**📅 Date:** ➤ ⌈ [[2025-10-08-Wed〚 Behavioural Biases and Market Characteristics〛]]⌋ **💭 What:** ➤ 行为经济学和传统金融的假设的区别 - Traditional Finance 1. Investors are **rational** and **risk-averse**. 2. ==Efficient Market Hypothesis== 3. Investors maximise **expected utility** using all information logically - Behavioral Finance 1. Not always rational 2. Influenced by psychological biases, emotions, and cognitive errors 3. Lead to **market patterns** that traditional models cannot fully explain ➤ 居安思危 VS Loss Aversion **👀 Snap:** >[!info] Bias >1. **Overconfidence Bias（过度自信偏差）** >2. **Herding Behaviour（羊群行为）** >3. **Loss Aversion / Prospect Theory（损失厌恶 / 前景理论）** >4. **Anchoring（锚定效应）** >5. **Representativeness Bias（代表性偏差）** >6. **Availability Bias（可得性偏差）** >7. **Mental Accounting（心理账户）** >8. **Self-Attribution Bias（自我归因偏差）** ⇩ 🅻🅸🅽🅺🆂 ⇩ **🏷️ Tags**: #💰/Economy **🗂 Menu**: ➤⌈[[✢ M O C ➣ 10 ⌈O C T - 2 0 2 5⌉ ✢|2025 - O C T - MOC]]⌋ ➤⌈[[✢ L O G ➢ 10 ⌈O C T - 2 0 2 5⌉ ✢|2025 - O C T - LOG]] ⌋ #👾/Private ➤ ⌈[[💰L069-04- M5-Case Study- Behavioural Biases in Action]]⌋ ➤ ⌈[[💰 L070-02+ 8 Behavioral Bias Correction]]⌋ ➤ ⌈[[💰 L070-03 -Market Phenomena Not Explained by Traditional Finance]]⌋ ➤ ⌈[[💰L070-04+Matthew Effect (马太效应)]]⌋ --- ## 📚 Traditional Finance vs. Behavioural Finance ### I. 🧮 Traditional Finance Assumptions - Investors are **rational** and **risk-averse**. - 投资者是理性的，对风险是厌恶的 - Markets are **efficient** — ==prices reflect all available information== (**Efficient Market Hypothesis**). - 市场是有效的 - Investors maximise **expected utility** using all information logically. ### II.🧠 Behavioural Finance - ==Challenges== traditional assumptions — investors are **==not always rational==**. - Decisions are influenced by **psychological biases**, **emotions**, and **cognitive errors**. - These biases can lead to **market patterns** that traditional models cannot fully explain. --- ## 🌍 How Behavioural Biases Affect Markets Individual biases can **aggregate** into **systematic market phenomena**, causing **prices and returns** to deviate from rational models. --- ## 📝 Bias Types and Market Effects | **Bias Type** | **Description** | **Resulting Market Characteristics** | **Example** | | ----------------------------------- | ---------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | | **Overconfidence Bias** 过度自信偏差 | Investors overestimate their knowledge or ability to predict outcomes. | Excessive trading volume, bubbles, volatility above fundamentals. | Dot-com bubble (late 1990s): investors believed they could find “the next Amazon.” | | **Herding Behaviour** 羊群行为 | Investors follow the crowd instead of independent analysis. | Momentum effects, bubbles, crashes. | Bitcoin rally (2017), GameStop squeeze (2021). | | **Loss Aversion / Prospect Theory** | Losses feel more painful than equivalent gains. | Underreaction after losses; “disposition effect” (holding losers too long). | Retail investors held Lehman Brothers in 2008, hoping to “break even.” | | **Anchoring** 锚定效应 | Fixating on reference points (e.g., past prices, forecasts). | Price resistance levels; delayed adjustment to new info. | Post–COVID crash: investors anchored to pre-crash prices. | | **Representativeness Bias** 代表性偏差 | Overgeneralizing from recent trends. | Overreaction to earnings surprises; short-term momentum, long-term reversals. | Tesla 2020 surge → overvaluation of smaller EV startups. | | **Availability Bias**可得性偏差 | Decisions based on easily recalled or recent events. | Overemphasis on headline news or shocks. | After 9/11, airline risk overestimated → sector undervalued. | | **Mental Accounting** 心理账户 | Segregating money into separate “mental accounts.” | Suboptimal diversification; dividend preference. | “Bonus money” put into risky stocks, “salary” in safe assets. | | **Self-Attribution Bias** | Success credited to skill, failure blamed on luck. | Reinforces overconfidence and excessive risk-taking. | 2020–21 bull market traders attributed gains to skill → reckless in 2022 correction. | --- ## 🧠 Key Takeaways - Behavioral biases are **systematic**, not random. - They **explain anomalies** in price movements, bubbles, crashes, and trading behavior. - Recognizing biases helps investors develop **better risk management** and **strategic discipline**.