Glossary of Investor and Trader Types

First Version: May 2, 2019

Prepared by Cumhur EKINCI* (ekincicu@itu.edu.tr), Oğuz ERSAN, Nihan DALGIÇ and Abdullah KAYACAN

Supported by TUBITAK ARDEB Research Grant (Project No 117K908)

Any errors remain the responsibility of the authors. To contribute, please contact the corresponding author.

By Subject

By Alphabetical Order

Fast Trader

A trader who acts at a higher speed when compared to other agents in a market. A fast trader is considered to be at an advantageous position with respect to speed. This advantageous position arises from either technological capabilities or the ability to faster analyze and respond to advances in the market or both. Although a fast trader is not necessarily a high-frequency trader or collocated trader, the trader type is usually referred to one of these (See e.g. Hoffmann, 2014; Brogaard et al., 2015; Van Kervel, 2015). See also Slow Trader, High-frequency Trader.

Slow Trader

A trader who, when compared to other traders, is relatively slow in submitting orders. This fact most probably results in an unfavorable situation for the trader, since detected opportunities have already been realized by fast traders. Slow traders tend to be individual and unsophisticated traders (See e.g. Hoffmann, 2014; Brogaard et al., 2015; Van Kervel, 2015). See also Fast Trader.

Automated Trader

(or Computerized trading) Refers to the trader type performing automated trading. In automated trading, each step of the trading process, from order submission to execution, is automated. See also Electronic trading.

High-frequency Trader

(or Low Latency Trader) A trader who is engaged in heavy message traffic within very low latencies, i.e. microseconds or even nanoseconds. High-frequency trading is a subset of algorithmic trading. The concept is independent of an underlying strategy. It mostly concerns the technological capabilities of the trader which allows high speed in analyzing and finalizing an order. One large branch of HFT is market making activities which provide liquidity to the market. Other include trading on various arbitrage strategies.

Extensive amount of investment is made on technological developments enabling marginal increases in order submission speeds. For example, a 3 ms reduction in the information transmission time between Chicago and New York markets has been realized by an estimated cost of $500m (Laughlin et al., 2014). Through new micro-chips, trades can be sent in 740 billionths of a second (O’Hara, 2015).

Various academic studies report that up to more than half of the trading activity is generated by high-frequency traders in developed markets (see e.g. Brogaard et al. 2014; Brogaard, 2010; Hagströmer and Norden, 2013; Menkveld, 2014). According to O’Hara (2015), more than 98% of all orders are cancelled, a figure brought by massive numbers of order submission by HFTrs.

HFT has been broadly studied in the financial markets literature since its emergence in early 2000’s. Its impacts on the markets are examined with respect to various aspects such as positive and negative externalities of HFT on liquidity, volatility, price discovery and social welfare; the role of HFT through unfavorable market conditions; and HFT profits. See also Low-frequency Trader, Algorithmic Trader, Fast Trader.

Collocated Trader

Refers to the trader with a place in the colocation. Having computers at the same place with the exchange’s computer servers enables the collocated firms to access financial data at a marginally faster speed. The low latency difference between the accession times of ordinary traders and the collocated traders generates an asymmetric positioning. While this low latency advantage suggests that collocated traders are mostly HFTrs, trading of collocation may not fully be HFT. Acquiring a seat in the colocation is quiet costly even for HFT firms. Thus, firms may end up in performing their non-HFT activity from the colocation as well. Similarly, not all the HFT activity takes place in the colocation. Aitken et al. (2014 and 2015) study the introduction of colocation services and HFT emergence in various countries. See also High-frequency Trader.

Low-frequency Trader

(or High latency Trader; or nonHFTr) A trader who is not engaged in high-frequency trading. In current financial markets, most of the trading activity excluding the one from HFT firms’ can be attributed to low-frequency traders. On the contrary to ‘HFT’, the term is not a commonly used one. The term ‘nonHFTrs’ usually replaces the term in terminology. See also High-frequency Trader.

Human Trader

(or Traditional Trader) Human traders, on the contrary to so called robot traders, algorithms or algos, are traders who perform trades by personally intervening in the analysis of current status, decision making and order submission / or execution. While machine learning algorithms continuously get stronger, human traders still preserve advantages in decision making processes. On the other hand, effects of various behavioral biases previously documented for humans may be reduced in the case of robots. See also Algorithmic Trading.

Robot Trader

A robot trader replaces a traditional trader in the sense it analyzes available information and trades based on certain rule sets. Psychological difficulties in trading is reduced via the use of a robot trader. On the other hand, rule sets and criteria to trade should be pre-determined with care since spontaneity is traded-off.

Off-floor Trader

A trader submitting orders via off-floor terminals. On the contrary to on-floor transactions which are conducted through the terminals in the trading halls, off-floor transactions occur off the trading halls, i.e. broker offices, off-floor terminals. See e.g. Campbell et al. (1991), Ding et al. (2014) and Easley et al. (2014) for further insight.

Electronic trading

On the contrary to earlier forms of trading such as paper-based trading and phone trading, electronic trading organizes the trading platform such that the trading process is conducted electronically. Electronic trading yield to immensely increased turnover and liquidity; more transparency and lowered transaction costs. See e.g. Angel et al. (2015). As opposed to floor trading, most electronic trading platforms are anonymous in the sense they do not provide information on trader identity. However, these systems offer insight into limit order book (Franke and Hees, 2000; Grammig et al., 2001). See also Automated Trading.

Asymmetrically Informed Trader

(or Heterogeneously Informed Trader) An asymmetrically informed trader, when compared to other traders in the market, has a different level of exposure (a superior access in the common use) to the information. In this sense, the trader type is similar to informed trader. However, the existence of various asymmetrically informed traders may imply the heterogeneity among the informed traders as well. See also Informed Trader.

Manipulator

(or Deceptive Trader) Illegal trading to maintain artificial prices. Layering, Quote stuffing, spoofing are considered as manipulative trading. See also Quote Stuffer, Spoofing.

Discretionary Liquidity Trader

(or Strategic Liquidity Trader) A strategically acting liquidity trader who plans the timing and amount of liquidity provided. The game between discretionary liquidity traders and informed traders has various aspects. A main one relies on the liquidity traders’ attempt not to affect the prices. Those liquidity traders would prefer times when the market is thick. Thus, discretionary liquidity traders tend to trade together resulting in market concentration. However, informed traders also tend to trade in these times when it is harder to discover the impact of their private information. Consequently, discretionary liquidity traders leave the market in the conditions of higher information asymmetry (Admati and Pfleiderer, 1988). See also Informed Trader, Liquidity Trader and Non-discretionary Liquidity Trader.

Nondiscretionary Liquidity Trader

Liquidity trader with no intension to strategically determine the timing and amount of the liquidity he/she will provide. See e.g. Admati and Pfleiderer (1988) and Foster and Viswanathan (1990) for theoretical applications. See also Liquidity Trader and Discretionary Liquidity Trader.

Misinformed Trader

A trader trading with an invaluable private information in the sense the information is irrelevant, biased or misinterpreted. Misinformed trader is usually modelled as the agent who act based on invalid signals (see e.g. Subrahmanyam, 1996). See also Informed Trader.

Habit Investing

The type of trading that arises due to the correlated trading activity of institutional investors in securities with certain characteristics. Since, these securities are highly attractive for various institutional investors, the securities face concentrated trading activity in specific time periods. Sias (2004) defines habit investing as “a special case of characteristic herding because institutional investors follow each other into and out of the same stocks (herd) as a result of their attraction to securities with the same characteristics that cause them to hold similar portfolios” The concept is closely related to the institutional herding studies. (See e.g. Sias, 2004; Gavriilidis et al., 2013; Economou et al., 2015). See also Parallel Trading.

Information-oriented Technical Trader

As Gradojevic and Gençay (2013) refer to Harris (2003), technical trading may be profitable when informed traders make systematic mistakes. Technical traders acting on detected mistakes of informed traders are then information-oriented technical traders. Gradojevic and Gençay (2013) claim that profit opportunities are quiet low because informed traders usually learn from past actions and correct existing mistakes. See also Technical Trader, Informed Trader.

Monopolist Informed Trader

An informed trader is monopolist in case there is no other informed trader in the market. Two cornerstone theoretical studies in the market microstructure literature differ with respect to the existence of a monopolistic nature in the private information. Kyle (1985) model assumes a monopolistic informed trader while Glosten and Milgrom (1985) incorporate competition among multiple informed traders. When there is no competition in the market, various variations occur in the impacts of private information. For example, Back et al. (2000) suggest that financial markets would be more efficient when there is a monopolist informed trader. This is closely related to the literature on the competition between informed traders and overall information asymmetry in markets. See also Asymmetrically Informed trader, Informed Trader.

Poorly Informed Trader

A trader who trades based on a signal with low precision and/or high uncertainty. The trader acting on the private information differentiates from an uninformed trader, however, the quality of private information is low which tends to result in a disadvantageous position. Poorly informed traders are prone to herd since the lack of quality in their private information may require exogenous contribution. The terms poorly informed and well informed traders can also be alternatively used for uninformed and informed traders in some studies. See also Misinformed Trader, Informed Trader.

Irrational Informed Trader

Vast majority of studies in the relevant literature assume informed traders to be rational. Few studies distinguish between rational and irrational informed traders. Namely, the irrational ones receive private information, however, exhibit behavioral biases such as overconfidence and overreaction (or under reaction) (See e.g. Wang, 1998; Gong and Liu, 2016). See also Informed Trader.

Patient Trader

A patient trader’s one concern is the timing (and cost) of trading activity in addition to prices. A patient trader’s preference is towards the periods when the market is more liquid. One goal or a potential utility of patient trader is to reduce transaction costs (Chacko et al, 2008). The type of trader is frequently studied within the liquidity and transaction costs topics in the literature. See also Impatient Trader, Long-term Trader.

Naive Trader

A naive trader is associated with unsophisticated trading behavior. For example acting on rumors (Eren and Ozsoylev, 2006) or not to infer information from observing equilibrium prices (Jennings and Barry, 1983). See also Unsophisticated Trader.

News Trader

News trading can refer to the trading activity based on new public information arrival (See e.g. Black, 1995; Meyer et al., 2013). The term can also replace (private) information based trading. See also Informed Trader.

Front-runner

Front running is to step in front of large orders with a goal of short term profit. It is based on the anticipated price effect of upcoming large orders. Two main types of front running are the practices of brokers and traders. Front running brokers act on the information they provide from their clients, i.e. requests of large order submissions. This type of front running is illegal. A second type of front running can be performed by traders relying on announced or anticipated large trades of fund and portfolio management firms. This type of front running also targets positive profits, while being legal. High-frequency traders are in advantageous position in anticipating and front running large orders (Harris, 2013). A front-runner, acting on the revealed information on upcoming trades, is a parasitic trader. See also Parasitic Trader, Short-term Trader, High-frequency Trader.

Non-speculative Trader

A non-speculative trader trades with non-speculative purposes, i.e. liquidity needs, rebalancing and tax purposes. Pure non-speculative traders are prone to losses against better informed traders when they need to trade for these purposes (See Harris, 2003). See also Speculator, Liquidity Trader.

Parasitic Trader

Harris (1997) defines parasitic traders as traders who seek profits by exploiting the revealed information of other traders. Harris (1997, 2003) claim that parasitic traders do not provide liquidity nor contribute to price efficiency.

Noise Trader

A trader who does not trade with fundamental values regarding an asset price. Introducing the concept, “Noise”, Black (1986) explains noise trading as relying on the existing noise in the market rather than information. In other words, “trading on noise as if it is information”. As Black (1986) points out, noise trading substantially adds to the trading volume, but more importantly, it enables the overall trading mechanism by incorporating uncertainty with respect to information asymmetry in the market.

Insider Trader

Insider trading is the trading activity performed by an agent that is based on a firm-specific information that she has access to. The type of insider trading that is widely studied is the illegal one, i.e. agent’s profit motivated trading activity prior to the point in time the information becomes public. Insider trading frequently occurs prior to firm-specific announcements. Large body of literature examine time periods surrounding these announcements (See e.g. John and Lang, 1991 for dividend announcements; Keown and Pinkerton, 1981 for M&A announcements; Karpoff and Lee, 1991 for new issue announcements). Insider trading is a type of informed trading in the sense an insider trades with a superior private information. See also Informed Trader.

Liquidity Trader

(or Liquidity-based Trader; or Liquidity-motivated Trader) On the contrary to trades of informed, trades of a liquidity trader have liquidity purposes, i.e. either to liquidate positions or make new investments. In contrast to non-discretionary liquidity traders, discretionary liquidity traders act strategically to set the timing and the amount of liquidity traded. Harris (2003) explicitly differentiates between liquidity traders and profit motivated traders. Theoretical models such as the one of Easley-O’Hara (1987) assume uninformed traders as liquidity traders. See also Informed Trader, Uninformed Trader, Discretionary Liquidity Trader, Non-discretionary Liquidity Trader.

Background Trader

In contrast to the profit-oriented traders who seek trading profits, background trader refers to the investors in the market (see Wah and Wellman, 2016).

Dark Trading

Dark trading refers to trading by preventing impacts of trade on the market. At the same time, it enables traders to hide their intention and information until the execution time with no pre-trade transparency. Share of dark trading is substantially high and growing higher (from 17% in 2008 to 37% in 2014, presented by Comerton-Forde and Futnins, 2015). Recently, trade execution algorithms have mitigated the impacts of large block trades (Comerton-Forde and Futnins, 2015).

Limit Order Trader

Trading with placement of limit orders provide liquidity and immediacy to the market (Glosten, 1994; Handa and Schwartz, 1996). See also Price-contingent Trading, Patient Trader.

Spread Trader

A spread trader can refer to a trader in a transaction of an option spread. Alternative definition is a trader who seek to exploit mispricing in spreads.

Contrarian Trader

A trader with a contrarian trading strategy invests in underperforming assets with the expectation of a reversal. A contrarian trader may search for value stocks or high book-to-market and low P/E ratio stocks. See also Momentum Trader.

Momentum Trader

(or Trend-following Trader) A momentum trader has the expectation of the continuity of a trend. Momentum strategy focuses on growth stocks or low book-to-market and high P/E ratio stocks See also Contrarian Trader.

Positive Feedback Trader

A positive feedback trader trades upon receiving a positive feedback from the market. Specifically, the trader buys (sells) observing a rise (decline) in the market. This type of trading is closely related to momentum trading. However, rather than a systematic trading strategy it is more likely to be associated with herding behavior. See also Momentum Trader, Negative Feedback Trader.

Negative Feedback Trader

Negative feedback trader can be considered a contrarian trader. The trader acts in the opposite of market direction. See also Contrarian Trader, Positive Feedback Trader.

Informed Trader

A trader who trades with a private information reflecting a qualified position with respect to existence of information. This can either result from the privileged (or early) access to information (insider trading) or better skills to analyze and interpret new information. In both cases, arising information asymmetry generates a comparative advantage on the side of informed traders. Informed trading is also referred as information based trading and trading with private information in the literature.

There is a broad branch of studies in the market microstructure literature regarding the nature of information asymmetry, measurement and the impacts of informed trading. Among others, Probability of Informed Trading (PIN) measure developed by series of papers (see e.g. Easley and O’hara, 1987, 1992; Easley et al., 1996) has been extensively used. See also Uninformed Trader, Liquidity Trader.

Uninformed Trader

The trader type is commonly used in theoretical papers to represent traders other than the informed traders. Those traders are attached with a liquidity purpose of trading. The distribution among informed and uninformed traders, as well as the number of informed traders and quality of private information determine the nature of information asymmetry. See also Informed Trader, Liquidity Trader, Monopolist Informed Trader.

Market Maker

A market maker provides liquidity to the market in the sense of showing up as a counterparty in trades when a buyer or a seller of an asset arrives to the market. A market maker bears inventory risk which refers to a potential loss from a change in the price of the asset through the holding period. This is compensated by the existence of a bid ask spread which enables the market maker to buy low and sell high. Market makers are market participants who are ready to buy and sell financial instruments in order to provide liquidity in the market (Bagehot, 1971). They are usually large financial institutions such as banks and brokerage houses due to the size of securities needed in order to fill buy and sell orders. See also Specialist, Supplemental Liquidity Provider, Broker, Dealer.

Parallel Trading

Parallel trading may refer to the correlated trading in financial markets, e.g. by certain types of investors. For example, Kraus and Stoll (1972) define the similar trading behavior and timing of institutional investors as parallel trading. Parallel trading may also refer to the trading at the hours when there multiple platforms to trade same asset exist, e.g. the study of the simultaneous trading hours of electronic trading platform, Globex and floor trading in Tse and Bandyopadhyay (2006). See also Habit Investing.

Predatory Trading

Trading which targets to exploit the needs of other traders liquidizing their positions. In case of anticipation of selling activity of other traders, predatory traders may sell as well prior to buying back at lower prices. This potentially results in a profit of predatory trader on the cost of liquidizing traders (Brunnermeier and Pedersen, 2005).

Price-contingent Trading

Conditional trading that is based on pre-specified price levels. Osler and Savaser (2011) state related four properties as: high kurtosis in order size distribution; clustering of trades within days and at certain prices; feedback between trading and returns. Easley and O’Hara (1991) argue that price-contingent orders may lead to large price movements and their existence result in faster update of prices and larger spreads by market maker. See also Limit Order Trader, Algorithmic Trading, Technical Trader.

Profit Motivated Trader

A profit motivated trader is expected to trade solely (or mainly) with the motivation of profit from the trade. Harris (2003) defines two main types of traders as profit motivated traders and utility traders. On the contrary to profit motivated traders, utility traders aim to obtain a benefit other than trading profits. For example, liquidizing positions, investing in preferred assets or hedging.

Quant Trader

(or Quantitative Trader) A trader who trades with quantitative and computational skills and knowledge. Quant traders potentially; rely on bulk amount of information in the market, use mathematical models to analyze and interpret and may benefit from algorithmic trading and more specifically high-frequency trading. See also Algorithmic Trading, High-frequency Trader.

Quote Stuffer

A quote stuffer places bulk amount of orders with the main goal of slowing the latency of electronic system. The placed orders tend to have prices far from best bid and ask with the intension of occupying a place rather than execution (O’Hara, 2014; Jain and Jordan, 2017). See also Manipulator.

Spoofing

(or Quote Dangling) Placing and instantaneously canceling limit orders with the intension of shading the quote process (O’Hara, 2014). See also Manipulator.

Short Seller

A trader who with the goal of profiting from a price fall applies short selling, i.e. selling an asset that is borrowed.

Buy Side Investor

Buy side investors are assumed to be the firms, e.g. fund management firms, buying assets for their own or on behalf of their investors, relying on various strategies. See also Sell Side Investor.

Sell Side Investor

On the contrary to buy side investors, sell side investors attempt to derive strategies and recommendations to share with clients, so that they can invest. Brokerage firms and investment banks are considered as sell side investors. See also Buy Side Investor.

Small Trader

(or Lightly Capitalized Trader; or Under-Capitalized Trader; or Low-volume Trader; or Low-Income Trader) Small traders are characterized with regard to the value of their trading activity; their investments’ value is relatively low (Lee, 1992). Thus, they can be classified according to a value-based threshold approach that takes price movements into consideration. Small traders may also refer to a group of investors who hold positions that are less than the required reporting thresholds designated by the relevant securities exchange. See also Odd-lot Trader, Medium-sized Trader, Large Trader.

Medium-sized Trader

Medium-sized traders cover all market participants whose trade value falls between that of small and large traders. Therefore, their classification depends heavily on the cut-off points that determine small and large trader classes. Occasionally, medium-sized trades are regarded as a “buffer-zone” between the trades of large and small investors since the former is unlikely to place very small buy or sell orders due to higher transaction costs but may split their trades in order to disguise their identity (Bhattacharya, 2001; Lee and Radhakrishna, 2000; Miller, 2010). See also Small Trader, Large Trader.

Large Trader

(or Highly Capitalized Trader) Large traders represent the portion of market participants who execute relatively high value trading activities. They hold substantially large positions exceeding the threshold specified by the relevant securities exchange; as such large traders are required to report their trading activities (https://www.investopedia.com). Large traders are likely to mirror a group of traders dominated by more sophisticated institutional investors (i.e. funds). On the other hand, many institutional traders enact trades under the large trade threshold (Kryzanowski and Zhang, 1996). See also Medium-sized Trader, Small Trader, Institutional Trader.

Odd-lot Trader

Odd-lot traders are small traders: the amount of their order for a particular security is below the required reporting thresholds set by the relevant securities exchange. Odd-lot trades were essentially associated with individual traders who buy and sell securities for their personal account and therefore, were assumed to have little information about future price variations ( O’Hara et al., 2014). Generally, those trades are not reported on publicly available real-time trading volume and price data but are detectable in proprietary feeds purchased by sophisticated traders (https://www.sec.gov/marketstructure/research/). However, odd-lot trades may form a considerable fraction of trades in certain financial markets due to very high-priced securities or algorithmic trading practices and thus, may contain higher level of information than presumed. See also Small Trader, Individual Trader, Retail Trader, Algorithmic Trader.

High-turnover Trader

(or Volume Trader). High-turnover traders buy and sell securities more frequently compared to low-turnover traders over a certain period. Research show that they are outperformed by low-turnover traders (Bushee, 1998). Barber and Odean (2000) suggest that investors’ portfolio return diminishes as their turnover increases. In the same paper, traders’ high turnover is also related to a well-known behavioral bias: overconfidence. See also Low-Turnover Trader, Overconfident Trader.

Low-turnover Trader

Low-turnover traders buy and sell securities less frequently compared to high-turnover traders  during a specified period. Research indicate that low-turnover trading by institutional investors reduces their myopic investment behavior (Bushee, 1998). See also High-Turnover Trader, Institutional Trader, Myopic Trader, Small Trader.

Large Block Trader

(or Block Trader) Large block traders buy or sell securities in large quantities. Individual investors are less likely to make a large block trade since it involves significantly large number of securities. Thus, large block trading is mostly linked to institutional investors (i.e. funds) and may have a considerable price impact on the market if the trading volumes become very large (Yin, 2016). See also Large Trader, Individual Trader, Institutional Trader, High-Turnover Trader.

Domestic Trader

(or Local Trader) Domestic traders represent individual and institutional investors who buy and sell securities in their home country. A bulk research investigate whether domestic traders possess informational superiority over foreign traders. The degree of this advantage may vary with regard to the level of relevant market’s transparency. For instance, Ferreira et al. (2017) find an information advantage of domestic institutional investors in more opaque countries where the official language is not English. On the other hand, their findings point out to equally well performing portfolios of both domestic and foreign traders on average. Academic studies reach mixed results regarding the performance of domestic and foreign traders (Dvořák, 2005; Choe et al., 1999). Seasholes (2004) argue that foreign investors perform better than domestic investors. Swan and Westerholm (2017) examine the trading activities of domestic individual traders versus foreign investors in Finland and they find that the former outperforms the latter. They also conclude that, in this case, it would be incorrect to categorize individual domestic traders in Finland as noise traders. See also Foreign Trader, Noise Trader.

Foreign Trader

(or Overseas Trader) Foreign traders include individual and institutional traders who buy or sell securities in a country other than their home country. Research usually suggest that foreign institutional traders are more sophisticated than domestic investors but have a disadvantage on the information gathering side (Covrig et al., 2006; Dahlquist et al., 2002). Hence, they tend to invest on larger stocks with worldwide recognition or low cost of information acquisition. Foreign traders are also more likely to favor momentum strategies of buying past winners and selling past losers (Lin and Swanson, 2003; Grinblatt and Keloharju, 2001). See also Momentum Trader.

Cross-market Trader

(or Multimarket Trader) Cross-market trader refers to any person or entity investing in multiple markets (Büyükşahin and Robe, 2014; Goyenko and Ukhov, 2009; Chng, 2009). In other words, if a trader is active in both commodity and stock markets he/she can be considered as a cross-market trader. Investing in both domestic and foreign markets may also account for cross-market trading activity.

Individual Trader

(or Retail Trader) Individual traders, also referred to as retail traders, buy or sell securities for their personal accounts. They can be classified as small investors since they are more likely to invest in smaller amounts in comparison with institutional traders. Individual traders generally underperform standard benchmarks, tend to display the characteristics of disposition effect (hold losing securities and sell winning ones), and are likely to hold undiversified portfolios (Barber and Odean, 2013). A bulk of research examining herding level of investors point out to irrational behavior-driven herding of individual traders (Hsieh, 2013). See also Institutional Trader, Small Trader.

Institutional Trader

Institutional traders buy or sell securities for a company, a group or an organization. They typically are concentrated in developed markets. They trade larger volumes in comparison with individual traders. Institutional traders are professional, experienced, sophisticated and/or better-informed investors as such it is expected that they trade in a more rational behavior-driven manner. However, reputational concerns may lead institutional traders to ignore market signals and follow the crowd (Scharfstein and Stein, 1990). See also Individual Trader, Sophisticated Trader, Experienced Trader, Informed Trader, Large Trader.

Professional Trader

Professional trader may refer to any person, corporation, clearing house, proprietorship, or partnership authorized to give investment services. In contrast to professional traders, private investors represent any person who trade securities for his/her own account, without providing investment services. See also Individual Trader.

Sophisticated Trader

(or Seasoned Trader; or High-ranking Trader) Sophisticated trader refers to investors having more experience and/or a better skill set to analyze investment opportunities compared to unsophisticated (or unskilled) traders. Sophisticated traders include financial analysts as well as institutional traders (Anderson, 1981; Day, 1986). Although sophisticated investors may be associated to trading activities in large quantities, they may also split their orders in order to disguise their identity. Investor sophistication doesn’t increase monotonically with the size of trade (Diether et al., 2005; Barclay and Warner 1993). Thus, both total trading activity and invested capital may be good proxies to measure investor sophistication (Hirshleifer et al., 2008). See also Experienced Trader, Institutional Trader, Unsophisticated Trader.

Unsophisticated Trader

(or Beginner Trader; or Inexperienced Trader; or Unskilled Trader) Traders with limited knowledge about the factors influencing prices. Unsophisticated traders have little trading skills and/or experience. See also Individual Trader, Small Trader, Sophisticated Trader.

Proprietary Trader

Proprietary trader refers to financial institutions such as investment banks and brokerage houses which trade utilizing the firm’s own capital. Consequently, those traders expect to generate more profit from the market in place of earning from commission fees. Fecht et al. (2016) investigate the conflict of interest faced by institutions offering investment services to their customers besides practicing proprietary trading activities. The results indicate that the clients of those institutions generate less profit in comparison with their peers in the long-term. See also Institutional Trader.

Interbank Trader

Interbank trader tries to find trading opportunities of any mispriced currencies, readjusts his/her position quickly and at a low cost (Puksamatanan, 2010). Some interbank trading occurs among banks and financial institutions on behalf of large customers. However, interbank trading is mostly proprietary, meaning that it utilizes the banks' own capital. Interbank trading requires large value transactions. Hence, very large portion of all foreign exchange transactions consists of interbank trades. See also Proprietary Trader.

Privileged Trader

In financial markets, a limited number of traders hold special privileges such as detailed and rapid access to order flow information or market making. Friedman (1993) states that those privileges are awarded to some traders in order to overcome a public goods problem. According to the results of his experimental research, call market rapid order flow access and last-mover privileges prove profitable and those privileges does not decrease market performance. Rapid and detailed access to order flow information in continuous market constitute privileges that are profitable and increase market performance. On the other hand, market making is a very profitable privilege but it diminishes market performance. See also Market Maker.

Mutual Fund Trader

A mutual fund trader represents a securities investment trust company that raises funds from many investors and trades domestically. O’Neal (2004) shows that short-term mutual fund traders tend to invest in higher risk mutual funds. Greene and Hodges (2002) suggest active mutual fund traders generate return dilution in international open-end mutual funds due to the difference between the time funds from investors arrive and the time those funds are invested in risky assets. See also Large Trader, Institutional Trader, Sophisticated Trader.

Bullish Trader

(or Optimistic Trader) Bullish traders refers to optimistic investors who have a positive opinion on the market or on a security and consequently, they may favor buying (Ulibarri et al., 2009; An et al., 2014). The terms “bullish trader” and “optimistic trader” are generally used interchangeably in the literature. Bullish traders may suffer from “bull traps” since it is challenging to distinguish an upward reversal from a bull trap. See also Bearish Trader.

Bearish Trader

(or Pessimistic Trader) A bearish trader believes that the return of a security will be below average (Brown and Cliff, 2004). Bearish traders may also represent investors who expect a downward direction for the market as a whole. The terms “bearish trader” and “pessimistic trader” are used interchangeably in the finance literature. Bearish traders may favor short positions since they seek profit from the decrease in securities’ or market’s value. See also Bullish Trader.

Bayesian Trader

Bayesian traders make inferences and form their portfolios by using the features of Bayesian investment analysis. They are better at detecting the relative quality of portfolios during downturns compared to that of during upturns. Schmalz and Zhuk (2018) show that Bayesian risk-averse traders react more to news during downturns if they are uncertain about a security’s future cash flows and risk loadings. See also Risk-averse Trader.

Biased Trader

Biased trader refers to any investor deviating from his/her original trading strategy or plan due to cognitive biases. Those biases are mostly linked to irrational behavior-driven trading patterns. The biases which traders face include but not limited to overconfidence, overreaction, herding, anchoring and conservatism. Kilicay-Ergin et al. (2012) consider an agent-based financial model and find that biased traders affect price dynamics. See also Irrational Trader, Rational Trader, Overconfident Trader, Overreacting Trader, Herding Trader, Conservative Trader.

Rational Trader

(or Unbiased Trader) Rational traders make investment decisions in order to obtain optimal portfolios in accordance with the risk or reward level of their preference (Ajayi and Mehdian, 1994; Dumas et al., 2005). Most of the conventional financial models assume investor rationality. However, behavioral finance considers emotional and behavioral biases of traders which lead them to making irrational behavior-driven financial decisions. See also Irrational Trader.

Boundedly Rational Trader

(or Rule-of-thumb Trader) Boundedly rational trader refers to market participants who have incomplete information, limited cognitive capacities and insufficient information processing skills (Kukacka and Baruník, 2016). Fundamentalists are often categorized as rational traders but sometimes regarded as boundedly rational long-term traders while chartists utilize heuristics and general principles resulting from experience and practice (in other words, rules-of-thumb) and are considered as boundedly rational speculators (Fritz et al., 2015). See also Fundamental Trader, Rational Trader, Long-term Trader, Speculator, Noise Trader.

Irrational Trader

Irrational traders are subject to cognitive biases such as overconfidence, overreaction, herding, anchoring, conservatism, and disposition. Kogan et al. (2006) find that irrational traders affect price dynamics even if they do not take substantial positions. Ma et al. (2017) investigate the causes of market crashes and conclude that the irrational behavior of traders have a more serious impact on market crashes compared to fundamental factors. See also Biased Trader, Rational Trader.

Disposition Trader

Disposition traders tend to hold losers too long and sell winners too early (Shefrin and Statman, 1985). Research indicate that the disposition effect can cause price momentum (Grinblatt and Han, 2005; Shumway and Wu, 2005). The link between prospect theory -which states that under risk and uncertainty investors make their decisions based on potential values and favor potential gains over potential losses- and the disposition effect is the subject of many academic studies as well. However, the results are mixed probably due to certain assumptions in the proposed equilibrium models. For instance, Barberis and Xiong (2009) investigate two models, namely annual and realized loss/gain models and conclude that the former fails but the latter can predict the disposition effect. On the other hand, Li and Yang (2013) consider a full equilibrium model and find that if traders make their decisions based on prospect theory, this situation generate a disposition effect.

Overconfident Trader

Overconfident traders are overly optimistic investors who put too much weight on the precision of their information signals (Kirchler and Maciejovsky, 2002). Chuang and Susmel (2011) investigates the behavior of individual and institutional investors in the overconfidence bias framework and show that the former investor group proves more overconfident compared to the latter. Chou and Wang (2011) examine overconfidence and disposition biases in various types of traders and their results are in line with Chuang and Susmel (2011)’s finding; both biases are more pronounced in individual traders trading behavior. See also Optimistic Trader, Individual Trader, Institutional Trader, Disposition Trader, Biased Trader, Unsophisticated Trader.

Overreacting Trader

Overreacting traders overbuy or oversell securities due to their emotional responses to new information. Mahani and Poteshman (2008) suggest that, in options market, traders who overreact to new information about the underlying securities are unsophisticated investors. Research also suggest that there are two main causes of investor overreaction bias: under-reaction (Hong and Stein, 1999) and overconfidence (Daniel et al., 1998). See also Unsophisticated Trader, Overconfident Trader, Biased Trader.

Dumb Trader

(or Dumb Money Trader; or Foolish Trader) Dumb traders are average traders who do not have time, skills, experience and patience to analyze investment opportunities appropriately. Hence, dumb money is mostly sourced from individual traders and is assumed to move prices away from fundamental values. Akbas et al. (2015) utilize mutual fund flows as a proxy for dumb money and they find that the latter intensifies growth, accrual and momentum anomalies. See also Unskilled Trader, Inexperienced Trader, Impatient Trader, Unsophisticated Trader.

Smart Trader

(or Smart Money Trader) Smart traders are usually institutional investors. They are sophisticated traders who have a better understanding and skill set to analyze investment opportunities. Akbas et al. (2015) utilize hedge fund flows as a proxy for smart money and they find that the latter contributes to the correction of cross-sectional mispricing. See also Institutional Trader, Sophisticated Trader.

Herding Trader

Herding traders ignore their own private information and instead, they follow the crowd. Research suggest that the two main underlying features of herding are rational (unintentional) and irrational (intentional) behavior. Herding traders may destabilize prices if their trading behavior is irrational in nature (Bikhchandani et al., 1992; Kremer and Nautz, 2013). Irrationally herding trader’s concentration can be different in developed and emerging markets: the market value of institutional traders is generally greater in developed markets (Angela-Maria et al., 2015; Hsieh et al., 2011) and those traders are mostly expected to show the characteristics of rational behavior driven herding. See also Rational Trader, Irrational Trader, Biased Trader.

Heuristic-driven Trader

Heuristic-driven traders make quick decisions and utilize heuristic methods such as trial error, intelligent guesswork, and elimination when they buy and sell securities. Ormos and Timotity (2016) define heuristic-driven traders as contrarians and introduce a model where informed, uninformed and heuristic-driven traders coexist. Their results suggest that the probability of heuristic-driven trading remains constant over time. See also Contrarian Trader, Informed Trader, Uninformed Trader.

Impatient Trader

Impatient traders are short-term traders who require immediate trade execution; they tend to place market orders. See also Patient Trader, Short-term Trader, Myopic Trader.

Myopic Trader

(or Short-sighted Trader) Myopic traders, also referred to as short-sighted traders, focus too much on short-term outcomes of investment opportunities and disregard long-term value information (Tirole, 1982). Bhushan et al. (1997) examine myopic trader models and find that traders’ myopia is not a necessary or sufficient condition to generate noisy prices. See also Short-term Trader, Impatient Trader.

Sentiment-based Trader

(or Sentiment-oriented Technical Trader) Sentiment-based traders make their buy or sell decisions mostly with the help of technical indicators and based on common price patterns. Individual traders are considered as natural candidates for this type of investor (Kumar and Lee, 2006; Stambaugh et al., 2012). On the other hand, DeVault et al. (2014) find evidence that institutional investors constitute sentiment-based traders assuming commonly utilized sentiment indicators are able to capture investor sentiment. See also Individual Trader, Institutional Trader.

Conservative Trader

(or Risk-averse Trader; or Defensive Trader) Conservative traders prefer to bear low level of risk when making investment decisions. Thus, they may avoid high volatility securities and tend to hold mostly less risky T-bills compared to aggressive traders. Brandouy et al. (2012) show that, in the presence of short selling, the earnings of both conservative and aggressive traders are not the highest. On the other hand, traders who tend to bear a moderate level of risk are most likely to survive in the long-run. See also Aggressive Trader.

Aggressive Trader

(or Risk-lover Trader; or Offensive Trader) Aggressive traders prefer to bear high level of risk when making investment decisions. Thus, they may avoid low risk T-bills and tend to hold mostly volatile securities in their portfolios (Brandouy et al., 2012). They also are most likely to implement active trading strategies. See also Conservative Trader, Active Trader.

Risk-neutral Trader

Risk-neutral traders are insensitive to the riskiness of an investment opportunity and can be situated in the middle of two extreme levels of risk bearing trader types: risk-lovers and risk-averse traders. They make their decisions based on the expected returns and not the riskiness of investment opportunities (Routledge et al., 2000). See also Conservative Trader, Aggressive Trader.

After-hours Trader

(or Round-the-clock Trader; or Evening Trader; or Extended Hours Trader) After-hours traders buy and sell securities directly between themselves outside (before or after) of an exchange’s standard trading hours and through an automated system. However, it does not mean 24-hours trading. After-hours trading is important since most of the quarterly earnings announcements occurs outside of the regular trading hours (Jiang et al., 2012). It is argued that after-hours traders mostly consist of informed traders (Barclay and Hendershott, 2003; Barclay and Hendershott, 2008). This should lead to quick price adjustments. However, Li (2016) shows that new information is reflected in prices slowly in after-hours trading. See also Informed Trader, Intraday Trader.

Intraday Trader

(or Day Trader) Intraday traders sell the securities they have bought on the same trading day (or vice versa). Kuo and Lin (2013) investigate the behavior of individual day traders and find that day traders are more experienced individual traders but they are overconfident in their trading activities. See also Individual Trader, Experienced Trader, Overconfident Trader, Short-term Trader, Active Trader.

Carry-forward Trader

(or Overnight Trader) Carry-forward traders do not close their positions on the same day and carry them forward overnight. Riedel and Wagner (2015) show that overnight tail risk component is greater than that of intraday. See also Intraday Trader, Long-term Trader.

Long-term Trader

(or Buy-and-hold Trader; or Long-horizon Trader) Long-term traders buy securities and hold them for a prolonged time horizon (weeks, months…etc.). They are mostly small or medium-sized traders who do not have time or skills to be actively and frequently involved in trading activities. Long-term traders are mostly concerned with fundamental values (Müller et al., 1997). They also pay attention to large price fluctuations which only occurs at low frequencies (Gençay et al., 2010). See also Small Trader, Medium-sized Trader, Active Trader, Fundamental Trader, Short-term Trader.

Short-term Trader

(or Short-horizon Trader) Short-term traders buy and sell securities at a high frequency such as minutes or days. Yan and Zhang (2007) show that short-term traders who buy and sell securities more actively are better informed and their predictive power is better than the long-term traders. Cella et al. (2013) indicate short-term traders put pressure on prices during negative market-wide shock periods. Cremers and Pareek (2016) find that short-term traders are associated with momentum and subsequent return reversal anomalies. See also Active Trader, Long-term Trader.

Round-trip Trader

(or Wash Trader) Round-trip traders buy and sell same security frequently and inflate the trading volume with the purpose of manipulating the market. Thus, such practices are illegal. See also Illegal Trader, Pattern Day Trader, Intraday Trader.

Pattern Day Trader

Pattern day traders are described as “investors who trade the same stock four or more times in five business days” (Kuo and Lin, 2013). See also Round-trip Trader.

Occasional Trader

Occasional traders invest in securities less frequently compared to regular traders who often engage in trading activities (for instance, every week). Occasional traders buy and sell securities when they detect an apparent investment opportunity. Schmittmann et al. (2014) investigate the impact of weather on individual traders by differentiating between regular and occasional traders. They show that although the weather does not affect the overall trading activity of regular traders, it influences their buy decisions. The results also suggest that the trading level of occasional traders is smaller during good weather days.

Experienced Trader

Experienced traders utilize diverse financial indicators and techniques to develop trading strategies; they are knowledgeable, sophisticated and strategic investors. Dufwenberg et al. (2005) show that in a market where both experienced and inexperienced traders coexist, experienced traders act as price stabilizers even though they form one third of the market participants. Akiyama et al. (2014) reach a similar result with their experimental market where experienced traders constitute one fifth of inexperienced traders. See also Strategic Trader, Sophisticated Trader, Institutional Trader.

Junior Trader

(or Novice Trader; or Sophomore Trader) Junior traders work usually for brokerage companies or hedge funds as entry-level analysts. They are mostly inexperienced traders who manage small accounts and assist a senior trader. Oberlechner and Osler (2012) conduct a survey in order to compare junior and senior traders in the framework of overconfidence and conclude that both type of traders are similar in their overconfident trading behavior. See also Senior Trader, Overconfident Trader.

Senior Trader

Senior traders are higher rank traders who are more experienced than junior traders and they manage larger accounts. Oberlechner and Osler (2012) conduct a survey in order to compare junior and senior traders in the framework of overconfidence and conclude that both type of traders are similar in their overconfident trading behavior. See also Junior Trader, Overconfident Trader, Experienced Trader.

Fundamental Trader

(or Fundamentalist) Fundamental traders make their buy and sell decisions based on the fundamental value information such as earnings announcements, stock splits, mergers and acquisitions, economic and political factors. For instance, buy-and-hold traders can be classified as fundamental traders. Assuming fundamental traders do not know the true fundamental value of assets, Jacob Leal (2015) show that the fundamentalists’ memory is an important factor affecting price behavior. See also Buy-and-hold Trader, Value Trader.

Value Trader

Value traders try to detect underpriced securities; they seek profit by buying them and selling the overpriced instruments. They make use of fundamental value information when making their trading decisions. Hogan et al. (2004) test value trading strategies and find evidence against the efficient markets hypothesis. See also Fundamental Trader.

Trend Trader

(or Trend Chasing Trader) Trend traders are technical traders (also referred to as chartists) who seek profit by following the trends in the market. Their trading strategies include the use of the following indicators: moving average and chart patterns. Trend traders are not concerned about fundamentals. Bulk of the research on currency markets until late 90’s suggest that trend trading strategies were profitable (Sweeney, 1986; Dooley and Shafer, 1984). However, more recent studies mostly show that trend trading is no longer profitable as suggested before (Neely et al., 1997; Liu, 2007). See also Technical Trader, Fundamental Trader.

Technical Trader

(or Chartist) Technical traders utilize charts and graphs and technical indicators such as moving average, trend, and gap analysis; they try to detect common patterns in historical trading data when they make buy and sell decisions. Chiarella and Ladly (2016) assume that the market consists of two investor types, namely informed and technical traders. They find evidence of higher returns generated by technical traders. However, they suggest that technical traders increase trading costs although they act as liquidity suppliers. Wang and Sun (2015) investigate whether technical trading is associated with information discovery or herding behavior; they conclude that the former holds when the market is not fully efficient and that the technical trading strategies generate profit. See also Trend Trader.

Marginal Trader

According to Marginal Trader Hypothesis a marginal trader is “a trader relatively free of judgment bias who consistently bought and sold at prices very close to the equilibrium price” (Forsythe et al., 1992). In this sense, marginal traders can be considered as arbitrageurs who contribute to price corrections. Forsythe et al. (1992) show that marginal traders invest more actively and generate higher returns. See also Arbitrageur, Rational Trader, Active Trader.

Broker

Brokers act as intermediaries between buyers and sellers; they charge a commission or fee in return for executing those buy and sell orders and financial advice. Bergstresser et al. (2009) disentangle between broker-sold and direct-sold mutual funds and investigates the advantages of broker services from the perspective of investors. The findings suggest that brokers’ services provide benefits. See also Inter-dealer Broker, Market Maker, Dealer.

Dealer

Dealers buy and sell securities on a business basis and for their own accounts whereas primary dealers are designated firms that are authorized to deal directly in government securities. Dealers can also be clients of brokers. They act as market-makers in the over-the-counter market. S’Souza (2008) investigates the role of foreign exchange dealers. The results shows that, in foreign exchange markets, dealers provide intraday and overnight liquidity. See also Broker, Inter-dealer Broker, Market Maker.

Inter-dealer Broker

Inter-dealer brokers are intermediaries who arrange and execute trades between dealers; they act as market-makers in the over-the-counter market. They deal with large block orders. Studies suggest that, in foreign exchange market, most of trades occurs via inter-dealer brokers (Lyons, 1995; Stenfors, 2017). See also Dealer, Market Maker, Large Block Trader.

Specialist

(or Designated Market Maker) Specialists differ from voluntary liquidity providers because they have formal obligations to quote at the best bid or offer. Clark-Joseph et al. (2017) show that designated market makers improve liquidity in an electronic marketplace and removing them cause significantly diminished liquidity in the market. On the other hand, they find that voluntary market makers impact is negligible. See also Market Maker, Supplemental Liquidity Provider.

Supplemental Liquidity Provider

Supplemental liquidity providers were introduced by NYSE. They have similar but lighter obligations compared to Designated Market Makers (https://www.investopedia.com). See also Specialist, Market Maker.

Open Outcry Trader

(or Pit Trader) Open outcry traders use verbal and hand signal communication in trading pits of securities exchanges. This trading method contribute to transparency of information. However, currently, most exchanges utilize automated trading systems instead of pit trading. Orlowski (2015) find that the shift from pit to electronic trading increased significantly the volume traded but not the price volatility of Treasury bonds. See also Floor Trader.

Floor Trader

(or Downstairs Trader) Floor traders are market participants who buy and sell securities for their own account from the floor of an exchange. Thus, they were used to utilize open outcry method but they switched to electronic trading systems (https://www.investopedia.com). Battalio et al. (2007) suggest that floor trading being rich in terms of information and liquidity provision is linked to the reputation of floor traders. See also Open Outcry Trader.

Upstairs Trader

(or Block Facilitator, Block Positioner) Upstairs trading involves executing transactions in off-exchange market. Hence, it is criticized for pushing down the public prices in exchange markets. Harris (1993) suggest that upstairs traders mostly provide liquidity to large block traders and thus, are referred to as block facilitators or block positioners.

Speculator

Speculators refers to market participants who are willing to take more risk in return for more profit compared to average investors. They rely on price movements instead of fundamental values. Speculators are more likely to buy and sell securities in shorter-term than traditional traders. Bulk of research suggest that speculators amplify price fluctuations and destabilize markets during market turmoil (Di Maggio, 2016; Cifarelli and Paladino, 2010). See also Fundamental Trader.

Active Trader

Active traders buy and sell securities frequently. They hold securities for short periods. They are most likely to be day traders, swing traders, momentum traders or scalpers (Banerjee and Hung, 2013). See also Passive Trader, Technical Trader, Short-term Trader, Day Trader, Swing Trader, Momentum Trader, Speculator, Scalper.

Passive Trader

Passive traders spend limited time on trading decisions and usually rely on brokers. They also act slower compared to active traders. The holding period of passive traders are relatively long. See also Active Trader, Long-term Trader.

Alpha Trader

Alpha represents abnormal return. Alpha traders make use of an investment performance measure against a benchmark, namely alpha, when making their buy and sell decisions (https://www.investopedia.com). See also Beta Trader.

Beta Trader

Beta measures the risk or volatility of securities. Thus, beta traders utilize beta coefficient as a measure of systematic risk of their portfolio (https://www.investopedia.com). See also Alpha Trader.

Calendar Spread Trader

Calendar spread trading is a neutral trading strategy. It is linked to futures or options market since it refers to taking a short or long position on the same underlying asset with same exercise price but different delivery date at the same time (https://www.investopedia.com).

Flash Trader

Flash trading refers to the use of high-speed computers in order to reach order information before it is available in public marketplace. Hence, its main advantage is generating higher profits from spreads but it is criticized as it can lead to flash crashes (https://www.investopedia.com). See also High-frequency Trader.

Triangular Arbitrage Trader

Triangular arbitrage traders profit from the discrepancies between three currency exchange rates. Fenn et al. (2009) show that triangular arbitrage opportunities exist but has decreased due to increase in price efficiencies. See also Arbitrageur.

Volatility-timing Trader

Volatility-timing trading strategies involves adjusting a portfolio based on volatility. Traders using volatility-timing strategies lower their exposure to risky assets if the volatility of their previous return is relatively high and vice versa. They sell during crisis periods (Moreira and Muir, 2017). See also Beta Trader.

Sunshine Trader

Sunshine traders disclose the information about their high volume transactions before the order is entered (Chung et al., 2007). De Frutos and Manzano (2014) show that for noise traders, the incentive for sunshine trading is not linked to the order size however; sunshine trading is mostly practiced among investors with higher liquidity needs.

Directional Trader

Directional traders base their buy and sell decisions on the direction of a specific security’s or market index. Cherian and Vila (1997) demonstrates that the presence of directional traders reduce the bid-ask spread in options. They also find that directional traders have a more important impact on subsequent stock prices compared to volatility traders.

Dual Trader

Dual traders are brokers who trade both for their customers and their own account. Fishman and Longstaff (1992) find that dual traders and their customers profit more than non-dual traders and their customers respectively. See also Broker.

References

Admati, A. R., & Pfleiderer, P. (1988). A theory of intraday patterns: Volume and price variability. The Review of Financial Studies, 1(1), 3-40.

Aitken, M., Cumming, D., & Zhan, F. (2014). Trade size, high-frequency trading, and colocation around the world. -European Journal of Finance, 1-21.

Aitken, M., Cumming, D., & Zhan, F. (2015). High frequency trading and end-of-day price dislocation. Journal of Banking & Finance, 59, 330-349.

Ajayi, R. A., & Mehdian, S. (1994). Rational investors’ reaction to uncertainty: Evidence from the World’s major markets. Journal of Business Finance & Accounting, 21(4), 533-545.

Akbas, F., Armstrong, W. J., Sorescu, S., & Subrahmanyam, A. (2015). Smart money, dumb money, and capital market anomalies. Journal of Financial Economics, 118(2), 355-382.

Akbas, F., Armstrong, W. J., Sorescu, S., & Subrahmanyam, A. (2015). Smart money, dumb money, and capital market anomalies. Journal of Financial Economics, 118(2), 355-382.

Akiyama, E., Hanaki, N., & Ishikawa, R. (2014). How do experienced traders respond to inflows of inexperienced traders? An experimental analysis. Journal of Economic Dynamics and Control, 45, 1-18.

An, B. J., Ang, A., Bali, T. G., & Cakici, N. (2014). The joint cross section of stocks and options. The Journal of Finance, 69(5), 2279-2337.

Anderson, R. (1981). The usefulness of accounting and other information disclosed in corporate annual reports to institutional investors in Australia. Accounting and Business Research, 11(44), 259-265.

Angel, J. J., Harris, L. E., & Spatt, C. S. (2015). Equity trading in the 21st century: An update. The Quarterly Journal of Finance, 5(01), 1-39.

Angela-Maria, F., Maria, P. A., & Miruna, P. M. (2015). An empirical investigation of herding behavior in CEE stock markets under the global financial crisis. Procedia Economics and Finance, 25, 354-361

Back, K., Cao, C. H., & Willard, G. A. (2000). Imperfect competition among informed traders. The journal of finance, 55(5), 2117-2155.

Bagehot, W. (1971). The only game in town. Financial Analysts Journal, 27(2), 12-14.

Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. Journal of Finance, 55, 773–806.

Barber, B. M., & Odean, T. (2013). The behavior of individual investors. In Handbook of the Economics of Finance (Vol. 2, pp. 1533-1570). Elsevier.

Barberis, N., & Xiong, W. (2009). What drives the disposition effect? An analysis of a long-standing preference-based explanation. The Journal of Finance, 64(2), 751-784.

Barclay, M. J., & Hendershott, T. (2003). Price discovery and trading after hours. The Review of Financial Studies, 16(4), 1041-1073.

Barclay, M. J., & Hendershott, T. (2008). A comparison of trading and non-trading mechanisms for price discovery. Journal of Empirical Finance, 15(5), 839-849.

Barclay, M. J., & Warner, J. B. (1993). Stealth trading and volatility: Which trades move prices?. Journal of Financial Economics, 34(3), 281-305.

Battalio, R., Ellul, A., & Jennings, R. (2007). Reputation effects in trading on the New York Stock Exchange. The Journal of Finance, 62(3), 1243-1271.

Bergstresser, D., Chalmers, J. M., & Tufano, P. (2009). Assessing the costs and benefits of brokers in the mutual fund industry. The Review of Financial Studies, 22(10), 4129-4156.

Bhattacharya, N. (2001). Investors' trade size and trading responses around earnings announcements: An empirical investigation. The Accounting Review, 76(2), 221-244.

Bhushan, R., Brown, D. P., & Mello, A. S. (1997). Do noise traders “create their own space?”. Journal of Financial and Quantitative Analysis, 32(1), 25-45.

Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of political Economy, 100(5), 992-1026.

Black, F. (1986). Noise. The journal of finance, 41(3), 528-543.

Black, F. (1995). Equilibrium exchanges. Financial Analysts Journal, 51(3), 23-29.

Brandouy, O., Mathieu, P., & Veryzhenko, I. (2012). Risk aversion impact on investment strategy performance: a multi agent-based analysis. In Managing Market Complexity (pp. 91-102). Springer, Berlin, Heidelberg.

Brandouy, O., Mathieu, P., & Veryzhenko, I. (2012). Risk aversion impact on investment strategy performance: a multi agent-based analysis. In Managing Market Complexity (pp. 91-102). Springer, Berlin, Heidelberg.

Brogaard, J. (2010). High frequency trading and its impact on market quality. Northwestern University Kellogg School of Management Working Paper, 66.

Brogaard, J., Hagströmer, B., Nordén, L., & Riordan, R. (2015). Trading fast and slow: Colocation and liquidity. The Review of Financial Studies, 28(12), 3407-3443.

Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267-2306.

Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of empirical finance, 11(1), 1-27.

Brunnermeier, M. K., & Pedersen, L. H. (2005). Predatory trading. The Journal of Finance, 60(4), 1825-1863.

Bushee, B. 1998. The influence of institutional investors on myopic R&D investment behavior. The Accounting Review 73, 19-45

Bushee, B. 1998. The influence of institutional investors on myopic R&D investment behavior. The Accounting Review 73, 19-45

Büyüksahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 38-70.

Campbell, J., LaMaster, S., Smith, V. L., & Van Boening, M. (1991). Off-floor trading, disintegration, and the bid-ask spread in experimental markets. Journal of Business, 495-522.

Cella, C., Ellul, A., & Giannetti, M. (2013). Investors' horizons and the amplification of market shocks. The Review of Financial Studies, 26(7), 1607-1648.

Chacko, G. C., Jurek, J. W., & Stafford, E. (2008). The price of immediacy. The Journal of Finance, 63(3), 1253-1290.

Cherian, J. A., & Vila, A. F. (1997). Information trading, volatility, and liquidity in options markets.

Chiarella, C., & Ladley, D. (2016). Chasing trends at the micro-level: The effect of technical trading on order book dynamics. Journal of Banking & Finance, 72, S119-S131.

Chng, M. T. (2009). Economic linkages across commodity futures: Hedging and trading implications. Journal of Banking & Finance, 33(5), 958-970.

Choe, H., Kho, B. C., & Stulz, R. M. (1999). Do foreign investors destabilize stock markets? The Korean experience in 1997. Journal of Financial Economics, 54(2), 227-264.

Chou, R. K., & Wang, Y. Y. (2011). A test of the different implications of the overconfidence and disposition hypotheses. Journal of Banking & finance, 35(8), 2037-2046.

Chuang, W. I., & Susmel, R. (2011). Who is the more overconfident trader? Individual vs. institutional investors. Journal of Banking & Finance, 35(7), 1626-1644.

Chung, D. Y., Isakov, D., & Pérignon, C. (2007). Repurchasing shares on a second trading line. Review of Finance, 11(2), 253-285.

Cifarelli, G., & Paladino, G. (2010). Oil price dynamics and speculation: A multivariate financial approach. Energy Economics, 32(2), 363-372.

Clark-Joseph, A. D., Ye, M., & Zi, C. (2017). Designated market makers still matter: Evidence from two natural experiments. Journal of Financial Economics, 126(3), 652-667.

Comerton-Forde, C., & Putninš, T. J. (2015). Dark trading and price discovery. Journal of Financial Economics, 118(1), 70-92.

Covrig, V., Lau, S. T., & Ng, L. (2006). Do domestic and foreign fund managers have similar preferences for stock characteristics? A cross-country analysis. Journal of International Business Studies, 37(3), 407-429.

Cremers, M., & Pareek, A. (2014). Short-term trading and stock return anomalies: Momentum, reversal, and share issuance. Review of Finance, 19(4), 1649-1701.

Dahlquist, M., Pinkowitz, L., Stulz, R. M., & Williamson, R. (2002). Corporate Governance, Investor Protection, and the Home Bias. Tuck-JQFA Contemporary Corporate Governance Issues II Conference.

Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under-and overreactions. The Journal of Finance, 53(6), 1839-1885.

Day, J. F. (1986). The use of annual reports by UK investment analysts. Accounting and Business Research, 16(64), 295-307.

de Frutos, M. Á., & Manzano, C. (2014). Market transparency, market quality, and sunshine trading. Journal of Financial Markets, 17, 174-198.

DeVault, L., Sias, R., & Starks, L. (2014). Who are the sentiment traders? Evidence from the cross-section of stock returns and demand.

Di Maggio, M. (2016). Market turmoil and destabilizing speculation.

Diether, K., Lee, K. H., & Werner, I. (2005). Can short-sellers predict returns? Daily evidence.

Ding, S., Hanna, J., & Hendershott, T. (2014). How slow is the NBBO? A comparison with direct exchange feeds. Financial Review, 49(2), 313-332.

Dooley, M. P., & Shafer, J. (1984). Analysis of short-run exchange rate behavior: March 1973 to November 1981. Floating exchange rates and the state of world trade and payments, 43-70.

Dufwenberg, M., Lindqvist, T., & Moore, E. (2005). Bubbles and experience: An experiment. American Economic Review, 95(5), 1731-1737.

Dumas, B., Kurshev, A., & Uppal, R. (2005). What can rational investors do about excessive volatility and sentiment fluctuations? (No. w11803). National Bureau of Economic Research.

Dvorák, T. (2005). Do domestic investors have an information advantage? Evidence from Indonesia. The Journal of Finance, 60(2), 817-839.

Easley, D., & O'hara, M. (1987). Price, trade size, and information in securities markets. Journal of Financial economics, 19(1), 69-90.

Easley, D., & O'HARA, M. (1991). Order form and information in securities markets. The Journal of Finance, 46(3), 905-927.

Easley, D., & O'hara, M. (1992). Time and the process of security price adjustment. The Journal of finance, 47(2), 577-605.

Easley, D., Hendershott, T., & Ramadorai, T. (2014). Leveling the trading field. Journal of Financial Markets, 17, 65-93.

Easley, D., Kiefer, N. M., O'hara, M., & Paperman, J. B. (1996). Liquidity, information, and infrequently traded stocks. The Journal of Finance, 51(4), 1405-1436.

Economou, F., Gavriilidis, K., Kallinterakis, V., & Yordanov, N. (2015). Do fund managers herd in frontier markets—and why?. International Review of Financial Analysis, 40, 76-87.

Eren, N., & Ozsoylev, H. N. (2006). Hype and dump manipulation.

Fecht, F., Hackethal, A., & Karabulut, Y. (2018). Is proprietary trading detrimental to retail investors?. The Journal of Finance, 73(3), 1323-1361.

Fenn, D. J., Howison, S. D., McDonald, M., Williams, S., & Johnson, N. F. (2009). The mirage of triangular arbitrage in the spot foreign exchange market. International Journal of Theoretical and Applied Finance, 12(08), 1105-1123.

Ferreira, M. A., Matos, P., Pereira, J. P., & Pires, P. (2017). Do locals know better? A comparison of the performance of local and foreign institutional investors. Journal of Banking & Finance, 82, 151-164.

Fishman, M. J., & Longstaff, F. A. (1992). Dual trading in futures markets. The Journal of Finance, 47(2), 643-671.

Forsythe, R., Nelson, F., Neumann, G. R., & Wright, J. (1992). Anatomy of an experimental political stock market. The American Economic Review, 1142-1161.

Foster, F. D., & Viswanathan, S. (1990). A theory of the interday variations in volume, variance, and trading costs in securities markets. The Review of Financial Studies, 3(4), 593-624.

Franke, G., & Hess, D. (2000). Information diffusion in electronic and floor trading. Journal of Empirical Finance, 7(5), 455-478.

Friedman, D. (1993). Privileged traders and asset market efficiency: A laboratory study. Journal of Financial and Quantitative Analysis, 28(4), 515-534.

Fritz, A., Stein, M., & Weber, C. (2015). The role of heterogeneous agents in fuel markets: Testing tales of speculators in oil markets (No. 05/15). EWL Working Paper.

Gavriilidis, K., Kallinterakis, V., & Ferreira, M. P. L. (2013). Institutional industry herding: Intentional or spurious?. Journal of International Financial Markets, Institutions and Money, 26, 192-214.

Gençay, R., Gradojevic, N., Selçuk?, F., & Whitcher, B. (2010). Asymmetry of information flow between volatilities across time scales. Quantitative Finance, 10(8), 895-915.

Glosten, L. R. (1994). Is the electronic open limit order book inevitable?. The Journal of Finance, 49(4), 1127-1161.

Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of financial economics, 14(1), 71-100.

Gong, F., & Liu, H. (2016). Asymmetric information, heterogeneous prior beliefs, and public information. International Review of Economics & Finance, 46, 100-120.

Goyenko, R. Y., & Ukhov, A. D. (2009). Stock and bond market liquidity: A long-run empirical analysis. Journal of Financial and Quantitative Analysis, 44(1), 189-212.

Gradojevic, N., & Gençay, R. (2013). Fuzzy logic, trading uncertainty and technical trading. Journal of Banking & Finance, 37(2), 578-586.

Grammig, J., Schiereck, D., & Theissen, E. (2001). Knowing me, knowing you:: Trader anonymity and informed trading in parallel markets. Journal of Financial Markets, 4(4), 385-412.

Greene, J. T., & Hodges, C. W. (2002). The dilution impact of daily fund flows on open-end mutual funds. Journal of Financial Economics, 65(1), 131-158.

Grinblatt, M., & Han, B. (2005). Prospect theory, mental accounting, and momentum. Journal of financial economics, 78(2), 311-339.

Grinblatt, M., & Keloharju, M. (2001). What makes investors trade?. The Journal of Finance, 56(2), 589-616.

Hagströmer, B., & Norden, L. (2013). The diversity of high-frequency traders. Journal of Financial Markets, 16(4), 741-770.

Handa, P., & Schwartz, R. A. (1996). Limit order trading. The Journal of Finance, 51(5), 1835-1861.

Harris, L. (1997). Order exposure and parasitic traders. University of Southern California working paper.

Harris, L. (2013). What to do about high-frequency trading.

Harris, L. E. (1993). Consolidation, fragmentation, segmentation, and regulation. Journal of Finance, 48, No. 3, pp. 1092-1093.

Harris, L., 2003. Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press, New York, USA.

Hirshleifer, D. A., Myers, J. N., Myers, L. A., & Teoh, S. H. (2008). Do individual investors cause post-earnings announcement drift? Direct evidence from personal trades. The Accounting Review, 83(6), 1521-1550.

Hoffmann, P. (2014). A dynamic limit order market with fast and slow traders. Journal of Financial Economics, 113(1), 156-169.

Hogan, S., Jarrow, R., Teo, M., & Warachka, M. (2004). Testing market efficiency using statistical arbitrage with applications to momentum and value strategies. Journal of Financial economics, 73(3), 525-565.

Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading, and overreaction in asset markets. The Journal of finance, 54(6), 2143-2184.

Hsieh, M. F., Yang, T. Y., Yang, Y. T., & Lee, J. S. (2011). Evidence of herding and positive feedback trading for mutual funds in emerging Asian countries. Quantitative Finance, 11(3), 423-435

Hsieh, S. F. (2013). Individual and institutional herding and the impact on stock returns: Evidence from Taiwan stock market. International Review of Financial Analysis, 29, 175-188.

Jacob Leal, S. (2015). Fundamentalists, chartists and asset pricing anomalies. Quantitative Finance, 15(11), 1837-1850.

Jain, P., & Jordan, S. J. (2017). Cancellation latency: The good, the bad, and the ugly. Financial Management, 46(2), 377-407.

Jennings, R. H., & Barry, C. B. (1983). Information dissemination and portfolio choice. Journal of Financial and Quantitative Analysis, 18(1), 1-19.

Jiang, C. X., Likitapiwat, T., & McInish, T. H. (2012). Information content of earnings announcements: Evidence from after-hours trading. Journal of Financial and Quantitative Analysis, 47(6), 1303-1330.

John, K., & Lang, L. H. (1991). Insider trading around dividend announcements: Theory and evidence. The Journal of Finance, 46(4), 1361-1389.

Karpoff, J. M., & Lee, D. (1991). Insider trading before new issue announcements. Financial Management, 18-26.

Keown, A. J., & Pinkerton, J. M. (1981). Merger announcements and insider trading activity: An empirical investigation. The journal of finance, 36(4), 855-869.

Kilicay-Ergin, N., Enke, D., & Dagli, C. (2012). Biased trader model and analysis of financial market dynamics. International Journal of Knowledge-based and Intelligent Engineering Systems, 16(2), 99-116.

Kirchler, E., & Maciejovsky, B. (2002). Simultaneous over-and underconfidence: Evidence from experimental asset markets. Journal of Risk and Uncertainty, 25(1), 65-85.

Kogan, L., Ross, S. A., Wang, J., & Westerfield, M. M. (2006). The price impact and survival of irrational traders. The Journal of Finance, 61(1), 195-229.

Kraus, A., & Stoll, H. R. (1972). Parallel trading by institutional investors. Journal of Financial and Quantitative Analysis, 7(5), 2107-2138.

Kremer, S., & Nautz, D. (2013). Short-term Herding of Institutional Traders: New Evidence from the German Stock Market. European Financial Management, 19(4), 730-746.

Kryzanowski, L., & Zhang, H. (1996). Trading Patterns of Small and Large Traders around Stock Split Ex-Dates. Journal of Financial Research, 19(1), 75-90.

Kukacka, J., & Baruník, J. (2016). Simulated ML estimation of financial agent-based models (No. 2016/07). Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies.

Kumar, A., & Lee, C. (2006). Retail investor sentiment and return comovements. The Journal of Finance, 61(5), 2451-2486.

Kuo, W. Y., & Lin, T. C. (2013). Overconfident individual day traders: Evidence from the Taiwan futures market. Journal of Banking & Finance, 37(9), 3548-3561.

Kuo, W. Y., & Lin, T. C. (2013). Overconfident individual day traders: Evidence from the Taiwan futures market. Journal of Banking & Finance, 37(9), 3548-3561.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, 1315-1335.

Laughlin, G., Aguirre, A., & Grundfest, J. (2014). Information transmission between financial markets in Chicago and New York. Financial Review, 49(2), 283-312.

Lee, C. M. (1992). Earnings news and small traders: An intraday analysis. Journal of Accounting and Economics, 15(2-3), 265-302.

Lee, C. M., & Radhakrishna, B. (2000). Inferring investor behavior: Evidence from TORQ data. Journal of Financial Markets, 3(2), 83-111.

Li, J. (2016). Slow price adjustment to public news in after-hours trading.

Li, Y., & Yang, L. (2013). Prospect theory, the disposition effect, and asset prices. Journal of Financial Economics, 107(3), 715-739.

Lin, A. Y., & Swanson, P. E. (2003). The behavior and performance of foreign investors in emerging equity markets: Evidence from Taiwan. International Review of Finance, 4(3-4), 189-210.

Liu, S. (2007). Currency derivatives and exchange rate forecastability. Financial Analysts Journal, 63(4), 72-78.

Lyons, R. K. (1995). Tests of microstructural hypotheses in the foreign exchange market. Journal of Financial Economics, 39(2-3), 321-351.

Ma, T., Lee, K. H., Lai, C. H., & Lee, Y. S. (2017). Rational or Irrational? A Comprehensive Studies on Stock Market Crashes.

Mahani, R. S., & Poteshman, A. M. (2008). Overreaction to stock market news and misevaluation of stock prices by unsophisticated investors: Evidence from the option market. Journal of Empirical Finance, 15(4), 635-655.

Menkveld, A. J. (2014). High-Frequency Traders and Market Structure. Financial Review, 49(2), 333-344.

Meyer, S., Schroff, S., & Weinhardt, C. (2013). (Un) skilled Leveraged Trading of Retail Investors.

Miller, B. P. (2010). The effects of reporting complexity on small and large investor trading. The Accounting Review, 85(6), 2107-2143.

Moreira, A., & Muir, T. (2017). Volatility-Managed Portfolios. The Journal of Finance, 72(4), 1611-1644.

Müller, U. A., Dacorogna, M. M., Davé, R. D., Olsen, R. B., Pictet, O. V., & von Weizsäcker, J. E. (1997). Volatilities of different time resolutions—analyzing the dynamics of market components. Journal of Empirical Finance, 4(2-3), 213-239.

N. Banerjee, A. N. U. R. A. G., & Hung, C. H. (2013). Active momentum trading versus passive ‘naive diversification’. Quantitative finance, 13(5), 655-663.

Neely, C., Weller, P., & Dittmar, R. (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of financial and Quantitative Analysis, 32(4), 405-426.

O’Hara, M. (2014). High-frequency trading and its impact on markets. Financial Analysts Journal, 70(3), 18-27.

O’Hara, M. (2015). High frequency market microstructure. Journal of Financial Economics, 116(2), 257-270.

O’Hara, M., Yao, C., & Ye, M. (2014). What's not there: Odd lots and market data. The Journal of Finance, 69(5), 2199-2236.

Oberlechner, T., & Osler, C. (2012). Survival of overconfidence in currency markets. Journal of Financial and Quantitative Analysis, 47(1), 91-113.

Oberlechner, T., & Osler, C. (2012). Survival of overconfidence in currency markets. Journal of Financial and Quantitative Analysis, 47(1), 91-113.

O'Neal, E. S. (2004). Purchase and redemption patterns of US equity mutual funds. Financial Management, 63-90.

Orlowski, L. T. (2015). From pit to electronic trading: Impact on price volatility of US Treasury futures. Review of Financial Economics, 25, 3-9.

Ormos, M., & Timotity, D. (2016). Market microstructure during financial crisis: Dynamics of informed and heuristic-driven trading. Finance Research Letters, 19, 60-66.

Osler, C., & Savaser, T. (2011). Extreme returns: The case of currencies. Journal of Banking & Finance, 35(11), 2868-2880.

Puksamatanan, J. (2010). Live and Learn with Interbank Trader: The Trading Decision-Making Process from Thai Interbank Traders’ Perspective.

Riedel, C., & Wagner, N. (2015). Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns. Journal of International Financial Markets, Institutions and Money, 39, 53-64.

Routledge, B. R., Seppi, D. J., & Spatt, C. S. (2000). Equilibrium forward curves for commodities. The Journal of Finance, 55(3), 1297-1338.

Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. American Economic Review, 465-479.

Schmalz, M., & Zhuk, S. (2018). Revealing downturns. Centre for Economic Policy Research.

Schmittmann, J. M., Pirschel, J., Meyer, S., & Hackethal, A. (2014). The impact of weather on German retail investors. Review of Finance, 19(3), 1143-1183.

Seasholes, M. S. (2004). Re-examining information asymmetries in emerging stock markets. University of California at Berkeley working paper.

Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of finance, 40(3), 777-790.

Shumway, T., & Wu, G. (2005). Does disposition drive momentum?.

Sias, R. W. (2004). Institutional herding. The Review of Financial Studies, 17(1), 165-206.

S'Souza, C. (2008). The role of foreign exchange dealers in providing overnight liquidity (No. 2008, 44). Bank of Canada Working Paper.

Stambaugh, R. F., Yu, J., & Yuan, Y. (2012). The short of it: Investor sentiment and anomalies. Journal of Financial Economics, 104(2), 288-302.

Stenfors, A. (2017). Bid-ask spread determination in the FX swap market: competition, collusion or a convention?. Journal of International Financial Markets, Institutions and Money.

Subrahmanyam, A. (1996). On speculation, index futures markets, and the link between market volatility and investor welfare. Financial Review, 31(2), 227-263.

Swan, P. L., & Westerholm, P. J. (2017). Are Domestic Household Investors Better Performers than Foreign Institutions? New Evidence from Finland. In Behavioral Finance: Where do investors’ biases come from? (pp. 115-155).

Sweeney, R. J. (1986). Beating the foreign exchange market. The Journal of Finance, 41(1), 163-182.

Tirole, J. (1982). On the possibility of speculation under rational expectations. Econometrica: Journal of the Econometric Society, 1163-1181.

Tse, Y., & Bandyopadhyay, P. (2006). Multi-market trading in the Eurodollar futures market. Review of Quantitative Finance and Accounting, 26(3), 321-341.

Ulibarri, C. A., Anselmo, P. C., Hovespian, K., Tolk, J., & Florescu, I. (2009). ‘Noise-trader risk’and Bayesian market making in FX derivatives: rolling loaded dice?. International Journal of Finance & Economics, 14(3), 268-279.

Van Kervel, V. (2015). Competition for order flow with fast and slow traders. The Review of Financial Studies, 28(7), 2094-2127.

Wah, E., & Wellman, M. P. (2016). Latency arbitrage in fragmented markets: A strategic agent-based analysis. Algorithmic Finance, 5(3-4), 69-93.

Wang, F. A. (1998). Strategic trading, asymmetric information and heterogeneous prior beliefs. Journal of Financial Markets, 1(3-4), 321-352.

Wang, T., & Sun, Q. (2015). Why investors use technical analysis? Information discovery versus herding behavior. China Finance Review International, 5(1), 53-68.

Yan, X., & Zhang, Z. (2007). Institutional investors and equity returns: Are short-term institutions better informed?. The review of financial studies, 22(2), 893-924.

Yin, C. (2016). The optimal size of hedge funds: conflict between investors and fund managers. The Journal of Finance, 71(4), 1857-1894.