Order-driven Market Research Articles

Proprietary Trading and the Information Advantage of Brokers in Order-Driven Markets

Proprietary Trading and the Information Advantage of Brokers in Order-Driven Markets

Tick Size and Informed Trading: Evidence from the Taiwanese Stock Market

We adopt the Sandås model for order-book equilibrium to examine informed trading on the Taiwanese stock market, a purely order-driven call-auction market. We find that adverse-selection cost is low for well-known stocks with high liquidity and low volatility, but cost is high for monitoring the order books of those stocks. Our empirical results show that the impact of adverse selection is greatest at the beginning of each trading day and that informed traders engage in stealth trading, supporting the stealth trading hypothesis. Finally, with the special tick size rules on the market, both adverse-selection cost and monitoring cost decline as tick size decreases.

Liquidity of Tunisian Stock Market: A Panel Var Estimation

The purpose of this paper is to estimate the functions impulsions-response of liquidity on the Tunisian Stock Exchange (TSE). We will use the methodology proposed by Abrigo and Love (2016). Our study is done on an order-driven market. The data is composed of high frequency data of orders listed on the TSE for the period April 2014 to June 2014. Inspired of the study of Jarnecic and Snape (2014), we apply a panel VAR model to stocks traded in continuous in order to examine the dynamic interactions between spread, volatility, size and frequency of transactions. Then we study the liquidity of the TSE through the impulse response function of the Panel VAR model. Our findings show dynamic relationships between spread, volatility, size and frequency of trading. Some differences exist in the dynamics of liquidity when we take into account the trading intensity of the stock. Furthermore, we note that shocks are absorbed after three gaps of 45minutes.

How noise trading affects informational efficiency: Evidence from an order-driven market

We use full order book data from the Australian Securities Exchange to investigate how noise trading affects informational efficiency of securities prices. In aggregate, noise trading harms price efficiency. However, this is driven mainly by higher levels of noise trading, indicating a non-linear effect. Further, behind the aggregate effects lies rich heterogeneity in how noise trading affects informational efficiency cross-sectionally. Noise trading harms informational efficiency of large and liquid stocks but can be beneficial in small and illiquid stocks, indicating that noise trading affects different stocks differently.

Market microstructure: a comparative study of Bombay stock exchange and national stock exchange

PurposeThe present study seeks to investigate the relative edge between the market microstructure of the two leading stock exchanges of the Indian capital market, that is BSE and NSE with a focus on analysing their trading mechanism, efficiency, liquidity and volatility.Design/methodology/approachWe analyse the microstructure of BSE and NSE on the basis of: (1) trading mechanism – ownership structure, listing of securities, trading system and settlement and clearing process; (2) information efficiency using unit root test, serial correlation, runs test, variance ratio and the ARIMA model; (3) liquidity using trading statistics no. of listed Companies, market capitalisation, no. of trades etc. and (4) volatility using standard deviation and GARCH(1,1) model.FindingsA comprehensive scrutiny on microstructure of BSE and NSE makes it evident that the two leading stock exchanges of India are mostly similar and leave no scope to choose between them. Both the exchanges are demutualised corporate entities with a fully automated trading system in an order-driven market, informationally inefficient as evidenced by the predictability of returns, have shown tremendously growing trading statistics and by and large a declining trend in volatility over the years.Practical implicationsUnderstanding the components of the microstructure black-box will provide the regulatory bodies with an intellectual framework to strengthen the market architecture. Both the exchanges will get aware of the dynamics of trading, can grow to be more competitive and attract more firms for listing and investors for trading of securities. Also, investors, portfolio managers and equity analysts will be able to make better investment strategies by understanding how the market works.Originality/valueResearch in the area of market microstructure has been severely neglected, especially in the context of the Indian market. India is the world's fastest growing economies and we have witnessed tremendous reforms in the capital market. The past two and a half decades have brought about several innovations via demutualisation, screen-based trading, emergence of clearing corporations, innovative financial products and intense use of IT in the Indian stock market. A spurt of reforms and the emerging environment make it crucial to deeply analyse the market structure and design of two premier stock exchanges of India – BSE and NSE.

Information content of order imbalance in an order-driven market: Indian Evidence

This article examines the information content of order imbalance (OIB), using 195 most active stocks from the National Stock Exchange of India. The private information of order flow has a persistent impact on prices, and makes the short-term returns predictable. The predictability appears strong for the first five minutes, and perishes within thirty minutes. Furthermore, the information inherent in the order flow impacts the deeper levels of limit-order book, indicating the presence of informed traders beyond the best quotes. Overall, the results support the information asymmetry hypothesis for the pure order-driven market of India.

Broker Network Connectivity and the Cross-Section of Expected Returns

We examine the relationship between broker network connectivity and stock returns in an order-driven market. Considering all stocks traded in Borsa Istanbul between January 2006 and November 2015, we estimate the monthly density, reciprocity and average weighted clustering coefficient as proxies for the broker network connectivity. Our firm-level cross-sectional regressions indicate a negative and significant predictive relationship between connectivity and one-month ahead stock returns. Our analyses also show that stocks in the lowest connectivity quintile earn 1.0% - 1.6% monthly return premiums. The connectivity premium is stronger in terms of both economic and statistical significance for small size stocks.

Liquidity commonality on National Stock Exchange: size and sector effect

ABSTRACT This study investigates the sources of liquidity commonality in an emerging Asian market. Specifically, the demand side and supply side factors were tested in an order-driven Indian stock market. The results indicate that growth in the industrial production and bank credit reflecting the supply side variables are found to have significant influence on liquidity commonality. However, compare to the supply side, demand side factors reflecting the trading behaviour of domestic and foreign institutional investors exhibited higher influence. Study has further extended the literature by investigating the size and sectorial effects and found heterogeneous impact on commonality.

Adaptive Robust Control in Continuous-Time

We propose a continuous-time version of the adaptive robust methodology introduced in Bielecki et al. (2019). An agent solves a stochastic control problem where the underlying uncertainty follows a jump-diffusion process and the agent does not know the drift parameters of the process. The agent considers a set of alternative measures to make the control problem robust to model misspecification and employs a continuous-time estimator to learn the value of the unknown parameters to make the control problem adaptive to the arrival of new information. We use measurable selection theorems to prove the dynamic programming principle of the adaptive robust problem and show that the value function of the agent is characterised by a non-linear partial differential equation. As an example, we derive in closed-form the optimal adaptive robust strategy for an agent who acquires a large number of shares in an order-driven market and illustrates the financial performance of the execution strategy.

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.

How do Normal Traders and Sharp Traders Make Profits in the Chinese Security Market?

In this paper, we consider the strategic interaction between the normal and sharp traders in a dynamic limit-order security market and its impact on the Chinese security market at different market volatility levels. We find that when the proportion of sharp traders is less than a threshold in an order-driven market, the sharp traders who submit limit orders will get more returns than the normal traders. The participation of sharp traders in the market can increase the total social welfare of all the traders. In addition, we show that:(1) when the market volatility level is generally low, the short-term sharp traders benefit from larger volatility; (2) when the market volatility level is generally high, the insider/cheating sharp traders with high-frequent trading rather than the short-term sharp traders benefit from extreme high volatility; (3) when the market volatility level is moderate, the sharp traders can increase market liquidity.

SHIFT: A Highly Realistic Financial Market Simulation Platform

This paper presents a new financial market simulator that may be used as a tool in both industry and academia for research in market microstructure. It allows multiple automated traders and/or researchers to simultaneously connect to an exchange-like environment, where they are able to asynchronously trade several financial assets at the same time. In its current iteration, this order-driven market implements the basic rules of U.S. equity markets, supporting both market and limit orders, and executing them in a first-in-first-out fashion. We overview the system architecture and we present possible use cases. We demonstrate how a set of automated agents is capable of producing a price process with characteristics similar to the statistics of real price from financial markets. Finally, we detail a market stress scenario and we draw, what we believe to be, interesting conclusions about crash events.

Spoofing and Price Manipulation in Order-Driven Markets

ABSTRACT We model the trading strategy of an investor who spoofs the limit order book (LOB) to increase the revenue obtained from selling a position in a security. The strategy employs, in addition to sell limit orders (LOs) and sell market orders (MOs), a large number of spoof buy LOs to manipulate the volume imbalance of the LOB. Spoofing is illegal, so the strategy trades off the gains that originate from spoofing against the expected financial losses due to a fine imposed by the financial authorities. As the fine increases, the investor relies less on spoofing, and if the fine is large, the investor does not spoof the LOB. The arrival rate of buy MOs increases because other traders interpret the spoofed buy-heavy LOB as an upward pressure on prices. When the fine is low, spoofing considerably increases the revenues from liquidating a position. Spoofing increases the PnL because (i) the investor employs fewer MOs to draw the inventory to zero and benefits from roundtrip trades, which stem from spoof buy LOs that are ‘inadvertently’ filled and subsequently unwound with sell LOs; and (ii) the midprice trends upward when the book is buy-heavy; therefore the spoofer sells the asset at better prices.

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.

The double-edged role of social learning: Flash crash and lower total volatility

• We study how agents’ social learning behavior influences market volatility and how the flash crash emerges. • In the usual case, social learning plays a role in reducing total price volatilities and stabilizing the market. • In some excitable regimes, social learning instead acts as the critical factor contributing to a flash crash. • The lower volatility associated with social learning in the stable regime is crucial to give birth to a flash crash. • Many robust simulation experiments have been done to confirm our results. In this paper, we study how agents’ social learning behavior influences market volatility and how the flash crash emerges. We build a model of order-driven market, in which agents use a combination of four components to form their return anticipations: a social learning component, a chartist component, a fundamentalist component and a noise induced component. By numerical simulations, we find that social learning plays a double-edged role in market volatilities. On the one hand, social learning plays a role in reducing total price volatilities and stabilizing the market. On the other hand, in some excitable regimes, social learning instead acts as the critical factor contributing to a flash crash. More interestingly, the lower volatility associated with social learning in the stable regime is crucial to give birth to a flash crash. In addition, we do some robust analyses on the roles of social learning by running the model under many different parameter settings. With the increase of the social learning innate parameter, both the average draw-down and draw-up, the average spread and the average gap show a downward trend. Meanwhile, the tail exponents for the draw-downs and draw-ups also show a downward trend, confirming that social learning plays a double-edged role. The chartist belief can stabilize the market when the social influence is not so trusted, while the chartist belief transfers to contribute to the market instability when the social influence is sufficiently trusted. The fundamentalist belief shows quite opposite impacts in respect to the chartist belief. Finally, we summarize typical return and volatility patterns before a flash crash, which will give some inspirations to regulators and investors.

Price Dynamics in an Order-Driven Market with Bayesian Learning

In this paper, we have developed a model of limit order book with learning mechanism and investigated its price dynamics. In this model, continuous Bayesian learning is introduced to describe the dynamics of self-adjusting learning mechanism of agents, which can result in some important stylized facts of limit order markets. This study also provides some behavioral explanations for these well-known stylized facts that are commonly observed in the financial markets.

Speed and trading behavior in an order-driven market

This paper examines how the speed of order submission affects investor behavior when submitting orders in an order-driven market. We provide a theoretical model where the speed of investor and limit order placement is non-monotonic. This is rationalized by mid-speed traders submitting initial orders further away from the market price to avoid order pick-offs by faster traders when the underlying asset changes, at the same time, they can still benefit by revising quotes against slower traders. We also show that market orders and marketable limit orders are used differently depending on investor speed. Fast traders prefer marketable limit orders to market orders more than slow traders do, since slow traders face higher costs of marketable-intended limit orders when an order is not executed immediately.

The Equity Market and Its Price Discovery Risk: An Empirical Study for the CAC40 Stock Market Index

This paper studies the price discovery dynamics in an order-driven market. Based on the transaction data on individual stocks, the paper focuses on the study of the monthly evolution of normalized volatility ratios on Euronext Paris for the CAC40 stocks before and after the implementation of the Markets in Financial Instruments Directive (MiFID). Since 2007, this European directive ended the national rule of order concentration and increased the fragmentation among trading venues. The normalized volatility ratio is used as a measure of the market quality from the point of view of price discovery. A significant inefficient price determination is observed at the opening period of the market before and after the implementation of MiFID, unlike the closing period. This finding is attributable to the complexity of price discovery following a period of non-trading and relatively heavy information releases. The opening half-hour percentage of High Frequency buyers, the opening half-hour number of trades and the monthly average number of transactions are significant explanatory variables of the volatility accentuation at the opening in the post-MiFID context. The market quality could be improved at the opening half-hour for the CAC40 stock Index on Euronext Paris.

The Performance of China Stock Market, Based on the Analysis of Implicit Transaction Cost

This paper is based on the stocks listed on the Shanghai and Shenzhen stock exchanges for full year in the early period of 1992 to the end of 2016, using Hasbrouck sampling method to estimate the implicit transaction cost of Chinese order-driven market, and observing the performance of stock market and affecting factors based on the implicit transaction costs. Empirical results show: the implicit transaction cost is an unavoidable cost for investors, and the performance of China stock market is not ideal. Finally, it is suggested that China stock market should improve the trading system, strengthen the information disclosure system and optimize the equity system of listed companies.

A New Estimation for Informed Trading Based on SSNF Method

This paper estimates the information-based trading using spatially selective noise filtration (SSNF) method. The SSNF method is a kind of filtration technique based on the different spatial correlation of the wavelet transform at several adjacent scales. Using SSNF method, the information shock caused by the informed traders could be extracted from the prices effectively, then the PINs at different scales could be calculated. The measure of informed trading can capture some asymmetric information properties, which is consistent with some empirical consensuses. Furthermore, compared with the MLE method in EKOP model, the method has computational facilities in avoiding the overflow or underflow problem, the boundary solutions problem and the initial values problem. And the method could be applied to the high-frequency world in both order-driven and quote-driven market.