量化基本面系列之二：交易热度监控体系探讨

Quantitative Models and Construction Methods 1. Model Name: Amihud Illiquidity Indicator - Model Construction Idea: Measures the price impact of trading volume to assess the liquidity level of an asset. A higher value indicates lower liquidity. [11][12][13] - Model Construction Process: The formula is: $Amihud = \frac{1}{D} \sum_{d=1}^{D} \frac{\left| R_{i,d} \right|}{Vol_{i,d}}$ Where: - $D$: Number of trading days in the window - $R_{i,d}$: Absolute return of security $i$ on day $d$ - $Vol_{i,d}$: Trading volume of security $i$ on day $d$ This indicator reflects the sensitivity of price to trading volume. A higher value indicates that smaller trading volumes cause larger price changes, implying lower liquidity. [12][13] 2. Model Name: Pastor-Stambaugh Liquidity Indicator - Model Construction Idea: Based on the reversal of asset returns to measure liquidity. Assets with lower liquidity tend to exhibit higher return reversals. [14] - Model Construction Process: The formula is: $r_{i,d+1}^{e} = \alpha + \beta_{i} r_{i,d} + \gamma_{i} sign(r_{i,d}^{e}) \cdot v_{i,d} + \epsilon_{i,d+1}$ Where: - $r_{i,d+1}^{e}$: Excess return of security $i$ on day $d+1$ - $r_{i,d}$: Return of security $i$ on day $d$ - $v_{i,d}$: Trading volume of security $i$ on day $d$ - $\gamma_{i}$: Liquidity indicator, with a significantly negative value indicating poor liquidity. [14] 3. Model Name: Turnover Rate Indicator - Model Construction Idea: Reflects the trading activity of an asset by measuring the frequency of its turnover. Higher values indicate higher market liquidity. [15] - Model Construction Process: The turnover rate is calculated as: $Turnover\ Rate = \frac{Trading\ Volume}{Market\ Capitalization}$ Where: - $Trading\ Volume$: Total trading volume of the asset - $Market\ Capitalization$: Total market value of the asset. [15] 4. Model Name: Component Stock Diffusion Indicator - Model Construction Idea: Measures the consistency of trends among individual stocks within an industry to assess crowding. Higher values indicate a more crowded market. [16] - Model Construction Process: The indicator is calculated as the proportion of stocks in an industry that exhibit a bullish trend, defined as the closing price being above the short-term, medium-term, and long-term moving averages. [16] 5. Model Name: Component Stock Pairwise Correlation Indicator - Model Construction Idea: Quantifies the homogeneity of stock movements within an industry to evaluate crowding. Higher values indicate stronger synchronization and higher crowding. [17] - Model Construction Process: The indicator is the average of pairwise correlation coefficients of returns among all component stocks in an industry over a given window. [17] 6. Model Name: Component Stock Return Kurtosis Indicator - Model Construction Idea: Captures the extremity of trading by analyzing the tail characteristics of return distributions. Higher kurtosis indicates more extreme returns, suggesting heightened market crowding. [18] - Model Construction Process: The indicator is the average kurtosis of daily cross-sectional returns within a window. Kurtosis measures the "peakedness" or "flatness" of a distribution, with higher values indicating fatter tails. [18] 7. Model Name: Heat Indicator - Model Construction Idea: Uses principal component analysis (PCA) to measure the contribution of a single industry to systemic market risk, reflecting its trading heat. [21][22] - Model Construction Process: The formula is: $AR_{m} = \frac{\sigma_{m}^{2}}{\sum_{j=1}^{N} \sigma_{j}^{2}}$ $C_{i} = \frac{\sum_{j=1}^{n} AR_{j} \cdot \frac{\left| EV_{i}^{j} \right|}{\sum_{k=1}^{N} \left| EV_{k}^{i} \right|}}{\sum_{j=1}^{n} AR_{j}}$ Where: - $N$: Total number of industries - $n$: Number of principal components - $\sigma_{m}^{2}$: Variance of the $m$-th principal component - $\sigma_{j}^{2}$: Variance of the $j$-th industry return - $EV_{i}^{j}$: Exposure of the $j$-th principal component to the $i$-th industry. A higher value indicates that the industry contributes more to systemic market risk, suggesting higher trading heat. [21][22] 8. Model Name: Herding Effect Indicator - Model Construction Idea: Captures the consistency of market participants' behavior. A significant negative value indicates strong herding behavior, often signaling extreme market sentiment and crowded trading. [23][24] - Model Construction Process: The formula is: $CSAD_{t} = \gamma_{0} + \gamma_{1} \left| R_{m,t} \right| + \gamma_{2} R_{m,t}^{2} + \mathcal{E}_{t}$ Where: - $CSAD_{t}$: Cross-sectional absolute deviation of returns on day $t$ - $R_{m,t}$: Market return on day $t$ - $\gamma_{2}$: Herding effect indicator. [23][24] 9. Model Name: Closing Price-Trading Volume Correlation Indicator - Model Construction Idea: Analyzes the stability of the relationship between price and trading volume to predict potential trend reversals. Persistent negative correlation often signals overtrading and potential reversals. [25] - Model Construction Process: The indicator is the correlation coefficient between the series of closing prices and trading volumes of an index. [25] 10. Model Name: Trading Volume Share Indicator - Model Construction Idea: Reflects the concentration of trading in a specific sector or industry. Higher values indicate higher trading concentration and potential overheating. [26] - Model Construction Process: The indicator is calculated as the daily trading volume of a sector or industry divided by the total market trading volume. [26] Model Backtesting Results 1. Amihud Illiquidity Indicator: No specific backtesting results provided 2. Pastor-Stambaugh Liquidity Indicator: No specific backtesting results provided 3. Turnover Rate Indicator: No specific backtesting results provided 4. Component Stock Diffusion Indicator: No specific backtesting results provided 5. Component Stock Pairwise Correlation Indicator: No specific backtesting results provided 6. Component Stock Return Kurtosis Indicator: No specific backtesting results provided 7. Heat Indicator: No specific backtesting results provided 8. Herding Effect Indicator: No specific backtesting results provided 9. Closing Price-Trading Volume Correlation Indicator: No specific backtesting results provided 10. Trading Volume Share Indicator: No specific backtesting results provided Historical Similarity Analysis Results - Using the Wind Satellite Index (866125.WI) as an example, historical similar segments were identified based on metrics such as component stock count, trading volume share, and market capitalization. - For the next 60 trading days: - Average maximum return: 12.79% - Average time to peak: 33 days - Average peak trading volume share: 4.48% [42][46][49]