Order-driven Stock Market Research Articles

COVID-19 pandemic sentiment and stock market behavior: evidence from an emerging market

PurposeThis study aims to examine the relationship between pandemic sentiment (PS) and stock market returns in an emerging order-driven stock market like India.Design/methodology/approachThis study uses nonlinear causality and wavelet coherence techniques to analyze the sentiment-returns nexus. The analysis is conducted on the full sample period from January to December 2020 and further extended to two subperiods from January to June and July to December to investigate whether the associations between sentiment and market returns persist even several months after the outbreak.FindingsThis study constructs two novel measures of PS: one using Google Search Volume Intensity and the other using Textual Analysis of newspaper headlines. The empirical findings suggest a high degree of interrelationship between PS and stock returns in all time-frequency domains across the full sample period. This interrelationship is found to be further heightened during the initial months of the crisis but reduces significantly during the later months. This could be because a considerable amount of uncertainty regarding the crisis is already accounted for and priced into the markets in the initial months.Originality/valueThe ongoing coronavirus pandemic has resulted in sharp volatility and frequent crashes in the global equity indices. This study is an endeavor to shed light on the ongoing debate on the COVID-19 pandemic, investors’ sentiment and stock market behavior.

Executing trades in style: retail investors vs. institutions

ABSTRACT Employing an exhaustive sample of trades made by all investors in a fully automated order-driven stock market, we examine trade execution prices between retail and institutional investors. Retail investors execute both purchases and sales systematically at better prices than domestic institutions but receive better prices than foreign institutions only when executing sales. Importantly, we also find some evidence consistent with the hypothesis that retail investors have a comparative advantage in executing trades of small-cap stocks. Based on a population of retail trades, our findings are not consistent with the stereotype arising from earlier studies that retail investors are noise traders.

When one stock share is a biological individual: a stylized simulation of the population dynamics in an order-driven market

The demand–supply relationship plays an important role in an order-driven stock market. In this paper, we propose a stylized model by defining demand (supply) over a stock at a certain time as how many shares are on the bid (ask) side, which includes all buy (sell) limit orders and buy (sell) market orders. Also, we will treat the two types of shares as two different species with interaction (a single share corresponds to an individual of one species) and will construct and apply generalized Lotka–Volterra equations (Hofbauer and Sigmund in Evolutionary games and population dynamics, Cambridge University Press, Cambridge, 1998) to simulate how their population evolve based on some properties or assumptions of an order-driven market, and also on the heterogenous beliefs among traders. The model suggests that the population of bid and ask shares moves either to a fixed point or periodically without the impact of external information. Also, our model gives a reason, though it is not perfect, explaining why stock prices can behave chaotically.

High Frequency Trading and Price Jumps in the Stock Market

In this paper, we investigate liquidity supply and demand around price jumps in a pure order driven stock market using a detailed tick frequency data set on the Euronext 100 index. The advantage of this database is to allow us to disentangle two major evolutions in European financial markets: the emergence of high frequency trading and the implementation of multilateral trading facilities. We generate average 2-minute trading volume interval and assess liquidity dynamics through an extensive set of order book-based measures (liquidity supply) and trade-based measures (liquidity demand). Furthermore, we also consider order submission dynamics and investor types activity around price jumps. We find the origin of market disruptions lies in a low liquidity supply while at the opposite liquidity demand slows down. All our results suggest a higher involvement of high frequency trading activity in the market around price jumps. To emphasize our findings, we conduct bidirectional Granger causality tests that support our results.

The position profiles of order cancellations in an emerging stock market

Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price level in the limit-order book (LOB) and the position in the queue at each price level. We study the profiles of these two order cancellation positions through rebuilding the limit-order book using the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We find that the profiles of relative price levels where cancellations occur obey a log-normal distribution. After normalizing the relative price level by removing the factor of order numbers stored at the price level, we find that the profiles exhibit a power-law scaling behavior on the right tails for both buy and sell orders. When focusing on the order cancellation positions in the queue at each price level, we find that the profiles increase rapidly in the front of the queue, and then fluctuate around a constant value till the end of the queue. These profiles are similar for different stocks. In addition, the profiles of cancellation positions can be fitted by an exponent function for both buy and sell orders. These two kinds of cancellation profiles seem universal for different stocks investigated and exhibit minor asymmetry between buy and sell orders. Our empirical findings shed new light on the order cancellation dynamics and pose constraints on the construction of order-driven stock market models.

Investors' information advantage and order choices in an order-driven market

We set out in this study to examine advantaged investors' order choices by computing gains and losses from executed orders in a pure order-driven stock market, the Taiwan Stock Exchange. We carry out an event study on the profitability of order categories around annual earnings announcements. We use a unique and extremely comprehensive dataset which can accurately classify executed orders by order size and aggressiveness for each investor group. We find that, as a group, individual investors are less informed about imminent corporate earnings announcements and the related value implications. Domestic institutions with better local connections have access to privileged information, resulting in significant trading profits in the pre-event window. Informed domestic institutions tend to employ large-sized orders to take up all of the available liquidity. Although limited in terms of private information, foreign institutions with superior expertise accrue profits by trading conservatively through the use of small- to medium-sized orders and less aggressive prices. Order flows are more serially correlated in the pre-event period, especially medium-sized orders from foreign institutions. The results are robust after controlling for various market condition variables. They also hold to different lengths of pre-event window and in the absence of price impacts.

Dancing in the dark: post-trade anonymity, liquidity and informed trading

We analyze the impact of post-trade anonymity on liquidity and informed trading in an order driven stock market. The German stock market introduced the Central Counterparty (CCP) in March 2003 for German equities traded on its anonymous electronic trading platform Xetra leading to a major change in its existing transparency regime. Before the introduction trader IDs were revealed to the counterparties of a trade, with the introduction of the CCP even after the transaction the traders remain anonymous. Previous theoretical and empirical research documents that pre-trade anonymity results in increased liquidity, while results on post-trade anonymity are mixed. We find a significant increase in liquidity measured through a reduction of 25% in implicit transaction costs. We also document that the arrival rate of informed traders is reduced in the anonymous setting. Following recent findings of Bloomfield et al. (J Finan Econ 75:165–199, 2005) that informed traders take on the role of liquidity providers we interpret our findings as indication that informed traders change their behavior in providing liquidity more aggressively in an anonymous environment.

Breeds of risk-adjusted fundamentalist strategies in an order-driven market

This paper studies an order-driven stock market where agents have heterogeneous estimates of the fundamental value of the risky asset. The agents are budget-constrained and follow a value-based trading strategy which buys or sells depending on whether the price of the asset is below or above its risk-adjusted fundamental value. This environment generates returns that are remarkably leptokurtic and fat-tailed. By extending the study over a grid of different parameters for the fundamentalist trading strategy, we exhibit the existence of monotone relationships between the bid–ask spread demanded by the agents and several statistics of the returns. We conjecture that this effect, coupled with positive dependence of the risk premium on the volatility, generates positive feedbacks that might explain volatility bursts.

OPTION PRICING WITH FEEDBACK EFFECTS

The paper provides a continuous time model for order-driven stock market. The model allows to derive a nonlinear PDE as a modification of Black–Scholes equation for option pricing with a local volatility as a function of the stock price. The solution can be expanded in series in the parameter, which relates to the size of option market. The first-order correction for the option price increases the price of a European call. The second-order correction for volatility allows to describe the "volatility smile".

Fundamentalists clashing over the book: a study of order-driven stock markets

Agent-based models of market dynamics must strike a compromise between the structural assumptions that represent the trading mechanism and the behavioural assumptions that describe the rules by which traders make their decisions. We present a structurally detailed model of an order-driven stock market and show that a minimal set of behavioural assumptions suffices to generate a leptokurtic distribution of short-term log-returns. This result supports the conjecture that the emergence of some statistical properties of financial time series is due to the microstructure of stock markets.

Liquidity and stock returns in pure order-driven markets: evidence from the Australian stock market

This paper examines the relationship between liquidity and stock returns in the pure order-driven stock market of Australia. The bid–ask spread, turnover rate, and amortized spread are used as proxies for liquidity. In addition to liquidity, other factors that have been found to influence stock returns, such as beta and size, are also considered. Seemingly unrelated regressions (SUR) and the cross-sectionally correlated timewise autoregressive (CSCTA) model form the methodological basis for this research. A small liquidity premium is found in the Australian market, which persists for the entire year. There is also strong evidence of a negative size effect.

The Probability of Informed Trading and the Performance of Stock in an Order-Driven Market

In this paper we estimate the probability of informed trading (Pi) in an order-driven stock market as well as perform a comprehensive analysis on the interrelations among probability of informed trading and three common performance indicators, i.e., liquidity, volatility and efficiency. We find that uninformed traders exhibit price chasing behavior even over very short time interval and that volatility in stock price attracts uninformed traders. Using 3SLS which takes into consideration the endogeneity of the probability of informed trading and the liquidity, volatility and efficiency measures, our empirical results provide new evidence on market microstructure literature. We find that Pi and the volatility and liquidity of stocks are simultaneously determined. Higher Pi leads to lower liquidity and higher volatility, and vice versa. Liquidity can explain volatility and efficiency, while the opposite is not true. Firms with larger size, higher ownership concentration and lower turnover have higher probability of informed trading.