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Register a new userWeekly idiosyncratic risk metrics and idiosyncratic momentum: Evidence from the Chinese stock market
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This paper focuses on the weekly idiosyncratic momentum (IMOM) as well as its risk-adjust
rate research
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Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin & Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extracting important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the textit{composite} sector is an information source of the whole stock market, while the textit{non-bank financial} sector is the information sink. We also find that the textit{non-bank finance}, textit{bank}, textit{computer}, textit{media}, textit{real estate}, textit{medical biology} and textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the textit{non-bank finance} and textit{bank} sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.
We investigate the probability distribution of the volatility return intervals $tau$ for the Chinese stock market. We rescale both the probability distribution $P_{q}(tau)$ and the volatility return intervals $tau$ as $P_{q}(tau)=1/bar{tau} f(tau/bar{tau})$ to obtain a uniform scaling curve for different threshold value $q$. The scaling curve can be well fitted by the stretched exponential function $f(x) sim e^{-alpha x^{gamma}}$, which suggests memory exists in $tau$. To demonstrate the memory effect, we investigate the conditional probability distribution $P_{q} (tau|tau_{0})$, the mean conditional interval $<tau|tau_{0}>$ and the cumulative probability distribution of the cluster size of $tau$. The results show clear clustering effect. We further investigate the persistence probability distribution $P_{pm}(t)$ and find that $P_{-}(t)$ decays by a power law with the exponent far different from the value 0.5 for the random walk, which further confirms long memory exists in $tau$. The scaling and long memory effect of $tau$ for the Chinese stock market are similar to those obtained from the United States and the Japanese financial markets.
Bid-ask spread is taken as an important measure of the financial market liquidity. In this article, we study the dynamics of the spread return and the spread volatility of four liquid stocks in the Chinese stock market, including the memory effect and the multifractal nature. By investigating the autocorrelation function and the Detrended Fluctuation Analysis (DFA), we find that the spread return is lack of long-range memory, while the spread volatility is long-range time correlated. Moreover, by applying the Multifractal Detrended Fluctuation Analysis (MF-DFA), the spread return is observed to possess a strong multifractality, which is similar to the dynamics of a variety of financial quantities. Differently from the spread return, the spread volatility exhibits a weak multifractal nature.
Traders adopt different trading strategies to maximize their returns in financial markets. These trading strategies not only results in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we present a detailed analysis on how the market behaviors are correlated with the structures of traders in trading networks based on audit trail data for the Baosteel stock and its warrant at the transaction level from 22 August 2005 to 23 August 2006. In our investigation, we divide each trade day into 48 time windows with a length of five minutes, construct a trading network within each window, and obtain a time series of over 1,100 trading networks. We find that there are strongly simultaneous correlations between the topological metrics (including network centralization, assortative index, and average path length) of trading networks that characterize the patterns of order execution and the financial variables (including return, volatility, intertrade duration, and trading volume) for the stock and its warrant. Our analysis may shed new lights on how the microscopic interactions between elements within complex system affect the systems performance.
Loneliness (i.e., the distressing feeling that often accompanies the subjective sense of social disconnection) is detrimental to mental and physical health, and deficits in self-reported feelings of being understood by others is a risk factor for loneliness. What contributes to these deficits in lonely people? We used functional magnetic resonance imaging (fMRI) to unobtrusively measure the relative alignment of various aspects of peoples mental processing of naturalistic stimuli (specifically, videos) as they unfold over time. We thereby tested whether lonely people actually process the world in idiosyncratic ways, rather than only exaggerating or misperceiving how dissimilar others views are to their own (which could lead them to feel misunderstood, even if they actually see the world similarly to those around them). We found evidence for such idiosyncrasy: lonely individuals neural responses during free viewing of the videos were dissimilar to peers in their communities, particularly in brain regions (e.g., regions of the default-mode network) in which similar responses have been associated with shared psychological perspectives and subjective understanding. Our findings were robust even after controlling for demographic similarities, participants overall levels of objective social isolation, and their friendships with each other. These results suggest that being surrounded predominantly by people who see the world differently from oneself may be a risk factor for loneliness, even if one is friends with them.
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