A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/19/10066 |
| _version_ | 1797480455271874560 |
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| author | Chung-Hong Lee Hsin-Chang Yang Xuan-Qi Su Yao-Xiang Tang |
| author_facet | Chung-Hong Lee Hsin-Chang Yang Xuan-Qi Su Yao-Xiang Tang |
| author_sort | Chung-Hong Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:00:17Z |
| format | Article |
| id | doaj.art-200edc602ce244a98b39283d2ec0a39f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:00:17Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-200edc602ce244a98b39283d2ec0a39f2023-11-23T19:50:26ZengMDPI AGApplied Sciences2076-34172022-10-0112191006610.3390/app121910066A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor SystemChung-Hong Lee0Hsin-Chang Yang1Xuan-Qi Su2Yao-Xiang Tang3Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, TaiwanDepartment of Information Management, National University of Kaohsiung, Kaohsiung 811726, TaiwanDepartment of Finance, National Kaohsiung University of Science and Technology, Kaohsiung 824005, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, TaiwanTo 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.https://www.mdpi.com/2076-3417/12/19/10066affective computingartificial intelligencehuman–computer interactionbehavioral financepersonality traits |
| spellingShingle | Chung-Hong Lee Hsin-Chang Yang Xuan-Qi Su Yao-Xiang Tang A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System Applied Sciences affective computing artificial intelligence human–computer interaction behavioral finance personality traits |
| title | A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System |
| title_full | A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System |
| title_fullStr | A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System |
| title_full_unstemmed | A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System |
| title_short | A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System |
| title_sort | multimodal affective sensing model for constructing a personality based financial advisor system |
| topic | affective computing artificial intelligence human–computer interaction behavioral finance personality traits |
| url | https://www.mdpi.com/2076-3417/12/19/10066 |
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