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

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.

[1]  Aram Galstyan,et al.  Information transfer in social media , 2011, WWW.

[2]  Barry W. Johnson.  Algorithmic trading & DMA : an introduction to direct access trading strategies , 2010 .

[3]  T. Dimpfl,et al.  Using Transfer Entropy to Measure Information Flows Between Financial Markets , 2013 .

[4]  Olivier J. J. Michel,et al.  The relation between Granger causality and directed information theory: a review , 2012, Entropy.

[5]  Leonidas Sandoval,et al.  Structure of a Global Network of Financial Companies Based on Transfer Entropy , 2014, Entropy.

[6]  Anil Deane,et al.  The Impact of Abnormal News Sentiment on Financial Markets , 2015 .

[7]  James R. Crotty,et al.  Structural causes of the global financial crisis: a critical assessment of the ‘new financial architecture’ , 2009 .

[8]  W. Fong,et al.  Investor sentiment and the MAX effect , 2014 .

[9]  Jeffrey Wurgler,et al.  Investor Sentiment in the Stock Market , 2011 .

[10]  Leonidas Sandoval Junior.  Cluster formation and evolution in networks of financial market indices , 2011, Algorithmic Finance.

[11]  Tim Loughran,et al.  Textual Analysis in Accounting and Finance: A Survey: TEXTUAL ANALYSIS IN ACCOUNTING AND FINANCE , 2016 .

[12]  T. Dimpfl,et al.  Analyzing volatility transmission using group transfer entropy , 2018, Energy Economics.

[13]  Mark Rubinstein,et al.  Rational Markets: Yes or No? The Affirmative Case , 2000 .

[14]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[15]  M. Rubinstein.,et al.  Rational Markets: Yes or No? The Affirmative Case , 2006 .

[16]  Bill McDonald,et al.  Textual Analysis in Accounting and Finance: A Survey , 2016 .

[17]  R. C. Merton,et al.  Presidential Address: A simple model of capital market equilibrium with incomplete information , 1987 .

[18]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[19]  Charles M. Jones,et al.  Does Algorithmic Trading Improve Liquidity? , 2010 .

[20]  Mikhail Prokopenko,et al.  On Thermodynamic Interpretation of Transfer Entropy , 2013, Entropy.

[21]  Steve Y. Yang,et al.  Information Transition in Trading and Its Effect on Market Efficiency: An Entropy Approach , 2021, Financial Mathematics and Fintech.

[22]  Price Discovery and the Cross-section of High-Frequency Trading , 2016 .

[23]  A. Seth,et al.  Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.

[24]  Y. Tse,et al.  Competition For Order Flow, Market Quality, And Price Discovery In The Nasdaq 100 Index Tracking Stock , 2003 .

[25]  W. Ebeling,et al.  Guessing probability distributions from small samples , 1995, cond-mat/0203467.

[26]  Lingfeng Liu,et al.  An Entropy Measure of Non-Stationary Processes , 2014, Entropy.

[27]  M. Nardo,et al.  Walking Down Wall Street with a Tablet: A Survey of Stock Market Predictions Using the Web , 2016 .

[28]  Tarun Chordia,et al.  Liquidity and Autocorrelations in Individual Stock Returns , 2005 .

[29]  B. Malkiel.  The Efficient Market Hypothesis and Its Critics , 2003 .

[30]  Leonidas Sandoval Junior.  Structure and causality relations in a global network of financial companies , 2013, 1310.5388.

[31]  George Kokolakis,et al.  Bregman divergences in the (m×k)-partitioning problem , 2006, Comput. Stat. Data Anal..

[32]  Loretta Mastroeni,et al.  Modeling the flow of information between financial time-series by an entropy-based approach , 2019, Ann. Oper. Res..

[33]  Jonathan M. Nichols,et al.  Linearized Transfer Entropy for Continuous Second Order Systems , 2013, Entropy.

[34]  Jing Chen,et al.  Applications of a multivariate Hawkes process to joint modeling of sentiment and market return events , 2017 .

[35]  Y. Tse,et al.  Price discovery on the S&P 500 index markets: An analysis of spot index, index futures, and SPDRs , 1999 .

[36]  F. Benedetto,et al.  Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis , 2020, Energy Economics.

[37]  Thomas E. Copeland,et al.  A Model of Asset Trading under the Assumption of Sequential Information Arrival , 1976 .

[38]  Guido Caldarelli,et al.  S 1 Appendix , 2016 .

[39]  Josep Domingo-Ferrer,et al.  Practical Data-Oriented Microaggregation for Statistical Disclosure Control , 2002, IEEE Trans. Knowl. Data Eng..

[40]  H. Kantz,et al.  Analysing the information flow between financial time series , 2002 .

[41]  George Kokolakis,et al.  On the Discrepancy Measures for the Optimal Equal Probability Partitioning in Bayesian Multivariate Micro-Aggregation , 2008, J. Classif..

[42]  H. Kleinert,et al.  Rényi’s information transfer between financial time series , 2011, 1106.5913.

[43]  Joel Hasbrouck,et al.  Intraday Price Formation in Us Equity Index Markets , 2000 .

[44]  Sofus A. Macskassy,et al.  More than Words: Quantifying Language to Measure Firms' Fundamentals the Authors Are Grateful for Assiduous Research Assistance from Jie Cao and Shuming Liu. We Appreciate Helpful Comments From , 2007 .

[45]  Gideon Saar,et al.  The Competitive Landscape of High-Frequency Trading Firms , 2017 .

[46]  Stelios D. Bekiros,et al.  Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets , 2017, Eur. J. Oper. Res..

[47]  A. Shleifer,et al.  Inefficient Markets: An Introduction to Behavioral Finance , 2002 .

[48]  Jianfeng Yu,et al.  Investor Sentiment and the Mean-variance Relation , 2005 .