Artificial intelligence in financial services: a qualitative research to discover robo-advisory services

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ankita Bhatia ◽  
Arti Chandani ◽  
Rizwana Atiq ◽  
Mita Mehta ◽  
Rajiv Divekar
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
Financial Services ◽  
Future Research ◽  
Individual Investors ◽  
Content Type ◽  
Advisory Services ◽  
Wealth Management ◽  
Management Scenario ◽  
Indian Stock Market

Purpose The purpose of this study is to gauge the awareness and perception of Indian individual investors about a new fintech innovation known as robo-advisors in the wealth management scenario. Robo-advisors are comprehensive automated online advisory platforms that help investors in managing wealth by recommending portfolio allocations, which are based on certain algorithms. Design/methodology/approach This is a phenomenological qualitative study that used five focussed group discussions to gather the stipulated information. Purposive sampling was used and the sample comprised investors who actively invest in the Indian stock market. A semi-structured questionnaire and homogeneous discussions were used for this study. Discussion time for all the groups was 203 min. One of the authors moderated the discussions and translated the audio recordings verbatim. Subsequently, content analysis was carried out by using the NVIVO 12 software (QSR International) to derive different themes. Findings Factors such as cost-effectiveness, trust, data security, behavioural biases and sentiments of the investors were observed as crucial points which significantly impacted the perception of the investors. Furthermore, several suggestions on different ways to enhance the awareness levels of investors were brought up by the participants during the discussions. It was observed that some investors perceive robo-advisors as only an alternative for fund/wealth managers/brokers for quantitative analysis. Also, they strongly believe that human intervention is necessary to gauge the emotions of the investors. Hence, at present, robo-advisors for the Indian stock market, act only as a supplementary service rather than a substitute for financial advisors. Research limitations/implications Due to the explorative nature of the study and limited participants, the findings of the study cannot be generalised to the overall population. Future research is imperative to study the dynamic nature of artificial intelligence (AI) theories and investigate whether they are able to capture the sentiments of individual investors and human sentiments impacting the market. Practical implications This study gives an insight into the awareness, perception and opinion of the investors about robo-advisory services. From a managerial perspective, the findings suggest that additional attention needs to be devoted to the adoption and inculcation of AI and machine learning theories while building algorithms or logic to come up with effective models. Many investors expressed discontent with the current design of risk profiles of the investors. This helps to provide feedback for developers and designers of robo-advisors to include advanced and detailed programming to be able to do risk profiling in a more comprehensive and precise manner. Social implications In the future, robo-advisors will change the wealth management scenario. It is well-established that data is the new oil for all businesses in the present times. Technologies such as robo-advisor, need to evolve further in terms of predicting unstructured data, improvising qualitative analysis techniques to include the ability to gauge emotions of investors and markets in real-time. Additionally, the behavioural biases of both the programmers and the investors need to be taken care of simultaneously while designing these automated decision support systems. Originality/value This study fulfils an identified gap in the literature regarding the investors’ perception of new fintech innovation, that is, robo-advisors. It also clarifies the confusion about the awareness level of robo-advisors amongst Indian individual investors by examining their attitudes and by suggesting innovations for future research. To the best of the authors’ knowledge, this study is the first to investigate the awareness, perception and attitudes of individual investors towards robo-advisors.

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janin Karoli Hentzen ◽  
Arvid Hoffmann ◽  
Rebecca Dolan ◽  
Erol Pala
Keyword(s):  
Artificial Intelligence ◽  
Financial Services ◽  
Credit Scoring ◽  
Future Research ◽  
Content Type ◽  
Financial Behaviors ◽  
Theoretical Perspectives ◽  
Actor Networks ◽  
And Performance ◽  
Future Work

PurposeThe objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.Design/methodology/approachCombining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.FindingsThe results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.Research limitations/implicationsThe review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.Originality/valueThe authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

Personality traits and motivation of individual investors towards herding behaviour in Indian stock market

Kybernetes ◽  
2019 ◽  
Vol 49 (2) ◽  
pp. 384-405
Author(s):  
Sharda Kumari ◽  
Bibhas Chandra ◽  
J.K. Pattanayak
Keyword(s):  
Stock Market ◽  
Personality Traits ◽  
Structural Equation ◽  
Small Sample ◽  
Individual Investors ◽  
Motivating Factors ◽  
Content Type ◽  
Emotional Factors ◽  
Cognitive Capability ◽  
Indian Stock Market

Purpose The purpose of this paper is to investigate the relationships between personality, motivating factors and herding behaviour of individual investors. Investors’ personality has been classified consonant to the personality traits (compliant, aggressive and detached) encapsulated in Horney’s tripartite model. Design/methodology/approach To carry out this study, the author surveyed 363 individual investors of the Indian stock market using a structured questionnaire. Structural equation modelling is used to empirically test the relationships between personality, three motivating factors (cognitive capability, emotional factors and social factors) and herding behaviour. Findings The result reveals that, expect compliant personality, none shows proclivity towards herding behaviour. Investors possessing compliant personality are more influenced by social motivating factors; however, cognitive factor motivates aggressive personality, inhibiting herding behaviour. Furthermore, investors having detached personality are not influenced by any motivating factors of herding. Research limitations/implications The limitation is the difficulty in generalizing the results to overall country populations as the Indian stock market has a huge turnover every day, and the author’s survey consisted of only small sample of individual investors. Practical implications The outcomes of this study could possibly unveil a new insight to discern the behaviour of individual investors in the Indian stock market. Originality/value The influences of personality on investment choices have been investigated before, but the influence of personality specifically on herding behaviour has not being adequately investigated in an emerging economy like India, as very scanty literature is available on the influence of personality on herding behaviour. The study addresses this gap and further explores the association of personality with different motivating factors that cause herding bias.

The unspoken facets of buying by individual investors in Indian stock market

2019 ◽  
Vol 11 (3) ◽  
pp. 324-351
Author(s):  
Ripsy Bondia ◽  
Pratap Chandra Biswal ◽  
Abinash Panda
Keyword(s):  
Stock Market ◽  
Lived Experiences ◽  
Design Methodology ◽  
Building Blocks ◽  
Process Approach ◽  
Individual Investors ◽  
Content Type ◽  
Behavioral Biases ◽  
Indian Stock Market ◽  
Variance Approach

PurposeThe purpose of this paper is to develop an in-depth contextualized understanding of individual investors’ buying decision in Indian stock market. Specifically, it provides answers to: how do individual investors make buying decision in stock market; and how and when do biases set in during such decisions. The paper also brings forward some aspects of individual’s journey as an investor.Design/methodology/approachGiven the exploratory nature of this study, the paper takes a step away from typically used variance approach and instead uses a process approach. The authors do in-depth one-on-one interview, where each respondent shares his/her lived experiences as an investor retrospectively. To understand buying decision, each respondent is asked to elaborate three significant buying transactions carried out by him/ her in stock market.FindingsSocio-cultural factors are found to have significant influence in inducing respondents to enter market. “Safe” vs “Risky” mental account emerges as the prominent stock categorization done by Indian investors. Three building blocks, namely, Identification, Rationalization and Further Validation emerge as the building blocks that culminate into buying decision of individual investors. The biases are seen to play a dual role in such decisions; as Attention Boosters and Rationales.Originality/valueThis study, to the best of authors’ knowledge, is first of its kind which amalgamates behavioral biases with phenomenon such as attention and Rationalization, to understand “how” behavioral biases set in during buying decision of individual investors.

Impact of herding on the returns in the Indian stock market: an empirical study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sunaina Kanojia ◽  
Deepti Singh ◽  
Ashutosh Goswami
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Market Condition ◽  
Individual Investors ◽  
Herd Behavior ◽  
Market Conditions ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Content Type ◽  
Indian Stock Market

PurposeHerd behavior has been studied herein and tested based on primary respondents from Indian markets.Design/methodology/approachThe paper expounds the empirical evidence by applying the cross-sectional absolute deviation method and reporting on herd behavior among decision-makers who are engaged in trading in the Indian stock market. Further, the study attempts to analyze the market-wide herding in the Indian stock market using 2230 daily, 470 weekly and 108 monthly observations of Nifty 50 stock returns for a period of nine years from April 1, 2009 to March 31, 2018 during the normal market conditions, extreme market conditions and in both increasing and decreasing market conditions.FindingsIn a span of a decade witnessing different market cycles, the authors’ results exhibit that there is no evidence of herding in any market condition in Indian stock market primarily due to the dominance of institutional investors and secondly because of low market participation by individual investors.Originality/valueThe results reveal that there is no impact of herd behavior on the stock returns in the Indian equity market during the normal market conditions. It highlights that the participation of individuals who are more prone to herding is more evident for short-run investments, contrary to long-term holdings.

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ankita Bhatia ◽  
Arti Chandani ◽  
Rajiv Divekar ◽  
Mita Mehta ◽  
Neeraja Vijay
Keyword(s):  
Decision Making ◽  
Loss Aversion ◽  
Investment Decision ◽  
Equation Modeling ◽  
Individual Investors ◽  
Digital Innovation ◽  
Content Type ◽  
Advisory Services ◽  
Wealth Management ◽  
Investment Decision Making

Purpose Innovation 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/approach Overconfidence 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. Findings The 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/implications The 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 implications This 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 implications This 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/value This 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.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Investment environment, stock market perception and stock investments after stock market crash

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Tariqul Islam Khan ◽  
Siow-Hooi Tan ◽  
Lee-Lee Chong ◽  
Gerald Guan Gan Goh
Keyword(s):  
Stock Market ◽  
Market Performance ◽  
Mediating Effect ◽  
Individual Investors ◽  
Investment Environment ◽  
Stock Market Crash ◽  
Content Type ◽  
Stock Market Performance ◽  
Market Perception ◽  
Stock Investments

PurposeThis study examines how the importance of external investment environment factors affect stock market perception, and how stock market perception affects stock investments after stock market crash witnessed by individual investors in one of the emerging stock markets.Design/methodology/approachA cross-sectional survey was administrated among 223 individual investors who experienced stock market crash in 2010–2011 in Bangladesh, and the proposed model was tested by the partial least squares-structural equation modeling PLS-SEM model.FindingsFindings show that the importance of Bangladesh's stock market performance, government policy, economic issues and neighboring country's stock market performance has effects on investors' stock market perception. This perception, in turn, decreases monthly stock trading and short-term investment horizon. The findings further show the mediating effect of stock market perception.Practical implicationsInvestors need to carefully consider the external investment environment when they form their stock market perception, as this perception drives stock investments. Analogously, regulators should ensure releasing timely and updated statistics on external investment factors.Originality/valueAddressing those investors who encountered stock market crash, a set of external investment environment issues, stock market perception and stock investments are new in the literature.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

Research on financial innovations: an interdisciplinary review

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad G. Nejad
Keyword(s):  
Financial Services ◽  
Business Models ◽  
Academic Research ◽  
Research Literature ◽  
Future Research ◽  
Breeding Ground ◽  
Financial Industry ◽  
Content Type ◽  
New Era ◽  
Financial Innovations

PurposeThe financial industry offers a unique setting to study innovations. Financial innovations have fueled the growth of economies, markets and societies. The financial industry has successfully become the breeding ground for innovative services, processes, business models and technologies. This study seeks to provide a holistic view of the literature on financial innovations, synthesize the research findings and offer future directions for research in light of three market developments that are disrupting the industry and opening up a new era for the financial services industry. Disruptions from within and outside the industry offer new generations of radically innovative services. Moreover, new generations of consumers differ from previous generations in their needs and wants and look for innovative ways to handle their financial needs. Finally, significant developments related to financial innovations have emerged in Asia and developing countries.Design/methodology/approachThis study systematically reviews the academic research literature on financial innovations in two phases. The first phase provides a quantitative review of 546 journal articles published between 1990 and 2018. In the second phase, the study synthesizes the extant research on financial innovations and maps them in five research areas: firms' introduction and adoption of FIs, financial innovation development, the outcomes of financial innovations, regulations and intellectual property, and consumers.FindingsThe analysis found that disciplines differ with regard to the employed research methodologies, the units of analysis, sources of data and the innovations they examined. A positive trend in the number of published articles during this period is observed. However, studies have primarily focused on the USA and Europe and less so on other parts of the world. The literature synthesis further identifies research gaps in the available research that highlight future research opportunities in light of the three market disruptions. The financial services industry is on the brink of a new era due to disruptions from within and outside the industry and the entrance of new generations of consumers. Moreover, the financial industry has successfully become the breeding ground for innovative services, processes and business models. Therefore, financial innovations offer promising opportunities for bridging the gap between research on product and service innovations.Research limitations/implicationsThe work provides a holistic and systematic overview of extant research on financial innovations and highlights future research opportunities in light of the three disruptive market developments. It helps researchers take advantage of the opportunities in studying financial innovations while maintaining industry relevance.Originality/valueThe study is the first to review and synthesize the academic research literature on financial innovations across marketing, finance and innovation disciplines. In addition, the study highlights three primary disruptive forces in the financial industry and identifies future research directions in light of these disruptive forces.

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