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Recurrence interval analysis of trading volumes
1School of Business, East China University of Science and Technology, Shanghai 200237, China.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 28, 2010
Summary
This study analyzes trading volume recurrence intervals in Chinese stocks. Findings reveal power-law scaling and memory effects in trading volumes, linking large volumes to significant price returns.
Area of Science:
- Quantitative Finance
- Financial Market Analysis
- Statistical Mechanics
Background:
- Trading volume is a key indicator of market activity.
- Understanding the statistical properties of trading volumes is crucial for market analysis.
- Recurrence interval analysis provides insights into the temporal dynamics of financial data.
Purpose of the Study:
- To investigate the statistical properties of recurrence intervals between high trading volumes.
- To analyze the memory effects in trading volume recurrence intervals.
- To explore the relationship between trading volumes and price returns using recurrence interval analysis.
Main Methods:
- Recurrence interval analysis applied to trading volumes of Chinese stocks and indices.
- Goodness-of-fit tests including Kolmogorov-Smirnov (KS) statistic and Cramér-von Mises criterion.
- Conditional probability distribution and detrended fluctuation function analysis.
Main Results:
- The tail of the trading volume recurrence interval distribution exhibits power-law scaling.
- Both short-term and long-term memory effects are identified in trading volume recurrence intervals.
- Large trading volumes tend to follow large price returns, with stronger comovement for significant volumes.
Conclusions:
- Trading volume dynamics display statistical similarities to price return dynamics.
- Memory effects in trading volume recurrence intervals suggest predictable patterns.
- The interplay between trading volume and price returns is significant, especially during periods of high activity.
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