A novel artificial autonomous system for supporting investment decisions using a Big Five model approach

Cabrera‐Paniagua, Daniel; Rubilar-Torrealba, Rolando

doi:10.1016/j.engappai.2020.104107

Cited by 11 publications

(5 citation statements)

References 63 publications

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“…Investors can use sentiment research and news sentiment indicators to assess market mood and adapt their investment strategy. Using big data and market sentiment to alter investments can help investors make better judgments (Cabrera-Paniagua & Rubilar-Torrealba, 2021). Big data, especially sentiment analysis, has revolutionized portfolio management.…”

Section: Literature Review Big Data Utilization and Investment Behaviormentioning

confidence: 99%

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

Zhang,

Sidik

2024

J INFORM SYSTEMS ENG

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The investing behavior of Chinese families is undergoing a dramatic transition in the context of the digital financial era, impacted by factors such as big data use, AI adoption, financial literacy, digital literacy, and risk aversion. Although prior research has offered useful insights into certain components, a thorough examination of their linked dynamics has been lacking. The purpose of this research was to look into how big data usage, AI adoption, financial literacy, digital literacy, and risk aversion influence investment behavior among Chinese households. Additionally, it aimed to learn more about how risk aversion and digital literacy function as mediators in these relationships. A questionnaire-based survey of 370 Chinese families was employed as part of the quantitative research methodology. The study employed AMOS to find the relationship between variables. The research found that big data usage, AI adoption, financial literacy, and digital literacy significantly and favorably influenced Chinese households' investment behavior. It was discovered that digital literacy mediated the linkages between the adoption of technology and investment decisions. Furthermore, risk aversion reduced the effects of financial literacy and big data usage on investment behavior. This study added to the body of knowledge by providing a comprehensive framework that incorporates several aspects impacting investment behavior. It shed insight into the complicated dynamics of technology uptake and literacy, as well as their impact on investment decisions. The study went beyond individual components to investigate their interactions, resulting in a more complex view of modern investment behavior. This study has broad-ranging effects that will help investors, financial institutions, governments, educators, and researchers. The focus on a particular setting and self-reported data are two important constraints that must be acknowledged. Future studies can investigate longitudinal dynamics and cross-cultural variances to further our understanding of investment behavior in the digital age.

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Section: Literature Review Big Data Utilization and Investment Behaviormentioning

confidence: 99%

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

Zhang,

Sidik

2024

J INFORM SYSTEMS ENG

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“…We consider a representative economic agent who lives for infinite periods of time, has access to the financial market and seeks to maximize the expected utility function in each period through consumption in each period. This theoretical approach allows the use of appropriate analytical and computational tools for the modeling of an agent, such as the value function iteration methods (see [46,47]). This combination is described by the following equation:…”

Section: Economic Modelmentioning

confidence: 99%

“…The model described in the figures considers a CRRA type utility function for its simulation with U = c 1−σ t /(1 − σ), where σ = 2 corresponds to the risk aversion value, y t = 0.1 for all t, an interest rate i = 3%, a depreciation rate δ = 0, an intertemporal discount value ρ = 0.97, and a technological parameter A = 1.9 for the case of an individual with high entrepreneurial skills and A = 1.5 for the case of an individual with low entrepreneurial skills. For the estimation of the trajectory, an optimization process is performed by verifying the highest utility value for each of the possible values of t * , using a methodology of defining a policy function (see [46,47]). The value of t * corresponds to the moment at which the utility of the economic agent is maximized and therefore the moment at which the entrepreneur decides to quit their job in order to start a business.…”

Section: Economic Modelmentioning

confidence: 99%

Econometric Modeling to Measure the Social and Economic Factors in the Success of Entrepreneurship

et al. 2022

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The purpose of this research is to develop a theoretical model of the entrepreneurial decision of individuals and to measure the effect of a group of variables on this decision. The effects of variables such as income, age, gender, level of education, and entrepreneurial skills are studied. The results show that innovation and entrepreneurship are linked to the personal characteristics of individuals and the social context in which they develop, thus making it possible to guide social policies for the development of the economy. Logit and probit functions were used to measure the effect of the variables on the entrepreneurship phenomenon. The main findings of this research indicate that the variables with a significant impact on the success of entrepreneurship and innovation are income, age, gender, skill, and the interaction between the opportunity and education variables.

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“…Chitra [54] used Systematic Random Sampling (SRS) to analyze the personality traits and investment patterns of 97 investors and to confirm the association between the majority of investors who had extraversion and emotional stability traits and investment behavior. In addition, other financial behavior researchers based their work on the Big Five personality traits; for example, Cabrera [55] proposed an adaptive intelligent system (AAS) for stock investment, designing five modules, named Investment Profile Manager, Market Data Manager, Market Analyzer, Portfolio Analyzer, and Decision Engine. The system was adjusted for five personality traits and investment market fluctuations.…”

Section: Author Participants Classes Of Human Behavior Signalmentioning

confidence: 99%

A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System

Lee

Yang²,

Su³

et al. 2022

Applied Sciences

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To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics.

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A novel artificial autonomous system for supporting investment decisions using a Big Five model approach

Cited by 11 publications

References 63 publications

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

Econometric Modeling to Measure the Social and Economic Factors in the Success of Entrepreneurship

A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System

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