Markov Chain Analysis of Stock Order Flows During the 2018 Trade War

Summary

This study models high-frequency order sequences from stocks in six sectors during the 2018 US–China trade war. It uses a first-order, time-homogeneous discrete-time Markov chain, checks the Markov assumption with a chi-square test, and estimates transition probabilities by maximum likelihood. Heat maps and derived chain statistics are used to compare order behavior across high- and low-volatility days.

The analysis reports that high-volatility days feature more add and delete orders, which the authors interpret as active traders placing limit orders and then deleting many of them. High stationary probabilities and short mean recurrence times for these order types support that interpretation. Similar spectral gaps and entropy rates across volatility conditions suggest comparable trading strategies, while recurring full executions in Finance and Banking are read as evidence of resilience. The document provides no sample details, numerical estimates, or causal identification, so claims about trader intent and sector resilience should be treated as interpretations of the observed order data.

Key ideas

• The study represents stock order sequences with a first-order, time-homogeneous Markov chain.
• A chi-square test evaluates the Markov assumption, and transition probabilities are estimated by maximum likelihood.
• Add and delete orders become more prominent on high-volatility days, according to the reported chain statistics.
• Similar spectral gaps and entropy rates suggest order dynamics remain broadly alike across volatility conditions.
• Recurring full executions in Finance and Banking are interpreted as resilience during the trade war.

Tags

# High-Frequency Stock Market Order Transitions during the US-China Trade War 2018: A Discrete-Time Markov Chain Analysis


# High-Frequency Stock Market Order Transitions during the US-China Trade War 2018: A Discrete-Time Markov Chain Analysis









Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the USA-China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test. We estimate the transition probability matrix of the sequence using maximum likelihood estimation. From the heat-map of these matrices, we found the presence of active participation by different types of traders during high volatility days. On such days, these traders place limit orders primarily with the intention of deleting the majority of them to influence the market. These findings are supported by high stationary distribution and low mean recurrence values of add and delete orders. Further, we found similar spectral gap and entropy rate values, which indicates that similar trading strategies are employed on both high and low volatility days during the trade war. Among all the sectors considered in this study, we observe that there is a recurring pattern of full execution orders in Finance & Banking sector. This shows that the banking stocks are resilient during the trade war. Hence, this study may be useful in understanding stock market order dynamics and devise trading strategies accordingly on high and low volatility days during extreme macroeconomic events.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.