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longbridge-behavioral-finance

Behavioral finance application framework — identify cognitive biases in markets (overreaction, underreaction, disposition effect, anchoring, herding), translate them into quantifiable trading signals (momentum / reversal), and assess whether current market sentiment shows systematic bias. Triggers: "行为金融", "认知偏差", "过度反应", "反应不足", "处置效应", "锚定效应", "羊群效应", "市场情绪偏差", "行為金融", "認知偏差", "過度反應", "反應不足", "處

27estrelas
Atualizado há 4 meses

Ver no GitHub ↗ Licença: MIT

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longbridge-behavioral-finance

Apply behavioral finance theory to identify exploitable market inefficiencies — map common cognitive biases to measurable price/volume patterns using Longbridge data.

Response language: match the user's input language — Simplified Chinese / Traditional Chinese / English.

When to use

  • "这只股票是不是过度反应了", "did the market overreact to this news", "這隻股票是否過度反應"
  • "动量效应还是反转效应", "momentum or reversal for this sector"
  • "处置效应怎么影响散户行为", "how does disposition effect affect retail traders"
  • "现在市场有没有羊群效应", "is there herding in the market right now"
  • "锚定效应怎么影响股价", "how does anchoring bias affect stock prices"

For quantitative factor testing (IC/IR), use longbridge-factor-research. For market temperature index, use longbridge-market-temp.

Bias catalogue and tradeable signals

1. Overreaction (过度反应)

Theory: Investors overweight recent bad/good news, pushing prices beyond fundamental value. De Bondt & Thaler (1985).

Signal: Long-term reversal. Stocks down 30–50% over 12M outperform over the next 12M; top performers underperform.

Detect:

longbridge kline <SYMBOL> --period day --count 252 --format json

Compare 12M return vs peer group. If a stock is in the bottom 10% of sector returns, screen for reversal setup.

2. Underreaction (反应不足)

Theory: Investors are slow to update beliefs; price drifts gradually toward fair value after earnings surprises.

Signal: Post-earnings announcement drift (PEAD). Buy after positive earnings surprise; price continues rising for 1–3 months.

Detect: Use longbridge-earnings to identify beats. Track price drift with longbridge kline --period day --count 60.

3. Disposition Effect (处置效应)

Theory: Investors sell winners too early and hold losers too long (Shefrin & Statman, 1985).

Market impact: Selling pressure on recent winners creates resistance near recent highs; support near cost basis concentrations.

Detect: High capital inflows after a price surge = retail profit-taking.

longbridge capital <SYMBOL> --format json

4. Anchoring Bias (锚定效应)

Theory: Investors anchor to arbitrary reference prices (52-week high, IPO price, round numbers).

Signal: 52-week high breakout tends to persist (stocks resist breaking prior highs but accelerate once broken).

Detect: Fetch 52-week high from longbridge calc-index <SYMBOL> --format json or static data.

5. Herding (羊群效应)

Theory: Investors follow the crowd, amplifying trends beyond fundamentals.

Signal: Abnormal volume + price acceleration without fundamental catalyst = herding. Also: analyst consensus clustering.

Detect:

longbridge market-temp --format json          # Market sentiment index 0-100
longbridge capital <SYMBOL> --format json     # Capital flow concentration

If market temperature > 80 and a single sector dominates capital inflow → herding warning.

6. Overconfidence (过度自信)

Theory: Investors overestimate precision of their forecasts, leading to under-diversification and excess trading.

Market impact: High turnover in bull markets; individual stocks show higher volatility than fundamentals justify.

Detect: Turnover rate spike from longbridge quote <SYMBOL> --format json (turnover_rate field).

Workflow

  1. Identify which bias the user is asking about (or scan all six).
  2. Fetch relevant data (kline / market-temp / capital flow).
  3. Map observed price/volume pattern to the bias.
  4. Quantify signal strength: magnitude, duration, persistence.
  5. Suggest a trading implication (entry / exit / avoid) with explicit caveats.

CLI

longbridge kline --help
longbridge market-temp --help
longbridge capital --help

longbridge market-temp --format json
longbridge kline <SYMBOL> --period day --count 60 --format json
longbridge capital <SYMBOL> --format json

Output

Present:

  1. Identified bias and academic basis.
  2. Observable evidence from Longbridge data (specific numbers).
  3. Tradeable implication (signal direction, horizon, conviction).
  4. Risks: when the bias does not persist (e.g. mean-reversion fails in trending markets).
  5. Disclaimer: behavioral signals are probabilistic, not deterministic.

Error handling

| Situation | 简体回复 | 繁體回覆 | English reply | |---|---|---|---| | command not found: longbridge | 请安装 longbridge-terminal 或检查 MCP 配置。 | 請安裝 longbridge-terminal 或檢查 MCP 配置。 | Install longbridge-terminal or check MCP config. | | stderr: not logged in | 请运行 longbridge auth login。 | 請執行 longbridge auth login。 | Run longbridge auth login. | | Insufficient price history | 历史数据不足,无法可靠识别偏差信号,请延长观察期。 | 歷史數據不足,請延長觀察期。 | Insufficient history to identify bias reliably; extend the lookback. |

Related skills

  • longbridge-market-temp — market sentiment temperature index
  • longbridge-capital-flow — intraday capital flow and large/small order split
  • longbridge-factor-research — quantitative factor IC/IR analysis
  • longbridge-anomaly — unusual price/volume movements

File layout

skills/longbridge-behavioral-finance/
└── SKILL.md
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