Dynamic multiscale analysis of causality among mining stock prices
Tao Wu and
Resources Policy, 2022, vol. 77, issue C
This paper combines the ensemble empirical mode decomposition(EEMD) method, the transfer entropy(TE) method and complex network theory to analyze the causal relationships among Chinese mining stock prices on multiple time scales. The results show that the strength of the causal relationship among mining stocks increases with cycle growth and that the causal relationship between stocks and subindustries is asymmetric and time-varying. On the three time scales, the weighted in-degree, weighted out-degree and weighted betweenness centrality of the five subindustries all show a trend of increasing and decreasing at the same time. In addition, we construct high and low transfer entropy portfolios and find that the high transfer entropy portfolio has higher investment returns when the stock market is good, and the low transfer entropy portfolio has less investment loss when the stock market is bad. Investors are advised to pay attention to the stocks included in the high transfer entropy portfolio when the market is good, and vice versa when the market is bad. Regulators are advised to pay attention to the subindustries with heavy positions in the high transfer entropy portfolio.
Keywords: Mining stocks; Multiple analysis; Causal relationship; Complex network (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001568
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