With the remarkable development of Fintech in the financial industry in recent years, many traditional financial products and services are now available online. One of the areas of innovation is in robo-advising [1
]. Robo-advising is a platform that consists of interactive and intelligent components, through which customers can receive personalized investment services online, instead of by making appointments with human advisors. It is expected to be the next step in the evolution of asset management and financial advice [3
]. Further, to the enforcement of social distancing due to the coronavirus disease 2019 (COVID-19) pandemic, robo-advising is of great importance in the development of non-contact advisory services.
There are three major steps involved in robo-advising: product configuration, matching, and maintenance [1
]. In the product configuration step, a robo-advisor identifies the customer’s investment needs and objectives, and measures the risk and investment related to the customer based on profiling. In the matching step, a robo-advisor processes the collected information and generates a recommendation for the customer. In the maintenance step, a robo-advisor tracks the asset performance and reacts to changes by rebalancing the portfolio. In other words, risk profiling for robo-advising is the first step in achieving the sustainability of investment products and services, because a comprehensive risk assessment is not only a regulatory requirement but also builds trust and develops relationships with customers [7
], as well as contributing to financial inclusion [10
]. However, it is still at a nascent stage [11
] and surprisingly little research has been conducted in this field [7
]. Correspondingly, two important questions that may draw researchers’ and practitioners’ attention include: (i) What are the important risk factors for profiling customers’ risk that we have to consider in robo-advising? (ii) How can we develop an organized and theoretical framework and protocol for systematic research on robo-advising?
There are two important behavioral theories on risk preference—expected utility theory and prospect theory. In expected utility theory [13
], choices are coherently and consistently made by weighing the outcome of actions by their probabilities. If an investor is risk averse, s/he would refuse a fair gamble. A concavity of the utility function is expected. However, prospect theory suggests “several classes of choice problems in which preferences systematically violate the axioms of expected utility theory” (p. 263) [16
]. It indicates that investors value losses and gains differently and they make decisions based on perceived gains rather than perceived losses [17
]. We may conclude that risk preferences, tolerance, and attitudes are multi-dimensional and multitude and not easily assessed [18
Currently, banks and investment service providers are used to adopt their own risk assessment or profiling questionnaires to collect customers’ information relating to their attitudes toward risk and their risk tolerance [9
] and the assessment results from the questionnaire is often a major input into the asset management and financial advice [20
]. These financial institutions usually design the questionnaires and develop the questions with reference to scientific research and/or through their internal investment advisory teams [21
]. In addition, the questionnaires have to meet the compulsory requirements of regulatory commissions. Though the accuracy of the questions may not be a problem, there is no recognized standard in regards to which questions to include or exclude, nor to determine the types of questions that should be asked or an appropriate length for the questionnaire [22
]. This paper seeks to investigate the risk profiling questions commonly asked by banks and investment service providers to assess their customers’ risk in regard to investments, and then aims to group them into different risk factors through content analysis. These important risk factors are recommended for inclusion as part of the robot-assisted profiling of an investor’s risk preference. We also make suggestions for the launch of such automated investment services.
2. Materials and Methods
We collected representative risk assessment questionnaires from banks and investment service providers (e.g., insurance and financial planning companies, and government-subvented fund management organizations) in both Hong Kong and outside of Hong Kong.
We first searched for risk assessment questionnaires through Google and Microsoft search engines, using the following key words: “risk assessment questionnaire”, “risk profile questionnaire”, “risk tolerance questionnaire”, “investor profile questionnaire”, and “investment risk profiling questionnaire”. We gathered publicly available questionnaires from the targeted company types. Some of the questionnaires had a score assigned to each option in a question, so it was possible to compute our own risk grade or score by adding up the scores of all the options selected. We further obtained questionnaires via personal accounts and mobile apps. We needed to be existing customers of some companies to access their questionnaires. In these instances, when we completed a questionnaire online, we would receive a risk grade or score, but there would be no follow-up action from the companies. In total, we collected 20 questionnaires with 180 questions.
Of these 20 questionnaires, 11 are used in financial institutions in Hong Kong and one of them is used in a government-subvented fund management organization, while nine questionnaires are used outside Hong Kong. Non-Hong Kong financial institutions are located in various countries, including Australia, Canada, the UK, and the USA. The length of the questionnaires varies from six to 13 questions, excluding questions regarding personal information. The result of 14 questionnaires (10 from Hong Kong and four from overseas) is a risk class/score/tolerance level ranging from low to high risk, while the result of five questionnaires from outside Hong Kong provides a recommended asset allocation of different asset classes. One questionnaire from Hong Kong does not include a result. A summary of the questionnaires is shown in Table 1
. We profiled the institutions that provided the questionnaires for our analysis: Five are commercial banks, seven are investment banks, seven provide insurance and/or financial planning services, and one is a government-subvented body. All of them are well-established institutions; some are market leaders. The backgrounds and the diversified business nature of the institutions provide strong evidence that the questionnaires are trustworthy and representative. A profile of the institutions is shown in Appendix A
We use inductive content analysis to examine the content of the questionnaires because no previous studies deal with this area; there is thus no theory to test [23
]. The inductive approach identifies patterns through repeated observations and comparisons of the raw materials and then develops key themes through inductive reasoning, which reduces the number of specific observations to broad generalizations. Unlike quantitative research, qualitative research often concerns developing a depth of understanding rather than a breadth [24
]. The question of what sample size is needed for qualitative research is frequently asked by individual researchers [25
]. Some researchers suggest that qualitative sample sizes of ten may be adequate for sampling among a homogenous population [26
]. In addition, in a meta-analysis of 560 academic qualitative studies, the distribution of sample sizes used was found to have sample sizes that were multiples of ten [27
]. In this regard, the 20 questionnaires can be of importance and are useful to generate great insight.
First of all, the 180 questions are classified into different types of questions according to the manifest meanings both in the questions and the options available to the respondents. The number of types created is not pre-set and is solely based on examining the nature of the questions and their options to identify the numbers and types of similarities. In other words, all the types are derived directly from the questionnaires themselves.
Different companies have different styles in terms of how they ask questions. Taking the question type “Investment Time Horizon” as an example, one of the companies asks in a very direct manner:
Question: In general, what is the time period intended for your financial investment?
Less than 1 year
1 year to less than 3 years
3 years to less than 5 years
5 years to less than 8 years
8 years or above
While another company puts it this way:
Question: When do you expect to start withdrawing your investment?
Above 20 years→ 11–20 years→ 6–10 years→ 1–5 years→ Less than 1 year
By examining the two questions above and the options provided in answer to them, the two questions are obviously asking for the same information; we thus categorized them as the same type. As a result, about 97% of the 180 questions are grouped into 15 different types. The approximately 3% of unclassified questions are usually background information questions (e.g., “What is your total monthly income?”) or firm-specific questions (e.g., “Please select up to six currencies you may consider for investments in this account”). The name of each type is simply a direct description of the nature and manifest meaning of the questions grouped under that type. For comparison purposes, we break down the “question type” distribution by Hong Kong questionnaires and non-Hong Kong questionnaires (Table 2
), so that we can easily investigate whether or not there are any differences in questionnaire design between Hong Kong and overseas companies. Relatively, Types 1, 2, 3, 4, 6, 7, 9, 10, and 12 are popular questions, accounting for 79.4% of the 180 questions. We provide a brief summary of these classified types in Appendix B
4. Reliability and Validity
The reliability and validity of the factor identification process must be considered. As with all methodologies, reliability and validity are the most fundamental issues associated with the application of content analysis [34
]. The author in [34
] suggests: “To make valid inferences from the text, it is important that the classification procedure be reliable in the sense of being consistent: Different people should code the same text in the same way. Also, the classification procedure must generate variables that are valid” (p. 12). Two methods of checking for reliability and validity were used in the present study.
First, we asked a research assistant who was not involved in any other aspect of this study but was familiar with the process of category generation in content analysis to read through the 20 questionnaires, then create his own identification systems and identify risk factors independently. We then compared and discussed our work. To further ensure the classification was clear and representative enough, we invited an academic in finance and a risk professional to review the classification and risk factors identified. The reviewers agreed with the classification and risk factors identified. Only minor changes to the wording were made.
The second check for reliability and validity is to cross-validate the findings with the risk assessment questionnaires designed by other recognized international financial institutions not included in the sample of this study. Their questionnaires are available only to their existing customers. Deloitte and Wealthfront [35
] do not disclose their questionnaires but they do share the methodologies they use to assess individual risk preference on their own websites. Their common characteristics are the use of several factors to determine the risk score/category of a customer. Each factor consists of several questions in the questionnaire. These factors can be broadly described as the ability to take risks, tolerance of risk, and attitude toward risk. Information concerning these factors is obtained both directly and indirectly from the questions related to investment goals, investment experience, and hypothetical market situations, etc. Exploring the design of the questionnaires used by these two financial institutions enhances the reliability and validity of our findings.
There exists no extant comprehensive analysis of the methods involved in robo-advising and calls have thus been made for more research on robo-advising [37
]. In this study, we use content analysis to understand existing risk assessment and identify patterns through the investigation and comparisons on the 180 questions in the 20 questionnaires. We then develop five important risk factors to profile individual risk preference through inductive reasoning. This can help us to set boundary to build theory for robo-advising risk profiling [40
]. The analysis results match Bhatia et al.’s [11
] idea to design a robo-advisory service based on investors’ profiles, risk tolerance, and risk analysis, as well as Singh and Kaur’s [41
] suggestion to consider risk tolerance, stage in life, net worth, experience with investments, and investment objectives in wealth management.
How different banks and investment service providers make use of the five identified risk factors in the assessment of individual risk preference through robo-advising could vary. The methods used should comply with company risk policies and align with its business strategies. In regards to the construction of risk assessment questionnaires, such as how to design a question, how many questions should be derived from each risk factor, how much weight should be carried by each factor, and how to calculate the final risk scores/grades, it is recommended that practitioners draft a preliminary version first and then conduct a customer survey to collect customer feedback. It is then possible to validate the risk assessment results with the customer data. For example, a sample of customers who hold or have ever held investment products could be invited to complete a draft version of the risk profiling online. Next, their feedback can be gathered to fine-tune the questionnaire and their preliminary risk scores/grades can be cross-checked with the risk categories of the investment products at hand. An extra benefit of a customer survey is that it enables research into customers’ adoption of robo-advising.
In addition to integrating these five risk factors into the risk profiling assessment, we make five crucial implementation recommendations below in order to make the risk profiling involved in robo-advising even more successful.
First, the total number of questions should be limited to make the message clear that this assessment is much simpler and briefer than human assessments have been in the past. Second, questions should be simple and straightforward, to allow customers to provide valid responses instantly without the help of human consultants. Third, customer responses throughout the assessment should be consistent. If response inconsistency exists across any risk factors, the robot should prompt the customer to re-input the responses. Fourth, the risk scores/grades should be updated on a regular basis and the next update time should be determined by the company hosting the robo-advising. The information provided by investors is primarily based on one’s own perceptions and investment decisions are mostly driven by emotions. Hence, the degree of risk one could take or how much loss one could bear is likely to change from time to time, especially when suffering investment loss. This reminds those with new experience of the price fluctuation of their investment products to review their risk preference. The last recommendation is that customers cannot ask to adjust their risk scores/grades within a short period of time, to avoid customers timing the market by buying investment products with risk grades higher than their original risk score/grade.
Two challenges presented by robo-advising are the lack of personal customization available for clients [42
] and the lack of confidence in the ability of algorithms to perform tasks [43
]. From a theoretical viewpoint, this study is a useful starting point for the development of an organized and theoretical framework and protocol for systematic research on robo-advising. It also represents a critical starting point for the development of methodology that can be used to evaluate risk and to recommend appropriate services and products to clients who have different risk preferences and needs. These elements are important in building trust with customers. Regarding practical significance, with our findings, managers can gain a better understanding of the questions that are useful for risk profiling and ways in which to develop risk profiling for robo-advising.
Robo-advising has become increasingly important for the sustainability of investments, as it can not only minimize costs and enable 24/7 services, but is also contact free [44
]. Financial institutions providing wealth management have to make a sound risk profiling model [11
]. However, there is no recognized standard for the robot-assisted profiling of an investor’s risk preferences and there are limited numbers of studies on robo-advising in the literature. The current study attempts to fill in this research gap. It develops a framework on risk profiling through content analysis and provides answers in regards to what kinds of questions are relevant to clients’ risk profiling and how to assess their relevance.
In this study, we focus on the Hong Kong environment, but similar research can be replicated in other regions or countries, subject to their cultural, economic, and financial characteristics. Future research could also compare differences in the risk profiling of robo-advising in different regions or countries, and develop a methodological framework for personalized services offered through robo-advising. To the best of our knowledge, this is a pioneering study in the field of profile risk and robo-advising. The present study provides an organized and theoretical framework for researchers to use when conducting further studies aiming to integrate individual investors’ risk preferences into robo-advising.