behavioral-finance

# Behavioral Finance Applications

## Overview

Translate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.

Applicable scenarios:
- Behavioral interpretation and parameter optimization for momentum / reversal strategies
- Contrarian signals when market sentiment becomes extreme
- Debiasing mechanisms in portfolio construction
- Capturing behavior patterns specific to retail-driven China A-share markets

## Core Concepts

### Overreaction and Underreaction

**Underreaction** → momentum effect:
```
Mechanism: anchoring bias + conservatism
  Investors anchor on old information and update insufficiently to new information
  After an earnings beat, the stock price digests it gradually rather than all at once
China A-share evidence:
  - Earnings-guidance beats still produce 3-5% excess return over the following 20 days
  - After analyst rating upgrades, momentum often persists for 1-3 months
Quant signal:
  SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days
  Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)
```

**Overreaction** → reversal effect:
```
Mechanism: representativeness heuristic + availability bias
  Investors extrapolate recent trends too aggressively and ignore mean reversion
  Panic / euphoria drives reactions beyond what fundamentals support
China A-share evidence:
  - Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)
  - Big annual losers often earn 5-10% excess return the next year
Quant signal:
  Bottom 10% of 250-day return -> buy and hold for 250 days
  RSI(5) < 10 -> short-term rebound signal (5-10 days)
```

**Key distinction**:
| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |
|------|------------------|------------------|
| Time scale | 1-12 months | <1 week or >12 months |
| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |
| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |

### Cognitive Bias Checklist

**Individual decision biases**:
| Bias | Manifestation | Quant Detection | Debiasing Strategy |
|------|------|----------|------------|
| Loss aversion | Hold losing stocks, sell winners too early | Holding period: losing positions > winning positions by 2-3x | Pre-set stop-loss line and execute mechanically |
| Overconfidence | Overtrading, concentrated positions | Monthly turnover > 100%, single-stock weight > 30% | Limit the number of trades per month |
| Anchoring effect | Anchoring to entry price / historical highs | Abnormal volume expansion near the entry price | Use relative valuation instead of absolute price |
| Confirmation bias | Focus only on information that supports the existing view | Single-source information, ignoring bearish news | Force reading the opposing view |
| Recency bias | Overweight recent events | Recent gains/losses have too much influence on position size | Lengthen the evaluation window (≥60 days) |
| Framing effect | Same information framed differently leads to different decisions | Decision differences between return format and absolute-PnL format | Evaluate consistently in return space |

**Group behavior biases**:
| Bias | Manifestation | China A-share Characteristics | Quant Indicator |
|------|------|---------|----------|
| Herding | Chasing rallies and panic-selling together | Extremely fast sector rotation (3-5 days) | Intra-sector stock correlation > 0.8 |
| Information cascades | Ignoring private information and following public signals | Sector follow-through after a leader stock hits limit-up | Sector return on the day after leader-stock limit-up |
| Attention effect | Buying stocks that attract attention | Explosive turnover in limit-up / news-driven stocks | Abnormal turnover > 3x average |

### Investor Sentiment Cycle

```
Fear -> Caution -> Optimism -> Excitement -> Euphoria -> Denial -> Panic -> Fear
  |        |        |        |        |       |        |
 Bottom   Recovery  Mid-uptrend  Pre-top   Top   Early selloff  Pre-bottom

Quant sentiment indicators:
  1. Closed-end fund discount: discount > 15% -> extreme fear -> buy signal
  2. Margin-financing growth: monthly growth > 20% -> euphoria -> reduce position
  3. New account openings: weekly openings > 2x average -> overheated market
  4. Turnover ratio: All-A daily turnover > 3% -> euphoric; < 0.5% -> deeply depressed
  5. Number of limit-up stocks: > 100 -> euphoric; < 10 -> weak
```

## Analysis Framework

### 1. Disposition-Effect Signal

**Principle**: investors tend to sell winners and hold losers. Once winning positions are largely cleared, selling pressure eases; when trapped holders are deeply underwater, selling pressure can also ease.

```
China A-share application:
  Compute the profit ratio in the chip-distribution structure:
  - Profit ratio > 90% and shrinking volume -> winners are reluctant to sell -> may continue rising
  - Profit ratio > 90% and expanding volume -> winners are exiting -> topping signal
  - Profit ratio < 10% and shrinking volume -> low willingness to cut losses -> bottom stabilization
  - Profit ratio < 10% and expanding volume -> panic selling -> short-term oversold

Quant implementation:
  capital_gain_overhang = (current_price - avg_cost) / avg_cost
  where avg_cost is approximated by 60-day VWAP
  CGO > 0.2 -> strong unrealized gains, watch for disposition-effect selling pressure
  CGO < -0.3 -> deeply trapped holders, selling pressure may actually ease
```

### 2. Composite Sentiment Indicator

```python
# Multi-dimensional sentiment score (0-100, 50 = neutral)
sentiment_components = {
    'turnover_ratio': normalize(all_a_turnover, historical_percentile),      # weight 25%
    'margin_growth': normalize(monthly_margin_growth, his