Master Thesis of MSc Finance
Research on Female Investment Behaviour in internet finance in China
Thesis Supervisor： Egle Karmaziene Student name (number): Xuemeng Lu (S3760065)
Abstract
Investors behaviour is always the trending topic in the field of finance. This paper investigates whether gender make differences in investor behaviour; if it does, what differences are between female and male investors behaviour; and why female investors behave distinctively. The result suggests that there are significant differences between female and male investors in some aspects and several factors such as perceived risk contribute to these distinctions. Compared with male investors, female investors have wider range of expected annual return of investment and de-investment on high-risk internet financial products such as P2P and Crowdfunding. They are more likely to be encouraged by their colleges and professional opinion leaders. Age, gender, income and education levels are all significantly related with investment behaviour on the internet finance.
I. Introduction
1.1 Motivation
Internet finance is an emerging financial investment model, which rises with the development of the Internet and the internet shopping. In general, Internet financial products refer to all financial products managed through the Internet. Such innovative financial management methods represented by P2P online loan have been widely concerned and recognized by investors since their foundation. Internet financial products initially appeared in European and American countries. In 2005, the establishment of Zopa, the first P2P online lending company in the UK, marked the birth of online lending industry. Since then, more and more microfinance lending platforms have been established. In China, the first P2P online lending platform was established in 2007. In June 2013, the Ant Financial Group joined hands with Tianhong Fund to launch "Yu 'E Bao", which marked that the development of Internet financial products in China has entered a new era. In recent years, the rapid popularization of the internet in China has provided a huge and solid network, terminal and user base for the rise of Internet financial products. In 2019, the number of mobile Internet users reached 1.086 billion (Statista, 2019). In 2016 and 2017, the transaction volume of Internet financial products reached 20 trillion yuan (2.9 trillion dollar) and 28 trillion yuan (4.1 trillion dollar) respectively (iResearch, 2018). By the end of 2017, the total transaction volume of China's Internet financial products had reached nearly 15 trillion yuan (2.17 trillion dollar), which accounted for nearly 20% of the total GDP of China in 2017. As an emerging innovative industry, internet finance is booming in China, with the market size reaching 5.36 trillion yuan (775.7 billion dollar) in 2018. internet finance has become a new form of financial market and one of the most concerned frontier areas in academic research.
Herzenstein, etc., 2011). On the other hand, when there is negative information appearing in the market of Internet financial products, the investment behaviour of investors will be abnormal and have an impact on Internet financial products. For example, in China, according to the data of Puyi Standard Financial Data Platform, in 2018 there were as many as 40 P2P platforms were exposed to negative news, involving over 120 billion yuan of funds in just one week from July 2 to July 8. The intensive explosion of Internet financial platforms directly caused Internet financial investors felt panic, which led to the market default of financial products. This fully demonstrates the vulnerability of Internet Financial Investor to special events. It can be seen that compared with the effective trial production of traditional finance, investor behaviour has a stronger impact on internet finance and a deeper connection. Therefore, when the traditional financial efficient market theory fails to explain a series of anomalies in the Internet financial market, relevant research on the behaviour of Internet financial investors can help explain the anomalies in the financial market. Therefore, it is necessary to pay attention to the behaviour of Internet financial investors.
1.2 Gap in the literature
In this paper, the female investors are the focus because according to research, female investors are taking more and more important part in the financial investment. Over the past three decades, women's average earnings have increased by 63%, and more than a third of working women worldwide earn more than their husbands. With the rapid increase of women's income around the world, the economic status of urban women in China is also rising rapidly (The Economist, 2018). The research results of Chinese Financial Intelligence Index Report (2011) show that women have more control on financial funds than men. Women are taking an important position in family financial management and they hope to balance income and expenditure and maintain value and increase value through financial management. Their recognition and demand for financial management are more obvious than men. In the increasingly fierce competition in the wealth management market, financial institutions need to segment the market and determine the target customers. Compared with the strong demand for financial services, the supply of financial services for women in China is relatively lagging behind. The single financial channels or tools cannot meet the diversified demand of women for financial services. According to the research results of Chinese Financial Intelligence Index Report (2011), the fraction of urban females who actually conduct financial management (45%) are lower than the females who consider it is necessary to conduct financial management (56.8%). What is more, when asked why they didn’t conduct financial management, 36.3% said that they didn’t know the channel or method of financial management.
So, based on the background of research and gap in literatures, the research question can be abstracted as:
What are differences between female and male investors on internet finance? What factors cause these differences?
1.3 Contribution
This paper mainly discusses how female investors behave when they invest in the internet finance in China. Firstly, I review related theories and literatures about the internet finance and investor behaviour to find that what are specific characteristics of the internet finance and what factors may make influence on investor behaviour. Then by conducting a survey, I collect 267 answers from Chinese investors about how they behave on the internet finance. By using the t-test and OLS regression, I find that female investors’ behaviour is different with that of male investors. They have differences on perceived risk, expected return, range of expected return of investment and de-investment and social influence. Moreover, in addition to gender, the age, income, investment experience and gender are also significant influential factors of investors behaviour on the internet finance.
have higher risk tolerance than male investors. Secondly, this study fills the gap in the current research on investor behaviour in internet finance, and deeply analyses the important role of gender in the difference of female and male investor behaviours. Thirdly, this study is of practical significance, which helps financial institutions better understand female investors and provides reference for future research on female investors' investment preferences.
II. Literature review and background theory
2.1 Literature review and hypothesesLiteratures on female investor generally compare the difference of investment behaviour between men and women. How gender make impact on investors’ behaviour? It is believed that risk sensitivity is the mediation. Researchers think that female investors earn as the same as male investors while female investors are more risk-averse and more negative to expected value (Wang, 1994; Nancy Ammon Jianakoplos and Alexandra Bernasek, 1998). Hartog and other researchers (2002) conclude that female investors are more risk-adverse through empirically analysing the data of Arrow-Pratt risk. And the age and whether their mothers have received higher education are also significantly affect their risk-aversion behaviour. Donkers, Melenberg and Soest (2001) found that female and the elderly hold a more negative attitude towards risk through the questionnaire of choosing lottery. Watson and McNaughton (2007) analyzed the pension fund data of an Australian university and reached the same conclusion as Hartog. Moreover, they found that female investors preferred low-risk pension after controlling for income and age. Dickson (1967) finds in 18s century that nearly one third of British government land tax bond is held by female, which shows female investors prefer short-term and low-risk products. Schubert and other researchers (1999) finds that there is no significant difference between female and male on risk propensities in the field of investment and insurance, but women are more averse in the frame of gambling. Some find that women are more risk-averse than men but the gender effect is reduced once education, knowledge and access, marital status and wealth taken into account (Rutterford & Maltby, 2007). Some researchers find that Female investors believe more on professional financial consultants than men (Shanghai Advanced Institute of Finance, 2018).
Based on the literature reviews on female and male investors behaviour, the hypotheses of the research are formulated below:
Hypothesis 2: Female investors have stronger risk aversion tendency than male investors when they invest internet financial products;
Then what factors affect investors' investment behaviour? Are these factors affecting both men and women investors in the same extent? Personal characteristics of investors are considered to be significant influential factors. Chen and Han (2012) found that compared to American investors, Chinese investors are more risk averse to platform with low security. Home bias exists in the investment behaviour. Lin and Viswanathan (2015) used the transaction data before October 2008 of Prosper, a P2P platform, to investigate whether there is local preference on the Internet financial platform. They used a variety of methods to test the results and found local preferences on P2P online lending platforms. They then examined whether local preference was a rational behaviour or a behavioural deviation, and the empirical results showed that local preference was more caused by an emotional factor rather than an economic thinking. Investing in local borrowers could not bring higher returns to investors. However, there are also some researchers found that nationality has no impact on investment behaviour. Studies by MacCrimmon and Wehrung (1990) indicate that there is no significant relationship between nationality/educational level and the risk-prone behaviour; There was a significant relationship between risk preference behaviour and success factors such as higher education, higher income and higher social status. Morse (1998) believes that there is no significant relationship between risk knowledge and actual risk in investment. There is no significant relationship between the risk level reported by investors and the actual overall risk level. There was no significant relationship between investor risk level and age, education and gender. In terms of the influence of personality on risk level, Brockett and Golden (2007) believe that the stimulus-seeking personality will lead investors to be willing to accept higher risk level.
So based on literatures, I summarize the hypothesis 3 that:
Hypothesis 3: Personal traits such as age and income have significant impact on the investment behaviour of both male and female investors;
2.2 Internet Finance
2.2.1 Characteristics of internet finance
is named as internet finance. The internet finance can be defined as the financing activities through internet facilities such as mobile, internet financial institution, financial application and so on (Houa, et al., 2016). Traditional financing activities are conducted through security market (direct finance) or bank (indirect finance). Compared with traditional finance, the internet finance is created based on the convenience, availability and effectiveness of internet applications. Considering that there are large varieties of internet financial products, in this research the internet finance specially indicates that the fund products jointly launched by internet companies and fund institutions, such as Yu’E Bao, a monetary fund launched by Alibaba and Tianhong Asset Management, as well as the P2P Network Loans and Crowd Funding.
Compared with traditional finance, Internet financing saves a lot of time and cost for investors and provides them with more diversified financing options. Several reasons contribute to this. Firstly, in terms of initial investment, internet finance decreases the threshold of initial investment amount, or even sets no threshold. For example, investors can invest any amount to but Internet fund in the Yu 'E Bao. Secondly, in terms of investment period, compared with the limitation of fixed-long period of traditional financial management, the investment period of many Internet financial products is flexible. That is to say, these financial products are redeemable at any time, which provides a lot of liquidities for investors. Thirdly, on product selection, according to different credit characteristics and risk characteristics, the Internet financial can provide abundant benefits to choose, the comparison of different characteristics of products just by browsing the web, can be realized in the traditional bank financing often need to run the Banks to achieve, therefore, the Internet financial greatly save the time cost of investors. Allow investors to use the fragment time to choose the financial products, to allow a small financial investment, allowing early redemption, increase the diversity of products and acceptance, to a great extent, to expand the potential investors, even who are far away from the bank financial group, also become the Internet a large team of financial services. This is unthinkable in the age of traditional money management.
2.2.2 Current Research on internet finance
There are several topics in the research of the internet finance, such as the development of the internet finance, the innovation of internet financial products, the research on the regulation and model of the internet finance.
and Philip Strahar (2002) hold that internet finance is a way to increase the current traditional financial system, but it is not completely separated from the traditional financial system. internet finance can help the financial industry complete financial disintermediation, merger and acquisition in the industry, and increase the number of credit issuance. Lihui Lin, Xianjun Geng and Andrew Whinston (2001) believe that based on the development trend of internet finance in the past, the industry will face greater and faster challenges now and in the future. The financial industry may adopt the mode of mixed operation and the barriers to entry between different industries will be broken down. New products or services are increasingly available, forcing the current financial services to take on a new look. In order to continue the current comparative advantages, traditional industries must develop diversified services and personalized services. In its 2001 report, the Bank of International Settlements (bis) pointed out that at this stage, internet finance and traditional finance are developing in a mutually innovative way. internet finance makes traditional finance more networked and service-oriented. However, traditional finance enables internet finance to innovate mode and improve user experience faster. Kim (2005) summarized and studied the operation mode and tools of e-commerce transactions and found that third-party payment plays a key role in e-commerce transactions. E-commerce can develop rapidly precisely because the payment system is convenient and fast. Richard (2005) studied the role of insurance in the e-commerce platform and found out what can be improved by the seller's demand for customers. Greinet (2009) focused on the lending business of Internet platform. The emergence of discovery platform has brought considerable development to both sides. For the platform, considerable capital returns were obtained, and for the borrower, the financing was successful and the credit rating index was improved.
2.3 Investor Behaviour
2.3.1 What is investor behaviour
The investment behaviour is one of the focus of the behaviour finance research. The behaviour finance discusses the impact of psychological factors, external factors and other factors on the investors’ investment decisions. The investment behaviour indicates the specific investment activity conducted by investors under the drive of certain investment motivation to achieve certain objectives. The investment behaviour can be defined as rational and irrational one, based on some standards. The topic aims to research on the influence of the rising and development of internet finance on the investment behaviour of female investors in China.
external factors such as environment. That is why behaviour taken and that is why it is important to research on investor behaviour. For the internet finance, the investment behaviour can be identified in this way: in order to reach the goal of earning future revenues, investors use effective methods to collect, analyse and process all kinds of information related to the securities and to conduct decision-making activities such as determining the investment objectives, buying and selling financial products. There are many researches on investor behaviour in traditional financial market than in the field of Internet financial market.
2.3.2 Influential factors
This part discusses the influential factors of the investor behaviour. Researchers found that various factors including security system of the platform, perceived risk of investors, intrinsic risk of the financial products as well as characteristics of customers are all significantly related to the investor behaviour.
Researchers have discussed several factors that may impact on investment behaviour of internet financial customers’ decision. It is found by Capon, Fitzsimons and Prince (1996) that investors considered about non-financial factors when they made decision. IT system security can be taken as one of the main risks of internet finance. Customers will make their investment decision based on IT security. The safer it is, the more willing of investors to use the internet financial product (Allameh et al, 2011). In addition, privacy security is also important. Research by Elissar Toufaily, Nizar Souiden and Riadh Ladharin (2013) found that the privacy safety would make effect on customers to choose whether invest or not. The main reason is that the privacy safety will make influence on customers’ reflection and trust on internet retailer and thus affect their decision.
Perceived risk plays an important role in investment behaviour. Some researchers, however, did the research from the perceived risk and found that the individual perceived risk to internet investment and attitude towards guarantee service are the most important factors that affect the investment decision of investors (Antony, 2009). Donkers, Melenberg and Soest (2001) found that gender and age are two significant factors on perceived risk and the income and education level are related as well. Watson and McNaughton (2007) found that when investors loss, they would like to take more risk and risk aversion increases with age in both men and women. But they found that there was no relationship between education and knowledge level and risk appetite.
that the crowdfunding platform, liability of capital founder, return rate and novelty of programmes will make impact on investment behaviour. Researchers find investment behaviour is related to financial knowledge (Hilgert, et al., 2003), gender (Farrell, et al., 2016), age, investment scope and risk attitude (Veld-Merkoulova, 2011). In addition to investors’ characteristics, some researchers also find that easy-use system, information safety and social influence of internet platform will also affect investor behaviour by using TAM model. (Yoon & Steege, 2013) (Venkatesh, et al., 2003) (Cox & Stuart U, 1964).
III.
Methodology and Data
3.1 Methodology and regression model
This research uses different methods to test three hypotheses and find out conclusion. For the first hypothesis, the summary of data is enough to find out the conclusion. Then t-test will be used to test second hypothesis. The third hypothesis is tested by using the OLS regression.
Hypothesis 1: Female investors are more likely to be encouraged by social influence;
For the first hypothesis, the questionnaire collect data about how many people will be influenced by their friends, family, colleges, success story and others. By comparing the differences between these data and differences of data characteristics of female sample and male sample, I find out whether female or male investors are more easily to be influenced by others and which kind of influence play more significant role.
Hypothesis 2: Female investors have stronger risk aversion tendency than male investors when they invest internet financial products;
In addition to the t-test of the risk values of the three Internet financial products, it is also necessary to test the range of acceptable returns of investors. Acceptable return range measures the range of investors' expected return on investment and de-investment, which is one of the measures of investors' risk sensitivity in this study. By carrying out the t-test on the rate of return on investment, the rate of return on de-investment, the range and sensitivity of acceptable returns, I can further understand the differences between male and female investors in the treatment of product risks and returns, as well as the changes in returns. In addition, testing the risks and expected returns of different products simultaneously can better understand investors' views and evaluations of different Internet financial products, which affects their investment behaviours of different products.
Then, based on the confirmation that the gender has a significant impact on investors' risk assessment and the range of acceptable expected return, Through the comparison between male and female investors risk assessment of three kinds of Internet financial products and the range of acceptable return on investment, I study whether female are more risk-adverse than male investors. That’s how I test second hypothesis. And factors are listed below:
Inf_r/p2p_r/cf_r: Risk rating of Internet Fund/ P2P/Crowdfunding by investors;
Inf_in/p2p_in/cf_in: Expected annual return of investors to invest on Internet Fund/ P2P/
Crowdfunding;
Inf_de/p2p_de/cf_de: Expected annual return of investors to de-invest on Internet Fund/
P2P/Crowdfunding;
Inf_range/p2p_rang/cf_range: The difference between Inf_in/p2p_in/cf_in and
Inf_de/p2p_de/cf_de;
Inf_sen/p2p_sen/cf_sen: Risk sensitivity of investors to invest on Internet Fund/ P2P/
Crowdfunding; Equals to the ratio of Inf_range/p2p_rang/cf_range on the Inf_in/p2p_in/cf_in.
Hypothesis 3: Personal traits such as age and income have significant impact on the investment behaviour of both male and female investors;
For the third hypothesis, the OLS model is used to test which factor significantly affect the investment behaviour on the internet finance. In order to build the regression model, I review some literatures to set a regression model.
Knight (2002) identified the differences between the risk and uncertainty. The risk is the percentage or probability of the possible event. That is to say the risk is the probabily distribution of known event. The uncertainty is the uncertain probability of uncertain event. That means the result of event is unknow. Investors intend to give a percentage to the uncertain event and this percentage can be used as the risk sensitivity of investors.
return rate of investing and de-investing measures the risk sensitivity of investors. The social influence measures whether the friend, relative, college, experts and successful investors have impact on investors’ behaviour.
In order to measure whether the gender is a significant factor of these three indexes in the internet finance, several factors are chosen as control variables. In the area of investment behaviour on internet finance, there are several factors have been suggested to be influential. Research by Freedman and Jin (2011) finds that P2P investor cannot identify the market risk in the initial stage of the investment, but they can decrease the portfolio risk through gradual self-learning. This suggests that investment experience and knowledge background make impact on investors’ behaviour. Li and He (2006) find that risk control, interest payment frequency, investment period and product scale will make effect on investment behaviour. Han and Li (2016) find that the crowdfunding platform, liability of capital founder, return rate and novelty of programmes will make impact on investment behaviour. Researchers find investment behaviour is related to financial knowledge (Hilgert, et al., 2003), gender (Farrell, et al., 2016), age, investment scope and risk attitude (Veld-Merkoulova, 2011). In addition to investors’ characteristics, some researchers also find that easy-use system, information safety and social influence of internet platform will also affect investor behaviour by using TAM model. (Yoon & Steege, 2013) (Venkatesh, et al., 2003) (Cox & Stuart U, 1964).
Table 3.1 Variables of the research
Variables Measurement
Explained Variables
Investment behaviour
Invest on internet finance or not (Inv_intf)
Explanatory
Variables Gender Gender (gender)
Control Variables Personal traits
Age
Education level (edu) Income
Saving City Industry Cohabit
Investment experience (inv_exp)
The regression model：
In this research, 5% is taken as significance level. In other words, if the P-value is lower than 5%, then the null hypothesis is rejected, otherwise the null hypothesis can’t be rejected.
3.2 Questionnaire Design and data collection
The questionnaire mainly contains two parts (See Appendix 1). The first part aims to collect participants’ opinion and behaviour on the Internet financial platform. The second part is the personal information of participants. Participants are volunteer for participation and they are informed about the aim of questionnaire and the research in advance. Participants can stop the survey or recall the data if they want. The participation is anonymous and participants’ personal information are kept in confidential. There are 21 questions in total. Ten of them measure the risk and return range of participants and other eleven questions are about personal information. We put personal information at the second part in case that participants exit the questionnaire halfway, so that I can still collect significant information as much as I can.
In the first part, there are nine questions about the internet finance and one about the power of social influence on investors. In these nine questions, the risk rating, expected return rate of investment and de-investment of the Internet fund, P2P and Crowd Funding are asked respectively. The first three questions are about the Internet fund. The first question asks participants to rate the risk of the Internet fund in the scale of 1 to 10. If participants think the Internet fund is very safe, they rate 1 while they rate 10 if they think it is very risky. The aim of this question is to measure how risky do they think the Internet Fund is. The scale is used in order to quantitatively measure the risk of the Internet Fund. The quantitative data is more convenient to conduct the research and data analysis.
those of the Internet fund. The reason why I put three Internet financial products in this order is because that they have different risk and return. In general, the Crowdfunding is riskier than P2P and P2P is riskier than the Internet fund. It is reported that the annual return rate of the Internet fund is in the range of 4.3% to 6.3% in 2018, while that of the P2P is 9.45%. The annual return rate of the Crowdfunding project is 37.3% for 1 million RMB investment, 25.95% for 1 to 5 million RMB investment and 13.8% for more than 5 million RMB investment.
The tenth question is about the social influence. Participants are asked to choose which would encourage them to invest in Internet Fund. The social power of advertisement, family, friend, college and success story are listed. There is not a consistent conclusion on whether female or male investors more easily follow others in existing research. The result of this question is compared in the latter part to see how female and male investors are influenced by others and whether there is any difference.
Following this question, the gender, age, education level, monthly income, saving, living city, industry and cohabit situation are asked. These questions are listed in order to collect personal information of investors. These factors play the role of control variables to make sure I can find out accurately whether there is significant difference between female investors and male investors. The literature review part has discussed what factors will make impact on investors’ behaviour and that is why these factors are chosen as control variables.
The investment experience is also mentioned. It is believed that whether investors have investment now or before will make impact on investors’ behaviour. Whether investors have investment on internet financial products is also asked and the assets they currently hold are asked.
The questionnaire is designed as digital version. Both the English and Chinese version are designed in order to eliminate the language barrier. Participants can choose to answer questionnaire in English, Chinese or bilingual version. The English version for international participants is uploaded on Qualtrics and distributed through Facebook. Due to the firewall of the Chinese internet, the questionnaire for Chinese participants is uploaded to Wenjuanxing1, which is the top questionnaire platform in China and distributed through the Wechat, Tencent QQ2 and Weibo3.
The questionnaire is distributed online and offline. There are in total 325 responses and 267 of them are kept. The invalid questionnaires are deleted due to several reasons. Firstly, some
1 Wenjuanxing is the most powerful research tool in China. More information available in:
https://medium.com/@williamkuo1988/china-market-research-tool-e612d559b649
2 Wechat and Tencent QQ are the most popular online communication application. More information available in:
https://www.tencent.com/en-us/index.html
participants do not know well about the internet finance and they rate risk or give expected return blindly. For example, some people give higher expected annual rate of de-investment than that of investment, which is illogical. Secondly, some quit survey halfway and there is few useful information. Thirdly, according to the test of credibility, some responses are suggested to deleted to improve the credibility.
3.3 Research Structure
The research begins with the proposed questions that “how female investors behave in the internet finance in China” under the context that Chinese internet financial is growing rapidly and there are more and more female investors. Then the literatures are reviewed about the theories in the field of internet finance and investor behaviour. The difference between female and male investors are also reviewed. Based on the literature review, the questionnaire is designed as the main methodology to collect data. Then the data is analysed by using t-test in order to find the differences between female and male investors on the Internet financial investment. The conclusions are summarized and the recommendations are given to both investors and financial institutions.
IV.
Data
4.1 Summary of Data
This summary of data is based on 267 effective response with 211 female participants (79%) and 55 male participants (21%). The summary of data is presented in the Table 4.1 with different panels. In general, the average, maximal, minimal, average of female sample and male sample are summarized to demonstrate the basic characteristics of data. Graphs such as bar are depicted to show the trend or comparison more clearly.
Table 4.1 Data summary
Panel A Risk scale of internet finance given by female and male
Total average
Maximal Minimal Female
average
Male average
Internet Fund 4.39 10 1 4.08 5.66
P2P 6.03 10 1 5.73 7.29
Crowd funding 6.79 10 3 6.67 7.37
Panel B Expected annual return rate to invest
Total average
Maximal Minimal Female
average
Male average
Internet Fund 6.04% 30% 1% 5.82% 6.81%
P2P 15.28% 30% 1% 16.04% 12.33%
Crowd funding 25.17% 200% 1% 26.65% 19.78%
Panel C Expected annual return rate to de-invest
Total average
Maximal Minimal Female
average Male average Internet Fund 2.94% 10% 1% 2.87% 3.17% P2P 7.25% 20% 1% 5.82% 6.39% Crowd funding 7.92% 150% 1% 7.46% 10.21% Panel D Range Total average
Maximal Minimal Female
average
Male average
Internet Fund 3.10% 20.00% 0.00% 2.95% 3.64%
P2P 8.03% 10.00% 0.00% 10.22% 5.94%
Crowd funding 17.25% 50.00% 0.00% 19.19% 9.57%
Panel E Social influence
Option Female Female% Male Male% Percentage
Advertisement 77 14.89% 8 5.59% 12.69%
Family investing 109 21.08% 31 21.68% 20.90%
Friend investing 111 21.47% 37 25.87% 23.58%
College investing 127 24.56% 25 17.48% 22.69%
Success stories of investing 89 17.21% 35 24.48% 18.51%
Others 1 0.19% 3 2.10% 0.60%
None 3 0.58% 4 2.80% 1.04%
Total 517 100.00% 143 100.00% 100.00%
Panel F Personal Background
Total average
Maximal Minimal Female
average
Male average
Age 31.7 55 17 33 26.5
Save fraction of income 32.01% 90.00% 0.00% 33.94% 30.48%
Panel G Graduation Panel H Industry
option Number Percentage option Number Percentage
<High school 6 2.25% Student 51 19.10%
High School 9 3.37% Academia 74 27.72%
Bachelor 192 71.91% Finance 36 13.48%
Master 58 21.72% Manufacturing 43 16.10%
Ph.D. 2 0.75% Government 51 19.10%
Panel I Monthly income Others 12 4.49% option Number Percentage Panel J Investment experience
<3000 (<400) 24 8.99% option Number Percentage
3000-5000 (€400-€660) 18 6.74% Yes, I have investment currently. 191 71.54% 5000-10000 (€660-€1300) 199 74.53% Yes, I had investment previously. 44 16.48% 10000-15000 (€1300-€2000) 14 5.24% No. I don’t have any investment
experience.
32 11.99%
15000-20000 (€2000-€2600) 5 1.87% Panel K Investment experience
20000-25000 (€2600-€3300) 3 1.12% option Number Percentage
25000-30000 (€3300-€4000) 3 1.12% Yes 199 74.53%
>30000 (>€4000) 1 0.37% No 68 25.47%
Panel L Holding asset
option Number Percentage option Number Percentage
Deposit 229 22.50% Fund 78 7.66%
Investment PPE 52 5.11% Foreign Currency 47 4.62%
Social Security 88 8.64% Derivatives 48 4.72%
Stock 84 8.25% Bond 40 3.93%
Loan 63 6.19% Commodities 40 3.93%
Financial products 71 6.97% Real Estate 68 6.68%
Female investors rate lower than male investors on all three products (See Graph 4.1), which shows that internet financial products are much safer in the views of female investors than in the views of male investors.
Comparing the expected annual return rate to invest and de-invest on three financial products, it can be found that female and male investors have different range of expected return. Firstly, the female investors expect lower return rate on the internet fund but higher rate on P2P and Crowd Funding for investment than male investors. Female investors have lower expected return on male investors on internet financial products for de-investment. This means female investors have lower limitation and would like to bear lower return rate. The reason can be explained by risk rating. Female investors rate lower on risk than male investors do, that’s why male investors are more careful and withdraw investment on higher level. But it is interesting to find that male investors do not require higher return rate to compensate the high risk they take when they decide to investment. Compare to male investors, although female investors withdraw in a lower level, they require higher when they invest. This results a larger range of female investors than that of male investors. The higher the range, the lower the sensitivity. The result shows that female investors are less sensitive on change of expected return than male investors, which shows female investors are actually less risk-averse than male investors. This result is against with the previous literatures that female investors are more risk-adverse (see eg. Wang, 1994; Nancy Ammon Jianakoplos and Alexandra Bernasek, 1998; Annika E. Sunde´n and Brian J. Surette, 1998; Schubert, 1999), for female investors are more likely to invest than male investors when they facing with the same internet financial products, because female investors rate lower risk on these products. The reason behind this behaviour is unknown but it encourages female investors to purchase high risk products. This is against the theory of Dickson (1967) who find female investors prefer short-term and low-risk products.
4 4.5 5 5.5 6 6.5 7 7.5 8
Internet Fund P2P Crowd funding
ris
k
ra
tin
g
Graph 4.1 Risk rating of investors on internet financial products
(1-10)
The result of power of social influence demonstrates that most of investors are influenced by friends, college and family. The Graph 4.2 and 4.3 clearly show the differences distribution of social influence on female and male investors. The Panel E in Table 4.1 also shows how many clicks on each social influence and their percentage in total clicks. Both female and male investors would be encouraged by family, friend, college and success stories, but female investors are more likely to be influenced by college rather than other factors while male investors prefer success stories rather than college investing. It is also obvious that female investors are more likely to be encouraged by advertisements than male investors. This result is consistent with the conclusion suggested by previous literatures. It is reported that Female investors believe more on professional financial consultants than men (Shanghai Advanced Institute of Finance, 2018) and in this research, female investors believe more on advertisement and college (who are strangers) while male investors rely more on family and friends investing. Thus, we can conclude that the Hypothesis I will not be rejected but specific variety of social influence should be taken into consideration when assess social influence on investors.
The average age of total participants is 32. Most of them earns ￥5000-10000 (€660-1300) monthly and save 30% of their salary. Most of them have degree of bachelor but this data can’t represent the whole Chinese female because most of respondents live in urban with high education level and people who live in rural area are not covered.
When asking whether they have investment now or had before, 71.54% of respondents have investment currently. 74.53% of participants have investment experience on internet finance. They hold various kinds of assets.
4.2 Regression and t-test
The Table 4.2 below shows the regression and t-test result of on Hypothesis II. In order to find out whether there are differences between female and male investors on risk aversion tendency,
15% 21% 21% 25% 17% 0%1%
Graph 4.2 Social Influence on female
Advertisement Family investing
Friend investing College investing
Success stories of investing Others
None 6% 22% 26% 17% 24% 2%3%
Graph 4.3 Social Influence on male
Advertisement Family investing
Friend investing College investing
Success stories of investing Others
I conduct t-tests of gender on risk rating, annual return rate of investment and de-investment as well as the risk sensitivity of investors on three financial products (Internet fund, P2P and Crowdfunding). Several conclusions can be summarized from the table 4.2.
Table 4.2 Results of regression and t-test on Hypothesis II
Regression t-test
explained explanatory R^2 Adjusted R^2 Coef t Standard error con F p Inf_ri gender 0.0709 0.0674 1.339919 4.5 *** 2.772308 20.21 *** Inf_in gender 0 -0.0038 0.000834 0.05 0.963 0.066625 0 0.9628 Inf_de gender 0.0112 0.0074 0.004032 1.73 * 0.023703 2.99 0.0848 Inf_sen gender 0.0017 -0.002 -0.01844 -0.68 0.499 0.531059 0.46 0.4994 Inf_range gender 0.0003 -0.0048 -0.00608 -0.23 0.82 0.04664 0.05 0.8197 p2p_ri gender 0.0926 0.0892 1.325749 5.2 *** 4.426154 27.05 *** p2p_in gender 0.0916 0.0882 -0.03405 -5.17 *** 0.195979 26.74 *** p2p_de gender 0.0161 0.0124 0.0065 2.08 ** 0.049797 4.33 ** p2p_sen gender 0.1086 0.1053 -0.13843 -5.68 *** 0.763682 32.3 *** p2p_range gender 0.0889 0.0843 -0.03529 -4.38 *** 0.14711 19.22 *** cf_ri gender 0.0169 0.0132 0.426883 2.14 ** 6.273846 4.56 ** cf_in gender 0.0305 0.0268 -0.06572 -2.89 *** 0.335387 8.32 *** cf_de gender 0.0105 0.0067 0.024071 1.68 * 0.047545 2.81 * cf_sen gender 0.1392 0.136 -0.18207 -6.55 *** 0.880879 42.85 *** cf_range gender 0.1006 0.0961 -0.08418 -4.7 *** 0.302716 22.05 ***
Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Secondly, the t-tests of the regression of risk rating on gender are all significant for three products, which shows that there is significant difference between female and male investors on their view on risk rating of Internet financial.
Thirdly, there is significant difference between female and male investors on their range on p2p products and crowdfunding but not difference on internet fund. In other words, female and male investors have different range of expected annual return rate for investment and de-investment. The reason why they have difference on P2P and Crowdfunding but not internet fund may be because that the internet fund is too safe to be taken into consideration. The highest annual return rate of internet fund is 6.8% in 2018 and the volatility range of annual return rate is small, so the difference between investors with different gender is not significant. In order to find out what factors significantly affect investors behaviour and whether they paly different role in female and male investors, I conduct following tests on Hypothesis III.
The following tests aim to test what factors make impact on differences between female and male investors. Based on Hypothesis III, the test mainly focus on the influence of personal traits of investors on their investment behaviour on internet finance. Firstly, in order to find whether all factors have the same impact on the female sample and the male sample, I conduct the following regression. Table 4.12 Shows that female and male investors behaviours are affected by different factors. For both female and male investors, investment behaviour is significantly related with their investment on the internet finance, but investment experience has a stronger explained power on female’s behaviour that on that of male investors. For female investors, the age is a significant factor. For male investors, saving plays an important part in their investment on the internet finance.
Table 4.12 investment behavior OLS for female and male investors
The dependent variable is the investment activity on the internet finance (Int_intfi). Standard errors are reported in parentheses. P-values smaller than 0.01, 0.05 and 0.10 are indicated by ***, **, and *, respectively.
The test equation is: 𝑖𝑛𝑣𝑖𝑛𝑡𝑓𝑖 = 𝛽1+ 𝛽2age + 𝛽3𝑖𝑛𝑣𝑒𝑥𝑝+ 𝛽4𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + 𝛽5𝑖𝑛𝑐𝑜𝑚𝑒 + 𝛽6𝑠𝑎𝑣𝑖𝑛𝑔 + 𝛽7𝑐𝑖𝑡𝑦 + 𝛽8𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝛽9𝑐𝑜ℎ𝑎𝑏𝑖𝑡
(1) (2)
VARIABLES male female
industry -0.017 0.008 (0.037) (0.011) cohabit -0.019 0.100* (0.124) (0.053) Constant 0.534 0.327** (0.500) (0.150) Observations 55 212 Adjusted R-squared 0.261 0.678
Then I conduct an OLS regression to find which factor is significantly related with the investment activity on the internet finance for the whole sample. The explained factor is
inv_intfi, which indicates whether investors invest on the internet finance or not. From the table
4., only age, investment experience, city, and industry are significant in the level of 0.05 while the gender is insignificant. After deleting education, saving, city, industry and cohabit, all explanatory factors and the constant are significant and the adjusted R-square is acceptable. I found that age, income, investment experience (inv_exp) and gender are significant influential factors on investing in Internet financial products (inv_intfi). The r-square of the regression is 0.7528 and adjusted r-square is 0.7491. The gender, age and investment experience are positively related with the investment behaviour while the income is negatively related with the investment behaviour. What’s more, the coefficient of age and income is much smaller than gender and investment experience, indicating that the latter two factors are more powerful.
Table 4.13 investment behavior OLS for female and male investors
The dependent variable is the investment activity on the internet finance (Int_intfi). Standard errors are reported in parentheses. P-values smaller than 0.01, 0.05 and 0.10 are indicated by ***, **, and *, respectively.
(0.013) cohabit 0.103* (0.052) Constant 0.261 (0.181) 0.514*** Observations 267 (0.086) Adjusted R-squared 0.546
V. Results
5.1 ConclusionThis research mainly discusses how female investors behave on the internet finance in China and what factors make impact on their behaviour. Literatures in the field of investors behaviour and internet finance are discussed. Through the survey research, I find that female investors have their special characteristics and there are several factors drive their behaviour. Some researchers think that female investors are more risk-averse than male investor (see eg. Wang, 1994; Nancy Ammon Jianakoplos and Alexandra Bernasek, 1998; Annika E. Sunde´n and Brian J. Surette, 1998; Schubert, 1999). However, in this research, I find that female have larger range of the expected annual return on the investment and de-investment than male investors on P2P and Crowdfunding. In other words, they are not more risk-averse than male investors. In addition, female investors rate internet financial products in lower level risk than male investors do. The reason behind this behaviour is unknown but it encourages female investors to purchase high risk products. This is against the theory of Dickson (1967) who find female investors prefer short-term and low-risk products. Female investors are more likely to encouraged by college investment and this can be explained by research of Shanghai Advanced Institute of Finance in 2018 that female investors believe more on professional financial consultants than male investors do.
5.2 Limitation
There are still some limitations of the research. Firstly, the sample is not so representative due to the limitation of time and resources. The participants are mostly urban residents who only take a small part in Chinese total population. The salary of most participants is ￥5000-10000 (€660-1300) and it is too limited. More samples should be reached, such as the poor and the extreme rich people. The limitation of sample brings negative impact on data analysis. In general, the income should be significantly and strongly related with investment behaviour while in this research, its effect is week. In other words, the diversity of the sample is week. Secondly, due to the limitation of survey method, there are only 21 questions on questionnaire. Questions are limited to the facial phenomenon. It is better if there is interview and more deep information can be collected, so as to analyse the psychological motivation of investment. Thirdly, this research only analyse data on three financial products. However, there are various type of products in Chinese internet financial market. It is insufficient to list only three products. In addition, there are lots of varieties of these products. It would be better if the research can focus on one specific product and deeply analyse investors’ behaviour.
5.3 Recommendation
Based on the conclusion of the research, some recommendations are proposed. For female investors, they are encouraged to learn more financial knowledge and actively search for financial products. It would be wise if they use internet and invest on Internet financial products. In addition, when female investors rate risk of financial products, it would be better to trust in professionals to correctly measure the risk scale and thus they can ask for beneficial return rate. For financial institutions, it would be wise to catch female customers for they are rising in the financial market. Financial institutions can specifically design a financial product for their target customers who are nearly 30 years old and have her own plan to manage wealth. High-risk product is possible for female investors have higher High-risk range than male investors. Advertisement might be an effective way to attract young female investors.
VI.
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VII.
Appendix
1. Questionnaire
This survey contains 21 questions on Internet investment and takes at most 5 minutes to complete. The findings will only be used for writing the dissertation of MSc of Finance at the University of Groningen. Your private information will be kept confidential.
1. Internet Fund is a kind of financial product issued jointly by fund companies and internet companies, such as Yu’E Bao, a kind of capital fund launched by Tian Hong Fund and Alipay. Investors can buy fund products online to earn interest.
a) On a scale of 1 to 10, in your opinion how risky Internet Fund is? (10 means the most risky)
b) What expected annual return in % would make you invest in Internet Fund? c) What annual return in % would make you withdraw the investment in Internet
Fund?
2. P2P (Peer-to-Peer lending) platforms match lender and borrower on the internet based on their personal profile. Lenders can invest in P2P platforms to earn higher interest payment than banks. The amount of debt is limited by the credit profile of borrower.
a) On a scale of 1 to 10, in your opinion how risky P2P is? (10 means the most risky)
b) What expected annual return in % would make you invest in P2P?
c) What annual return in % would make you withdraw the investment in P2P? 3. Crowdfunding refers to raising funds for projects through group purchase or
pre-purchase online. Investors can get products or equity as payback.
a) On a scale of 1 to 10, in your opinion how risky Crowdfunding is? (10 means the most risky)
b) What expected annual return in % would make you invest in Crowdfunding? c) What annual return in % would make you withdraw the investment in
Crowdfunding?
4. Which of the following would encourage you to invest in Internet Fund? Advertisement
Family investing Friend investing College investing
Others None
5. What is your gender? Female
Male Other
6. Which year were you born?
7. What is your highest education background? Lower than High school
High School Bachelor Master PH.D.
8. What is your monthly income in RMB? <3000 (<€400) 3000-5000 (€400-€660) 5000-10000 (€660-€1300) 10000-15000 (€1300-€2000) 15000-20000 (€2000-€2600) 20000-25000 (€2600-€3300) 25000-30000 (€3300-€4000) >30000 (>€4000)
9. What fraction of you monthly income do you save? (%) 10. Where do you live?
Beijing Shanghai Guangzhou Shenzhen Provincial capital Outside China Other cities
No
13. Do you currently have investment? Or did you previously have investment? Yes, I have investment currently.
Yes, I had investment previously.
No. I don’t have any investment experience.
14. Do you currently have investment in internet financing products (Fund, P2P, Crowdfunding etc.)
Yes. No.
