# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 5 Discussion  
In this study, we collected customers' personalized behavioral data by quantifying human behavior signals, and then extracting different modality types of data through various deep learning methods to analyze customer personality traits. The resulting personality trait scores can be used as a reference for predicting an individual's investment risk tolerance. The experimental results are discussed in detail as follows:  
1. In past research work, it was recognized that personality traits can profoundly affect people's habits, behaviors, and even decision-making. In behavioral finance theory, it is believed that investors are easily affected by psychological and behavioral factors that affect investment judgment, leading to irrational investment behavior. Therefore, in this research work, we explored the impact of many personality traits on investment and utilized deep learning techniques to extract deep features of human behavioral signals as client personality traits. We used multimodal data fusion techniques to address the biggest problem associated with unimodal techniques, i.e., that only one-sided personality traits can be learned. 2. The experimental results of this work are shown in Tables 3-6. In this case study, we found that facial expression features performed relatively well on unimodal measurement tasks. In non-verbal communication, we could clearly observe the changes in facial expressions; the degree of emotion, thought and attention conveyed by facial expressions is more obvious, and the influence of text features is the least obvious. This is mainly because the information content of each person's reply may be relatively similar, so the model cannot accurately judge the change in each personality trait. 3. As discussed earlier, previous research showed that investor risk taking is highly correlated with personality traits [66]. For example, according to [PERSON] [66], conscientious people are determined, methodical, dependable, persistent, and punctual, and do not take higher risks impulsively. People with high openness to experience generally tend to conduct new experiments and take higher risks [67]. Extroverted people are more optimistic about life and events. Their positive attitude towards life and events may increase the overvaluation of the market and the undervaluation of possible risks. On the other hand, a negative attitude and narrow focus can lead to an overestimation of risk and may lead to the loss of profitable investment opportunities [68]. People with low agreeableness are generally skeptical and curious, consider more information than people with high agreeableness, and ultimately take fewer risks and make more computational decisions [54]. People with low neuroticism feel greater anxiety when making risk-taking decisions [69; 70]. Similarly, in our case study, we used questionnaires and Pearson's correlation coefficient analysis to perform correlation analysis between the personality trait scores and risk tolerance of 32 subjects. The results showed that openness, extroversion, and neuroticism were highly correlated with risk tolerance in investing. People with personality traits higher in openness, extraversion, and lower in neuroticism were able to take higher risks. These results are similar to the research findings in [66]. 4. Although we used the Big Five personality traits as the basis of the client's personality in this research work to judge client risk tolerance in financial investment behavior, we still need to confront the complexities of factors that influence individual investment behavior. For example, the age, work, and education level of clients are also influential. In this work, we did not include these factors. We only took personality as the main factor to explore the impact of personality traits on investment behavior. The results coincide with the use of traditional questionnaires in behavioral finance research methods. 5. Moreover, the study has several limitations.