behavioral-finance

name behavioral-finance description Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies. category analysis 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 # 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, historical_percentile), # weight 25% 'new_high_ratio' : normalize(new_high_ratio, historical_percentile), # weight 20% 'limit_up_count' : normalize(limit_up_count, historical_percentile), # weight 15% 'fund_discount' : normalize(closed_end_fund_discount, historical_percentile), # weight 15% (inverse) } sentiment_score = weighted_sum(components) # > 80: extreme greed -> cut exposure below 60% # 60-80: optimistic -> maintain normal exposure # 40-60: neutral -> keep exposure unchanged # 20-40: pessimistic -> add gradually # < 20: extreme fear -> increase exposure above 80% 3. Behavioral Optimization of Momentum Strategies Traditional momentum (sorting by past 12-month returns) is unstable in China A-shares. A behavioral-finance perspective suggests the following optimizations: Optimization 1: Separate sentiment momentum from fundamental momentum Sentiment momentum = part of recent price rise with no fundamental support -> short-term reversal Fundamental momentum = price rise consistent with earnings revisions -> can persist Trade: buy stocks with "strong fundamental momentum + weak sentiment momentum" Optimization 2: Attention-weighted momentum High-attention retail names reverse faster Indicator: if abnormal turnover > 3x average, cut momentum holding period by 50% Example: if a normal momentum basket holds for 60 days, high-attention names hold only 30 days Optimization 3: Combine cross-sectional momentum and time-series momentum Cross-sectional: relative strength (top 20% in return ranking) Time-series: absolute trend (price > MA60) Both satisfied -> strong signal; only one satisfied -> half position 4. Contrarian Trading Signals Extreme-fear buy conditions (at least 3 items): □ Shanghai Composite RSI(5) < 15 □ All-A daily turnover < 0.5% □ Weekly margin-financing decline > 5% □ Limit-up count < 10 and limit-down count > 50 □ Closed-end fund discount > 15% Extreme-greed sell conditions (at least 3 items): □ Shanghai Composite RSI(5) > 90 □ All-A daily turnover > 3% □ Weekly margin-financing growth > 10% □ Limit-up count > 150 □ Weekly increase in new account openings > 100% Output Format Behavioral-finance analysis report: === Market Sentiment Diagnosis === Date: 2026-03-28 Sentiment score: 72/100 (optimistic bias) Current phase: transition from optimism to excitement === Behavioral-Bias Signals === Overreaction detection: 127 stocks rose > 15% in the past 5 days -> 65% probability of short-term reversal Disposition effect: winner-clearing ratio is low (35%) -> overhead selling pressure remains Herding effect: sector correlation 0.85 -> severe follow-the-leader behavior, divergence likely soon === Strategy Recommendations === Momentum strategy: shorten holding period from 60 days to 30 days (market attention is elevated) Contrarian signal: not triggered (sentiment is not yet extreme) Position suggestion: maintain 70% exposure, and prioritize names with "strong fundamental momentum + weak sentiment momentum" === Debiasing Checklist === □ Are you overconfident because of recent profits? -> check position concentration □ Are you anchored to your entry price? -> re-evaluate using current PE/PB □ Are you ignoring bearish information? -> force yourself to read bearish research reports Notes High retail participation in China A-shares : behavioral-bias signals are more pronounced than in US equities, but sector rotation is also faster, so momentum windows should be shorter Lag in sentiment indicators : margin-financing balance is released T+1, and new account openings are weekly, so they are not suitable for intraday trading Structural changes : after 2019, foreign capital and quant participation rose, so the effectiveness of traditional behavioral-finance signals may have weakened Behavioral factors correlate with traditional factors : disposition-effect factors correlate about 0.3-0.5 with momentum, so control collinearity Overfitting risk : behavioral stories are easy to explain after the fact, so out-of-sample validation is mandatory Extreme sentiment is rare : extreme fear / greed appears only 2-3 times per year, so strategy capacity is limited Dependencies pip install pandas numpy scipy