Whose trades convey information? Evidence from a cross-section of traders

This paper contributes empirically to our understanding of informed traders. It analyzes traders’ characteristics in a foreign exchange electronic limit order market via anonymous trader identities. We use six indicators of informed trading in a cross-sectional multivariate approach to identify traders with high price impact. More information is conveyed by those traders’ trades which—simultaneously—use medium-sized orders (practice stealth trading), have large trading volume, are located in a financial center, trade early in the trading session, at times of wide spreads and when the order book is thin.     

Limit Order Trading

We analyze the rationale for limit order trading. Use of limit orders involves two risks: 1) an adverse information event can trigger an undesirable execution, and 2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. Our empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.     

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.     

Optimal high-frequency trading with limit and market orders

We propose a framework for studying optimal market-making policies in a limit order book (LOB). The bid–ask spread of the LOB is modeled by a tick-valued continuous-time Markov chain. We consider a small agent who continuously submits limit buy/sell orders at best bid/ask quotes, and may also set limit orders at best bid (resp. ask) plus (resp. minus) a tick for obtaining execution order priority, which is a crucial issue in high-frequency trading. The agent faces an execution risk since her limit orders are executed only when they meet counterpart market orders. She is also subject to inventory risk due to price volatility when holding the risky asset. The agent can then also choose to trade with market orders, and therefore obtain immediate execution, but at a less favorable price. The objective of the market maker is to maximize her expected utility from revenue over a short-term horizon by a trade-off between limit and market orders, while controlling her inventory position. This is formulated as a mixed regime switching regular/impulse control problem that we characterize in terms of a quasi-variational system by dynamic programming methods. Calibration procedures are derived for estimating the transition matrix and intensity parameters for the spread and for Cox processes modelling the execution of limit orders. We provide an explicit backward splitting scheme for solving the problem and show how it can be reduced to a system of simple equations involving only the inventory and spread variables. Several computational tests are performed both on simulated and real data, and illustrate the impact and profit when considering execution priority in limit orders and market orders.     

The use of undisclosed limit orders on the Australian Stock Exchange

This paper investigates the use of undisclosed limit orders on the Australian Stock Exchange (ASX). Our findings suggest that undisclosed limit orders are used to reduce the option value of limit orders. We find no evidence that undisclosed limit orders are more frequently used by informed traders than disclosed limit orders. The effects of recent changes in undisclosed order regulation are also examined. We find that the enhancement in pre-trade transparency, through tightening the undisclosed order regulation in October 1994, resulted in a significant decline in trading volume. The impact of the second regulation change in October 1996, which further tightened undisclosed order regulation, resulted in a less significant trading volume reduction.     

Market or limit orders?

In this paper we investigate the problem of optimal order placement of an asset listed on an exchange using both market and limit orders in a simple model of market dynamics. We seek to understand under which settings it is optimal to place limit or market orders. Limit orders typically lower transaction costs but increase the risk of incomplete order execution, whereas market orders typically have higher transaction costs but are guaranteed to be executed. Rather than considering order book dynamics to determine if a limit order is executed we rely on price dynamics for this. We look at implementation shortfall in this setup with market impact of trading and propose a dynamic program to find the optimal placement of both market and limit orders for risk-neutral and risk-averse traders. With this we find a bound on the expected cost of trading and show that a trader who behaves optimally should always expect to pay less to trade less. We then solve the dynamic program numerically and examine optimal order placement strategies. We find that the decision between market and limit orders is sensitive to price volatility, risk aversion, and trading costs.     

A detailed investigation of the disposition effect and individual trading behavior: a panel survival approach

This article uses a panel survival approach to analyze the trading behavior of foreign exchange traders. We concentrate on a detailed characterization of the shape of the disposition effect over the entire profit and loss regions. In doing so, we investigate the influence of a number of trading characteristics on the impact of the disposition effect. These trading characteristics include: special limit order strategies, trading success, size and the experience of our investors. Our main findings are that (i) the disposition effect has a nonlinear shape. For small profits and losses we find an inverted disposition effect, while for larger ones, the usual positive disposition effect emerges. (ii) The inverted disposition effect is driven to a great extend by patient and cautious investors closing their positions with special limit orders (take-profit and stop-loss). The normal positive disposition effect is found to be intensified for impatient investors closing their positions actively with market orders. (iii) We show that unsuccessful investors reveal a stronger inverse disposition effect. (iv) Evidence that bigger investors are less prone to the disposition effect than smaller investors is also found.     

Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE

We study pre‐trade transparency by looking at the introduction of NYSE's OpenBook service that provides limit‐order book information to traders off the exchange floor. We find that traders attempt to manage limit‐order exposure: They submit smaller orders and cancel orders faster. Specialists' participation rate and the depth they add to the quote decline. Liquidity increases in that the price impact of orders declines, and we find some improvement in the informational efficiency of prices. These results suggest that an increase in pre‐trade transparency affects investors' trading strategies and can improve certain dimensions of market quality.     

Myopic loss aversion and margin of safety: the risk of value investing

Abstract

Most modern financial markets use a continuous double auction mechanism to store and match orders and facilitate trading. In this paper we develop a microscopic dynamical statistical model for the continuous double auction under the assumption of IID random order flow, and analyse it using simulation, dimensional analysis, and theoretical tools based on mean field approximations. The model makes testable predictions for basic properties of markets, such as price volatility, the depth of stored supply and demand versus price, the bid–ask spread, the price impact function, and the time and probability of filling orders. These predictions are based on properties of order flow and the limit order book, such as share volume of market and limit orders, cancellations, typical order size, and tick size. Because these quantities can all be measured directly there are no free parameters. We show that the order size, which can be cast as a non-dimensional granularity parameter, is in most cases a more significant determinant of market behaviour than tick size. We also provide an explanation for the observed highly concave nature of the price impact function. On a broader level, this work suggests how stochastic models based on zero intelligence agents may be useful to probe the structure of market institutions. Like the model of perfect rationality, a stochastic zero intelligence model can be used to make strong predictions based on a compact set of assumptions, even if these assumptions are not fully believable.     

Technology and liquidity provision: The blurring of traditional definitions

Limit orders are usually viewed as patiently supplying liquidity. We investigate the trading of one hundred Nasdaq-listed stocks on INET, a limit order book. In contrast to the usual view, we find that over one-third of nonmarketable limit orders are cancelled within two seconds. We investigate the role these “fleeting orders” play in the market and test specific hypotheses about their uses. We find evidence consistent with dynamic trading strategies whereby traders chase market prices or search for latent liquidity. We show that fleeting orders are a relatively recent phenomenon, and suggest that they have arisen from a combination of factors that includes improved technology, an active trading culture, market fragmentation, and an increasing utilization of latent liquidity.     

How efficiency shapes market impact

We develop a theory for the market impact of large trading orders, which we call metaorders because they are typically split into small pieces and executed incrementally. Market impact is empirically observed to be a concave function of metaorder size, i.e. the impact per share of large metaorders is smaller than that of small metaorders. We formulate a stylized model of an algorithmic execution service and derive a fair pricing condition, which says that the average transaction price of the metaorder is equal to the price after trading is completed. We show that at equilibrium the distribution of trading volume adjusts to reflect information, and dictates the shape of the impact function. The resulting theory makes empirically testable predictions for the functional form of both the temporary and permanent components of market impact. Based on the commonly observed asymptotic distribution for the volume of large trades, it says that market impact should increase asymptotically roughly as the square root of metaorder size, with average permanent impact relaxing to about two-thirds of peak impact.     

On the hidden side of liquidity

This article deals with the informativeness of iceberg orders, also known as hidden limit orders (HLOs). Namely, we analyze how the market reacts when the presence of hidden volume in the limit order book is revealed by the trading process. We use high-frequency book and transaction data from the Spanish Stock Exchange, including a large sample of executed HLOs. We show that just when hidden volume is detected, traders on the opposite side of the market become more aggressive, exploiting the opportunity to consume more than expected at the best quotes. However, neither illiquidity nor volatility increases in the short term. Furthermore, the detection of hidden volume has no relevant price impact. Overall, our results suggest that market participants do not attribute any relevant information content to the hidden side of liquidity.     

On the Feasibility of Automated Market Making by a Programmed Specialist

Securities trading is accomplished through the execution of orders. Admissible orders (e.g., market orders, limit orders) give rise to discontinuous aggregate demand functions, composed of many “steps.” Demand smoothing, or the balancing of excesses due to such discontinuities via intervention, is one of the most basic functions that could be assigned to a “specialist.” When the specialist's “affirmative obligation” is fully specified, his or her activity can in principle be automated. This paper is an attempt to assess, via simulation, some of the ramifications of using a “programmed specialist,” whose automated market making is limited to demand smoothing. A number of alternative rules of operation are simulated. Several of the rules performed well, especially the extremely simple rule that calls for the (computerized) specialist to minimize new absolute share holdings in each security at each trading point via “total” (as opposed to “local”) demand smoothing. Our results indicate that the underlying costs of demand smoothing are on the order of a fraction of a penny per share traded even in relatively thin markets.     

The informational effect and market quality impact of upstairs trading and fleeting orders on the Australian Securities Exchange

This paper investigates the informational effect of trading and market segmentation on the Australian Securities Exchange (ASX) paying particular attention to the recent phenomenon: fleeting orders.¹ Confirming theoretical predictions, this study finds that permanent price effect (PPE) is significantly greater in the central limit order book (LOB) than in the upstairs market and that less informed institutional trades are routed to the upstairs market. It also finds that a well functioning upstairs market often results in lower transaction cost, higher volatility and larger trade size on the ASX. In the context of fleeting orders specifically, it finds the informational effect and market quality impact of upstairs market to be weaker after removing fleeting orders, which subsequently leads to the conclusion that recently introduced execution algorithms, which leave a trace of fleeting orders, often result in lower PPE and are mostly used my uninformed liquidity traders.     

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.     

Trading Activity,Trade Costs and Informed Trading for Acquisition Targets and Acquirers

Abstract

Microstructure effects of tender offer acquisitions on targets and acquirers differentiated by listing venue and payment method are examined. Trading activity increases more for targets than for acquirers upon offer announcement. Investors are more likely to sell targets upon announcement using direct market orders against ask limit orders for cash payment offers. While target liquidity improves as spread costs fall and quoted depths increase, acquirer liquidity falls continuously to successful offer completion. Due to increased trading differences, temporary trade costs fall more for targets than for acquirers. Permanent trade costs decline over the tender offer cycle for both parties, and especially for targets for cash tender offers and for acquirers for shares tender offers. The probability of informed trading declines (remains constant) for targets (acquirers) because increased trading intensity is greater (the same) for uninformed versus informed traders. As expected, abnormal returns and changes in own-firm permanent return volatility are negatively (but weakly) and positively (and strongly) related, respectively, to changes in information asymmetry upon announcement.     

Effects of Market Reform on the Trading Costs and Depths of Nasdaq Stocks

The relative merits of dealer versus auction markets have been a subject of significant and sometimes contentious debate. On January 20, 1997, the Securities and Exchange Commission began implementing reforms that would permit the public to compete directly with Nasdaq dealers by submitting binding limit orders. Additionally, superior quotes placed by Nasdaq dealers in private trading venues began to be displayed in the Nasdaq market. We measure the impact of these new rules on various measures of performance, including trading costs and depths. Our results indicate that quoted and effective spreads fell dramatically without adversely affecting market quality.     

How do individual investors trade?

This paper examines how high-frequency trading decisions of individual investors are influenced by past price changes. Specifically, we address the question as to whether decisions to open or close a position are different when investors already hold a position compared with when they do not. Based on a unique data set from an electronic foreign exchange trading platform, OANDA FXTrade, we find that investors’ future order flow is (significantly) driven by past price movements and that these predictive patterns last up to several hours. This observation clearly shows that for high-frequency trading, investors rely on previous price movements in making future investment decisions. We provide clear evidence that market and limit orders flows are much more predictable if those orders are submitted to close an existing position than if they are used to open one. We interpret this finding as evidence for the existence of a monitoring effect, which has implications for theoretical market microstructure models and behavioral finance phenomena, such as the endowment effect.     

Execution Costs of Institutional Equity Orders

We compare institutional execution costs across the major U.S. exchanges using a sample of institutional equity orders in firms that switch exchanges. Execution costs including commissions are essentially indistinguishable across these exchanges. We also find the fraction of trading volume from momentum traders is greater on the NYSE than on either the Nasdaq or AMEX and that orders are more likely to be worked by an institution's trading desk on the NYSE than on the Nasdaq. These results suggest that institutions actively manage execution strategies, taking into account characteristics of the markets in which they trade.Journal of Economic Literature Classification Numbers: G10, G19, G20, G23.     

Order Imbalance in the FTSE Index Futures Market: Electronic versus Open Outcry Trading

Abstract:  This study examines trading activities before and after the transfer of the FTSE 100 index futures contract from open outcry to electronic trading. Daily order imbalance exhibits strong serial persistence in the electronic limit order market, but not in open-outcry trading. Both excess buying and selling reduce liquidity. In the electronic venue, prior market movements barely affect investors' buying or selling decisions. Excess buy orders do not generate any price impact, but sell orders do. Positive imbalances are more strongly autocorrelated than negative imbalances. No trading elements, such as order imbalance, volume, or open interest, are associated with volatility. Moreover, excess buying decreases volatility. Such evidence suggests that the development and growth of electronic trading has changed the dynamics of trading activities in many important ways.