The Flow of Information in Trading: An Entropy Approach to Market Regimes (original) (raw)
Related papers
Information Transition in Trading and Its Effect on Market Efficiency: An Entropy Approach
2021
The Efficient Market Hypothesis has been well explored in terms of daily responses to market movements and financial reports. However, there is lack of evidence about information efficiency after the popularization of intraday trading. We investigate the time series properties of information adopted in the intraday market, in particular the causality effects. We use 30-min market price and news data to represent the past market data and the public information respectively, so that our analysis is in line with the EMH framework. Traders’ responses to such information are associated with the financial crisis. There was strong overreaction to market data right before the 2008 crisis and traders tend to rely more on news data during the crisis. We confirm that, in terms of the intraday information efficiency, it is worthwhile to adopt both types of information. Furthermore, there is still room for improving the price discovery process to reveal such information more effectively.
Modeling the flow of information between financial time-series by an entropy-based approach
Annals of Operations Research, 2019
Recent literature has been documented that commodity prices have become more and more correlated with prices of financial assets. Hence, it would be crucial to understand how the amount of information contained in one time series (i.e. commodity prices) reflects on the other one (i.e. financial asset prices). Here, we address these issues by means of an entropybased approach. In particular, we define two new metrics, namely the Joined Entropy and the Mutual Information, to analyze and model how the information content is (mutually) exchanged between two time series under investigation. The experimental outcomes, applied on volatility indexes, oil and natural gas prices for the period 01/04/1999-01/02/2015, prove the effectiveness of the proposed method in modeling the information flows between the analyzed data. Keywords Information content • Modeling • Financial time-series • Volatility indexes • Crude oil spot prices • Entropy-based analysis JEL Classification C530 • C630 • G140 • Q470
Using transfer entropy to measure information flows between financial markets
Studies in Nonlinear Dynamics and Econometrics, 2013
We use transfer entropy to quantify information flows between financial markets and propose a suitable bootstrap procedure for statistical inference. Transfer entropy is a model-free measure designed as the Kullback-Leibler distance of transition probabilities. Our approach allows to determine, measure and test for information transfer without being restricted to linear dynamics. In our empirical application, we examine the importance of the credit default swap market relative to the corporate bond market for the pricing of credit risk. We also analyze the dynamic relation between market risk and credit risk proxied by the VIX and the iTraxx Europe, respectively. We conduct the analyses for pre-crisis, crisis and post-crisis periods.
The Impact of Financial and Macroeconomic Shocks on the Entropy of Financial Markets
Journal of Entropy, 2019
We propose here a method to analyze whether financial and macroeconomic shocks 1 influence the entropy of financial networks. We derive a measure of entropy using the correlation 2 matrix of the stock market components of the DOW Jones Industrial Average index. Using VAR 3 models in different specifications, we show that shocks in production or DJIA index lead to an 4 increase in the entropy of the financial markets. 5
Entropy Analysis of Financial Time Series
arXiv: Statistical Finance, 2015
This thesis applies entropy as a model independent measure to address three research questions concerning financial time series. In the first study we apply transfer entropy to drawdowns and drawups in foreign exchange rates, to study their correlation and cross correlation. When applied to daily and hourly EUR/USD and GBP/USD exchange rates, we find evidence of dependence among the largest draws (i.e. 5% and 95% quantiles), but not as strong as the correlation between the daily returns of the same pair of FX rates. In the second study we use state space models (Hidden Markov Models) of volatility to investigate volatility spill overs between exchange rates. Among the currency pairs, the co-movement of EUR/USD and CHF/USD volatility states show the strongest observed relationship. With the use of transfer entropy, we find evidence for information flows between the volatility state series of AUD, CAD and BRL. The third study uses the entropy of S&P realised volatility in detecting ch...
Iranian Journal of Finance, 2021
This work aims to analyze the relationship between stocks in the financial market of the Tehran Stock Exchange embedded in their transfer entropy. In this regard, the behavior of the transfer entropy between indices of 180 corporations of the Tehran Stock Exchange has been studied. Then the footprint of crises of the market has been searched in the trends of the transfer entropy. The result has been compared with the result of the analysis imposed on the stocks included in the Dow Jones industrial index in the stock exchanges of the United States. In order to investigate the financial crisis of the Tehran Stock Exchange, the stock price data of 180 companies in this market that were active in the period from 2008 to 2018 are analyzed. It is observed that the average pairwise transfer entropy of indices in the Dow Jones group declines
Entropy-Based Behavioural Efficiency of the Financial Market
Entropy
The most known and used abstract model of the financial market is based on the concept of the informational efficiency (EMH) of that market. The paper proposes an alternative which could be named the behavioural efficiency of the financial market, which is based on the behavioural entropy instead of the informational entropy. More specifically, the paper supports the idea that, in the financial market, the only measure (if any) of the entropy is the available behaviours indicated by the implicit information. Therefore, the behavioural entropy is linked to the concept of behavioural efficiency. The paper argues that, in fact, in the financial markets, there is not a (real) informational efficiency, but there exists a behavioural efficiency instead. The proposal is based both on a new typology of information in the financial market (which provides the concept of implicit information—that is, that information ”translated” by the economic agents from observing the actual behaviours) and...
Market efficiency: an information entropy perspective
International Journal of Management and Enterprise Development, 2018
The aim of this paper is to examine the market efficiency from an information entropy perspective. Specifically, we compare some emerging and developed markets to pinpoint efficiency of these markets in time. We also used the symbolic time series analysis to detect dynamics of the processes under investigation over the period 2003-2013. The results indicate that these markets show a dynamic market efficiency unlike what static tests seem to suggest. Moreover, emerging and developed markets are less efficient. This lower efficiency comes in parallel with crisis periods (financial or political). When the market is efficient, market returns move following a random position and information entropy reaches its maximum. However, when an event is particular, entropy decreases and the market is considered inefficient. When the market is inefficient, prices do not instantly reflect new information, which replicates an information comprehension process, through a learning process that is considered time-consuming.
Stock Returns, Market Trends, and Information Theory: A Statistical Equilibrium Approach
SSRN Electronic Journal
This paper attempts to develop a theory of statistical equilibrium based on an entropy- constrained framework, that allow us to explain the distribution of stock returns over di erent market trends. By making use of the Quantal Response Statistical Equilib- rium model (Scharfenaker and Foley, 2017), we recover the cross-sectional distribution of daily returns of individual company listed the S&P 500, over the period 1988-2019. We then make inference on the frequency distributions of returns by studying them over bull markets, bear markets and corrections. The results of the model shed light on the microscopic as well as macroscopic behavior of the stock market, in addition to provide insights in terms of stock returns distribution.
Entropy and predictability of stock market returns
Journal of Econometrics, 2002
We examine the predictability of stock market returns by employing a new metric entropy measure of dependence with several desirable properties. We compare our results with a number of traditional measures. The metric entropy is capable of detecting nonlinear dependence within the returns series, and is also capable of detecting nonlin-ear\a±nity" between the returns and their predictions obtained from various models thereby serving as a measure of out-of-sample goodness-of-¯t or model adequacy. Several models are investigated, including the linear and neural-network models as well as nonparametric and recursive unconditional mean models. We¯nd signi¯cant evidence of small nonlinear unconditional serial dependence within the returns series, but fragile evidence of superior conditional predictability (pro¯t opportunity) when using market-switching versus buy-and-hold strategies.