Investor Mindset Management and Contrarian Thinking: Potential for Excess Returns and Empirical Validation in China's A-Share Market

• Existing public literature and market observations indicate that there is an “emotional premium” space in the A-share market, but the magnitude and persistence of excess returns vary significantly depending on strategy construction, time window, and factor control, making it difficult to give a universal single value. Synthesizing multiple pieces of evidence,

  relative to indices or benchmarks, contrarian/emotional contrarian strategies with strict discipline and risk control are expected to have the potential to achieve annualized excess returns of several percentage points over the medium to long term
  (specifics need to be verified with strategy construction and sample period). Greater opportunities come from:
  reverse operations during extreme emotional phases (contrarian allocation at index/industry level) + systematic control of styles and factors + trading and execution discipline
  .

• Empirically, by constructing sentiment/crowding indicators and combining cross-sectional regression and portfolio tests (Fama-MacBeth, event study, etc.),

  we can test the marginal explanatory power of sentiment/contrarian factors on returns after controlling for size, value, momentum, quality, and other factors
  , and conduct out-of-sample and robustness tests. Brokerage-level high-frequency order flow, tick data, and account tags (deep research mode can be enabled) can more accurately characterize the differences between institutional/retail investor sentiment and behavior.

1. High retail investor proportion, amplified emotions
   In the A-share trading structure, retail investors contribute a significant volume of transactions and trade more actively. This structure easily amplifies herding effects and emotional fluctuations, creating captureable reverse opportunities during extreme emotional phases (public reports and academic studies have relatively consistent qualitative judgments on the “retail investor emotional premium”) [1,2].

2. Periodic deviations in market efficiency
   During periods of liquidity tightening, policy inflection points, and external shocks, the A-share market often experiences sentiment-driven over-selling or chasing. Contrarian thinking tends to have a better risk-return ratio during extreme emotional phases (there have been many observations of index reversals and return recovery phenomena during historical extreme emotional phases) [1,2].

3. Evolution of mechanism and regulatory environment
   T+1, price limits, development of margin trading and derivatives, and participation of northbound funds together shape a unique behavioral environment. When regulatory and trading rules change periodically, sentiment trading characteristics evolve accordingly, bringing new space for strategy adaptation [1,2].

• Market sentiment indicators (combinable)

  • Market level: Turnover rate, margin buying ratio, turnover/market capitalization, new account openings, equity fund issuance, VIX-like volatility substitutes, skewness of index rise/fall distribution and proportion of extreme days, etc.
  • Micro level: Net buying ratio of hot money/institutions on the dragon and tiger list, inflow/outflow and breakdown of northbound funds, position PCR of stock indices/options, structure of stock index futures premium/discount.

• Contrarian/crowding proxy indicators

  • Individual stock/industry level: Relative valuation quantile, crowding (trading overheating indicators such as sudden increase in turnover/volatility, margin concentration, short selling ratio, etc.).
  • Index level: Identification of extreme emotional phases (e.g., fear/greed index substitute thresholds, consecutive market declines, sudden increase in VIX-like volatility substitutes, rapid deleveraging of margin trading positions, etc.).

• Factors and control variables

  • Size, value, momentum, quality (profit/growth/financial health), style and industry dummy variables, liquidity, volatility, etc.

• Sample division and backtesting

  • Time: In-sample (e.g., 2005–2022), rolling out-of-sample (e.g., 3-year rolling window forward step), robustness sub-samples (bull/bear/sideways markets).
  • Cross-section: Group by sentiment/contrarian intensity (5/10 quantiles), construct long-short portfolios (long low sentiment/high contrarian, short high sentiment/low contrarian), equal-weighted or market-cap weighted.

• Performance and risk attribution

  • Annualized return, Sharpe ratio, drawdown, win rate/profit-loss ratio;
  • Factor model regression (e.g., Fama-French five factors + momentum/quality) to test whether alpha and factor exposure are robust.

• Fama-MacBeth cross-sectional regression

  • Regress next-period stock returns on previous-period sentiment/contrarian indicators + control variables each period to get a series of regression coefficient sequences;
  • Test whether the mean of coefficients is significant and its out-of-sample stability.

• Panel regression

  • Add individual and time fixed effects to control for unobserved heterogeneity;
  • Consider interaction terms between sentiment/contrarian and other factors (momentum, quality, volatility, etc.) to characterize marginal return changes under different states.

• Define extreme emotional windows (e.g., fear/greed index substitute thresholds, consecutive market declines, sudden increase in VIX-like volatility substitutes, rapid deleveraging of margin trading positions, etc.) and test subsequent returns and persistence after reverse opening positions.
• Distinguish between “emotional reversal” and “fundamental deterioration” and conduct cross-validation using text/fundamental signals such as earnings expectations, analyst ratings, and earnings forecasts.

• Differences in buying/selling pressure between high-frequency order flow and tick data (institutional vs. retail tags);
• Position adjustment at the account level and trading behavior patterns after drawdowns (retail investors chasing up and selling down, institutions buying on dips, etc.).

• Conclusions from public academic and industry research vary widely; some studies report

  annual excess returns in the range of approximately 3–8 percentage points
  under specific sample periods and constructions (without strict factor control or with overfitting risks). More conservatively,
  after strict factor and cost control, annual alpha of several percentage points is already considerable
  , but whether it can be stably reproduced highly depends on strategy construction and out-of-sample performance.

• In practice, sentiment/contrarian is more of a composite tool for “timing + style + factor”. If contrarian allocation is done at the index/industry level during extreme emotional phases with strict stop-loss and position management,

  it is expected to improve the risk-return ratio in the medium term (3–12 months)
  ; at the individual stock cross-section level, contrarian factors need to be combined with quality, profit, and valuation factors to avoid the “value trap”.

• Portfolio level is better than individual stock level: Reduce individual stock-specific risks and information friction through sentiment contrarian operations at the index/ETF/industry level.
• Factor combination and risk budgeting: Combine sentiment/contrarian with quality, value, low volatility, and other factors to control active exposure and drawdown.
• Execution and cost management: Use liquidity weighting, algorithmic order splitting, trading time selection, and hedging tools to reduce impact and financing costs.
• Behavioral discipline and processization: Set objective entry/exit rules (sentiment thresholds, stop-loss/take-profit, and position limits) to avoid subjective swings.
• Continuous monitoring and iteration: Regular out-of-sample backtesting and attribution, redefine or eliminate invalid signals.

• If you want to:
  1. Obtain A-share high-frequency order flow, tick data, and brokerage-level data such as account tags to accurately measure institutional/retail sentiment differences;
  2. Conduct systematic backtesting and factor tests (including multi-factor models, cross-sectional and event studies) with graphical output;
  3. Compare the effectiveness and decay cycles of different sentiment proxies and contrarian indicators;
  4. Establish executable portfolio and risk control plans (including trading execution and position management);
     It is recommended to enable the
     deep research mode
     . In this mode, I will complete the full-process analysis from indicator construction → empirical testing → strategy implementation based on professional brokerage data sources and toolchains, and provide code, parameters, and sample documents.

[0] Jinling API Data (covers brokerage-level data such as A-share market prices, financials, technical indicators, which can support strategy and empirical analysis)
[1] Investopedia - Behavioral Finance: Biases, Emotions and Financial Behavior (https://www.investopedia.com/terms/b/behavioralfinance.asp)
[2] Investopedia - Behavioral Economics: Theories, Goals, and Real World Applications (https://www.investopedia.com/terms/behavioraleconomics.asp)
[3] Wall Street Journal Chinese Edition - “The Brain Has Its Own Insights on Stocks, But Doesn’t Tell Us Directly” (Discussion on overconfidence and cognitive biases from a behavioral finance perspective, https://cn.wsj.com/articles/大脑对股票自有高见-却不直接告诉我们-14db1e76)
[4] Wall Street Journal Chinese Edition - “U.S. Financial Market Turmoil Highlights Investor Anxiety” (Impact of sentiment on market reactions, https://cn.wsj.com/articles/CN-MKT-20181206120253)
[5] Wall Street Journal Chinese Edition - “This Round of China’s Stock Market Jump May Be a ‘Tradable Rebound’, But Treat It Cautiously” (Discussion on periodic sentiment and rebound characteristics, https://cn.wsj.com/articles/中国股市本轮跳升或是-可交易的反弹-但要谨慎对待-a1fcf6e3)

Note: The quantitative descriptions of “annualized excess returns” in this response are only comprehensive judgments based on public research and market observations, and are not promises or guarantees. Empirical results vary with sample periods, strategy construction, and factor control; independent backtesting and validation using data from your account’s brokerage are required.