A market-clearing role for inefficiency on a limit order book

Limit order markets with stationary dynamics attract equal volumes of market orders and uncanceled limit orders, equalizing the supply and demand for liquidity and immediacy. To maintain this balance, market orders must share any benefit obtained by limit order traders from more efficient trading conditions, such as better order queuing policies. Therefore an efficient market places a low price on immediacy, producing small bid–ask spreads. Furthermore, when price-discreteness leads to a mainly constant spread, cutting the price tick raises surplus. This is modeled with a stochastic sequential game, using stationarity considerations to bypass direct analysis of traders’ intricate market forecasts.     

A simulation analysis of the microstructure of double auction markets

Abstract

We introduce an order-driven market model with heterogeneous agents trading via a central order matching mechanism. Traders set bids and asks and post market or limit orders according to exogenously fixed rules. We investigate how different trading strategies may affect the dynamics of price, bid-ask spreads, trading volume and volatility. We also analyse how some features of market design, such as tick size and order lifetime, affect market liquidity. The model is able to reproduce many of the complex phenomena observed in real stock markets.     

Undisclosed orders and optimal submission strategies in a limit order market

Reserve orders enable traders to hide a portion of their orders and now appear in most electronic limit order markets. This paper outlines a theory to determine an optimal submission strategy in a limit order book, in which traders choose among limit, market, and reserve orders and simultaneously set price, quantity, and exposure. We show that reserve orders help traders compete for the provision of liquidity and reduce the friction generated by exposure costs. Therefore, total gains from trade increase. Large traders always benefit from reserve orders, whereas small traders benefit only when the tick size is large.     

An evolutionary multi‐objective optimization of trading rules in call markets

We evaluate an agent‐based model featuring near‐zero‐intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices and which orders are executed, as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. We optimize these trading rules using a multi‐objective population‐based incremental learning algorithm seeking to maximize the trading volume and minimize the bid–ask spread. Our results suggest that markets should choose a small tick size if concerns about the bid–ask spread are dominating and a large tick size if maximizing trading volume is the main aim. We also find that unless concerns about trading volume dominate, time priority is the optimal priority rule. Copyright © 2011 John Wiley & Sons, Ltd.     

Liquidity provision with limit orders and a strategic specialist

This article presents a microstructure model of liquidity provisionin which a specialist with market power competes against a competitivelimit order book. General solutions, comparative statics andexamples are provided first with un-informative orders and thenwhen order flows are informative. The model is also used toaddress two optimal market design issues. The first is the effectof 'tick' size - for example, eighths versus decimal pricing- on market liquidity. Institutions trading large blocks havea larger optimal tick size than small retail investors, butboth prefer a tick size strictly greater than zero. Second,a hybrid specialist/limit order market (like the NYSE) providesbetter liquidity to small retail and institutional trades, buta pure limit order market (like the Paris Bourse) may offerbetter liquidity on mid-size orders.     

Hybrid markets, tick size and investor trading costs

This paper shows how the tick size affects equilibrium outcomes in a hybrid stock market such as the NYSE that features both a specialist and a limit order book. Reducing the tick size facilitates the specialist's ability to step ahead of the limit order book, resulting in a reduction in the cumulative depth of the limit order book at prices above the minimum tick. If market demand is price-sensitive, and there are costs of limit order submission, the limit order book can be destroyed by tick sizes that are either too small or too large. We show that trading cost is minimized at larger tick sizes for larger market orders, creating an incentive to submit smaller orders when tick size is reduced. With a smaller tick size, specialist participation increases and specialist profit increases slightly for small market orders, and considerably for large market orders.     

Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations

We built an artificial market model and investigated the impact of large erroneous orders on financial market price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analysed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term shorter than the time erroneous orders exist effectively prevent large price fluctuations. We also investigated effects of up-tick rules, adopting the trigger method that the Japan Financial Services Agency adopted in November 2013. We also investigated whether dark pools that never provide any order books stabilize markets or not using the model including one lit market, which provides all order books to investors, and one dark pool. We found that markets become more stable as the dark pool is increasingly used. We also found that using the dark pool more reduces the market impacts. However, if other investors’ usage rates of dark pools become too large, investors must use the dark pool more than other investors to avoid market impacts. When a tick size of a lit market is larger, dark pools are more useful to avoid market impacts. These results suggest that dark pools stabilize markets when the usage rate is under some threshold and negatively affect the market when the usage rate is over that threshold. Our simulation results suggest the threshold might be much larger than the usage rate in present real financial markets. This study is the first to discuss whether or not several concrete and actually adoptable regulations, including those that have never been employed (e.g. price variation limits with various parameters), could prevent large fluctuations of market prices, including those beyond our experience, using artificial market simulations, and to discuss quantitatively how spreading of dark pools beyond our experience could affect market price formations using the artificial market simulations. In short, this study is the first study to comprehensively discuss how regulations and financial systems beyond our experience could affect price formations using the artificial market simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Tick Size,Share Prices,and Stock Splits

Minimum price variation rules help explain why stock prices vary substantially across countries, and other curiosities of share prices. Companies tend to split their stock so that the institutionally mandated minimum tick size is optimal relative to the stock price. A large relative tick size provides an incentive for dealers to make markets and for investors to provide liquidity by placing limit orders, despite its placing a high floor on the quoted bid-ask spread. A simple model suggests that idiosyncratic risk, firm size, and visibility of the firm affect the optimal relative tick size and thus the share price.     

Price Clustering on the Tokyo Stock Exchange

This paper examines price clustering on the Tokyo Stock Exchange (TSE). Regardless of tick and lot size, prices ending in zero and five are the most popular. The TSE has no market makers or direct negotiation between traders; therefore, clustering is not explained by collusion or negotiation. Our evidence supports the attraction hypothesis. Clustering also extends to order book depth. There is evidence of strategic trading behavior as traders place orders one price tick better than zero and five to avoid queuing orders at prices ending in these digits. Strategic trading behavior declined and clustering increased when the market became anonymous.     

Clearing price distributions in call auctions

We propose a model for price formation in financial markets based on the clearing of a standard call auction with random orders, and verify its validity for prediction of the daily closing price distribution statistically. The model considers random buy and sell orders, placed employing demand- and supply-side valuation distributions; an equilibrium equation then leads to a distribution for clearing price and transacted volume. Bid and ask volumes are left as free parameters, permitting possibly heavy-tailed or very skewed order flow conditions. In highly liquid auctions, the clearing price distribution converges to an asymptotically normal central limit, with mean and variance in terms of supply/demand-valuation distributions and order flow imbalance. By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting a distinction between high- and low-volume auction price variance. To verify the validity of the model statistically, we predict a year's worth of daily closing price distributions for five constituents of the Eurostoxx 50 index; Kolmogorov–Smirnov statistics and QQ-plots demonstrate with ample statistical significance that the model predicts closing price distributions accurately, and compares favourably with alternative methods of prediction.     

A simulation analysis of the microstructure of an order driven financial market with multiple securities and portfolio choices

In this paper we propose an artificial market where multiple risky assets are exchanged. Agents are constrained by the availability of resources and trade to adjust their portfolio according to an exogenously given target portfolio. We model the trading mechanism as a continuous auction order-driven market. Agents are heterogeneous in terms of desired target portfolio allocations, but they are homogeneous in terms of trading strategies. We investigate the role played by the trading mechanism in affecting the dynamics of prices, trading volume and volatility. We show that the institutional setting of a double auction market is sufficient to generate a non-normal distribution of price changes and temporal patterns that resemble those observed in real markets. Moreover, we highlight the role played by the interaction between individual wealth constraints and the market frictions associated with a double auction system to determine the negative asymmetry of the stock returns distribution.     

The specialist's discretion: stopped orders and price improvement

When a market order arrives, the NYSE specialist can offer aprice one tick better than the limit orders on the book andtrade for his own account. Alternatively, the specialist can'stop' the market order, which means he guarantees executionat the current quote but provides the possibility of price improvement.My model shows that specialists can use stops to sample thefuture order flow before making a commitment to trade. I presentempirical evidence that both stops and immediate price improvementimpose adverse selection costs on limit order traders.     

Order dynamics: Recent evidence from the NYSE

We examine investor order choices using evidence from a recent period when the NYSE trades in decimals and allows automatic executions. We analyze the decision to submit or cancel an order or to take no action. For submitted orders, we distinguish order type (market vs. limit), order side (buy vs. sell), execution method (auction vs. automatic), and pricing aggressiveness. We find that the NYSE exhibits positive serial correlation in order type on an order-by-order basis, which suggests that follow-on order strategies dominate adverse selection or liquidity considerations at a moment in time. Aggregated levels of order flow also exhibit positive serial correlation in order type, but appear to be non-stationary processes. Overall, changes in aggregated order flow have an order-type serial correlation that is close to zero at short aggregation intervals, but becomes increasingly negative at longer intervals. This implies a liquidity exhaustion–replenishment cycle. We find that small orders routed to the NYSE's floor auction process are sensitive to the quoted spread, but that small orders routed to the automatic execution system are not. Thus, in addition to foregoing price improvement, traders selecting the speed of automatic executions on the NYSE do so with little regard for the quoted cost of immediacy. As quoted depth increases, traders respond by competing on price via limit orders that undercut existing bid and ask prices. Limit orders are more likely and market sells are less likely late in the trading day. These results are helpful in understanding the order arrival process at the NYSE and have potential applications in academics and industry for optimizing order submission strategies.     

Impact of the introduction of call auction on price discovery: Evidence from the Indian stock market using high-frequency data

Call markets are claimed to aggregate information and facilitate price discovery where continuous markets may fail. The impact of the introduction of call auction has not been found uniformly beneficial, possibly due to poor design or due to ‘thick market externalities’. This paper examines the reintroduction of opening call auction at the National Stock Exchange of India in 2010. The results suggest that the auctions attract very little volume, the intraday pattern of volume and volatility in the continuous market remains unchanged and a large fraction of price discovery, measured by the Weighted Price Contribution, still takes place in the first 15 min of continuous market. However, the market synchronicity has improved after the introduction of the auction. Our findings suggest that the ability to attract volume in the call auction for effective price discovery depends on the institutional settings and the characteristics of liquidity supply in the market.     

Multiple-limit trades: empirical facts and application to lead–lag measures

Order splitting is a standard practice in trading: traders constantly scan the limit order book and choose to limit the size of their market orders to the quantity available at the best limit, thereby controlling the market impact of their orders. In this article, we focus on the other trades, multiple-limit trades that go through the best available price in the order book, or ‘trade-throughs’. We provide various statistics on trade-throughs: frequency, volume, intraday distribution, market impact, etc., and present a new method for the measurement of lead–lag parameters between assets, sectors or markets.     

The next tick on Nasdaq

The Nasdaq stock market provides information about buying and selling interest in its limit order book. Using a vector autoregressive model of trades and returns, I assess the effect of the entire order book on the next tick. I also determine the influence of individual market makers and electronic networks and find evidence that the identity of market participants can be useful information. Finally, I produce a set of dynamic market price responses to buy and sell orders, and I find that these estimates vary with standard measures of liquidity.     

Market microstructure

It is assumed that a collection of market agents can be treated as a statistical ensemble. Their market activities are depicted as the stochastic generation of market orders according to a Poisson process. The objective is to effectively describe the ‘temporal microstructure’, or moment-to-moment trading activities in asset markets. Two basic models, ‘dealership’ vs. ‘auction’ markets (and their variants) are put forth. Implications are drawn from each model. The implications include several testable hypotheses regarding the aggregate behavior of markets and market-makers as well as some qualitative insight into the transaction-to-transaction nature of realistic exchange processes.     

Upstairs Market for Principal and Agency Trades: Analysis of Adverse Information and Price Effects

This paper directly tests the hypothesis that upstairs intermediation lowers adverse selection cost. We find upstairs market makers effectively screen out information-motivated orders and execute large liquidity-motivated orders at a lower cost than the downstairs market. Upstairs markets do not cannibalize or free ride off the downstairs market. In one-quarter of the trades, the upstairs market offers price improvement over the limit orders available in the consolidated limit order book. Trades are more likely to be executed upstairs at times when liquidity is lower in the downstairs market.     

Modeling stock pinning

This paper investigates the effect of hedging strategies on the so-called pinning effect, i.e. the tendency of stock's prices to close near the strike price of heavily traded options as the expiration date nears. In the paper we extend the analysis of Avellaneda and Lipkin, who propose an explanation of stock pinning in terms of delta hedging strategies for long option positions. We adopt a model introduced by Frey and Stremme and show that, under the original assumptions of the model, pinning is driven by two effects: a hedging-dependent drift term that pushes the stock price toward the strike price and a hedging-dependent volatility term that constrains the stock price near the strike as it approaches it. Finally, we show that pinning can be generated by simulating trading in a double auction market. Pinning in the microstructure model is consistent with the Frey and Stremme model when both discrete hedging and stochastic impact are taken into account.     

Data analytic approach for manipulation detection in stock market

The term “price manipulation” is used to describe the actions of “rogue” traders who employ carefully designed trading tactics to incur equity prices up or down to make profit. Such activities damage the proper functioning, integrity, and stability of the financial markets. In response to that, the regulators proposed new regulatory guidance to prohibit such activities on the financial markets. However, due to the lack of existing research and the implementation complexity, the application of those regulatory guidance, i.e. MiFID II in EU, is postponed to 2018. The existing studies exploring this issue either focus on empirical analysis of such cases, or propose detection models based on certain assumptions. The effective methods, based on analysing trading behaviour data, are not yet studied. This paper seeks to address that gap, and provides two data analytics based models. The first one, static model, detects manipulative behaviours through identifying abnormal patterns of trading activities. The activities are represented by transformed limit orders, in which the transformation method is proposed for partially reducing the non-stationarity nature of the financial data. The second one is hidden Markov model based dynamic model, which identifies the sequential and contextual changes in trading behaviours. Both models are evaluated using real stock tick data, which demonstrate their effectiveness on identifying a range of price manipulation scenarios, and outperforming the selected benchmarks. Thus, both models are shown to make a substantial contribution to the literature, and to offer a practical and effective approach to the identification of market manipulation.