Theoretical Discussion on Individual Investor Behavior from a Quantitative Finance Perspective: Possibilities for Machine Learning Applications ()
1. Introduction
The emergence of quantitative finance as a new scientific field has provided us with new perspectives and tools for understanding financial markets, thanks to its accurate data processing and complex mathematical models. In today’s era, characterized by an explosion of information, data is flooding into our lives at an unprecedented speed and scale, especially in the financial field (Zhou et al., 2023) . In the stock market, individual investors are required to face and process vast amounts of information and make complex investment decisions, which places extremely high demands on their economic behavior capabilities. However, what exactly is the economic behavior capability of individual investors in the stock market? How do these investors process their decision-making when faced with a sea of information and complex investment choices? These are the questions we need to delve into. In fact, the research on these questions not only has a significant impact on the investment decisions of individual investors themselves but also has crucial implications for the operation and supervision of the entire financial market (Zhou & Yu, 2021; Li, 2022) .
This study will start from the perspective of quantitative finance and conduct an in-depth analysis and research on the economic behavior capabilities of individual investors in the stock market. We will explore the key factors affecting individual investors’ investment decisions by deeply understanding their behavior patterns and decision-making processes, revealing the true state of their economic behavior capabilities. The research in this study can provide individual investors with scientific investment decision references, helping them make more rational and effective decisions in the complex stock market environment. It can also provide valuable insights for policymakers and financial institutions to promote the healthy and stable development of the financial market. Through the lens of quantitative finance, we can gain a better understanding of the economic behavior of individual investors, thus providing new perspectives and tools for the theoretical research and practical operation of the financial market.
2. Theories and Methods of Quantitative Finance
2.1. Definition and Development of Quantitative Finance
Quantitative finance is an interdisciplinary field that applies mathematical, statistical, and computational methods to finance, and it has become an essential tool in financial research and practice (Lee & Lee, 2020) . Its concept can be traced back to the pioneering work of Harry Markowitz, who devised the portfolio theory and introduced the concept of diversification, marking the origin of quantitative methods in finance (Smith, 2018) . The advancement and proliferation of computer technology, as well as the emergence of big data, play a crucial role in the progress of quantitative finance (Markowitz, 1952) . These technological leaps have enabled the development of complex models and tools, allowing researchers to dissect and analyze complex financial markets and products (Lee & Lee, 2020) .
Initially, quantitative finance was mainly used for the analysis of financial markets and products. Over time, its applications have expanded to include risk management, asset pricing, investment strategies, and financial engineering (Chen & Zhao, 2021) . In this regard, the research of Chen and Zhao focuses on the application of quantitative finance in risk management and financial engineering (Markowitz, 1952) . In the modern financial landscape, quantitative finance plays a crucial role in the analysis, prediction, and decision-making of financial markets. The research by Brown and Smith underscores the role of quantitative finance in financial market analysis and decision-making (Brown & Smith, 2022) . While quantitative finance has made significant progress, the inherent complexity and uncertainty of financial markets still require us to continue to explore and innovate.
2.2. Main Methods of Quantitative Finance and Their Applications
The primary methods of quantitative finance include mathematical models, statistical analysis, machine learning, and big data analytics. These methods are essential in various fields of financial market analysis, forecasting, and decision-making.
1) Mathematical models, such as the Black-Scholes model and the Capital Asset Pricing Model (CAPM), form the foundational tools of quantitative finance. They are widely used in pricing financial products, optimizing investment portfolios, and managing risk. For instance, the Black-Scholes model is a mathematical model used to calculate the pricing of European options and other financial derivatives. The CAPM is used to evaluate the expected return of a specific asset and determine its appropriate proportion in a portfolio.
2) Statistical analysis, including time series analysis, regression analysis, hypothesis testing, is one of the most commonly used methods in quantitative finance. Through statistical analysis, we can extract useful information from historical data, evaluate relationships between various financial variables, and predict future financial market trends. For instance, ARIMA and GARCH models, common in statistical analysis, are widely used for trend forecasting in financial markets.
3) Machine learning and big data analytics are emerging methods that have gained wide application in quantitative finance in recent years. Machine learning allows us to establish complex forecasting models, automatically extract and learn patterns in data, and improve the accuracy and efficiency of predictions. Big data analytics enable us to process and analyze large-scale data, mining deep information and knowledge within. Machine learning models, such as neural networks, support vector machines, and random forests, have applications in predicting stock prices, credit scoring, and risk management (Shen, 2023) .
In summary, quantitative finance offers a multitude of distinctive methods that are widely used in financial market analysis, forecasting, and decision-making. However, the complexity and uncertainty of financial markets remain a challenge, necessitating continued exploration and innovation for better understanding and prediction of financial markets.
3. Analysis of Economic Behavior for Individual Investors
3.1. Definition and Characteristics of Individual Investors
Individual investors, also known as retail investors or small investors, are individuals who participate in investment activities in the financial market under their own name. They seek to generate returns by buying and holding various financial assets such as stocks, bonds, funds, futures, etc. The characteristics of individual investors differ significantly from institutional investors, which include banks, insurance companies, fund companies, pension funds, and other large financial institutions. The unique traits of individual investors are primarily reflected in the following aspects:
1) Smaller Capital Scale: Compared to institutional investors, individual investors have significantly smaller capital scales. This limits their range of choices and strategies in the investment market. Their influence is not enough to cause significant changes in market prices, nor do they have the strength to compete with large institutions. However, despite their smaller capital scale, their large numbers mean that their collective behavior can have a significant impact on the market (Zhou, 2021) .
2) Limited Investment Knowledge and Experience: Most individual investors do not come from a financial professional background and lack professional investment knowledge and extensive investment experience. This means their investment decisions often rely more on personal intuition and emotions rather than rational analysis and judgment. In the information-overloaded financial market, they may feel confused and uncertain, thereby relying on simplified decision rules or the opinions of others (Deng & Li, 2022) .
3) Greater Influence of Behavioral Biases: Many studies show that the investment behavior of individual investors is often influenced by various behavioral biases such as overconfidence, representativeness bias, anchoring effect, etc. For example, overconfidence can lead them to trade excessively and overestimate their investment capabilities; representativeness bias may lead them to rely too much on recent information, overlooking long-term trends; the anchoring effect may cause them to rely excessively on a certain reference point, such as the purchase price when making decisions (Zhang, 2022a) .
In summary, individual investors play a significant role in the financial market. Although they have a smaller capital scale, limited knowledge and experience, and are susceptible to behavioral biases, their investment behaviors have a significant impact on the stability and efficiency of the market. Therefore, understanding and studying the characteristics and behaviors of individual investors is crucial for understanding and predicting the operation of financial markets.
3.2. Economic Behavior Analysis of Individual Investors
The economic behavior of individual investors is a complex process involving rationality and emotions. Cognitive biases such as overconfidence, anchoring effect, and representativeness bias often lead to deviations from rational expectations in investors’ decisions. For instance, overconfidence may cause investors to trade frequently, the anchoring effect makes investors rely too much on historical prices, and the representativeness bias makes investors overly dependent on simple patterns, ignoring unique investment opportunities. Investors’ risk preference and time preference also influence their investment behavior. They might be more inclined towards high-risk, high-return investments, or they may overly focus on short-term market fluctuations and returns, neglecting long-term value and risk. Additionally, personal traits, such as gender, age, education, and income level, also affect investment decisions. For instance, younger investors might be more willing to take risks, and investors in good economic conditions might have more resources to diversify risks. A deep understanding of the economic behavior of individual investors is crucial for predicting financial market dynamics.
3.3. Behavioral Biases and Decision-Making Process of Individual Investors
The investment behavior and decision-making process of individual investors are often influenced by behavioral biases. This influence is evident in many instances. For example, overconfidence bias may lead investors to overestimate their investment skills and market judgment ability. A classic example is the investor who frequently buys and sells stocks, believing they can accurately predict market trends. In reality, this excessive trading behavior often reduces investment returns due to transaction costs and potential capital losses. Another common behavioral bias is the anchoring effect, where investors overly rely on some initial information or value (the “anchor”) for decision-making, ignoring other relevant information. For instance, investors might overly rely on a stock’s historical high price as the “anchor” for buying, believing that the stock price will reach that level again. This view often overlooks changes in the market environment and company fundamentals.
Representativeness bias is where investors overly rely on simple judgments and classifications, ignoring the uniqueness of individual events. For instance, investors might believe that a stock that has performed well in the recent past will continue to rise in the future (Yu et al., 2022) . This judgment overlooks the fact that the stock price has already reflected the company’s excellent performance, and future prices will be influenced by many other factors. These behavioral biases play a key role in the decision-making process of individual investors. They profoundly impact investors’ behavior, sometimes leading them to make irrational decisions. Therefore, recognizing and understanding these biases are crucial for improving the decision-making process and enhancing investment efficiency.
4. Individual Investor Behavior from the Perspective of Quantitative Finance
4.1. Building Behavioral Models of Individual Investors in Quantitative Finance
From the perspective of quantitative finance, the construction of individual investor behavior models is mainly based on statistical and economic theories, especially Expected Utility Theory and Behavioral Finance Theory (Zhang, 2022b) . These models typically incorporate factors such as the investor’s risk tolerance, return expectations, market information, and personal characteristics, and use mathematical formulas to describe investor decision-making. Table 1 describes variables involved in building behavioral models for individual investors.
The foundation of Table 1 lies in the theoretical frameworks provided by the Expected Utility Theory and Behavioral Finance Theory. These theories offer key insights into how investors make decisions, which variables influence these decisions, and how these variables interact (Li et al., 2022) . For example, the Expected Utility Theory allows us to understand how investors choose portfolios based on their risk tolerance and return expectations. Meanwhile, the Behavioral Finance Theory provides a lens to explore how market information and personal characteristics may influence these decisions. Based on these variables, we can construct a basic model of individual investor behavior. For example, we can use Expected Utility Theory to describe how investors choose portfolios based on their risk tolerance and return expectations. We can then use Behavioral Finance Theory to consider the impact of market information and personal characteristics on investor decisions. Such models can help us understand and predict investor behavior, providing decision support for investment strategy formulation.
4.2. Data Analysis of Individual Investor Behavior in Quantitative Finance
From the perspective of quantitative finance, we can delve into understanding and analyzing the behavior of individual investors. This process involves a significant amount of data collection and processing, as well as deep data analysis using mathematics and statistics. For instance, consider the following transaction data in Table 2.
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Table 2. Transaction data for an investors.
In this data, we record key information including the investor’s transaction date, transaction type, transaction amount, stock code, market index, investor confidence index, and risk tolerance. From this data, we can observe changes in the investor’s trading behavior under different market conditions and personal circumstances. For example, when the market index decreases, the investor may reduce their trading volume, reflecting the impact of market conditions on their trading behavior. When the investor confidence index rises, the investor may be more inclined to buy stocks, showing the impact of investor expectations on their decisions. When investors have a higher risk tolerance, they may be more willing to make short-term trades, reflecting their attitudes and abilities to handle risk on their trading behavior.
Through such data analysis, we can better understand the behavior patterns of investors and reveal the various factors influencing their decisions. Furthermore, we can use this data to build predictive models, applying multivariate regression, cluster analysis, decision trees, and other statistical methods or machine learning algorithms. Based on an investor’s historical trading data and market conditions, we can predict their future trading behavior. These models can not only help investors better understand and improve their investment strategies but also provide useful information and references for market regulators and other investors.
4.3. Understanding and Interpretation of Individual Investor Behavior from a Quantitative Finance Perspective
From the perspective of quantitative finance, understanding and interpreting the behavior of individual investors requires in-depth analysis of behavior patterns, transaction data, and psychological factors. Investors’ behaviors are not always rational; variations in individual differences, market conditions, information factors, and behavioral biases can lead to various irrational phenomena in their behaviors. Investors often prefer short-term trades and frequently suffer losses in the short term, possibly indicative of low risk tolerance and a need for improvement in judging market dynamics. This situation is likely due to investors being influenced by market noise or psychological biases, such as overconfidence, representativeness bias, or anchoring effect. For such situations, we can interpret investor behavior through behavioral finance theory. For example, overconfidence might lead investors to overestimate their investment capabilities, leading to frequent trading. Or, they might be influenced by representativeness bias, paying too much attention to recent market dynamics while ignoring long-term investment trends. Alternatively, they might be influenced by the anchoring effect, relying too much on preliminary information or experience, and overlooking new market information. Meanwhile, we can also use machine learning algorithms to predict investor behavior. For instance, we can apply algorithms such as random forests or neural networks based on an investor’s historical trading data to predict their future trading behavior, thus providing more rational investment advice. Through in-depth data analysis and theoretical interpretation, we can better understand and predict individual investor behavior, helping investors improve their investment strategies, enhance investment efficiency, and provide valuable references for market regulation and policy formulation.
5. Conclusion
This study, using the perspective of quantitative finance, delves into and analyzes the behaviour of individual investors. We discovered that although the behavior of investors is influenced by several factors such as personal characteristics, market conditions, information acquisition abilities, and psychological biases, we can still elucidate some commonly existing behavior patterns and decision-making rules through analysis. Specifically, we noticed that different market environments and factors such as investor confidence index and risk tolerance significantly affect investor trading behavior. For instance, when the market index declines, investors may reduce their trading volume, reflecting the impact of market conditions on their trading behavior. When the investor confidence index is high, investors may be more inclined to buy stocks, indicating the influence of investor expectations on their decisions. When investors have a higher risk tolerance, they may be more willing to engage in short-term trading, which shows their attitude and capacity towards risk affecting their trading behavior. What’s more noteworthy is that we have identified the tremendous potential of machine learning in predicting the behavior of individual investors. Based on the historical trading data of investors, we can establish predictive models to effectively forecast their future trading behavior. Such predictions can provide robust support for the investors’ own decision-making and valuable references for market regulators.
In the future, we will further deepen this research, continuously excavating and exploring new data analysis methods and models. We aim to understand and predict the behavior of individual investors more accurately, provide more comprehensive and precise bases for investors’ decisions, and offer deeper insights for market supervision and policy-making. Through this approach, we aspire to serve investors better and contribute to the healthy and stable development of the financial market.