Robo Advisors Research Articles

Determinants of Technology Adaption Within the Framework of TOE: An Insurance Sector Perspective

Over the last four years, several interesting technology drivers have hit the market. Each of the technology has affected the insurance industry significantly. The cost of acquiring the customer through the digital medium is the least compared to the traditional methods. These changes in the industry could be analyzed based on the social, technological, economic, environmental, and political aspects. The social changes include non-interaction with customary channels including agents and using social media and online channels where they can make the decisions. Robo advisors, Internet of Things, digital platforms, telematics, telemetry, data analytics, and big data are the main disruptors in the insurance industry. Big data has enabled new pricing models with greater risk segmentation. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are used for predicting customer churn in the insurance industry.

INVESTASI REKSADANA SYARIAH DI APLIKASI BIBIT

The development of technology is very rapid so that it gives birth to new products that can make it easier for humans to make any transaction easily, the development of interest in conventional and sharia investment in Indonesia is starting to move but lacks experience and knowledge to implement, the seed application takes this opportunity by creating a robo advisor best to answer the problem. There are several kinds of investments contained in the seed application, making it easier for novice investors to make transactions, both conventional and Islamic investments. This study aims to assess the effectiveness and increase the attractiveness of sharia investment enthusiasts in seed applications. Making it easier for sharia investment developers to further develop it in accordance with the provisions of the DSN MUI fatwa no. 20/DSN/MUI/IV/2001.Keyword: Investment, Mutual Funds, Sharia, Bibit

The acceptance of financial robo-advisors among investors: The emerging market study

Robo-advisory services are a relatively new concept in the financial world. However, Epperson, Hedges, Singh, and Gabel (2015) report that many investors are extremely interested in employing robo-advisors to manage their finances. Nowadays, robo-advisers develop profiles of investors using very basic surveys to determine their investing preferences. The advantage of robo-advisory is that it charges far less than traditional private bankers (PB) since robo advisors do not require additional labour (Cho, 2019). Having considered the utility of such services, this research aims to examine the acceptance of financial robo-advisors. The findings indicate that average monthly income, value invested in financial instruments, and investment knowledge affect an acceptance of financial robo-advisors as regards their effects on profits, careers in finance, and the overall economic system, and vice versa. The drawback of the study is that the results demonstrate the relationship between independent and dependent variables without delving into each variable in detail. Thus, qualitative research may be necessary for addition to quantitative one to go further into the details. The paper suggests that providing information about robo-advisors for investors can enhance the understanding of robo-advisors leading to the increasing use of robo-advisors in Thailand.

Influence of Robo Advisory in Investment Decision

This research is conducted to identify the potential influence of robo advisory, equipped with its features and technological improvements that would aid and assist in investment decision, and compare the relevancy as well as its efficiency to human financial advisor. The objective of this research is to study the discourse of robo advisor’s phase of development and the understanding on behavioral aspect in financial investment, in order to gain proper understanding on investor’s preference in accessing financial advisory services in order to aid in their investment decision. In this respect, the research is conducted through qualitative study, conducted through the process of interviewing a sample of respondents with substantial experience and exposure to financial investment, and had utilized financial advisory services, in which their response are further studied in a case basis through Analytic Hierarchy Process, to find out the relevance and the potency of robo advisors in comparison to human financial advisors in adjusting to investor’s investment decision as well as detecting behavioral investment pattern to determine its preferences to either alternatives of financial advisory. The result of the research indicates stronger preferences to human financial advisors rather than robo advisors, and emphasizes financial literacy as the most influential criteria in determining financial investment decisions.

Do Financial Literacy and Technology Affect Intention to Invest in the Capital Market in the Early Pandemic Period?

Research aims: This paper discusses the effect of financial literacy and automatic investment technology on intention to invest in the capital market during the early pandemic.Design/Methodology/Approach: The research population was students studying economics and finance in institutions located in Yogyakarta Special Region Province. The sample of 384 respondents was obtained through questionnaires distributed online. To test the impact of financial literacy and automatic investment technology on intention to participate in the capital market, multiple linear regression was used.Research findings: The researchers found that financial literacy and automatic investment technology affected students’ intention to invest in the capital market. The number of students with a moderate level of financial literacy score dominated, followed by the students with low and high literacy scores. Besides, students’ background in economic and finance appeared inadequate to solely determine the financial literacy score.Theoretical contribution/Originality: This paper contributes to the investment area, especially related to the automatic investment technology “Robo advisor,” that is still rarely studied yet, which will be a significant issue in the future. It also provides empirical results, which explain the investment intention through financial literacy. Moreover, this study was conducted during the massive growth of investors in Indonesia during the pandemic.Practitioner/Policy implication: This study provides a useful reference to the financial sector, especially the capital market. Inclusive programs regarding financial literacy should be expanded for wider society to enhance their knowledge and dismiss lack of confidence in capital market participation. Private sectors providing automatic investment technology are suggested to continue developing a more convenient application to be accessible by a broader range of society.Research limitation/Implication: The research included only students as the sample; hence, further research may use a larger area of the sample with various backgrounds and ages. Other determinants, such as norms, environment, risk, and more advanced financial literacy measurement, can also be added to enrich future studies and literature.

Do sustainable consumers prefer socially responsible investments? A study among the users of robo advisors

Do people behave consistently when it comes to sustainability? With few exceptions, most previous studies of sustainable investment behavior rely on survey responses. Numerous studies have noted, however, that peoples’ talk is often cheap when it comes to sustainable choices. We use a financially incentivized choice to study the non-investment-related sustainable behavior of the clients of three German robo advisors and relate it to their investment decisions. We find that sustainable consumption translates into a higher likelihood of choosing a portfolio following a sustainable investment strategy among the clients of a digital wealth manager that offers both conventional and sustainable investments. Sustainable consumption also translates into a particularly strong interest in the launch of sustainable investment strategies among the clients of a conventional robo advisor. The provision of sustainable investment strategies can, next to performance and costs, be a selling point for a digital wealth manager. However, our results show that self-reported sustainable consumer behavior that is not backed up by the pertinent actions is not significantly related to sustainable investment choices. Our results lend further support to the notion that studying sustainability in terms of actual choices is crucial. We provide guidance to practitioners in the financial industry on identifying investors with a potential interest in sustainable investments.

Digital innovation in wealth management landscape: the moderating role of robo advisors in behavioural biases and investment decision-making

PurposeInnovation is the way of life and we see various innovative techniques and methods being introduced in our daily life. This study aims to focus on digital innovation in the wealth management domain. This study examines the effect of usage of robo-advisory services in investment decision-making and behavioural biases, i.e. overconfidence and loss aversion. Such studies are more pronounced in developed countries and little has been studied about investor behaviour in association with advisory services in developing countries such as India.Design/methodology/approachOverconfidence and loss-aversion biases, investment decision-making and advisory services questions are measured using a five-point Likert scale. The number of respondents was 172 investors. A purposive sampling is used for gathering responses from investors. Structural equation modeling model was run using AMOS 22 version software package.FindingsThe authors found that behavioural biases positively and significantly influence the irrationalities of investment decision-making. The findings of this study also provide empirical evidence that the usage of robo-advisory services, by individual investors, is still incapable of mitigating behavioural biases, such as overconfidence bias and loss-aversion bias.Research limitations/implicationsThe sample size of this study could be a limiting factor. This study is limited only to two biases, while other behavioural biases affect the investment decision-making of the investors, which can be considered for future research along with the impact of robo-advisory services in different socio-cultural backgrounds.Practical implicationsThis study will assist fintech start-ups, banks, architecture of robo advisors, product owners and wealth management service providers improvise their products, platforms and offerings of these automated advisory services. This could help individual investors to mitigate their behavioural biases in investment decision-making.Social implicationsThis study is useful to society as the awareness of robo-advisory services is very less, at present, and there is a need to increase the usage of these services to extend the benefit of this to the lower stratum of society. These services would be useful to all investors who find it difficult to afford financial advisors and help them mitigate their behavioural biases for investment decision-making.Originality/valueThis study is the first of its type that establishes the linkage between behavioural biases, digital innovation in fintech, i.e. robo-advisory services and individual investor’s investment decision-making in individual investor of the Indian stock market.

Acceptance of digital investment solutions: The case of robo advisory in Germany

The financial services sector is undergoing substantial change due to technological innovation and digitalization. Traditional banks face intensifying competition through the market entry of digital investment platforms that make use of automated investment advisory, so-called robo advisors. Based on replica of two German robo advisors, a sample of 96 participants assessed their intention to use such digital investment services. The results obtained using partial least squares (PLS) path modelling indicate that perceived usefulness and privacy are the most decisive factors with a one percent higher perceived usefulness (higher privacy) increasing usage intentions by 0.57 % (0.25 %). The results are robust to various socio-demographic and FinTech-related controls as well as alternative estimation procedures such as generalized structured component analysis (GSCA).

Comprehensive Analysis of Machine Learning Algorithms Used in Robo-Advisory Services

Adoption of Robo advisory is rapidly increasing among retail investors. Approximately 10% of all assets under management will be managed by them by 2025. Researchers continuously work on finding machine learning and deep learning algorithms which will accurately predict the stock price movement with less error. Various Robo advisor platform providers are working on different approaches like NLP (Natural language algorithm) based sentiment analysis, hybrid time series algorithms to limit human bias, which existed in the current financial advisory services. This research compared the performance of various forecasting algorithms, which can be used in the Robo advisory framework. RMSE (Root mean square value) value is taken into consideration in this research to compare the performance of algorithms as it is a good and simple loss function. It has been observed that newer algorithms like those proposed by Facebook are able to predict stock prices pretty well, but ARIMA outperformed all the algorithms which are considered in this research.

Robo-advisors (RAs): the programmed self-service market for professional advice

PurposeThis conceptual paper draws together an interdisciplinary approach to robo-advisors (RAs) as an example of an early and successful example of automated, programmed professional services.Design/methodology/approachLittle is known about the forces driving this change in the delivery of professional service. This work explores the drivers of RAs, the degree of disruption incurred by the introduction of RAs, and how, as RAs advance, trust in algorithmic authority aids in legitimating RAs as smart information.FindingsFrom the firms' perspective, the drivers include rebranding occasioned by the financial crisis (2008), the widening of the client base and the “on-trend” nature of algorithmic authority guided by artificial intelligence (AI) embedded in RAs. This examination of the drivers of RAs indicates that professional service automation is aligned with information society trends and is likely to expand.Practical implicationsExamining RAs as an indicator of the future introduction of programmed professional services suggests that success increases when the algorithmic authority in the programmed serves are minimally disruptive, trustworthy and expand the client base while keeping the knowledge domain of the profession under control of the industry.Originality/valueTreating RAs as an early instance of successfully embedding knowledge in AI and algorithmically based platforms adds to the early stages of theory and practice in the monetization and automation of professional knowledge-based services.

Machine Learning in Automated Asset Management Processes 4.1

The traditional (human driven) process of Asset Management has become automatized by algorithmic decision trading with so called Robo Advisors (RAs). With an increasing amount of publicly available financial data, the foundation for applying machine learning (ML) algorithms has been paved. We examine the question in which process steps of automated investment advice ML algorithms could be applied and investigate which implementations have already been placed on the market. As the following study shows, (surprisingly) ML is globally still under its development phase in Robo Advisory. German and Swiss FinTech companies thereby contribute about a third to the ML solutions in our sample. The most promising technique is the usage of Text Mining for sentiment analyses, which can be used for monitoring and rebalancing purposes or future performance forecasting. Furthermore, Text Mining algorithms can be helpful for reducing information asymmetries. Embedded into early warning systems, the derived sentiment scores can be used for hedging against future price losses. This approach would be inevitably linked to an increased access of highly sensible data. Furthermore, we try to provide an explanation for the lack of acceptance of the application of ML in RA distributions. Possible reasons for this can be found in the current MiFID II regulations, which are not specified for ML. Based on these insights, we formulate first recommendations for both the provider of RA solutions as well as for the regulator.

Robo Advisory Customer Groups: Who Requires Advice?The authors wish to thank Anselm Hüwe, Andreas Pfingsten (the editor), and an anonymous reviewer for their comments, suggestions, and valuable feedback.

Prior literature has often investigated how robo advisors can broaden their customer base. This study is based on the observation that some customers value the risk elicitation of robo advisors (guidance customers), whereas others value other aspects such as the simplicity and convenience of these services. Based on empirical robo advisory data, we build machine learning models to identify guidance customers. The models make predictions based on the financial knowledge of customers to a large extent. The age of a customer, the amount invested, income, and available assets are further important determinants.

Resilience in the Storm: Adaptive Robo-Advisors Outperform Human Investors during the COVID-19 Financial Market Turmoil

Despite the increasing popularity of robo-advisors (RAs), empirical evidence on their performance, especially during market downturns, is highly limited. We study the impact of using RAs on investment performance during the 2020 financial crisis caused by the COVID-19 global pandemic. We obtain daily portfolio and transaction data of investors on an online investment platform. Besides making investment decisions by themselves, investors can also leverage an RA system offered by the platform. We match RA users with other investors who did not use the RA with similar characteristics before the market crash, then compare their portfolio returns within four weeks after the crash. We find that RA users experienced significantly fewer losses during the market downturn. Moreover, our analyses of trading strategies show that RA users held less risky portfolios, whereas other investors stayed with their status quo and did not reduce the risk of their portfolios, which partly accounted for the performance discrepancy. During the subsequent period of market recovery, we observe that RA users were able to maintain their performance advantage. Overall, by trading adaptively to adjust the portfolio risk levels in response to market movements, the RA system mitigated losses and benefited its users. Further analyses of effect heterogeneity show that younger users or users with less investment experience benefited more from the RA, because they delegated a larger proportion of their assets to be managed by the RA. Our work offers a direct empirical assessment of RAs’ performance during a severe market downturn, and advances the understanding of algorithmic decision-making in the financial markets.

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

The current research demonstrates how conversational robo advisors as opposed to static, non-conversational robo advisors alter perceptions of trust, the evaluation of a financial services firm, and consumer financial decision making. We develop and empirically test a novel conceptualization of conversational robo advisors building on prior work in human-to-human communication and interpersonal psychology, showing that conversational robo advisors cause greater levels of affective trust compared to non-conversational robo advisors and evoke a more benevolent evaluation of a financial services firm. We demonstrate that this increase in affective trust not only affects firm perception (in terms of benevolence attributions or a more positively-valenced onboarding experience), but has important implications for investor behavior, such as greater recommendation acceptance and an increase in asset allocation toward conversational robo advisors. These findings have important implications for research on trust formation between humans and machines, the effective design of conversational robo advisors, and public policy in the digital economy.

Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets

One of the important contributions of the fourth industrial revolution is the introduction of robo advisors as alternates to conventional mutual funds. Robo advisors are mechanized platforms that use automated algorithms to provide financial advice to investors. This study compares the risk adjusted performance of these automated advisors to the conventional funds that were based in the US between the years 2016 and 2019. Our results show that on average, robo advisors demonstrate superior performance as compared to equity, fixed income, money market and hybrid funds. They also out performed three prominent equity indices, and the results remained robust for different specifications of the risk to reward models. The findings demonstrated that robo advisors not only provide easy access and cost effective advice, but also dominate in the risk adjusted performance.

Do Investors Rely on Robots? Evidence from an Experimental Study

Robo advice has moved its first steps in the Anglo-Saxon countries and is now rapidly gaining market share at a global level. The phenomenon fueled a growing and still not conclusive institutional debate about potential benefits and risks to financial consumers, based also on investors’ biases and behaviours that online platforms could trigger to the detriment of robo advisees. The present paper provides some insights into attitudes and behaviours that might prevail in a digital environment among young investors, representing the category of users potentially more involved by the development of the automated advice. In detail, the study investigates whether individuals’ propensity to follow the recommendation received from an advisor changes depending on whether the advisor is a human or a robot. The analysis is based on data collected through an ad hoc developed laboratory experiment run in the Cesare Lab of LUISS University with a sample of 180 students. Students were given an initial monetary endowment and were asked to choose between six different portfolios of financial activities; after being profiled through a questionnaire aimed at eliciting their risk tolerance (Grable and Lytton’s Risk Tolerance Quiz; 2003), they received the advice, either from a human advisor or from a robo advisor (i.e. via a computer platform) depending on the treatment they had randomly assigned before entering the experimental session. Then, they were asked again to choose among the six portfolios in order to capture whether the propensity to follow the recommendation depends on its source (human versus robo). Finally, participants were asked to answer several questions eliciting risk preferences, financial literacy (actual and perceived) and digital literacy, serving as control variables when modelling the probability to follow the advice. Our results show that the probability to follow the advice does not depend on the source of the recommendation but rather on the alignment between the self-directed choice made before receiving the advice and the recommendation subsequently received: the propensity to follow the advisor (either human or robo) increases if the advice confirms individual’s own beliefs about her/his investor profile. This result might be explained by referring to individuals’ attitude towards the so called ‘confirmation bias’. However, when the self-directed choice differs from the recommendation received, participants may be more likely to follow the advice given by a human advisor and less likely to follow the advice formulated by an algorithm. Also the gender of the advisor seems to matter: women tend to follow the advice provided by a female advisor more frequently compared to the case of the recommendation given by a male advisor. This work is part of a wider research on FinTech that CONSOB started in 2016, in collaboration with several Italian universities, with the aim of exploring opportunities and risks for investor protection and the financial system as a whole, related to the application of technological innovation to the provision of financial services. In particular, supplementing Lener, Linciano and Soccorso (2019, edited by) and Caratelli et al. (2019), this document widens the field of investigation by referring to a specific target of the population - the so called millennials and post-millennials – and using complementary and innovative methods. According to an evidence-based approach, insights from the present study may suggest specific investor protection initiatives, also in terms of financial education activities designed for a clearly-identified segment of the population (the so called millennials and post-millennials, in this case). Evidence from the present work might be extended further with respect to the consumers’ perception of the fairness of algorithms used to provide financial services, the cognitive heuristics and biases underlying decision making process and investments in the digital environment and nudges which may be used to enhance investor protection.

Robo-advisors and the financialization of lay investors

The burgeoning financial technology scene in Singapore has seen the emergence of robo-advisors, which aim to disrupt traditional financial advisories by using algorithms to automate client advising and investment recommendations. Using an ecologies concept to explore how lay investors are articulated into global financial networks through robo advisors, this paper contributes to studies on the “financialization of everyday life”. It argues that investors are rendered passive by the disciplinary tools of algorithms, contemporary finance theories and elements of robo-advisor platforms that feed into these sociotechnological assemblages. The state's role in embedding citizen investors in these human-machine relationships is considered. The fragmented landscape of free, nonprofessional online financial advice and the opaque qualities of investing algorithms make investor subject formation incomplete and uncertain, especially when markets are highly volatile. This paper explores how both financial inclusion and exclusion operate simultaneously in robo-advisors and argues that robo-advisors may weaken efforts to promote financial literacy and education.

로보어드바이저(RA)를 통한 증권거래와 투자자 보호

금융공학의 급속한 발전과 함께 AI 기반 RA가 저비용 등의 장점으로 투자자에게 매력적인 투자프로그램으로 관심을 끌고 있다. 그런데 RA에 의한 투자의 경우 이해상충의 문제는 물론 투자자문의 질적 수준에 따른 조언의무의 하자 문제 등 다양한 법적 쟁점이 도사리고 있다. 이에 대해 실무상 RA 알고리즘이 기존의 금융투자업자의 전문성 등에 미치지 못하고 있다는 점에서 자본시장법상의 의무를 그대로 적용하는 것에 무리가 있다는 신중론도 있지만, 이렇게만 본다면 오히려 기존의 금융투자업자에 비해 RA에 대한 규제가 완화되는 문제가 생기게 된다. 따라서 투자자보호의 측면에서 이에 대한 적극적인 규제가 필요하다. RA에 대한 이해상충 문제에 있어서, 규제 당국은 광범위한 수준에서 RA 업체에게 공시의무 등을 부과해야 할 것이다. 이해상충이 의도적으로 알고리즘에 프로그래밍되는 경우는 물론, RA가 상충되는 인센티브를 고의적으로 알고리즘에 통합시키는 경우에도 RA 업체에게 공시하도록 해야 한다. 특히 RA로 하여금 이해상충으로 인해 고객 각자에게 어느 정도 비용이 발생하는지 수치화하여 이를 의뢰인들에게 밝히도록 해야 할 것이다. RA의 조언의무와 관련하여서도 자본시장법상의 적합성원칙 및 설명의무를 부과하여야 할 것이다.

Glide Path Indexes

Investment glide paths set asset allocations through life, and they typically start aggressively for young investors and become progressively safer through time. Glide paths have gained popularity in target-date funds (TDFs) and have recently been integrated into computerized advice provided by so-called “Robo” advisors. Currently about $2 trillion is invested in TDFs, and there is another trillion dollars managed by Robos. Both are growing rapidly. In this article, the author examines two potential glide path indexes that TDF and Robo providers and investors might use. One standard is the aggregation of all mutual fund glide paths—an industry consensus standard. The other index candidate is a proposal for a better glide path. The author proposes an alternative glide path with more rigorous risk management, broader diversification, and an advanced decumulation algorithm in retirement. TDFs and Robo advisors are only as good as their glide paths. TOPICS:Wealth management, retirement, mutual fund performance Key Findings • Trillions of dollars are riding on glide paths in target-date funds and other investments. • Most glide paths lost more than 30% in 2008, and they have become riskier since. • Glide paths are still in their infancy and are improving.

Does FinTech Democratize Investing?

We examine how access to automated wealth managers affects households’ investment in financial markets and welfare across the wealth distribution. Our setting features novel microdata from a major U.S. robo advisor and a quasi-experiment in which the advisor reduces its account minimum by 90%. Based on a difference-in-difference estimator, the reduction increases middle-class households’ participation by 110% but does not affect wealthier or poorer households. We rationalize this behavior with a life cycle model calibrated using portfolio-level data. Our calibration suggests that the reduction significantly raises middle-class households’ welfare, and 65% of this gain reflects improved diversification.