High-frequency volatility path-length-weighted illiquidity
factor.formula
Calculate the length of a single K-line fluctuation path:
Daily volatility path length weighted illiquidity factor:
in:
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It represents the fluctuation path length of the jth K-line at the intraday frequency, which approximately represents the amplitude of price fluctuation during the K-line period. Among them, $High_j$, $Low_j$, $Close_j$, $Open_j$ represent the highest price, lowest price, closing price and opening price of the jth K-line respectively.
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It represents the transaction volume within the jth K-line at the intraday frequency. This value, as a weight, reflects the impact of trading volume on illiquidity. The larger the transaction volume, the smaller the impact of illiquidity.
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Indicates the number of K-line segments per day. For example, if 5-minute K-line data is used, it indicates the number of 5-minute K-lines per day. This parameter depends on the selected intraday data frequency.
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Indicates the period parameter of the daily moving average, that is, the average value of the past number of days is calculated. This parameter is used to smooth short-term fluctuations and make the factor more stable. For example, if d=20, it means taking the average value of the past 20 trading days.
factor.explanation
This factor effectively measures the illiquidity of stocks by calculating the volatility path length of high-frequency minute K-line data and weighting it with transaction volume. Compared with traditional illiquidity factors, it uses the richer microstructure information contained in high-frequency data to more accurately capture the liquidity risk caused by impact costs during stock trading. Specifically:
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Volatility path length (Shortcut): This parameter adopts the form of 2*(High - Low) - |Close - Open|, which better captures the intraday price fluctuation range than the simple (High-Low), thereby more accurately reflecting the path length of price fluctuations and can be regarded as a volatility proxy indicator based on high-frequency data.
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Transaction volume weighting: Using transaction volume as weight can effectively control the impact of transaction volume on liquidity shock. The higher the transaction volume, the smaller the illiquidity shock.
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Time series average: By averaging over the time series, the factor is smoothed, making it more stable, reducing noise, and better reflecting the illiquidity risk of stocks.
In a high-frequency data environment, this factor can more effectively capture market microstructure and provide a more accurate measure of illiquidity than traditional methods, which helps to improve the effectiveness and predictive ability of quantitative trading strategies.