This paper uses an experimental electronic market to investigate the effect of limited attention on the market maker’s ability to provide liquidity and, thus, on aggregate market liquidity. I find that higher demands on the market maker’s attention worsen her ability to provide liquidity but do not reduce the aggregate level of market liquidity. This effect is only significant in less active markets. Furthermore, the aggregate level of market liquidity remains unaltered across both highly active and inactive markets, suggesting a reactive strategy by informed traders who step in to compete with market makers during high information intensity periods when their attention allocation efforts are compromised. In fact, in markets with a higher information value, the effect of attention constraints on the liquidity provision ability of market makers is greater. This implies that informed traders may not only exploit their informational advantage against uninformed traders but they may also use it to reap a higher share of liquidity-based profits. Finally, the market maker’s trading performance measured by her profit share and ability to manage her inventory worsens when demands on her attention are greater.
Market microstructure models suggest that the presence of market makers is a key determinant of market liquidity.1 In these models, the costs to market makers of maintaining a market presence can have a significant impact on market liquidity and the price formation process [
Individuals have a limited capacity for information processing [
Previous studies have shown that the main challenge in examining the role of attention is the difficulty in measuring attention and its allocation within financial market settings [
In a market where market makers are an important source of liquidity, attention constraints could materially impact the market’s degree of liquidity. [
This paper contributes to the literature in the following ways: (1) to my knowledge, this is the first experimental study to observe the effects of attention constraints on financial markets. Several empirical studies have tested this relationship, but none have studied this effect within an experimental setting where the individual’s ability to allocate attention can be directly controlled; (2) it measures the individual’s attention constraints in a way that precludes the use of noisy and potentially endogenous empirical proxies. Recent studies have used proxies of attention that may allow for substantial confounding effects such as trading volume [
The remainder of this paper is organized as follows: Section 2 reviews the related literature; Section 3 describes the nature of the market and develops the hypotheses; Section 4 discusses the advantages and design of the experimental setting; Section 5 describes the statistical methodology; Section 6 presents the results; Section 7 concludes.
Early work in attention literature can be traced back to the notion of bounded rationality [
Traditional finance literature has attempted to explain empirical anomalies by emphasizing the role of market imperfections. For example, [
Researchers have attempted to complement the efforts of traditional models in explaining market anomalies by challenging the viability of the rationality assumption.4 In other words, they study financial markets under the premise that individuals’ limited capacity to process information may prevent them from making fully rational decisions, leading to less-than-optimal outcomes [
Although these studies focus on the timing and information content of earnings announcements as a way of measuring the effect of investors’ attention constraints, other studies have attempted to directly quantify the level of these constraints using empirical proxies. For example, [
The aforementioned literature is aimed at identifying the determinants of investor attention such as trading volume, the state of the market, and the timing of earnings announcements. There is, however, a parallel stream of literature on limited attention that focuses on the process through which individuals allocate their limited attention. For example, [
Examining the effects of attention constraints on investor behavior can be challenging due to the difficulty in observing how investors allocate their attention or even which securities are in their opportunity set [
The traditional microstructure models attribute the market maker’s decisions and their effects on market equilibrium to costs associated with inventory risks [
In summary, the finance literature has taken significant steps to challenge the notion of perfectly rational agents in support of the notion of bounded rationality [
In this section I discuss the basic structure of the market with a focus on the role of the market maker as a liquidity provider. Among the market design issues discussed in this section are the information structure and market transparency, the market maker’s management of inventory risk, and the structure of attention and its allocation across markets. I then formulate the hypotheses tested in this study.
Electronic markets have surged in popularity as a platform for trading not only equities but a wide spectrum of financial assets as well. There are a variety of ways in which these electronic markets can be and have been organized.6 Despite the varied and distinct structure of these markets, there seems to be a common theme: most equity markets contain some form of market making. A study by the U.S. Securities and Exchange Commission (SEC) finds that, as of September 2009, the three largest U.S. equity trading centers are the NASDAQ, NYSE and NYSE Arca, which account for 47.3% of the trading volume in National Market System (NMS) stocks.7 A quick glance at the trading rules for these three U.S. registered exchanges reveals a significant role played by some form of designated market maker.8 In an earlier study, [
In this paper, I use an experimental market setting that contains salient features of a market making system in an order-driven market:10 continuous trading with the required participation of market makers, different levels of pre- trade transparency across traders, price-time priority rules, instantaneous trade reporting rules, and order submission and cancellation functionality for both market and limit orders. In this setting, all potential buyers and sellers (informed and uninformed) can trade directly with each other via the limit order book (acting as competitors to the market maker), and can enter limit and market orders that trade against the market maker’s quotes (acting as counterparties to the market maker).
The Role of the Market Maker
In this experimental market, market makers act primarily as liquidity providers, submitting limit orders continuously from their own accounts. They are required to quote (binding) bid and ask prices at all times. At the same time, the market allows for public buyers and sellers (informed or uninformed) to trade directly with each other via the limit order book, effectively competing with the market maker. In addition, the market maker has exclusive access to the book and, thus, the order flow in real time. Other traders can only observe the best market bid and ask prices. Altogether, these features foster a trading environment where the market maker’s quotes reflect both her intentions as well as the interest of the entire market.
Market makers are required to post bid and ask prices on a continuous basis. This requirement, however, does not necessarily provide extra liquidity in the market. The market maker can avoid trading by entering orders that are non-competitive.11 There are several reasons, however, why this possibility may not play a substantial role in the behavior of the market maker. First, the trading behavior of the market maker is primarily profit-motivated. The main source of revenue for the market maker is the difference between the bid and the ask prices she quotes (i.e. the bid-ask spread). Therefore, the market maker has a strong incentive to trade a large volume of shares and this can only be achieved with aggressive quotes. Second, the main goal of this study is to uncover any potential differences in the market maker’s liquidity provision ability under different attention constraint scenarios. The level of liquidity provision under either scenario is not as important as the change in the level of liquidity provision across scenarios. Finally, studies have shown that designated market makers have a significant positive impact on the quality of markets, especially the less liquid ones. For example, [
Information and Disclosure
Undoubtedly, varying degrees of information in the market are the fundamental basis for trade.12 The varying degrees of information are the result of either (1) asymmetric information (i.e. traders may have access to different information sets) and/or (2) opinion dispersion (i.e. traders may perceive the same information set differently, creating disagreement regarding the asset value).
In a model with asymmetric information, the market maker loses on average to those traders with superior information. This phenomenon is widely accepted and known as adverse selection risk.13 There are several experimental studies that model the effect of adverse selection risk on the behavior of market makers. [
Information disclosure and the resulting transparency of the market, have significant implications on the degree of liquidity, the level of competition, and the informational efficiency of markets. [
This paper’s experimental market features a high degree of post-trade transparency for all traders. Trades are instantaneously reported to all traders. The degree of pre-trade transparency, however, is different across trader types in order to motivate trading and to simulate the main features of a market making system in an order-driven market [
Inventory Management
In addition to adverse selection risk, it is well understood that risk averse market makers must incorporate their inventory levels into their quote-setting process, especially during times of large order imbalances. This idea can be traced back to the work of [
In order to control for potential effects of inventory risk on the trading behavior of market makers, the experimental design lets market makers accumulate any ending inventory position without penalty. Any shares in inventory remaining at the end of trading are ignored, in the sense that they do not alter the profit/loss performance of the market maker.17 In this way, market makers are not required to protect themselves from the arrival of overnight news (i.e. overnight risk), eliminating inventory-driven trading such as the selling/buying of shares towards the end of the trading session in an attempt to end with a flat position (i.e. zero shares in inventory).18 The market maker does, however, manage her inventory during continuous trading to protect herself from informed trading (i.e. adverse selection risk). Increasing inventory deviations away from zero provide the market maker with information signals about trading decisions of other traders, some of who may be informed.
Overall, in this market, inventory management has an asymmetric and positive effect on the market maker’s trading behavior. The market maker can monitor her inventory and revise her quotes reducing her exposure to informed trading without the constraint of ending with a flat position. Thus, the experimental design not only encourages a more aggressive price-quoting behavior by the market maker, but it also helps isolate the effect of limited attention by removing the influence of a substantial non-attention factor. In this way, the design permits for a more robust examination of the effects of attention constraints on the market maker’s ability to provide liquidity to one or more markets.
Attention Allocation
Structural market features may influence the market maker’s ability to allocate attention and provide liquidity effectively. Two points are worth considering: (1) the availability of attention per unit of information intensity is fixed19, and (2) market makers may face competition from traders with lower attention constraints.
Most empirical studies look at the effect of market makers’ attention constrains on financial markets by examining different levels of information intensity. These studies use empirical proxies to measure the level of information intensity for a stock and, thus, its demand on the attention of traders. Some examples of those proxies are earnings news [
In this paper, the experimental market design keeps the amount of attention available for the set of securities fixed on a per-unit basis across low and high attention constraints environments.22 This allows me to control for the amount of attention allocated across stocks and isolate the effects attention constraints on the market maker’s ability to provide liquidity.
The second structural implication is the impact of other traders who face lower attention constraints (i.e. they trade on a smaller set of securities or trade on securities with lower information intensity). For example, informed traders may behave similar to market makers, yet trade on a fewer number of stocks. Previous studies find evidence that informed traders provide a significant amount of liquidity to the market [
This competitive feature is explicitly modeled in the experimental market by allowing market makers to trade in more than one stock while restricting the trading of informed traders to only one stock. This feature is similar to the non- centralized market making design in an order-driven market as described by [
The complexity of modern-day stock markets, coupled with the interdependence of trader’s decisions, makes empirical studies of traders’ behavior vulnerable to a myriad of confounding effects. Given the experimental nature of this paper and the impracticality of perfectly replicating complex institutions such as the NYSE’s Hybrid market, the experimental market implements only institutional features of interest in a simplified setting. These institutional features, however, are enough to provide a direct test of the paper’s relevant hypotheses and shed light on the behaviors of market makers in a less restrictive setting. Furthermore, this study aims to improve the robustness of previous empirical results by avoiding the use of noisy proxies, controlling for potential statistical and behavioral biases, isolating the pure effect of the market maker’s attention constraints, and addressing other confounding effects that have not been previously addressed or simply cannot be measured in empirical studies.
[
Hypothesis 1: The liquidity provision ability of the market maker and, thus, the degree of market liquidity decreases as the market maker’s attention constraints increase.
To test this hypothesis, the experimental design consists of two main environments: a condition where market makers are only responsible for providing liquidity to one market, and another condition where market makers are responsible for providing liquidity to two distinct markets simultaneously. The degree of liquidity is measured and compared across these two environments.
To support this hypothesis, I predict that the liquidity measures will deteriorate more significantly when the market maker is responsible for providing liquidity in two markets simultaneously relative to one market only. In other words, I expect to see market liquidity (e.g. bid-ask spread) as well as the amount of liquidity provided by the market maker (e.g. number of limit orders posted) worsen, on average, when the market maker operates in two simultaneous markets as opposed to only one.
Hypothesis 2: The reduction in the market maker’s liquidity provision (and overall market liquidity) caused by higher attention constraints is more evident for the least active securities.
To test this hypothesis, the experimental design consists of two distinct markets with different levels of trading activity. Both markets always have the same number of informed traders and market makers. One of the two markets (i.e. high activity market), however, consists of twice as many uninformed traders. Uninformed traders are given an exogenous motive for trading a given number of shares.25 Therefore, varying the number of uninformed traders across markets (two in one market and four in the other) produces different levels of trading activity.
To support this hypothesis, I predict that the liquidity measures will deteriorate more significantly in the market that has a low level of trading activity (i.e. relatively inactive) if market makers are responsible for trading in two markets simultaneously rather than one market only. In other words, I expect to see a significant increase in the bid-ask spread and a decrease in the number of limit order posted by the market maker only in the less active market, and when the market maker is responsible for trading on both markets. I do not expect to see any effect on the liquidity measures in markets with a high level of trading activity.
Numerous studies have examined the strategic behavior of traders and the information content of their strategies. Not only do traders’ decisions determine market prices but they also impact market liquidity. The trader’s decision as to whether to be patient or impatient has a direct impact on the degree of market liquidity.26 [
Similar to [
Evidently, valuable information drives the strategic behavior of traders with an informational advantage, as well as the behavior of uninformed traders whose trading decisions may be reactive. Informed traders possess information about the fundamental value while market makers can extract this information from their exclusive access to the order book. These two types of traders may use their information to compete for market making profits. Higher attention constraints on the market maker may then lower her ability to extract information and compete effectively. Moreover, the reduced ability of the market maker to extract information makes informed traders’ information even more valuable. This, in turn, increases the market maker’s adverse selection risk, further reducing her ability and/or willingness to provide liquidity. In order to explore the role played by information value and traders’ strategic behavior on liquidity across different attention constraints environments, I test the following hypotheses:
Hypothesis 3: Higher attention constraints lower the market maker’s abilities to extract information from the market, worsening her informational disadvantage (relative to informed traders) and hampering her ability to provide liquidity.
To test this hypothesis, the experimental design consists of securities (or trials) with different degrees of information value. Informed traders receive an information signal (i.e. a narrow range that contains the security’ fundamental value) prior to the start of trading. Some trading trials consist of fundamental values with large absolute deviations from the prior expected value of $50 (high information value) while other fundamental values are near the prior expected value (low information value). In this way, the value of the informed traders’ information is controlled across securities.
To support this hypothesis, I predict that the liquidity measures will deteriorate more significantly in markets with high information value (i.e. large informational advantage of informed traders) if market makers are responsible for trading in two markets simultaneously rather than one market only. In other words, I expect to see a significant increase in the bid-ask spread and a decrease in the number of limit order posted by the market maker only in markets where informed traders have a large informational advantage, and when the market maker is responsible for trading on both markets. I do not expect to see any effect on the liquidity measures in markets where informed traders have little to no informational advantage.
Hypothesis 4: The market maker’s ability to provide liquidity effectively, and thus, her trading performance (based on optimal quote revisions) is reduced as her attention constraints increase.
To test this hypothesis, I assess the market maker’s trading performance and liquidity provision abilities across the two attention-constraint environments. To measure the market maker’s trading performance, I consider her dollar profits. The market maker’s ability to optimally revise her quotes would be indicative of a superior trading performance and should, in turn, lead to higher profits.28
To support this hypothesis, I predict that the market maker profit share will be significantly lower when the market maker is responsible for providing liquidity in two markets simultaneously relative to one market only. In other words, I expect to see dollar profit, but more importantly, profit share decline, on average, when the market maker operates in two simultaneous markets as opposed to only one.
In this section, I provide a detailed description of both the experimental design and the laboratory market.
In this paper, I use a full-factorial repeated measures (balanced) design with a total of six factors (three within-subject factors and three between-subject factors).29 In such a design, each subject or experimental unit is observed repeatedly under different treatments.30 The objective of this design is to investigate factors affecting the ability of the market maker to provide liquidity. More specifically, the experiment was designed to examine how the liquidity provision ability of market makers differs with (1) market characteristics such as trading activity and time, (2) asset characteristics such as the relative value of the security’s information to traders, and, (3) behavioral characteristics such as constraints on the market maker’s attention capacity and the trading motives of different market participants.
In order to manipulate the level of trading activity, at the beginning of each trial, traders are randomly assigned to one of two markets. Excluding market makers, one market consists of two informed traders and four uninformed traders, while the other market consists of two informed traders and only two uninformed traders. All traders know the level of trading activity in their selected market.
Attention constraints on market makers are manipulated by varying the number of markets the market maker must attend to. In one trading environment, each market maker is constrained to trading in one market so she can allocate her entire effort and attention to one stock (i.e. low attention constraints). On the other trading environment, the market maker must attend to two markets by quoting and updating her bid and ask quotes in both markets simultaneously (i.e. high attention constraints).
Information value is manipulated relative to a prior expected value of $50. All traders know that fundamental values are randomly drawn from a normal distribution with a mean of 50. Only a subset of traders (i.e. informed traders) is given a narrow range containing the fundamental value (i.e. the information range). Thus, these traders have an informational advantage over the other traders. The magnitude of their informational advantage is a direct function of the value of their information. To manipulate this factor, some securities are given a value near the prior expected value of $50 (i.e. low information value) while other securities carry extreme values or values far away from the prior expected value (i.e. high information value).31 More specifically, securities with a high information value have realizations that are between $20 and $30 from the expected value, and securities with low information value have realizations that are no more than $10 from the expected value.
The traders’ trading motive is manipulated by randomly assigning them to different trader types. There are three types of traders (market makers, informed traders, and uninformed traders), and each type has unique characteristics across several dimensions such as information, market transparency, trading capabilities, sources of profit, among others. At the beginning of each trial, traders are randomly assigned to one of these three types. Overall, there are two market makers, four informed traders, and six uninformed traders in each trial.
Time is manipulated to characterize the liquidity provision strategy of the market maker. During the trading trial, trading decisions are examined at the end of each 24-second time interval for a total of five decision points. The segmentation of time facilitates the study of the time series properties of the market.
Overall, the experimental market uses a full-factorial repeated measures design with six factors and varying number of levels for each factor: attention constraints (high, low), information value (high, low), time (five time intervals of 24-second periods), trading activity (high, low), trader type (informed trader, uninformed trader, market maker), and cohort (four cohorts of 12 participants each). The first three are within-subject factors, as they vary across different observations coming from the same subject. The last three are between-subject factors, as they vary for different subjects, but remain constant for a single subject.
Controls
A primary benefit of an experimental study is the ability to control features of the experimental design that might influence behavior but are not the focus of the study [
Order effects result when the ordinal position of the treatments biases participant responses [
Carry-over effects (also known as learning or practice effects) can be significant in a repeated measures design. These effects occur when an effect from one treatment changes (carries over to) participants’ responses in the following treatment condition [
Trial # | Order 1 | Order 2 | |||
---|---|---|---|---|---|
Information value | Low constraints first | High constraints first | |||
Stock 1 | Stock 2 | Stock 1 | Stock 2 | ||
1 | |||||
2 | High | 27 | 29 | 27 | 29 |
3 | Low | 1 | 8 | 1 | 8 |
4 | |||||
5 | High | 20 | 22 | 20 | 22 |
6 | |||||
7 | Low | 4 | 6 | 4 | 6 |
8 | Low | 9 | 3 | 9 | 3 |
9 | High | 30 | 22 | 30 | 22 |
10 | |||||
11 | |||||
12 | Low | 10 | 2 | 10 | 2 |
13 | High | 26 | 21 | 26 | 21 |
14 | |||||
15 | Low | 1 | 9 | 1 | 9 |
16 | |||||
17 | Low | 7 | 5 | 7 | 5 |
18 | High | 25 | 30 | 25 | 30 |
19 | High | 24 | 29 | 24 | 29 |
20 |
Participants were given detailed written instructions and were told to carefully review these instructions prior to the trading sessions.38 In addition, all participants experienced extensive training for the experiment. They attended a 90- minute training session. The session consists of three parts: (1) a 30-minute discussion of the written instructions, including an overview of the experimental market and the trading software (e.g. trading functionality, mechanics of the limit order book, trading rules such as price-time priority, the role of the market maker, etc.), (2) a 15-minute trading simulation where participants learn the basics of the trading software by trading and openly discussing any challenges they may have in using the software, and (3) a 45-minute practice session where participants trade in the experimental markets replicating the exact dynamics of the experiment (e.g. random assignment to trader types and markets, trading trials with a pre-trading and a main trading phase, etc.).
Notwithstanding the importance of adequate training, financial experiments must offer participants monetary incentives. The fundamental method of experimental economics is to create a setting that implements some institutional features of interest and then provide participants with incentives to maximize utility within that setting [
The laboratory market resembles the mechanism of a market making system in an order-driven electronic market.41 This market contains traders with different trading motives and different levels of information (i.e. asymmetric information), and it allows for trading rules, reporting rules, and trading functionality that are similar to those used in many large stock exchanges worldwide [
The Market
The basic setting features a double-auction electronic market with the addition of market makers who are obligated to provide liquidity for a well-defined set of securities (i.e. one or two securities). The market allows for continuous trading, a limit order book that is visible to the market maker, limit order and market order functionality, price-time priority rules, post-trade transparency, and order cancellation capabilities. Besides the presence of market makers, the market contains informed traders who possess information about the fundamental value of the security and uninformed traders who face liquidity needs (i.e. they trade on the basis of exogenous non-information reasons).
The Traders
There are three trader types: (1) informed traders, (2) uninformed traders, and (3) market makers.42 Informed traders possess superior information regarding the fundamental value of the security. The value is determined before the trading trial starts and informed traders are shown a narrow range containing this value. For example, if the fundamental value is determined to be $23, informed traders may be told that the value is somewhere between $11 and $31. Informed traders can enter limit orders and market orders into the order book. They are able to see their own outstanding orders but they cannot see the orders of other traders. They can, however, see the market’s highest bid and the lowest ask. Informed traders earn a profit every time they buy (sell) shares at a price below (above) the fundamental value. Uninformed traders do not possess information regarding the value of the security. Instead they have a trading “target” which is known to them before the trading trial begins. The target may have different directions for different uninformed traders. For example, a trader may have a target of −20 shares (i.e. an instruction to sell 20 shares) while another trader may have a target of +20 (i.e. an instruction to buy 20 shares). The goal of uninformed traders is to meet their target at the most favorable prices possible. At the end of the trading trial, uninformed traders who fail to reach their targets receive a penalty of $100 for each share in inventory short of the target. For example, an uninformed trader with a target of 20 shares who never reaches a position of 20 shares and ends the trading trial with an inventory of 18 shares will receive a penalty of $200. The penalty is large enough that uninformed traders are better off hitting the exact target even if the prices at which they transact are unfavorable. The use of targets captures the notion that uninformed traders are trading for exogenous reasons related to liquidity needs. Similar to informed traders, uninformed traders can enter limit orders and market orders into the order book. They are only able to see their own outstanding orders but they can also see the highest bid and the lowest ask for the entire market. Uninformed traders take a loss every time they buy (sell) shares at a price above (below) the fundamental value. The third type of trader is the market maker. Similar to uninformed traders, market makers do not know the fundamental value. However, unlike the other two types of traders, market makers are able to see the entire limit order book (i.e. all outstanding limit orders entered by traders in the market). Market maker scan enter market orders but, unlike the other two trader types, they cannot enter limit orders. Instead, they quote a single bid price and a single ask price. Furthermore, market makers cannot cancel their quotes; they can only update their quotes. This feature prevents market makers from exiting the market and ensures their continuity as liquidity providers.
In summary, the three types of traders have substantially different degrees of market information accessibility and trading capabilities. More importantly, traders have substantially different motivations for trading. Informed traders trade with the aim of maximizing their profit based on their informational advantage. Uninformed traders trade with the aim of minimizing their loss while meeting their exogenous liquidity needs. Market makers have an obligation to trade and provide liquidity to the market; they are compensated for their liquidity provision services by earning the difference between their quoted bid and ask prices.
The Order Book
The laboratory market features an electronic book of orders (i.e. limit order book) with the participation of designated market makers. Traders are permitted to enter limit orders and market orders. Limit orders are instructions to buy (sell) securities at a price not higher (lower) than the instructed limit price. This type of orders provides the trader with price certainty (i.e. the trader will not buy (sell) at a price higher (lower) than the limit price) but it carries execution risk (i.e. the order may never execute). Market orders are instructions to buy (sell) securities at the best available price. Unlike limit orders, market orders do not provide traders with price certainty but they do eliminate execution risk.
The order book consists of two books: a bid book and an ask book. The bid book maintains a list of all outstanding bid orders and the ask book maintains a list of all outstanding ask orders. The order book allows for the automated crossing or execution of orders. All market orders immediately cross with the best-priced limit orders outstanding. In this sense, market orders are marketable. Limit orders can also be marketable. If a trader enters a bid (ask) order with a price higher (lower) than the lowest (highest) ask (bid), the orders immediately cross. Limit orders that are conservatively priced and fail to cross immediately after they are entered are known as non-marketable limit orders. These orders remain on the order book until they are crossed by a marketable order from another trader.
In this market, all bids and asks must have integer prices between 0 and 100, inclusive. The maximum number of shares allowed per order is one. Traders can, however, enter multiple orders at the same price. Traders can enter both limit orders and market orders at any time during the trading trial. Traders, except for the market maker, can also cancel their outstanding limit orders at any time. The trading functionality provides traders with the flexibility to cancel individual orders, cancel all bids (or asks) at once, or cancel all outstanding orders at once.
The Trading Trial
Each trading trial takes place in two phases: (1) pre-trading phase and (2) main trading phase.
Pre-Trading Phase: The pre-trading phase lasts 20 seconds during which traders can enter and cancel as many orders as they wish. During this period, no trades are executed. Marketable orders do not result in a trade. The crossing orders are simply kept on the order book and at the end of the pre-trading phase the order book is purged of these orders in the following way: if the highest bid crosses with the lowest ask, the more recent of the two orders is deleted from the book. This process is repeated until the highest bid price is below the lowest asking price.
Main Trading Phase: The main trading phase lasts 120 seconds during which traders are permitted to enter and cancel as many limit and market orders as they wish. During this period, trades can be executed. Here, participants can trade continuously and they are free to pursue dynamic order placement and cancellation strategies.
The Trading Session
The experimental design includes four cohorts of 12 traders each, for a total of 48 participants. A cohort is a group of traders who always trade together in one trading session. A trading session is a 90-minute period where participants take part in a series of independent trading trials. In other words, each cohort trades 20 securities sequentially. Each of the 20 trading trials consists of 12 participants randomly divided into two groups with each group trading on a different security or market. A group of at least 7 traders trades one security, while a group of at least 5 traders trade the other security. Before the beginning of each trading trial, traders are randomly assigned to a trader type, achieving the following overall trader type distribution: two market makers, four informed traders, and six uninformed traders. Each market has two informed traders each for all 20 trials. One market has four uninformed traders while the other has only two uninformed traders.43 In one block of 10 trials, each market maker is required to participate in only one market (i.e. low attention constraints environment). In the other 10 trials, both market makers are required to participate in both markets simultaneously (i.e. high attention constraints environment).
In this section, I provide a broad overview of the statistical methodology used to
analyze the experimental data. The exact nature of the statistical methodology depends on the hypothesis being tested and, thus, may be different for each hypothesis test in this paper. A more detailed description of the statistical methodology used for each hypothesis is provided in the next section.
The statistical methodology used to test this paper’s hypotheses is repeated- measures analysis of variance (ANOVA) with between-subject factors. As with any ANOVA, repeated-measures ANOVA tests the equality of means. However, repeated-measures ANOVA is used when all members of a sample are measured under several different treatments. As the sample is exposed to each treatment in turn, the measurement of the dependent variable is repeated. This design reduces the problem, common in experimental economics, of overstating statistical significance by assuming that repetitions of the same actions by the same group of subjects are independent events [
A factorial ANOVA provides a methodology to test a variety of effects: (1) a significant main effect means that there is a difference between at least two levels of a factor with respect to mean scores on the response (or dependent) variable, (2) an interaction is a condition in which the effect of one factor on the response variable is different at various levels of another factor, and (3) a significant simple effect means that there is a significant relationship between a factor and the response variable at a given level of another factor [
The relevant type of effect depends on the nature of the hypothesis and on the significance of the interaction effect. If the interaction effect is significant, there is no need to test for main effects, as these results would be misleading.45 Instead, it is appropriate to test for simple effects. If the interaction effect is not significant, however, simple effects may be ignored and main effects should be tested instead. For the sake of consistency, and more importantly, appropriateness, the statistical tests, when applicable, begin with a test of the interaction effect and then are sequenced as follows:
If the interaction effect is not significant, I proceed to test for main effects for each of the factors being considered. If any of the main effects are significant, I provide contrast tests and/or multiple comparison tests for any significant main effects.46
If the interaction effect is significant, I proceed to interpret a profile plot to gain some insight on the nature of the interaction.47 Then, I test for any relevant simple effects. Similar to main effects, if a given simple effect is significant, I provide contrast tests and/or multiple comparison tests.
The focus of this analysis is on the effect of attention constraints on the ability of market makers to provide liquidity to the market, and thus, on market liquidity. I begin with preliminary summary statistics to provide a sense of how typical is the aggregate behavior resulting from these experiments. I then examine the liquidity effect mentioned above.
experimental market behaves. Each panel divides trading into five 24-second intervals.
A particularly important aspect of this study is the inducement of different levels of trading activity across markets.48 To evaluate the success of my efforts, I compute the trading volume by summing over the number of trades in each time interval and averaging these numbers across all trials and cohorts. (a) provides the summary statistics of the trading volume measure for each market. As expected, the trading volume in the high-activity market is significantly larger than the trading volume in the low-activity market. Approximately 200 shares are traded in a typical high-activity market, while less than 130 shares are traded, on average, in low-activity markets. This difference in trading volume across markets is also consistent across time. Markets with an expected high level of activity have a larger volume of trading than the low-activity markets throughout the entire trading day. Furthermore, (a) shows that volume exhibits a slight “U” shape with high levels of trading activity near the second time interval and again toward the end of the trading day. The increase in trading volume during the last trading interval reflects the trading behavior of uninformed traders rushing to hit their trading targets. This shape is observed for both experimental markets and it is consistent with the observed shape of trading volume in equity markets. Overall, these results provide strong support for the ability of the experimental design to induce the desired level of trading activity across markets.
(b) shows the time series behavior of the market’s bid-ask spread. Each data point is computed as the average bid-ask spread for the 24-second time interval. Irrespective of market activity, the spread declines from an average of $17.5 in the first interval to an average of nearly $6 in interval five, resulting in a threefold decrease in the size of the spread. Panel C shows the average pricing error, calculated as the absolute value of the deviations of the mid-quote from the fundamental value. The pricing error decreases by an average of roughly 30% from the opening time to its lowest point near closing time. These patterns suggest that markets behave reasonably well, in light of theoretical, archival, and experimental studies. Of particular importance is that these experimental markets appear to gradually incorporate information, a feature consistent with market efficiency.
As a robustness check, I also compute the dollar mispricing in each of the two markets.
level of trading activity have an average dollar mispricing equal to −$0.11. Simi- lar to high-activity markets, this average is not statistically different from zero (p = 0.9427). These results imply that the experimental markets are efficient.
The ability of markets to incorporate information into security prices depends on informed traders exploiting their informational advantage, which should result in positive profits for the informed group as a whole. This implies that all other groups experience an information disadvantage and should expect to lose money. Market makers are, however, a profit-motivated group with privileged access to the market’s order flow. Their trading motive as well as informational access should allow market makers to mitigate and even eliminate their informational disadvantage as trading occurs. Uninformed traders, on the other hand, should expect to lose money to both informed traders and market makers.
The experimental markets indeed produce the expected results.
In summary, these results imply that informed traders not only have an informational advantage but they successfully trade on it. Also, market makers are able to successfully use their privileged access to order flow. This provides further evidence that the markets are well behaved.
Attention Constraints and Liquidity
This paper’s first hypothesis predicts that, when the demand on the market maker’s attention constraints is high, her liquidity-provision abilities worsen. This, in turn, decreases the aggregate level of liquidity in the market. In order to test this hypothesis, this paper looks at the main effect of attention constraints on two broad sets of liquidity measures: (1) market maker’s liquidity provision and (2) aggregate market liquidity. To measure the market maker’s ability to provide liquidity, I compute the following three measures: the number of limit orders submitted by the market maker, the proportion of total limit orders submitted by the market maker, and the submission rate (computed as the number of limit orders the market maker submits divided by the sum of her limit and market orders).50 To measure the aggregate level of liquidity, I use the quoted bid-asked spread and trading volume (computed as the number of shares traded within a time period).
(a) presents the measures of market maker liquidity provision. In markets with low attention constraints, market makers submitted an average of 49 limit orders per trial. This number was significantly reduced (to an average 22 limit orders) when the demands on the market markers’ attention were high. The difference in the number of limit orders submitted is statistically significant with a p-value less than 0.01. Perhaps a better measure of liquidity provision is the percentage of the total number of limit orders submitted by a trader. Under low attention constraints, market makers submitted 18.72% of all limit orders in a typical trial. This proportion decreased by 6.41% in trials with high attention
Attention constraints | # Limit | % Limit | Sub. rate |
---|---|---|---|
Low constraints | 49 | 18.72% | 47.50% |
High constraints | 22 | 12.31% | 51.60% |
Difference in means | 27 | 6.41% | −4.10% |
P-value | <0.01 | 0.02 | 0.40 |
Attention constraints | % Spread | $ Spread | Volume |
---|---|---|---|
Low constraints | 18.67% | $11.27 | 174 |
High constraints | 16.30% | $9.44 | 156 |
Difference in means | −2.37% | −$1.82 | −$17.90 |
P-value | 0.09 | 0.05 | 0.34 |
constraints. Once again, the difference between these two numbers is statistically significant (p = 0.02). Finally, the submission rate shows that there was no significant change in the market markers’ order choice across attention constraint conditions. This measure, however, does not assess the market maker’s contribution to market liquidity, but instead, describes her choice of trading strategy. Overall, (a) provides strong evidence on the negative effect of higher attention constraints on the market makers’ contribution to market liquidity.
(b) looks at the impact of attention constraints (imposed on market makers) on the aggregate level of market liquidity. A quick look at all three measures of market liquidity illustrates the absence of a negative relationship between attention constraints and liquidity. In fact, there seems to be an increase in liquidity when the demand on the market makers’ attention is greater. For example, the average percentage bid-ask spread in low-constraints markets is roughly 2.4% greater than in markets with high constraints (this difference is statistically significant with a p-value equal to 0.09). The average dollar spread yields similar results. Although trading volume remains unchanged across conditions, trading volume is a noisier measure of liquidity.
The contrast between the two sets of liquidity measures presented above could be explained by either the reaction of the market maker to higher attention demands placed on her, or the reaction of other traders (mostly informed) acting in an opportunistic manner. Market makers profit from the bid-ask spread, and having to allocate their attention across multiple tasks may push them to trade more aggressively (quote higher bids and lower asks) to achieve higher profits. This, in turn, narrows the spread. An alternative explanation is rooted in the reactions of other traders. Informed traders can exploit their informational advantage to generate profits but they can also earn additional income through liquidity provision. In markets where attention constraints on market makers are high, informed traders can step in to provide market making services, effectively competing against market makers. This would lead to either an improved or unchanged level of liquidity. A later section of this paper sheds some light on these ideas by evaluating the trading behavior of market makers and informed traders across different levels of attention constraints.
Attention Constraints, Liquidity, and Trading Activity
Reference [
(a) presents the interaction effect on market wide liquidity. The statistical tests provide no significant interaction effect across all three measures of market wide liquidity (percentage spread, dollar spread, and trading volume).51 The results are similar when I measure the liquidity provision of both market makers combined (see (b)). It is worth mentioning, however, that the percentage of limit orders submitted by market makers relative to all limit orders does respond to the interaction of trading activity and attention constraints. (b) shows that the share of market maker limit orders in low attention constraints markets is much larger when the market is less active relative to high-activity markets (the market makers’ average share of limit orders is 22% in low activity markets compared to 15% in high activity markets). Furthermore, there is a significant decline in the market makers’ share of limit orders when attention constraints intensify. This decline is only statistically significant (p = 0.05), however, for markets with a low
level of trading activity. These results support the prediction in [
Although the results from
The top section of this table shows measures of liquidity provision for the market maker operating only in markets with high activity. The bottom section summarizes the results for the low-activity market maker. The average number of limit orders submitted by either trader (# Limit) declines significantly as the
Activity/attention | # Limit | % Limit | Sub. rate |
---|---|---|---|
High/low | 48 | 15.31% | 45.30% |
High/high | 23 | 10.98% | 49.54% |
Difference in means | 25 | 4.33% | −4.24% |
P-value | 0.06 | 0.20 | 0.35 |
Activity/attention | # Limit | % Limit | Sub. rate |
---|---|---|---|
Low/low | 51 | 22.13% | 49.70% |
Low/high | 22 | 13.50% | 57.77% |
Difference in means | 29 | 8.63% | −8.07% |
P-value | 0.10 | 0.05 | 0.29 |
demands on their attention increase (p-values are equal to 0.06 and 0.10, respectively). The number of limit orders as a measure of liquidity, however, may be misleading as it does not control for the changes in market wide liquidity. To address this issue,
Attention Constraints, Liquidity, and Information Value
In this section, I consider how the informational advantage of informed traders may hinder the liquidity provision ability of the market maker especially when the demands on her attention are high. Higher attention constraints on the market maker may lower her ability to extract information from the order flow making her less able to compete with limit orders for the provision of liquidity. Moreover, the reduced ability of the market maker to extract information from the book makes informed traders’ information more valuable. This in turn, increases the market maker’s adverse selection risk further reducing her ability and/or willingness to provide liquidity. To test this hypothesis, the experimental design consists of markets where informed traders are provided with highly valuable information and markets where the value of information is low.
(a) shows the interaction effect on market wide liquidity. Once again, the broad measures of market liquidity do not show the expected negative relationship with the demand on the market makers’ attention. None of the interaction plots depicted in (a) provide statistically significant tests. (b) presents a very different story. The interaction effect is statistically significant for both the number and the proportion of limit orders submitted by market makers (p-values are 0.09 and 0.10, respectively). A closer look at the profile plots shows a dramatic attention-driven decline in the market makers’ liquidity provision, primarily for markets with high information value. The number of limit orders submitted by market makers declines from an average of 62 under low attention constraints to an average of 16 under high attention constraints (p < 0.01). The proportion of limit orders submitted by market makers declines from an average of 23% under low attention constraints to an average of 11% under high attention constraints (p < 0.01). Although markets with low information values also experience a decline in liquidity provision, the decline is neither economically meaningful nor statistically significant.
Overall, these results are consistent with this paper’s third hypothesis. More importantly, these results shed some light on the asymmetric impact of attention constraints on the different sets of liquidity measures. Higher attention constraints on market makers have a significant negative effect on their liquidity provision but no effect on the aggregate level of market liquidity. These results suggest that the higher informational advantage of informed traders allows them to not only better exploit the uninformed traders, but it also increases their incentive to compete with market makers and earn additional spread-based revenue, especially in markets where market makers face high attention constraints.
Attention Constraints, Liquidity, and Trading Performance
A second dimension of the market maker’s ability to provide liquidity is her effectiveness in revising her quotes and, in turn, her trading performance. To examine how effectively market makers provide liquidity under different degrees of attention constraints, this section looks at profit performance.
demands on the market makers’ attention are high.53
Although uninformed and informed traders do show an improvement in their trading performance across attention conditions, the increase in the average dollar profit of market makers does not necessarily mean a better trading performance. The market makers share of positive declines from 40% in a typical low attention constraints market to an average of 36% under high attention constraints.54 In fact, market makers are the only ones that see their profit share decline (informed traders keep their profit share at 60% while uninformed traders manage to turn their losses into a profit). These results provide evidence of a redistribution of profit share away from market makers when the demands on their attention increase.
tory, as market makers face higher attention constraints. Under low attention constraints, market makers end a typical trial with an inventory balance of 26 shares away from a flat position; this number doubles to an average of 51 shares under high attention constraints.
In summary, attention constraints have a meaningful negative effect on the market maker’s ability to provide liquidity effectively, and thus, on her trading performance. This effect is captured by a combination of a decline in the market maker’s share of the realized profits and her decreased ability to manage her ending inventory position.
A study released by the U.S. Securities and Exchange Commission in 2010 finds that roughly 50% of all U.S. equity trading volume occurs in exchanges that require some form of designated market maker. Furthermore, the advent of high-tech low latency trading, coupled with liquidity rebates offered to proprietary firms, makes market makers a sizable group in U.S. equity markets. This implies that market making not only provides equity markets with a vital source of liquidity but it is also a key determinant of overall market quality. This paper aims to explore the costs to market makers of providing liquidity, which has implications for many aspects of financial markets from return predictability to optimal asset allocation strategies.
Traditional models of market microstructure attribute the costs of market making to two primary sources: adverse selection risk and inventory risk. More recently, however, researchers have emphasized the role of behavioral factors on the function of market makers. In this study, I examine the effect of limited attention within a controlled experimental setting that precludes the use of noisy proxies, controls for potential statistical and behavioral biases, and isolates the pure effect of market maker’s attention constraints on liquidity.
As the constraints on the market maker’s attention become more significant, her ability to provide liquidity to the market may worsen. I test this prediction and find that, under high attention constraints, the number of limit orders as well as the proportion of limit orders submitted by the market makers decline significantly. Aggregate market liquidity, however, is not affected. These results suggest that while the market maker is busy attending to multiple tasks, other traders, such as informed traders, step in to compete for liquidity provision via limit order submissions.
Markets for less active stocks may source a larger portion of their liquidity from market making activities. At the same time, market makers have a lower financial incentive to allocate attention to those markets. These ideas suggest that the effect of limited attention may be greater in less active markets. Once again, I find that, although market wide liquidity is not affected by attention constraints at any level of trading activity, the market maker’s liquidity provision deteriorates. Consistent with this paper’s hypothesis, I find that limited attention has a significant negative impact on liquidity provision, primarily for market makers operating in less active markets.
The ability of market makers to extract information from order flow could worsen when attention constraints are high. This, in turn, could hamper their ability to provide liquidity effectively. Informed traders may be able to better exploit their information advantage during times when the market maker’s attention is highly constrained. I test this notion and find that informed traders use their informational advantage to not only exploit uninformed traders, but to also compete with market makers, especially in markets where they face high attention constraints. A related result is the deterioration in the market maker’s trading performance. I find evidence that higher attention constraints lower the market maker’s share of realized profits and lessen her ability to manage her inventory.
From a broad perspective, this study provides robust evidence on the importance of behavioral factors in the investor’s decision-making process as well as the quality of the markets in which these investors trade. A better understanding of the role on these factors on financial markets can help investors make better informed decisions and would lead to more informationally efficient markets. From a narrower perspective, this study sheds light on the effect of attention constraints on the market maker’s ability to provide liquidity while staying profitable. Market makers can be an invaluable source of liquidity, especially in less active markets. For this reason, it is important to understand how the liquidity of these markets suffers during times of heightened information intensity when the market makers’ attention capacity is compromised. This paper’s findings bring doubt to the notion that market makers (particularly designated market makers) are effective in providing liquidity and continuity to less active markets, especially under stressful times.
In addition to providing experimental evidence to help bridge the relationship between behavioral constraints, trading behavior and market quality, this paper has practical implications for both market design and portfolio allocation strategies. Market design is paramount especially for securities that are not actively traded, where the role of market makers is a key feature in preserving a fair and orderly functioning of these markets. Understanding the effect of attention constraints on market makers can contribute to more ideal designs. Furthermore, this effect can help enhance the portfolio allocation strategies of market participants providing market making services. These practical implications open the door for further research with the aim of creating more efficient market designs and structures where the liquidity of less active markets can be significantly enhanced. This research may address questions related to the need for market makers and potential incentive structures that may encourage or discourage their participation.
Cabrera, J.F. (2017) Market Making, Liquidity Provision, and Attention Constraints: An Experimental Study. Theoretical Economics Letters, 7, 862-913. https://doi.org/10.4236/tel.2017.74061
Experimental Trading Simulation
Instructions
In this trading session, you will participate in a total of 20 trials. In each trial, you will trade a different security that is valued in “laboratory dollars” (LAB$). At the end of the session, we will convert you trading gains into U.S. dollars (US$) to determine your payment. To trade these securities, you will be using the Financial Trading Services (FTS) trading software. Please refer to the sections below for a detailed overview of the FTS trading screen and instructions on how to login.
Ten Basic Terms
1. Bid―it is an order to buy shares at a stated price (the bid price). The bid price is the highest price the buyer is willing to pay for one share.
2. Ask―it is an order to sell shares at a stated price (the ask price). The ask price is the lowest price the seller is willing to receive for one share.
3. Bid book―it is a list ordered by price (highest price first) of all the bids traders have entered.
4. Ask book―it is a list ordered by price (lowest price first) of all the asks traders have entered.
5. Best bid―it is the bid with the highest price on the bid book.
6. Best ask―it is the ask with the lowest price on the book.
7. Entering a bid―a trader willing to buy shares at a stated price can submit a bid to the book. The bid will be held on the bid book until another trader chooses to “take” it.
8. Entering an ask―a trader willing to sell shares at a stated price can submit an ask to the book. The ask will be held on the ask book until another trader chooses to “take” it.
9. Taking a bid―a trader willing to sell shares can take the highest bid from the bid book in two ways: (i) she can directly sell to the highest bid or (ii) she can enter an ask with a price lower than the highest bid price.
10. Taking an ask―a trader willing to buy shares can take the lowest ask from the ask book in two ways: (i) she can directly buy from the lowest ask or (ii) she can enter a bid with a price higher than the lowest ask price.
The Trading Session
A trading session consists of trading 20 securities successively (i.e. 20 trials). Information about each security and each trader is generated right before the beginning of each trial. Traders will trade based on this information until the trial ends.55 Prior to the start of a new trial, new information is generated.
The Trading Trial
You will start each trading trial with zero cash and zero shares of the security, however, negative cash balances (cash borrowing) and negative share balances (short selling) are allowed.56 Trading in each trial will be split into two parts: pre-trading and main trading.
Pre-trading (20 seconds)
During the pre-trading period, traders can enter orders (bids and asks), but no one can take them. This means that no actual trading will occur during this period. At the end the pre-trading period, the highest bid and the lowest ask will be paired up. If they “cross” (the bid is priced higher than the ask) the more recent order will be deleted and a new pair will be matched. This will be repeated until there are no crossing orders remaining. The purpose of this pre-trading period is to allow traders to enter orders into the book before trading takes place. In other words, traders will “build” the book before trading can take place.
Main Trading (120 seconds)
During the main trading period, all traders can enter bids and asks, and can also take the bids and asks posted by the other traders (i.e. orders can cross). In other words, during this period trading takes place. Executions follow price/time priority, meaning that orders at most competitive prices will be executed first. Orders at the same price level will be executed in following time priority; orders submitted first are also executed first.
The Security Value
The range of permissible prices in this trading session is between 0 and 100. The “true” value of the security is generated using a uniform probability distribution. In other words, all values within the 0 [
Remember that the security value and the market price are not necessarily the same thing. The market price is determined by the amount traders are willing to pay or accept (i.e. transactions), and may change as trading progresses during each trial. The security value is determined prior to the start of each trial and does not change during the trial.
Types of Traders
Informed traders
They know a narrow range for the “value” of the security, which they learn right before trading starts. They trade because they have valuable information regarding the security value. Therefore, they will earn a profit every time they buy (sell) a share at a price below (above) the security value. Information received by informed traders on their screen would look like this:
“INFORMED: Your value estimate is within 44 to 64”
They can enter a bid (order to buy) by entering a price and left-clicking on “Submit Bid.”
They can enter an ask (order to sell) by entering a price and left-clicking on “Submit Ask.”
They can take bids (sell at highest bid) by left-clicking on “Sell to Bid.”
They can take asks (buy at lowest ask) by left-clicking on “Buy at Ask.”
They can cancel individual or multiple orders.
Informed traders can see the market’s best bid and best ask as well as their own orders on the book, but they cannot see other trader’s orders. Also, they will be restricted to trading only one of the two available securities (either stock “HIACT” or stock “LOACT”). Information and trading functionality (i.e. the ability to buy or sell) will only be available for the one security they are able to trade.
Liquidity traders
They do not know the “value” of the security (i.e. they are “uninformed” traders). Therefore, they trade because of reasons other than information related to the security value. Liquidity traders are given a “target” number of shares they are required to trade before the end of the trial. This target does not need to be in the same direction for all liquidity traders. For example, some traders may be given a target of +20 (i.e. buy a net amount of 20 shares) while other traders may be given a target of −20 (i.e. sell a net amount of 20 shares). Typical information received by liquidity traders on their screen would look like this:
“LIQUIDITY: Your target is −20 shares”
They can enter a bid (order to buy) by entering a price and left-clicking on “Submit Bid.”
They can enter an ask (order to sell) by entering a price and left-clicking on “Submit Ask.”
They can take bids (sell at highest bid) by left-clicking on “Sell to Bid.”
They can take asks (buy at lowest ask) by left-clicking on “Buy at Ask.”
They can cancel individual or multiple orders.
Similar to informed traders, liquidity traders can see the market’s best bid and best ask as well as their own orders on the book, but they cannot see other trader’s orders. Also, they will be restricted to trading only one of the two available securities (either stock “HIACT” or stock “LOACT”). Information and trading functionality (i.e. the ability to buy or sell) will only be available for the one security they are able to trade.
Liquidity traders incur a penalty of LAB$ 100 per share for failing to achieve their targets (once they reach their targets they are allowed to trade freely without penalty). These penalties are large enough that liquidity traders are always better off trading enough to hit their target, even if they must buy at very high prices or sell at very low prices to do so.
Dealers
They do not know the “value” of the security. They trade to provide liquidity to the market. They earn a profit from selling shares at the ask price and buying them at the bid price. In other words, they earn the difference between their bid price and their ask price (i.e. bid-ask spread). The security value does not affect the dealer’s profit.
Inventory management is a very important task for dealers. Very high (low) inventories may indicate that the dealer’s bid and ask prices are too high (low). Dealers profit from both buying and selling shares. Thus, they should set their bid and ask prices so that they create an incentive for other traders to take their bids and asks. Furthermore, they should ensure that their ask price is always higher than the bid price.
Although dealers are not informed about the value of the security, they are the only traders with access to all market information (i.e. they can observe the orders of all traders on the book). Typical information received by dealers on their screen would look like this:
“DEALER”
They can enter a bid (order to buy) by entering a price and left-clicking on “Submit Bid.”
They can enter an ask (order to sell) by entering a price and left-clicking on “Submit Ask.”
They can take bids (sell at highest bid) by left-clicking on “Sell to Bid.”
They can take asks (buy at lowest ask) by left-clicking on “Buy at Ask.”
They cannot cancel/clear any bids or asks. Bids (asks) can only be “revised” or updated by entering new a new bid (ask) order.
Dealers cannot enter multiple orders (bids and/or asks) on the book because each new order submitted by the dealer will replace the old order: (i) entering a new bid will automatically cancel her old bid and (ii) entering a new ask will automatically cancel her old ask. In other words, dealers simply update their orders (i.e. they enter quotes).
Dealers can see the market’s best bid and ask as well as the all orders on the book. They may be able to trade both securities simultaneously (stock “HIACT” or stock “LOACT”). Information and trading functionality (i.e. the ability to buy or sell) may be available for both securities.57
Note that informed traders and dealers do not have a target. They can end the trial with any number of shares in inventory and will not be penalized.
Basic Trading Rules
Once you enter an order on the book, you can only trade if someone takes the other side of your order. For example, if you have entered a bid to the book at LAB$ 20, it will remain on the book until another trader takes it (or until you cancel the order).
You can’t trade with yourself. If you enter an ask at LAB$ 70 and then you enter a bid at LAB$ 75, these two orders will not cross and they will both remain standing on the book.
The maximum number of shares (i.e. quantity) you can enter per order is 1. However, you can enter multiple orders at any price point. For example, if you enter 3 separate bids (one share each) at LAB$ 52, you will have 3 shares listed at the LAB$ 52 price point.
Getting Paid
You will start each trial with a zero cash balance and a zero share balance. At the end of each trial, the shares you own (“n”) pay an amount (“V”) equal to the security “true value.”
If you have a positive share balance (n > 0), then “n × V” LAB$ will be added to your cash balance.
If you have a negative share balance (n < 0), then “n × V” LAB$ will be subtracted from your cash balance.
The resulting cash balance is your trading gain/loss in LAB$. Any penalties assessed for failing to hit your exact target are deducted from your resulting cash balance decreasing your gain or increasing your loss.
Note that this method of calculating gains and losses applies only to informed and liquidity traders. Dealers’ profits are not affected by the security value. Below is a detailed description of how gains/losses are computed for each trader type:
Ø Informed and liquidity traders make money every time they buy a share for less than the security value or sell a share for more than this value. For example, buying a share with a security value of LAB$ 43 at a price of LAB$ 39 creates a gain of LAB$ 4. Selling that share at a price of LAB$ 55 creates a gain of LAB$ 12.
Ø Dealers make money every time they sell a share for more than the price they paid for it. For example, buying a share at a price of LAB$ 25 and then selling it at a price of LAB$ 31 creates a gain of LAB$ 6. You may also sell a share first at a price of LAB$ 34 to then buy it back at a price of LAB$ 30 for a gain of LAB$ 4.
Remember that you do not “get paid” in laboratory dollars (LAB$). These LAB$ need to be converted into US$ before you can get paid. The conversion will be done using the following formula:
US $ Winnings = Baseline + ( YourLAB $ − Type − AverageLAB $ ) × ConversionRate
You will not know the exact baseline or conversion rate. However, we will tell you three key facts:
1. The type-average LAB$ refers to the average LAB$ for only those traders that are of your same type (i.e. informed traders, liquidity traders, or dealers).
2. The baseline is a positive US$ amount. If your LAB$ at the end of the trial is equal to the average LAB$ for traders of your type, you will earn an amount of LAB$ equal to the baseline.
3. The conversion rate is positive, meaning that the more LAB$ you win, or the fewer you lose, the more US$ you take home.
The parameters (baseline and conversion rate) are set so that the average US$ winnings of US$15 per person per session (not including the training session). Finally, US$ are determined separately for each trading session. Losses in one session do not offset gains in another session.
Other Rules
Please do not talk with other traders or look at their computer screens without explicit permission from the experiment administrator. Please ask the administrator before leaving the room for any reason.
The Trading Screen
The FTS Trading Screen is the first screen that appears once you have successfully logged into the market. It is important to be familiar with its elements before beginning to trade. However, if at any time during trading you cannot remember what a particular object on the screen does, simply place your mouse over the object and a mouse-over description will appear.
Below is a description of the different features you will find on the trading screen (refer to the figure below):
1. Bid―it displays the current best (highest) bid (listing the price/quantity). If there is an asterisk (*) next to the price/quantity it means that your bid is the current best bid.
2. Ask―it displays the current best (lowest) ask (listing the price/quantity). If there is an asterisk (*) next to the price/quantity it means that your ask is the current best ask.
3. Security Name―this is the name of the security that you will be trading on each trial. There could be multiple security names listed. By left-clicking on a security name, you will access the security’s market screen.
4. Last―it displays the last traded price. This box changes color according to how the last traded price changed relative to the previous last traded price.
5. Buy WVAP―it specifies the trader’s volume-weighted average buying price. It is simply the average price the trader has paid for all shares purchased. It updates in real time.
6. Sell WVAP―it specifies the trader’s volume-weighted average selling price. It is simply the average price the trader has received for all shares sold. It updates in real time.
7. Time Left―each trial specifies a particular amount of time. Once the market begins, Time Left will begin to count down. The time will reset after the completion of each trial (a bigger screen with the time left can be found on the upper right hand corner of the screen).
8. Information Window―here you will see information regarding the security value and the target number of shares that you are required to trade. This window will also display the type of trader you are.
9. Position―it specifies your current position in a security (i.e. the number of shares that you currently hold in inventory). If this number is positive, then you have a long position. If the number is negative this means you have a short position.
10. Realized P & L―it specifies the profit (or loss) of a trader based only on the shares that have been both purchased and then sold (or vice versa). It ignores any shares in the trader’s inventory. It updates in real time.
11. Trading Controls―this section contains the buttons/fields that will allow you to trade. The buttons may be different for different traders. Please refer to the “Types of Traders” section (above) for a detailed explanation of the trading controls. Remember that to enter a bid and an ask on the book, you must first enter a price and a quantity:
a) Price―here you can enter a specified price for your order (i.e. bid price or ask price).
b) Quantity―here you can enter a specified quantity for your order. In this case, however, the quantity can only be equal to 1.
12. Order Book―this book collects all orders entered by all traders in the market. The bid book (left half) displays all bids with the highest-priced bid listed first. The ask book (right half) displays all asks with the lowest-price ask listed first. The top-listed bid and ask are the market BBO (best bid/offer, where offer and ask are equivalent terms).
13. Bid/Ask/Price Graph―it provides a graph of the evolution of the best bid, best ask, and traded prices over the trading trial. It updates in real time.
How to Login
You must wait until the administrator tells you that you can login. Once he/she does, you must follow these steps to login successfully:
1. Double click on the “Launch FTS System Manager” icon on your desktop.
2. Check “Student Applications.”
3. Check “Download again before running.”
4. On the drop-down menu, select “FTS Trader 2013 Version.”
5. Click on “Run Selected Application.”
6. The following screen will appear:
Enter the information provided to you by the administrator on the “Market IP Address”, “Trading Name” and “Password” (if required) cells. Leave all other cells untouched.
7. Click on “Connect to the Market”
Note: if you click on “Connect to Demo Market” you can see and interact with a demo of the trading screen. You do not need to enter any information to connect to the demo version. In order to get to the trading screen shown above, you first need to left-click on the red/white icon located to the right of the security name. This may be a good tool to become familiar with the software before the trading session).
8. You should now see the main trading screen. Wait for further instructions from the administrator.