Abstract
Abstract The paper adopts the financial physics approach to investigate influence of trading volume, market trend, as well as monetary policy on characteristics of the Chinese Stock Exchange. Utilizing 1-minute high-frequency data at various time intervals, the study examines the probability distribution density, autocorrelation and multi-fractal of the Shanghai Composite Index. Our study finds that the scale of trading volume, stock market trends, and monetary policy cycles all exert significant influences on micro characteristics of Shanghai Composite Index. More specifically, under the conditions of large trading volumes, loose monetary policies, and downward stock trends, the market possesses better fitting on Levy’s distribution, the volatility self-correlation is stronger, and multifractal trait is more salient. We hope our study could provide better guidance for investment decisions, and form the basis for policy formulation aiming for a healthy growth of the financial market.
Highlights
Our study finds that the scale of trading volume, stock market trends, and monetary policy cycles all exert significant influences on micro characteristics of Shanghai Composite Index
Since Mantegna and Stanley’s [1] introduction of the concept of “Econophysics” in 1995, financial physics has gradually developed into a new interdisciplinary field to apply finds that the truncated Levy distribution can describe the distribution characteristics of experience, and perform better than the Gaussian distribution
This study analyzes the effects of trading volume, stock market trend, and monetary policy on the probability distribution density, autocorrelation and multi-fractal of the Shanghai Stock Exchange Index using 1-min data at different time intervals
Summary
Since Mantegna and Stanley’s [1] introduction of the concept of “Econophysics” in 1995, financial physics has gradually developed into a new interdisciplinary field to apply finds that the truncated Levy distribution can describe the distribution characteristics of experience, and perform better than the Gaussian distribution. We contribute to the literature by exploring dif- tary policy interval as from Jun 28, 2015 to Oct 28, 2015, ferences in statistical laws such as probability distribution the steady monetary policy interval from Mar 28, 2016 to density, autocorrelation and multiple fractals under vari- Jul 28, 2016, and the tight monetary policy interval from ous trading volume scales, stock market trends and mone- Dec 21, 2016 to Apr 21, 2017. Our paper can directly verify the effectiveness of various trading volumes scales, stock market trends and monetary policies cycles, which will help to explain investor behaviors and guide investors’ investment decisions, and provide imthe trend chart of weekly data based on methods applied by Ref. Our paper possesses impor- the rising period is from Sep 12, 2014 to tant practical implications
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