)

For any month t, the monthly rate of return money market fund market portfolio equal to the weights of 25 money market funds multiplied by the sum of the monthly rate of return (i.e. R i t ), then have the expression of the monthly rate of return money market fund market portfolio R t E F ,

R t E F = ∑ i θ i R i t , for i = 1 , ⋯ , 25 (15)

Express the interbank offered rate as the return to interbank offered market. The interbank offered rates data are from Shanghai Interbank Offered Rate during the period from June 2013 through June 2014. The average interbank offered rate for a day, a week, a month and a quarter respectively at any month t, noted them as b t D , b t W , b t M , b t Q .

In panel A of

b t D | b t W | b t M | b t Q | MKRT | |
---|---|---|---|---|---|

Descriptive Statistics: Mean | 3.37 | 4.21 | 5.26 | 5.08 | 4.65 |

Descriptive Statistics: Standard Deviation | 1.12 | 0.98 | 0.91 | 0.38 | 0.51 |

Descriptive Statistics: Autocorrelation Coef. | 0.22 | 0.25 | 0.41 | 0.75 | 0.61 |

Correlation Matrix: b t D | 1 | ||||

Correlation Matrix: b t W | 0.94 | 1 | |||

Correlation Matrix: b t M | 0.81 | 0.90 | 1 | ||

Correlation Matrix: b t Q | −0.21 | 0.01 | 0.04 | 1 | |

Correlation Matrix: MKRT | −0.18 | 0.11 | 0.34 | 0.66 | 1 |

This table includes descriptive statistics for various interbank offered rates and for the rate of return to online money market fund market portfolio. b t D is the average interbank offered rate for a day. b t W , b t M and b t Q represent the average interbank offered rate for a week, a month and a quarter, respectively. The interbank offered rates data are from Shanghai Interbank Offered Rate. The rate of return to online money market fund market portfolio (MKRT) is the value-weighted return of 25 tops money market funds listed on the Hexun database. The time period is from June 2013 to June 2014.

higher than risk free rate (one year fixed saving rate, R f ). Chinese banks were short of cash, the interbank borrowing cost jumped, as huge expansion of Chinese online financial market at that period from June 2013 to June 2014.

In this section we present our main empirical results. First, we present results of a traditional estimate of the CAPM. We then discuss the level and significance of the risk premiums on the various measures of the excess return to interbank offered market, which are b t D , b t W , b t M , b t Q , and the excess return to online money market fund market portfolio related to the four proxies of the return to interbank offered market. Finally, we compare the CAPM and ECAPM model.

Intercept | NBR | SHIBOR | MKRT | R^{2} | Adj R^{2} | |
---|---|---|---|---|---|---|

CAPM | 1.491 | 0.18 | 0.16 | 0.09 | ||

CAPM: FMB t-statistic | 17.76 | 1.41 | ||||

ECAPM_D | 0.005 | −0.13 | 1.00 | 0.71 | 0.68 | |

ECAPM_D: FMB t-statistic | 0.06 | −6.04 | 21.41 | |||

ECAPM_W | −0.001 | −0.14 | 1.07 | 0.71 | 0.68 | |

ECAPM_W: FMB t-statistic | −0.02 | −5.70 | 23.33 | |||

ECAPM_M | 0.05 | −0.13 | 1.13 | 0.70 | 0.67 | |

ECAPM_M: FMB t-statistic | 0.58 | −4.89 | 23.09 | |||

ECAPM_Q | −0.278 | 0.11 | 1.00 | 0.68 | 0.65 | |

ECAPM1_Q FMB t-statistic | −2.03 | 1.31 | 15.70 |

The table includes the Fama and MacBeth (FMB) (1973) risk premiums estimated from the four CAPM and four ECAPM specifications [

offered rate, the interbank offered rate for a day, a week, a month and a quarter b D , b W , b M , b Q respectively, and refer to the estimated exchange online financial models as ECAPM_D, ECAPM_W, ECAPM_M and ECAPM_Q. A traditional estimate of the CAPM that excludes any measure of exchange online financial industry is presented in the top row of

Looking at the estimates under the excess return to national debt (NBR) column of

Now, focus on the estimates under the excess return to exchange online financial market, where are the interbank offered market and exchange online money market fund market. Looking at the estimates under the return to interbank offered market (SHIBOR) column of

The extent to which identifying the interbank offered market depends on the measure of statistical significance. In the case of the Fama-MacBeth t-statistics, the pattern is clear. The return to interbank offered market associated with successively more affluent return to exchange online financial asset has a more statistically significant effect on expected return. The t-statistic on the interbank offered market rises monotonically from −6.04 in the case of the ECAPM_D to 1.31 in the case of the ECAPM_Q.

Next, facing at the estimates under the return to exchange online money market fund market (MKRT) column of

Finally, compare the CAPM and ECAPM model. The traditional CAPM measures that results as measured by adjusted R 2 is ambiguous. The adjusted R 2 of the CAPM that ignores all exchange online financial market is 9% indicating that the simple CAPM explains a modest proportion of the variation in expected returns on exchange online financial asset. However, the EAPM measure that results in the largest improvement in model fit, as measured by adjusted R 2 is unambiguous. The improvement of fit is from adding the exchange online financial markets, which are the interbank offered market and the exchange online money market fund market. Looking at the fit measures in

Overall, the main results in

The version of the CAPM developed by Sharpe (1964) and Lintner (1965) has never been an empirical success in application to exchange online financial asset [

We measure the return to exchange online money market fund market using the return rate data from 25 top money market funds listed on the Hexun database in China. This data set provides a long time series on the return rates of the top exchange online money market fund in China because of rapid increase on Chinese money funds over June 2013 to June 2014 period. Unfortunately, such data cannot cover other countries’ online financial market, as US data is much difficult to obtain that its online financial market started much early but Paypal loses. But in China the saving ratio is constantly higher than the rest of world, when the Chinese online financial industry boom, for example Alipay, the rise is surprising high.

Our empirical analysis demonstrates that version of the ECAPM (ECAPM_D, ECAPM_W, ECAPM_M, ECAPM_Q) that identifies the return to interbank offered market with the interbank offered rate are successful in many dimensions. These versions of the ECAPM explain the returns on the interbank offered rate for one day, one week and one month more successfully than the ECAPM implemented with interbank offered rate for one quarter (ECAPM_Q). Moreover, the degree of fit is more improved in the model of ECAPM_D and ECAPM_W, which implementing the interbank offered rate for one day and one week, respectively.

We also find that the empirical performance of the ECAPM, implemented with the risks of interbank offered market and of exchange online money market fund market, is more compatible to apply than capital asset pricing model in part of exchange online capital asset. This finding is interesting because it suggests that exchange online financial capital asset pricing model is more successful in application.

It shows that some arguments of failure of online financial market in US, for example, the Paypal. The results of

These results consistently show that the exchange online risks of affluent the return to exchange online financial asset is the priced risk factors that successfully explain variation in expected return of exchange online financial asset. Moreover, implementing the ECAPM by identifying the return to exchange online financial asset with the interbank offered market and the exchange online money market fund market is more successful than an implementation employing a simple CAPM. We find that examining the ECAPM improves our understanding of the empirical link between risk and return in the exchange online financial market.

The author declares no conflicts of interest regarding the publication of this paper.

Yang, C.Y. (2019) Research on China’s Exchange Online Financial Market: An Exchange Online Financial Capital Asset Pricing Model. American Journal of Industrial and Business Management, 9, 1045-1058. https://doi.org/10.4236/ajibm.2019.94072