The Flow of Information in Trading: An Entropy Approach to Market Regimes.

Anqi Liu,Steve Y Yang,Jing Chen,Alan G Hawkes

doi:10.3390/e22091064

Anqi Liu, Steve Y Yang + Show 2 more

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https://doi.org/10.3390/e22091064

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Abstract

In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven trading activity that is revealed by the information flows from news sentiment to market returns. We argue that when certain trading behavior becomes dominant or jointly dominant, the market will form a specific regime, namely return-, news- or mixed regime. Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows. The proposed method can be expanded to make “causal” inferences on other types of economic phenomena.

Highlights

The financial market is a natural arena for information competition and investors often seek to collect and process information to assist their investment decisions marking [1,2]
Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows
If we identify information flows targeting the changes of the market returns, we can find out what causes the market movements, which is consistent with the theory of price discovery

Summary

Introduction

The financial market is a natural arena for information competition and investors often seek to collect and process information to assist their investment decisions marking [1,2]. Investigating how traders use information become vital to comprehensively analyze and understand important finance problems including price formation, price discovery and market efficiency [3,4,5,6,7,8]. New financial technologies offer greater capacity to process larger amount information more efficiently that would result in faster price discovery [9,10,11] and eventually more efficient market as the Efficient Market Hypothesis (EMH) states. The advancement of financial technologies has implicitly increased the complexity of the market; how the multiple information transmits and influences one another through trading decisions is much more complex and has exceeded what the EMH can describe. We endeavor to propose a new method based on entropy to identify the roles of different information sources in price formation within the context of a contemporary financial market

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