Influence of nonspecific factors on the interest rate of online peer-to-peer microloans in China

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Influence of nonspecific factors on the interest rate of online peer-to-peer microloans in China Jianfeng GUO a, b, Xiaojie LIU a, b, Changnan CUI c, Fu GU d, e, * a

Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China National Technology Transfer Center of Chinese Academy of Sciences, Beijing, China d Department of Industrial and System Engineering, Zhejiang University, Hangzhou, China e National Institute of Innovation Management, Zhejiang University, Hangzhou, China b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Attention Interest rate Microloan Online peer-to-peer lending Private lending

This work explores the influence of nonspecific factors, including internal parameters such as term, volume, and participants, and external ones such as risk-free interest rates, private lending interest rates, and online attention, on the average interest rate of peer-to-peer (P2P) microloans in China. Empirical findings reveal that with a positive coefficient, the private lending interest rate is the most significant influencing factor. China’s P2P market was changed on June 15, 2018; the effect of the private lending interest rate turned insignificant, and the negative coefficient of the loan term, short-term risk-free interest rate, and online attention became positive.

1. Introduction The popularity of online peer-to-peer (P2P) lending has increased in recent years owing to its association with financial liber­ alization (Dorfleitner et al., 2016; Iyer et al., 2016; Tang, 2019). P2P microloans directly pair up a borrower and a lender without any financial intermediation; lending occurs when a lender’s expected rate of return coincides with the interest that the borrower is willing to pay (Lin and Viswanathan, 2015; Wei and Lin, 2017). In other words, P2P lending interest rates liberalized (Ceyhan, 2011). Although the liberalization of interest rate increases the efficacy of credit allocation (Cho, 1988), both volatility and default probability are increased (Demirgüç-Kunt and Detragiache, 2001), particularly under weak regulation (Chaffee and Rapp, 2012; Huang, 2018). Therefore, examining influencing factors of P2P lending interest rate is crucial to decisions such as investment and regulation. Similar to the banking literature (Caporale and Pittis, 1997; Duburcq and Girardin, 2010), the influencing factors of P2P lending interest rate are investigated from internal and external angles. Borrower-related elements such as credit score, debt-to-income ratio (Herzenstein et al., 2008; Klafft, 2009), race (Pope and Sydnor, 2011), gender (Chen et al., 2017), and loan history (Ding et al., 2019); and other elements, like market mechanism (Ma et al., 2017), affiliated organizations (Chen et al., 2016), amount, term (Tao et al., 2017; Chen et al., 2018), liability (Zhou and Wei, 2020), and P2P lending platform characteristics (Wang et al., 2020) are examined as internal factors. Macroeconomic elements, such as government bond yield and unemployment level (Dietrich and Wernli, 2014; Foo et al., 2017), are considered external influencing factors because they are linked with private lending. These studies, however, are based on platforms with varied designs (Zhao et al., 2017), thereby affecting the generalization of results. Besides, the majority of the research uses cross-sectional data, which is susceptible to bias. * Corresponding author. E-mail address: [email protected] (F. GU). https://doi.org/10.1016/j.frl.2020.101839 Received 23 July 2020; Received in revised form 23 September 2020; Accepted 3 November 2020 Available online 6 November 2020 1544-6123/© 2020 Elsevier Inc. All rights reserved.

Please cite this article as: Jianfeng GUO, Finance Research Letters, https://doi.org/10.1016/j.frl.2020.101839

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In this paper, we use a unique panel dataset of daily frequency, spanning from August 21, 2013 to December 6, 2019, to investigate the influence of nonspecific factors on the average P2P lending interest rate of industrial scale. The internal factors include internal parameters such as term, volume, and participants of microloans, and the external variables are risk-free interest rates, private lending interest rates, and online attention on lending. Here we focus on the microloans in China because the country made annual P2P loans exceeding $20 billion, leading the development of online P2P lending (Thakor, 2020). Specifically, we divide the data into two phases based on regulators’ attitude and fraud: Phase 1 spans from August 21, 2013 to June 14, 2018, and Phase 2 covers June 15, 2018 to December 6, 2019. The empirical analysis indicates that all the included factors have significant effects (1% significance level) on P2P lending interest rates throughout the study period. The term, volume, and online attention have adverse effects. In contrast, participants, short-term risk-free interest rates, long-term risk-free interest rates, and private lending interest rates have positive influences. Besides, the private lending interest rate has the most significant impact, validating that P2P lending is a part of informal finance. The phased panel estimation results indicate that all the factors associated with the P2P lending interest rate are significant (1% significance level) during Phase 1. However, in Phase 2, the effect of the private lending interest rate turns insignificant (even at 10% significance level). The impact of the short-term risk-free interest rate, online attention, and loan term has changed from negative in Phase 1 to positive in Phase 2. These findings verify that China’s P2P lending market conditions were transformed since June 15, 2018. 2. Methodology 2.1. Data and variables Our daily panel dataset is comprised of the P2P microloans1 from 10 municipalities and provinces of China, including Beijing, Guangdong, Henan, Hubei, Hunan, Jiangsu, Shandong, Shanghai, Sichuan, and Zhejiang. The selection of the municipalities and provinces is due to their representativeness of the P2P lending industry in China. According to WangDaiZhiJia2, by the end of December 2019, the outstanding balance and trading volume of microloans in the above regions (except Henan and Hunan) were 468.08 and 39.61 billion RMB, respectively, accounting for 95.22% and 92.35% of the national totals, as shown in Appendix A. Notably, available data regarding the P2P lending in China is practically nonexistent before August 2013. On November 28, 2019, all online P2P platforms were ordered to cease operations by the end of 2021 (Leng and Tham, 2019) due to increased fraudulent activities and fundraisings (Chorzempa, 2018; Jao, 2019). Therefore, our data covers from August 21, 2013 to December 6, 2019. From an internal perspective, we think about three crucial factors. Like any loan (Benston, 1977), the loan term is undoubtedly an essential factor affecting the P2P interest rate. Total trading volume (reflecting the operational stability and prosperity of the P2P lending market (Fu et al., 2019)) and the total number of borrowers and investors (denoting the degree of activity of credit markets (François and Missonier-Piera, 2007)) are also included. We do not consider any specific factors such as appearances and writing because they vary noticeably from platform to platform. Referring to the Capital Asset Pricing Model (Treynor, 1961; Sharpe, 1964; Lintner, 1965; Mossin, 1966), the expected return rate of any risky asset consists of a risk-free interest rate and a risk premium. Therefore, the risk-free interest rate could affect the interest rate of online microloans, which are regarded as risky investment. Here we consider both short-term risk-free interest rate and longterm risk-free interest rate because short-term rates are more volatile than long-term rates, and long-term interest rates are compounds of short-term rates (Hansen and Scheinkman, 2009). The risk premium of P2P microloans depends on the demand and supply of funds in such market, which are affected by other private lending markets. Tang (2019) suggests that P2P lending market complements bank lending concerning small loans, indicating that P2P lending market is similar to other private lending markets. Thence, we include private lending interest rate. Specifically, we employ the private lending interest rate from the Wenzhou private credit market, which is the most representative private lending market in China (Qin et al., 2014). Since the P2P lending is garnering growing attention (Galloway, 2009; Chen et al., 2019), we also link the search intensity of online loan-related keywords from Baidu to indicate the attention on online microloans, based on the research of Da et al. (2011) and Da et al. (2015). The attention series is acquired from Baidu, a search engine that holds 70% of the market share in China. In summary, we include the loan term (Term), total trading volume (Volume), total number of borrowers and investors (Partic­ ipant), short-term risk-free interest rates (Rfs), long-term risk-free interest rates (Rfl), private lending rates (Rp), and search intensity of online loan-related keywords from Baidu (Baiduindex). Table 1 lists the variables and data sources, and Table 2 presents descriptive statistics. 2.2. Empirical models Our data are acquired from a daily-based panel dataset that allows for complicated analysis (Bollen and Brand, 2010). We refer to He et al. (2016) and use a panel model for the analysis. The basic model is as follows: Rateit = αi + β1i Termit + β2i Volumeit + β3i Participantit + β4i Rflit + β5i Rfsit + β6i Rpit + β7i Baiduindexit + μit

(1)

1 Only microloans from legitmate platforms are included in our dataset, while those from fraudulous platforms are excluded. The borrowers and lenders in these platforms are from every region of the country, as the microloans are made online. 2 https://www.wdzj.com. The website is entitled “网贷之家” (WangDaiZhiJia), which means “the home of online P2P lending”.

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Table 1 Selected variables and their data sources. Category

Variable

Definition

Source

UoM

Dependent variable Independent variable

Rate

The average annual interest rate of the P2P microloans in the ten selected provinces and municipalities The average loan term of the P2P microloans in the ten selected provinces and municipalities The total trading volume of the P2P microloans in the ten selected provinces and municipalities The total number of participants of the P2P microloans in the ten selected provinces and municipalities The ten-year yield on the Chinese government bonds.

DiYiWangDai (http://www. p2p001.com) DiYiWangDai (http://www. p2p001.com) DiYiWangDai (http://www. p2p001.com) DiYiWangDai (http://www. p2p001.com) China Bond Information (https://www.chinabond.com. cn) http://www.shibor.org/shibor/ web/html/index.html Wenzhou Private Finance Index (http://www.wzpfi.gov.cn) Baidu Index (https://index. baidu.com)

%

Term Volume Participant Rfl Rfs

The Shanghai interbank offered rate of the interbank loans that have similar terms to the P2P microloans. Comprehensive interest rate index of private finance in Wenzhou.

Rp Baiduindex

The combined search intensity of six online loan-related Chinese phrases, including Wangdai (“网贷”, online loan), Wangluojiedai (“网络借贷”, internet lending), Wangluodaikuan (“网络贷款”, network loan ), P2PWangdai (“P2P网贷”, P2P online loan), P2PWangluojiedai (“P2P网络借贷”, P2P internet lending), and P2PDaikuan (“P2P贷款”, P2P network loan).

Month 10,000 RMB % % %

Table 2 Descriptive statistics of the variables. Variable

Mean

Median

Max

Min

Std.dev

Observations

Rate Term Volume Participant Rfl Rfs Rp Baiduindex

13.97851 6.098954 47,928.36 34,658.35 3.508705 3.836307 17.53995 783.6777

11.81500 4.620000 6,319.000 3,713.000 3.492750 3.800000 16.74000 743.0000

194.0800 215.9200 646,400.0 1,168,816 4.722200 5.603700 21.20000 2,525.000

2.580000 0.140000 2.000000 1.000000 2.640100 2.600000 14.20000 66.00000

6.513700 4.747734 87,875.30 71,863.33 0.473774 0.840667 1.934768 261.5292

14,002 14,000 14,001 14,002 13,880 13,860 13,160 14,810

The dimension of each variable can be removed, and heteroscedasticity ignored by rewriting the above model in logarithmic form as follows: lnRateit = αi + β1i lnTermit + β2i lnVolumeit + β3i lnParticipantit + β4i lnRflit + β5i lnRfsit + β6i lnRpit + β7i lnBaiduindexit + μit

(2)

where the subscript i denotes the municipalities and provinces, αidenotes the unobservable individual effect, β denotes the coefficients of every explanatory variable, t denotes the date, and μit denotes the independent random disturbance term in the model. It should be noted that the following empirical analysis is based on Eq. (2) in logarithmic form. Furthermore, a phased panel estimation is conducted, with Phase 1 covering August 21, 2013 to June 14, 2018, and Phase 2 covering June 15, 2018 to December 6, 2019. June 15, 2018 is selected to be the boundary due to the following two reasons. The first is that on June 14, the financial supervision authorities stressed the importance of guarding against financial risks, with particular emphasis on the danger of financial products that promise high rates of return (Lujiazui Forum, 2018). The second is that on June 15, Tangxiaoseng, a renowned P2P platform with recorded microloans over 80 billion RMB, was forced to shut down due to frauds (Yi and Cheng, 2018). Subsequently, hundreds of fraudulent P2P platforms were closed, causing the downfall of the entire industry. This is not only reflected in the deterioration of the transaction situation (both the trading volume and outstanding balance began to reduce, see Appendix B), but also in the shrinking number of participants (see Appendix C). Since the particular day, the prosperity index of the Chinese P2P lending market declined rapidly (see Appendix D). 3. Empirical results Before estimation, we first conducted a preliminary statistical test. First, according to Appendix E, there is no multicollinearity among the variables. Then, the results of Appendix F and G indicate the stable long-term equilibrium relationship exists in the chosen variables. Hence, the panel model can be used to estimate the variables. After that, Appendix H shows the presence of individual effects (Guo et al., 2019) and a fixed effects model. Last, according to Appendix I, the Chow breakpoint test results validate the identified turning point of China’s online P2P lending market, that is, June 15, 2018; our phased panel estimation is credible.

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3.1. Complete period estimation Table 3 lists the estimation results of the entire study period. From August 21, 2013 to December 6, 2019, the online P2P lending interest rate is significantly affected (1% significance level) by all the chosen variables. Private lending interest rates, risk-free interest rates, and number of participants is positively correlated with the P2P interest rate, whereas other factors are negatively correlated with the P2P interest rate. The highest coefficient with the P2P lending interest rate is the private lending interest rate, confirming the observation of Tang (2019); P2P lending, as an informal finance sector, is complementary to bank lending. Besides, both short-term and long-term risk-free interest rates positively affect the P2P interest rate. Such finding indicates that P2P lending market can manifest, to a certain extent, the supply and demand of money related to macroeconomics. The P2P interest rate is also positively correlated with the number of participants. This can be explained that more people borrow money from this channel, the higher the interest rate would be; greater demand triggers higher cost. P2P lending interest rate has significant negative coefficients with the volume and term of microloans, being partially consistent with the observation of Chen et al. (2018) but contradicting the finding of Tao et al. (2017). Generally, microloan borrowers tend to be people who incur higher credit costs because they present higher default probabilities (Rostamkalaei and Freel, 2016). Therefore, microloan borrowers are more inclined to small-volume and short-term loans, given the high cost of credit. The P2P interest rate is also affected negatively by online attention, showing a good agreement with the finding of He et al. (2020); more people are paying attention on P2P lending, more funds would be flowed into such market, thereby lowering the interest rates. 3.2. Estimation of two periods Table 4 presents the estimation results of the data of the two study periods. The coefficients of the online P2P lending interest rate with all the included factors are significant (1% significance level) in Phase 1. By contrast, the private lending interest rate’s effect becomes insignificant (even at 10% significance level) in Phase 2. Additionally, although the extent of the effect of the private lending interest rate on the online P2P lending interest rate is markedly smaller in Phase 2, the value remains positive. Due to increasing frauds and other illegal activities, all Chinese P2P platforms are forced to convert into licensed microcredit lenders within at most two years (since November 2019). Thence, the marginalization of China’s P2P lending market in the informal finance sector is a likely reason for this outcome. Contrary to the estimation results of the entire period, the coefficient of the P2P lending interest rate with short-term risk-free interest rates in Phase 1 is negative. The possible reason is that in Phase 1, the P2P lending industry is booming (Huang, 2018; Thakor, 2020). Thence, its market price, i.e., the P2P lending rate, deviates from the general law of value in the short term, resulting in the inverse relationship between the interest rate and short-term risk-free interest rate. Table 4 also shows that the directions of the effects on the P2P interest rate of short-term risk-free interest rates, online attention, and loan term in Phase 2 changed to positive from negative. The occurrence of risk events and tightening regulation of this informal industry characterize this phase (Yu and Shen, 2019). Furthermore, the marginalization of this online P2P lending market are co-occurring. Consequently, as the benchmark interest rate in China, the short-term risk-free interest rate (Cao et al., 2018) has a positive coefficient and the most significant effect on the P2P lending interest rate. Given these conditions, with investors become more risk-averse, a positive coefficient occurs between the online attention and the P2P interest rate. Besides, borrowers may prefer to obtain long-term microloans instead of short-term ones due to lowered borrowing cost. Thus, the P2P interest rate and microloan term are positively correlated because of this liquidity premium. 3.3. Endogeneity A reverse causality issue may exist in our basic estimation, as the cause-and-effect relationship between P2P interest rates and online attention cannot be confirmed. Following the work of He et al. (2020), we use the lag term of online attention as the instrument variable, and perform 2SLS regression to solve the endogeneity problem. The results (see Appendix J and K) indicate that our previous Table 3 Results of complete period estimation. Variable

Coefficient

t-statistic

P value

const Term Volume Participant Rfl Rfs Rp Baiduindex Observations R2 Adjusted R2 F-statistic

-1.174197 -0.110384 -0.101854 0.082954 0.621365 0.132675 1.503212 -0.175385 12,421 0.803224 0.802970 3,164.504

-12.54141 -24.84123 -35.02155 31.14443 28.09649 10.43575 76.46008 -21.14476

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Note: This table reports the fixed effect estimation results, and variables are in logarithmic form. 4

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Table 4 Results of two periods estimation. The first phase: August 21, 2013 to June 14, 2018 Variable Coefficient const Term Volume Participant Rfl Rfs Rp Baiduindex Observations R2 Adjusted R2 F-statistic

1.106654 -0.128066 -0.190891 0.106930 0.693306 -0.075000 0.949549 -0.150726 9,931 0.842840 0.842587 3,323.026

The second phase: June 15, 2018 to December 6, 2019 Variable Coefficient const Term Volume Participant Rfl Rfs Rp Baiduindex Observations R2 Adjusted R2 F-statistic

1.323472 0.017677 -0.020545 0.037638 0.129685 0.193021 0.048643 0.037091 2,490 0.591169 0.588524 223.4971

t-statistic

P value

9.546839 -26.74417 -48.22120 29.99545 30.51716 -5.340330 38.85801 -17.28512

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

t-statistic

P value

7.846497 2.674006 -5.314157 11.93102 2.954948 8.245784 0.833440 2.932997

0.0000 0.0075 0.0000 0.0000 0.0032 0.0000 0.4047 0.0034

Note: This table reports the fixed effect estimation results, and variables are in logarithmic form.

findings still hold and our conclusion is robust. 4. Conclusion The effects of nonspecific internal factors, such as loan term, volume, and the number of participants, and external nonspecific factors, including risk-free interest rates, private lending interest rates, and public attention, on P2P lending interest rates are explored in this work. The private lending interest rate is found to have the highest positive coefficient with—and is thus the most influential nonspecific factor of—P2P interest rate during the studied period. Moreover, June 15, 2018 is confirmed to be the critical moment of China’s P2P lending market. On that day, the effect of the private lending interest rate on the P2P interest rate turned insignificant, and the negative coefficient of short-term risk-free interest rates, online attention, and loan term with P2P interest rate became positive. These changes verify that the conditions of China’s P2P lending market are transformed. Monitoring the changes in influencing factors like risk-free interest rates and the occurrences of some events are crucial to the regulation of private lending markets such as P2P lending market. Admittedly, many other factors are not included in this work and their influence on P2P interest rate could also be significant. CRediT authorship contribution statement Jianfeng GUO: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing - original draft, Writing - review & editing. Xiaojie LIU: Data curation, Formal analysis, Software, Visualization. Changnan CUI: Funding acquisition, Project administration, Supervision. Fu GU: Conceptualization, Methodology, Supervision, Validation, Writing - original draft, Writing - review & editing. Acknowledgments This study is financially supported by the National Natural Science Foundation of China (nos. 71671180 and 71832013). Appendices Appendix A Transaction information about the selected municipalities and provinces (except Henan and Hunan) by the end of December 2019. 5

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Municipalities and Provinces

Outstanding balance (in billion RMB)

Trading volume (in billion RMB)

Beijing Guangdong Hubei Jiangsu Shandong Shanghai Sichuan Zhejiang Total China Proportion

270.902 57.692 1.292 1.377 0.839 111.838 0.184 23.958 468.082 491.591 95.22%

24.701 5.697 0.207 0.223 0.172 4.03 0.066 4.509 39.605 42.886 92.35%

Appendix B The monthly trading volume and outstanding balance of the P2P lending industry in China. Source: www.wdzj.com.

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Appendix C The monthly number of investors and borrowers in the P2P lending industry in China. Source: www.wdzj.com.

Appendix D China’s Online Loan Monthly Prosperity Index. Source: www.wdzj.com. Note: The "China’s Online Loan Monthly Prosperity Index" was launched by WandDaiZhiJia to reflect the prosperity of China’s online loan industry. The index fluctuates between 0 to 200. When the prosperity index is greater than 100, it indicates that the status of the online lending industry tends to be improved; when the prosperity index is less than 100, it suggests that the status of the online lending industry tends to deteriorate. Appendix E Variance inflation factor (VIF) test results.

Variables

VIF

Term Volume Participant Rfl Rfs Rp Baiduindex

1.239402 2.489285 2.585152 3.201117 3.002169 1.520898 1.234448

Note: If the VIF of the variables is less than 10, it can be considered that there is no multicollinearity between the variables.

Appendix F Results of panel unit root tests.

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Variables levels Level data Rate Term Volume Participant Rfl Rfs Rp Baiduindex First differenced data △Rate △Term △Volume △Participant △Rfl △Rfs △Rp △Baiduindex

Deterministic trend

LLC statistics

ADF statistics

PP statistics

c, t c, t c, t c, t c, t c, t c, t c, t

-1.95956** 4.99567 24.4195 21.4305 -3.94279*** -0.16431 -6.67664*** -5.49271***

109.412*** 220.456*** 13.6819 12.7609 11.1148 3.34067 27.9025 154.921***

1869.06*** 2633.91*** 825.264*** 1135.48*** 5.61219 4.74297 1585.29*** 2515.78***

-

-73.5537*** -65.2465*** -57.7941*** -63.8760*** -90.3675*** -38.9899*** -58.0873*** -70.7971***

1710.83*** 1755.26*** 1589.75*** 1599.87*** 1653.05*** 1138.76*** 1696.10*** 1803.06***

184.207*** 184.207*** 184.207*** 184.207*** 1657.49*** 1613.05*** 184.207*** 184.207***

Note: Variables are in logarithmic form. For the level data, we consider both individual regional effects (c) and regional-specific time trends (t). For the first differenced data, we consider neither individual regional effects (c) nor regional-specific time trends (t). The selection of the lag length is based on SIC to adjust for autocor­ relation. ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively.

Appendix G Results of the panel cointegration test of the Kao method.

ADF

t-statistic

P value

-12.56852

0.0000

Note: The null hypothesis is that there is no cointegration relationship between variables.

Appendix H Results of the F-test and Hausman test.

Complete period: August 21, 2013 to December 6, 2019 Test Chi-Sq. statistic

P value

F-test Hausman test

0.0000 0.0000

F(9,12404) = 165.495812 Chi2(7) = 317.833330

The first phase: August 21, 2013 to June 14, 2018 Test Chi-Sq. statistic

P value

F-test Hausman test

0.0000 0.0001

F(9,9914) = 210.379086 Chi2(7) = 29.598684

The second phase: June 15, 2018 to December 6, 2019 Test Chi-Sq. statistic

P value

F-test Hausman test

0.0000 0.0000

F(9,2473) = 221.095537 Chi2(7) = 35.330569

Note: The null hypothesis of the F-test is that the real model is a pooled regression model; the null hypothesis of the Hausman test is that the real model is a random effects model.

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Appendix I Results of Chow breakpoint test.

Province/Municipality

F-statistic

Log-likelihood ratio

Wald statistic

Beijing Shanghai Jiangsu Zhejiang Shandong Henan Hubei Hunan Guangdong Sichuan

59.95828*** 41.53612*** 15.87621*** 63.08299*** 33.12995*** 64.68328*** 83.51993*** 25.21337*** 151.4855*** 273.2463***

412.5547*** 297.6793*** 122.3302*** 427.6807*** 242.5516*** 436.2887*** 539.6684*** 189.0706*** 863.8886*** 1273.469***

479.6663*** 332.2890*** 127.0097*** 504.6639*** 265.0396*** 517.4662*** 668.1594*** 201.7069*** 1211.884*** 2185.970***

Note: The null hypothesis is that there are no breaks at specified breakpoints. ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively.

Appendix J Results of complete period instrument variable estimation.

Variable

Coefficient

t-statistic

P value

const Term Volume Participant Rfl Rfs Rp Baiduindex Observations R2 Adjusted R2 F-statistic

-0.200252 -0.095285 -0.098656 0.084639 0.683987 0.123014 1.399452 -0.297357 12,419 0.799736 0.799477 3,181.616

-1.592683 -20.42658 -33.48231 31.45589 29.81812 9.572032 64.44502 -22.28993

0.1113 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Note: This table reports the 2SLS estimation results, and the instrument variable is the lag term of Baiduindex. Moreover, variables are in logarithmic form.

Appendix K Results of two periods instrument variable estimation.

The first phase: August 21, 2013, to June 14, 2018 Variable Coefficient const Term Volume Participant Rfl Rfs Rp Baiduindex Observations R2 Adjusted R2 F-statistic

1.966392 -0.115300 -0.190436 0.109553 0.728385 -0.074317 0.855397 -0.253450 9,929 0.840616 0.840358 3,325.339

The second phase: June 15, 2018, to December 6, 2019 Variable Coefficient

t-statistic

P value

12.97120 -22.88024 -47.75978 30.41289 31.36984 -5.254607 31.93318 -17.44234

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

t-statistic

P value (continued on next page)

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(continued ) The second phase: June 15, 2018, to December 6, 2019 Variable Coefficient const Term Volume Participant Rfl Rfs Rp Baiduindex Observations R2 Adjusted R2 F-statistic

1.171615 0.018323 -0.022272 0.036860 0.078504 0.197503 0.034300 0.076812 2,490 0.589538 0.586882 223.6034

t-statistic

P value

6.215803 2.762337 -5.584256 11.55627 1.503883 8.374411 0.581254 3.043148

0.0000 0.0058 0.0000 0.0000 0.1327 0.0000 0.5611 0.0024

Note: This table reports the 2SLS estimation results, and the instrument variable is the lag term of Baiduindex. Moreover, variables are in logarithmic form.

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