# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 2 Related Work
Finally, they concluded that neuroticism and openness, as well as fear and sadness, were predictors of risk aversion in the personality traits. [PERSON] [50] concluded that positive thinking can contribute to risk-averse behaviors and collected 100 subjects for a Big Five personality trait measure, which was statistically analyzed using Partial Least Squares (PLS) structural equation modeling. The results demonstrated that personality traits influenced an individual's risk-averse attitudes; it was found that affinity and emotional stability had negative effects on risk-averse ability, while conscientiousness and openness had significant positive effects. [PERSON] [51] collected quantitative scores from 599 investors through questionnaires and analyzed the influence of five personality traits on risk tolerance through correlation and regression tests. The results confirmed that agreeableness, conscientiousness, and openness were significantly correlated with risk tolerance. [PERSON] [52] used statistical methods such as correlation analysis and utilized the Kruskal-Wallis H Test to verify that impulsive personality traits had some influence on investors when they made investment decisions. [PERSON] [53] used the Proactive Personality Inventory to measure the proactivity of the subjects and to construct a regression model for analysis. The experimental results showed that investors maintained proactive traits so they could continue their investment behavior, while the opposite affected their willingness to invest. [PERSON] [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, [PERSON] [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. The results of the experiments on the official data from the New York Stock Exchange showed that Extraversion and Agreeableness accumulated the most wealth in the system. Using an ANOVA/Kruskal-Wallis model, [PERSON] [56] analyzed the influence of investors' personalities on short-term and long-term trading performance, analyzing the personalities through machine learning models, such as logistic, multilevel perceptron, and random forest. The experimental results showed that people with agreeableness, extraversion, and openness were able to achieve better long-term returns than people with highly neurotic traits. [PERSON] [57] proposed that features of handwriting could be mapped to five personality trait dimensions to identify financial behavior. Accordingly, the authors collected texts written by ten subjects using a CNN model and logistic regression, and selected seven features, such as spacing and ascending lines, for the classification task, achieving an accuracy of 63.97%. To sum up, there are a number of successful examples, such as education and job interviews, where personality trait analysis using human behavioral signals was applied. Although analyzing investment behavior based on five personality traits is a common method in investment applications, few people use behavioral signals to analyze investment behavior. Therefore, in this work, we develop a multimodal system for analyzing personality traits via behavioral signals. The system will help human financial advisors provide appropriate financial planning advice based on the deep personalities detected in people's investments.