  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.
