Core Viewpoint - The article explores the concept of contrarian investing, emphasizing its complexity beyond the simplistic notion of "buy low, sell high." It introduces a quantitative approach to measure contrarian investment capabilities through the concept of emotional beta, demonstrating that fund managers who achieve excess returns when investor opinions converge tend to perform better in the future [1][5][12]. Emotional Beta and Asset Returns - Contrarian investing is defined as taking positions contrary to the majority of investors. Turnover rate is used to represent the degree of investor disagreement, with lower turnover indicating greater consensus. Empirical results show that assets with low emotional beta often exhibit better future performance across various asset classes [2][6]. Quantitative Expression of Contrarian Investment Capability - The article constructs a contrarian investment capability factor based on fund holdings and fund returns. The average RankIC for the fund holding-based factor is -7.30%, with an annualized RankICIR of -0.92 and a win rate of 67.21%. The fund return-based factor shows an average RankIC of -8.92%, an annualized RankICIR of -1.04, and a win rate of 75.41%. The combined contrarian investment capability factor has an average RankIC of -10.85%, an annualized RankICIR of -1.39, and a win rate of 78.69% [3][66]. Characteristics of the Contrarian Investment Capability Factor - The contrarian investment capability factor exhibits low correlation with nine previously constructed selection factors, with absolute correlation values below 0.1. The introduction of this factor enhances the predictive power of a composite selection factor, increasing its average RankIC from 11.51% to 13.57% [4][73]. Market Adaptability and Predictive Power - Since 2015, the contrarian investment capability factor has shown high predictive power, with an average RankIC of -10.85% and an annualized RankICIR of -1.39. It has maintained strong performance even as other previously successful factors have experienced significant volatility [6][73]. Historical Examples of Contrarian Investors - Notable investors like Warren Buffett and John Templeton exemplify successful contrarian investing. Buffett's strategy involves buying undervalued stocks during market downturns, while Templeton capitalized on extreme pessimism during the Great Depression by investing in undervalued stocks [8][9][12].

Quantitative Models and Factor Construction Quantitative Models - **Model Name**: Extended CAPM with Sentiment Beta **Model Construction Idea**: Incorporates turnover rate changes as a proxy for investor sentiment to measure the sensitivity of asset returns to sentiment changes, extending the traditional CAPM framework[35][36] **Model Construction Process**: The model is expressed as: $R = \alpha + \beta_{MRT} \times MKT + \beta_{TO} \times \Delta TO + \varepsilon$ - $R$: Asset daily return (e.g., stocks, industries, funds) - $MKT$: Market factor, represented by CSI All Share Index daily return - $\Delta TO$: Change in turnover rate, calculated as: $$\Delta TO = \frac{Turnover_t}{\sum_{i=1}^{N} Turnover_{t-i}/N} - 1$$ - $\beta_{TO}$: Sentiment Beta, representing the sensitivity of asset returns to sentiment changes[35][36] **Model Evaluation**: Demonstrates strong predictive power for future asset performance, with lower Sentiment Beta assets generally outperforming higher Sentiment Beta assets[42][43][50] Quantitative Factors - **Factor Name**: Sentiment Beta (Stock Level) **Factor Construction Idea**: Measures the sensitivity of stock returns to changes in investor sentiment, represented by turnover rate changes[35][36] **Factor Construction Process**: - Calculate Sentiment Beta for each stock using the extended CAPM model - Neutralize the factor for industry and market capitalization effects using the following regression: $$\beta_{i,TO} = \alpha + \gamma_M \ln(mktcap) + \sum_{j=1}^{n} \gamma_j \times lnd_{j,i} + \varepsilon_i$$ - $\beta_{i,TO}$: Neutralized Sentiment Beta for stock $i$ - $mktcap$: Market capitalization of the stock - $lnd_{j,i}$: Dummy variable for industry classification[60][61] **Factor Evaluation**: RankIC mean of -2.75%, annualized RankICIR of -0.49, indicating strong predictive power for future stock returns[43][45] - **Factor Name**: Sentiment Beta (Industry Level) **Factor Construction Idea**: Measures the sensitivity of industry index returns to sentiment changes[47] **Factor Construction Process**: - Calculate Sentiment Beta for each industry index using the extended CAPM model - Group industries by Sentiment Beta and analyze future performance differences[47] **Factor Evaluation**: RankIC mean of -4.44%, annualized RankICIR of -0.29, with low Sentiment Beta industries outperforming high Sentiment Beta industries[47][51] - **Factor Name**: Sentiment Beta (Fund Level) **Factor Construction Idea**: Quantifies fund managers' contrarian investment ability based on the sensitivity of fund returns to sentiment changes[50] **Factor Construction Process**: - Calculate Sentiment Beta for each fund using the extended CAPM model - Group funds by Sentiment Beta and analyze future performance differences[50][52] **Factor Evaluation**: RankIC mean of -5.78%, annualized RankICIR of -0.48, with low Sentiment Beta funds outperforming high Sentiment Beta funds[52][55] - **Factor Name**: Contrarian Investment Ability Factor (Fund Holdings-Based) **Factor Construction Idea**: Aggregates the Sentiment Beta of stocks held by a fund to measure the fund manager's contrarian ability[58] **Factor Construction Process**: - Calculate stock-level Sentiment Beta using an extended Fama-French five-factor model with turnover rate changes - Aggregate stock-level Sentiment Beta weighted by fund holdings: $$FHB = \sum_{i=1}^{n} w_i \times \widehat{\beta}_{i,TO}$$ - $w_i$: Normalized weight of stock $i$ in the fund's holdings - $\widehat{\beta}_{i,TO}$: Neutralized Sentiment Beta for stock $i$[63][64] **Factor Evaluation**: RankIC mean of -7.30%, annualized RankICIR of -0.92, win rate of 67.21%[64][65] - **Factor Name**: Contrarian Investment Ability Factor (Fund Returns-Based) **Factor Construction Idea**: Directly measures fund managers' contrarian ability using fund return data[69] **Factor Construction Process**: - Extend the Carhart four-factor model by adding turnover rate changes: $$F_i = \alpha_i + \beta_{i,MRT} \times MKT + \beta_{i,SMB} \times SMB + \beta_{i,HML} \times HML + \beta_{i,UMD} \times UMD + \beta_{i,TO} \times \Delta TO + \varepsilon$$ - $\beta_{i,TO}$: Sentiment Beta for fund $i$[70][71] **Factor Evaluation**: RankIC mean of -8.92%, annualized RankICIR of -1.04, win rate of 75.41%[71][77] - **Factor Name**: Contrarian Investment Ability Factor (Composite) **Factor Construction Idea**: Combines holdings-based and returns-based factors to create a comprehensive measure of contrarian ability[75] **Factor Construction Process**: - Equal-weight the holdings-based and returns-based factors to form the composite factor[75] **Factor Evaluation**: RankIC mean of -10.85%, annualized RankICIR of -1.39, win rate of 78.69%[75][79] Backtesting Results of Factors - **Sentiment Beta (Stock Level)**: RankIC mean -2.75%, annualized RankICIR -0.49[43][45] - **Sentiment Beta (Industry Level)**: RankIC mean -4.44%, annualized RankICIR -0.29[47][51] - **Sentiment Beta (Fund Level)**: RankIC mean -5.78%, annualized RankICIR -0.48[52][55] - **Contrarian Investment Ability Factor (Holdings-Based)**: RankIC mean -7.30%, annualized RankICIR -0.92, win rate 67.21%[64][65] - **Contrarian Investment Ability Factor (Returns-Based)**: RankIC mean -8.92%, annualized RankICIR -1.04, win rate 75.41%[71][77] - **Contrarian Investment Ability Factor (Composite)**: RankIC mean -10.85%, annualized RankICIR -1.39, win rate 78.69%[75][79]