This paper proposes the Shannon entropy as an appropriate one-dimensional measure of behavioural trading patterns in financial markets. The concept is applied to the illustrative example of algorithmic vs. non-algorithmic trading and empirical data from Deutsche Börse's electronic cash equity trading system, Xetra. The results reveal pronounced differences between algorithmic and non-algorithmic traders. In particular, trading patterns of algorithmic traders exhibit a medium degree of regularity while non-algorithmic trading tends towards either very regular or very irregular trading patterns. JEL Classification: C40, D0, G14, G15, G2
We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stock...
While market is a social field where information flows over the interacting agents, there have been ...
This book presents selected entropy-based applications in economics, finance and management research...
After exchanges and alternative trading venues have introduced electronic execution mechanisms world...
In this study, we use entropy-based measures to identify different types of trading behaviors.1We de...
We investigate the relative information efficiency of financial markets by measuring the entropy of ...
After exchanges and alternative trading venues have introduced electronic execution mechanisms world...
Being equipped with a unique high-frequency dataset that enablesus to precisely identify algorithmic...
The Detrending Moving Average (DMA) algorithm can be implemented to estimate the Shannon entropy of ...
The most known and used abstract model of the financial market is based on the concept of the inform...
We use transfer entropy to quantify information flows between financial markets and propose a suitab...
Financial markets have undergone a dramatic technological transformation. Electronic and centralized...
In this paper, we analyse the time series of 12,000+ networks of traders in the E-mini S&P 500 stock...
AbstractThe application of entropy in finance can be regarded as the extension of information entrop...
At today\u27s stock markets, most of the trading volume is traded electronically. Thus, also ...
We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stock...
While market is a social field where information flows over the interacting agents, there have been ...
This book presents selected entropy-based applications in economics, finance and management research...
After exchanges and alternative trading venues have introduced electronic execution mechanisms world...
In this study, we use entropy-based measures to identify different types of trading behaviors.1We de...
We investigate the relative information efficiency of financial markets by measuring the entropy of ...
After exchanges and alternative trading venues have introduced electronic execution mechanisms world...
Being equipped with a unique high-frequency dataset that enablesus to precisely identify algorithmic...
The Detrending Moving Average (DMA) algorithm can be implemented to estimate the Shannon entropy of ...
The most known and used abstract model of the financial market is based on the concept of the inform...
We use transfer entropy to quantify information flows between financial markets and propose a suitab...
Financial markets have undergone a dramatic technological transformation. Electronic and centralized...
In this paper, we analyse the time series of 12,000+ networks of traders in the E-mini S&P 500 stock...
AbstractThe application of entropy in finance can be regarded as the extension of information entrop...
At today\u27s stock markets, most of the trading volume is traded electronically. Thus, also ...
We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stock...
While market is a social field where information flows over the interacting agents, there have been ...
This book presents selected entropy-based applications in economics, finance and management research...