Information Flow Analysis between EPU and Other Financial Time Series

Abstract

We investigate the strength and direction of information flow among economic policy uncertainty (EPU), US imports and exports to China, and the CNY/US exchange rate by using the novel concept of effective transfer entropy (ETE) with a sliding window methodology. We verify that this new method can capture dynamic orders effectively by validating them with the linear transfer entropy (TE) and Granger causality methods. Analysis shows that since 2016, US economic policy has contributed substantially to China-US bilateral trade and that China is making passive adjustments based on this trade volume. Unlike trade market conditions, China’s economic policy has significantly influenced the exchange rate fluctuation since 2016, which has, in turn, affected US economic policy.

Information flow between global financial market stress and African equity markets: An EEMD-based transfer entropy analysis

“…In the work of Eom et al [ 23 ], the authors concluded that the information flow evidences a property of time dependence which is influenced by the difference in the degree of efficiency in the stock market. Other existing literature within the body of information flow concentrates on economic policy uncertainty (EPU) and stock returns (see [ 14 , 24 , 25 ]). The findings evidence the relationship between the EPU and the stock returns which is consistent with the EMH and the market expectation hypothesis [ 35 , 36 ].…”

“…Given the relevance of the transfer entropy and decomposition approach in economics and finance [ 4 , 25 , 31 , 35 , 42 , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] ], it is necessary to employ this novel technique to quantify the flow of information from global financial stress to African stock markets. Although EEMD-based transfer entropy may not reveal the time-varying dynamics of the financial stress-stock markets nexus, its application is essential to delineate signal data into various modes that are representative of investment horizons (short-, medium-, and long-term), which are particularly relevant for market participants and policymakers amid systemic crisis periods.…”

Investor reaction to ESG news sentiment: evidence from South Africa

“…Transfer entropy is a model-free measure that can evaluate the flow of information between random variables in a time-directed manner. Thus, it provides an asymmetrical approach to measuring information transfer (Yao, 2020). The specific details of the computation and measurement of the statistics used in the two types of transfer entropy used in this study are outlined in the following subsections [1].…”

Optimization of Local Prediction Algorithm of Financial Time Series Based on Fuzzy Neural Network

“…Schnurr applied radial basis neural network to the prediction of exchange rate and obtained more accurate prediction of exchange rate [12] en, based on the financial time series forecasting model combining fuzzy neural network and GARCH, a specific mixed model is given for the data of the Shanghai Stock Exchange Index [13]. Yao uses Elman-NN to predict the stock composite index forecasting [14].…”