Information transfer between stock market sectors: A comparison between the USA and China

Information diffusion within financial markets plays a crucial role in the process of price formation and the propagation of sentiment and risk. We perform a comparative analysis of information transfer between industry sectors of the Chinese and the USA stock markets, using daily sector indices for the period from 2000 to 2017. The information flow from one sector to another is measured by the transfer entropy of the daily returns of the two sector indices. We find that the most active sector in information exchange (i.e., the largest total information inflow and outflow) is the {\textit{non-bank financial}} sector in the Chinese market and the {\textit{technology}} sector in the USA market. This is consistent with the role of the non-bank sector in corporate financing in China and the impact of technological innovation in the USA. In each market, the most active sector is also the largest information sink that has the largest information inflow (i.e., inflow minus outflow). In contrast, we identify that the main information source is the {\textit{bank}} sector in the Chinese market and the {\textit{energy}} sector in the USA market. In the case of China, this is due to the importance of net bank lending as a signal of corporate activity and the role of energy pricing in affecting corporate profitability. There are sectors such as the {\textit{real estate}} sector that could be an information sink in one market but an information source in the other, showing the complex behavior of different markets. Overall, these findings show that stock markets are more synchronized, or ordered, during periods of turmoil than during periods of stability.Comment: 12 pages including 8 figure

Random Walk Null Models for Time Series Data

Permutation entropy has become a standard tool for time series analysis that exploits the temporal and ordinal relationships within data. Motivated by a Kullback–Leibler divergence interpretation of permutation entropy as divergence from white noise, we extend pattern-based methods to the setting of random walk data. We analyze random walk null models for correlated time series and describe a method for determining the corresponding ordinal pattern distributions. These null models more accurately reflect the observed pattern distributions in some economic data. This leads us to define a measure of complexity using the deviation of a time series from an associated random walk null model. We demonstrate the applicability of our methods using empirical data drawn from a variety of fields, including to a variety of stock market closing prices

Characterizing Complexity Changes in Chinese Stock Markets by Permutation Entropy

Financial time series analyses have played an important role in developing some of the fundamental economic theories. However, many of the published analyses of financial time series focus on long-term average behavior of a market, and thus shed little light on the temporal evolution of a market, which from time to time may be interrupted by stock crashes and financial crises. Consequently, in terms of complexity science, it is still unknown whether the market complexity during a stock crash decreases or increases. To answer this question, we have examined the temporal variation of permutation entropy (PE) in Chinese stock markets by computing PE from high-frequency composite indies of two stock markets: the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). We have found that PE decreased significantly in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. One window started in the middle of 2006, long before the 2008 global financial crisis, and continued up to early 2011. The other window was more recent, started in the middle of 2014, and ended in the middle of 2016. Since both windows were at least one year long, and proceeded stock crashes by at least half a year, the decrease in PE can be invaluable warning signs for regulators and investors alike

The Evolution of Efficiency in the Chinese Stock Market

This dissertation examines the weak-form efficiency of the Chinese stock market and provides evidence on how the market efficiency evolved throughout the last three decades. The Shanghai Composite Index (SSEC) and the Shenzhen Component Index (SZSE) are the primary indicators of the Chinese stock market in this study. Both traditional economics and the complex systems’ methods are employed to evaluate market efficiency, with an additional focus on the effect of two parameter inputs (embedded dimension and noise filter) on entropy methods to improve their ability to detect phase transitions in stock market data. The traditional efficiency tests indicate that the Chinese stock market during the full sample period of 1990-2021 is inefficient, but some of the sub-sample periods indicate the weak-form efficiency, except for the ADF test. Meanwhile, the complex systems’ methods suggest that the level of randomness in returns increases over time. Additionally, I find that the bull periods of the Chinese market are less efficient than the bust periods, which may indicate that investors tend to commit more errors during the bull period. Generally, the study concludes that the complex systems’ methods provide a more comprehensive evaluation of the changes in the market efficiency than traditional methods. The empirical results suggest that the Chinese stock market is not completely efficient based on the traditional efficiency tests but the level of efficiency has improved over time based on the evidence of the complex systems’ analysis