Article

The Cost of Algorithmic Trading: A First Look at Comparative Performance

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Abstract

The authors examine transaction costs associated with algorithmic trading, based on a sample of 2.5 million orders, of which one million are executed via algorithmic means. The data permit a comparison of algorithmic executions with a broader universe of trades, as well as across multiple providers of model-based trading services. Algorithmic trading is found to be a cost-effective technique, based on a measure of implementation shortfall. The superiority of algorithm performance applies only for order sizes up to 10% of average daily volume, however. Algorithmic trading performance relative to a commonly used volume participation benchmark also is quite good, although certainty of outcome declines sharply with the size of the order. A clear link between performance and variability in performance relative to both benchmarks appears to be lacking. Although rough equality across providers is observed on average, this equality of performance breaks down quickly as order size grows.

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... Algorithmic Trading systems typically aim at achieving or beating a specified benchmark with their executions and may be distinguished by their underlying benchmark, their aggressiveness or trading style as well as their adaptation behaviour [15]. The volume-weighted average price (VWAP), which is calculated as the ratio of the value traded and the volume traded within a specified time horizon, commonly serves as a benchmark for (automated) trading [10]. Research on aggressiveness of orders is e.g. ...
... Empirical research found the execution quality of algorithms to be inferior to executions handled by a broker. Nevertheless, this underperformance can be overcompensated by the fact that algorithms can be offered at lower fees than human order handling [10]. Algorithms can be offered to customers at lower fees, as no (expensive) human traders are involved. ...
Article
As successful algorithmic trading systems constitute a priceless value to their operators, their procedures of trading are kept secret and only little is known about their adaptation behavior to current market developments. Based on a unique dataset provided by Deutsche Boerse AG the activity of computerized traders is analyzed. As the dataset provides high-precision timestamps a thorough analysis of submission, deletion and execution activities is enabled. Being able to distinguish algorithmic and non-algorithmic orders, empirical evidence on the different structures of algorithmic and non-algorithmic order flow is presented.
... This includes Bertsimas and Lo [6], Almgren and Chriss [1,2], Gatheral and Schied [22], Engle et al. [17], Predoiu et al. [37], Boulatov et al. [8] and other research surveyed in Gatheral and Shied 1 Large asset managers conduct dynamic trading strategies using in-house trading desks and also via principal and agency trading with external brokers. 2 Madhavan [32] discusses price improvement on order execution relative to VWAP. Domowitz and Yegerman [13] estimate empirical order-execution costs benchmarked relative to VWAP. 3 Hagströmer and Nordén [26] and Menkveld [34] show that high-frequency (HFT) market makers are an important source of intraday liquidity. A common feature of HFT market makers is that they have "very short time-frames for establishing and liquidating positions" SEC [41], which is consistent with a zero target inventory level. ...
... (1.9) 12 It is possible to extend our model to include noise-trader orders such that the floating stock supply becomes an exogenous stochastic process a(t) + b(t)Z t + c(t)B t where a, b, and c are deterministic functions of time t ∈ [0, 1], B t is the risk-factor Brownian motion in (1.4), and Z t is a Brownian motion independent of all other random variables. 13 If the terminal restriction (1.8) is eliminated, our model becomes simpler because the stock volatility becomes a free parameter and can, for example, be set to be one. The fact that competitive Radner equilibrium models without dividends have free volatilities is well-known; see, e.g., Theorem 4.6.3 in Karatzas and Shreve [28]. ...
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... Unsurprisingly, numerous research efforts have been dedicated to understating how this intense automation of trading impacts market dynamics (Chaboud et al., 2014). Previous studies have found that algorithmic trading improves market liquidity (Hendershott et al., 2011) and facilitates price discovery (Carrion, 2013;Brogaard et al., 2014;Hirschey, 2021), also contributing to decreasing trading costs (Domowitz and Yegerman, 2005;Kim, 2010). Nonetheless, it is important to underline that these positive externalities have been validated during "normal" market evolution (SEC, 2020), whereas algorithmic trading can diminish liquidity and exacerbate volatility during distressed markets, with dire economic consequences (Treleaven et al., 2013). ...
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... either best bid or ask price -are taken to be the decision price (also frequently referred to as the "arrival price"). For further details see Johnson (2010) and Domowitz and Yegerman (2005). ...
Article
We develop a sequential trade model of Iceberg order execution in a limit order book. The Iceberg-trader has the freedom to expose his trading intentions or (partially) shield the true order size against other market participants. Order exposure can cause drastic market reactions (“market impact”) in the end leading to higher transaction costs. On the other hand the Iceberg trader faces a loss-in-priority when he hides his intentions, as most electronic limit order books penalize the usage of hidden liquidity. Thus the Iceberg-trader is faced with the problem to find the right trade-off. Our model provides optimal exposure strategies for Iceberg traders in limit order book markets. In particular, we provide a range of analytical statements that are in line with recent empirical findings on the determinants of trader’s exposure strategies. In this framework, we also study the market impact also market impact of limit orders. We provide optimal exposure profiles for a range of hightech stocks from the US S&P500 and how they scale with the state-of-the-book. We finally test the Iceberg’s performance against the limit orders and find that Iceberg orders can significantly enhance trade performance by up to 60%.
... For order sizes up to 10% of daily average trading volume, algorithmic trading has been found to be a cost-effective method (Domowitz and Yegerman, 2005). The auto quote power of algorithms as a competitive advantage disappears when new entrants come with better models. ...
Article
Algorithmic trading has made a paradigm shift in Indian stock market. Popularity of algorithmic trading is gaining momentum among Indian traders and investors due to technological advancement. The objective of this paper is to apply Interpretive Structural Modeling (ISM) to develop a hierarchical structure among the key barriers of algorithmic trading in India. 11 barriers have been identified through the literature review which are then validated for significance, using a structured questionnaire, from the experts. ISM approach has been utilized to rank the barriers and analyze their mutual interactions. Subsequently, MICMAC analysis was conducted to reveal dependence and driving power of these barriers. MICMAC analysis also elicits the relative importance and interdependence between these barriers from the Indian context. A list of relevant barriers significantly helps the practitioners to take right decision while adopting algorithmic trading. The study has importance in the Indian context due to scarcity of research in this area.
... either best bid or ask price -are taken to be the decision price (also frequently referred to as the "arrival price"). For further details see Johnson (2010) and Domowitz and Yegerman (2005). ...
Article
Full-text available
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... The trader is then called the price setter as it can manipulate the trading price through its trading behavior in the market. For example, institutional traders which heavily rely on algorithmic trading or automatic trading strategies most likely belong to this type of trader, see (Domowitz & Yegerman, 2005). As algorithmic trading starts to prevail in global electronic trading platforms, it is thus reasonable to include the formulation of the price setter when developing the financial market model for the electronic trading platform. ...
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Under the background of the electronic security trading platform Xetra operated by Frankfurt Stock Exchange, we consider the Xetra auction market system (XAMS) from `bottom-up', which the interaction among heterogeneous traders and Xetra auction market mechanism generates non-equilibrium price dynamics. First we develop an integrative framework that serves as general guidance for analyzing the economic system from `bottom-up' and for seamlessly transferring the economic system into the corresponding agent-based model. Then we apply this integrative framework to construct the agent-based model of XAMS. By conducting market experiments with the computer implementation of the agent-based model of XAMS, we investigate the role of the price setter who assumes its trading behavior can manipulate the market price. The main finding is that the introduction of the price setter in the setting of XAMS improves market efficiency while does not significantly influence price volatility of the market.
... ppon (2011) for models where investors compete on their trading algorithm's speed. Monitoring also has important cross market competition implications as in Foucault and Menkveld (2008) and others. use execution data from Morgan Stanley algorithms to study the tradeoffs between algorithm aggressiveness and the mean and dispersion of execution cost. Domowitz and Yegerman (2005) study execution costs of ITG buy-side clients, comparing results from different algorithm providers. Several recent studies use comprehensive data on AT. Chaboud, Chiquoine, Hjalmarsson, and Vega (2009) study the development of AT in the foreign exchange market on the electronic broking system (EBS) in three currency pairs euro-dollar, ...
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We examine the role of algorithmic traders (AT) in liquidity supply and demand in the 30 DAX stocks on the Deutsche Boerse in January 2008. AT represent 52% of market order volume and 64% of nonmarketable limit order volume. AT more actively monitor market liquidity than human traders. AT consume liquidity when it is cheap, i.e., when the bid-ask quotes are narrow, and supply liquidity when it is expensive. When spreads are narrow AT are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. AT react more quickly to events and even more so when spreads are wide.
... ic trading models and their impact especially from an empirical perspective. The sparsely existing literature on the concept of Algorithmic Trading focuses on the investors' perspective. Yang & Jiu (2006) propose a framework to help investors to choose the most suitable algorithm. Konishi (2002) proposes an optimal slicing strategy for VWAP trades. Domowitz & Yegerman (2005) examine the execution quality of algorithms in comparison to traditional brokers' offering of stealth trading. They conclude that e.g. VWAP algorithms on average have an underperformance of 2bps. Nevertheless, this underperformance can be overcompensated by the fact that algorithms can be offered at lower fees than human stealth trading ...
Article
The concept of Algorithmic Trading emulates via electronic means a brokers core competency of slicing a big order into a multiplicity of smaller orders and of timing these orders to minimize market impact. Based on mathematical models and considering historical and real-time market data, algorithms determine ex ante or continuously the optimum size of the (next) slice and its time of submission to the market. Algorithmic trading models are gaining market share worldwide. As this might impact the order flow on the markets it is self-evident to investigate whether algorithmic trading can be categorized in the traditional way or whether it represents a new category of stylized trader. The paper assesses the upcoming sophisticated trading strategy of algorithmic trading against the background of the traditional categories of stylized traders in the literature, i.e. informed traders, momentum traders and noise traders. As a conclusion, in order to assess the of impact algorithmic trading on financial markets, the set-up of a new simulation model incorporating agents representing the specific properties and the trading behavior of algorithmic trading is proposed.
... As with the optimal trading strategies of Bertsimas and Lo and Almgren and Chriss, a key feature of VWAP trading is the fragmentation of large trades into smaller trades in order to minimize market impact. The comparative studies of Domowitz and Yegerman [8] and Werner [37] show that the execution costs of VWAP are comparable to, or lower than, other block (large) trading strategies. ...
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VWAP is the Volume Weighted Average Price of traded stock over a defined period. It is a metric of trade execution quality used by institutional traders to minimize the execution cost of large trades. A riskless VWAP trading strategy is not possible without knowledge of final market volume. We formulate a mean-variance optimal VWAP strategy by assuming knowledge of final volume and then project this onto the space of strategies accessible to the VWAP trader. The mean variance optimal VWAP trading strategy is the sum of two distinct trading strategies, a minimum variance VWAP hedging strategy and a `directional' price strategy independent of the hedging strategy and market VWAP. It is optimal for large volume VWAP traders to increase the size of the price `directional' trade for additional return.
... A final additional cost that may be unappreciated is transaction costs, which include commissions, bid-ask spread, and trade impact. There is a tradeoff whereby small traders with low trade impact generally have high commissions, cross the bid-ask spread to transact, and do not have sufficient experience or scale trading to do so in an optimal manner (algorithmic trading optimizes the size, frequency and limit order specifications, currently done thru computer-based execution of equity orders via direct market-access channels, Domowitz and Yegerman (2005)), while large institutions generate small commissions and trade based on strategies optimized through extensive trial-and-error, but in turn have a larger trade-impact due to their larger size. As brokers optimize over these three costs in a relationship, they must be considered simultaneously to get at the true trading costs. ...
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This paper presents a utility function refinement that explains the empirical irrelevance of risk to returns. The key is that in an environment where people care about relative wealth, risk is a deviation from what everyone else is doing, and therefore becomes like diversifiable risk in the CAPM, avoidable. Using an equilibrium or an arbitrage argument, a relative status utility function creates a zero risk-return correlation via a market model that implies a zero risk premium. This approach is described as being theoretically consistent, intuitive and a better description of the data.
... Anecdotal evidence indicates that a substantial amount of algorithmic trading by direct market access participants is conducted anonymously on the TSX. This may be because the lack of randomization in algorithmic trading makes such strategies more susceptible to frontrunning (Domowitz and Yegerman (2005)). In addition, potential conflicts of interest arise where brokers can identify their clients' algorithmic trading patterns and position themselves to take advantage of these anticipated trades (Patel (2006)). ...
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This paper examines the use, determinants and impact of anonymous orders in a market where disclosure of broker identity in the trading screen is voluntary. We find that most trading occurs non-anonymously, contrary to prior literature that suggests liquidity gravitates to anonymous markets. By strategically using anonymity when it is beneficial, traders reduce their execution costs. Traders select anonymity based on various factors including order source, order size and aggressiveness, time of day, liquidity and expected execution costs. Finally, we report how anonymous orders affect market quality and discuss implications for market design.
... For instance the volume-weighted average price (VWAP), which is calculated as the ratio of the value traded and the volume traded (number of shares) within a specified time horizon, commonly serves as a benchmark for (automated) trading. Empirical research found the execution quality of algorithms to be inferior to executions handled by a broker (Domowitz & Yegerman 2005). Nevertheless, this underperformance can be overcompensated by the fact that algorithms can be run at lower costs, as no (expensive) human traders are involved. ...
Conference Paper
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... They underline the high percentage of orders originating from Algorithmic Trading. Further, [18] show the business value of algorithms by comparing their overall trading costs with those of human brokers. On top, [19] highlight the importance of overall transaction costs. ...
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Smart Order Routing technology promises to improve the efficiency of the securities trading value chain by selecting most favourable execution prices among fragmented markets. To measure the extent of sub-optimal order executions in Europe we develop a simulation framework which includes explicit costs associated with switching to a different market. By analysing historical order book data for EURO STOXX 50 securities across ten European lectronic markets we highlight an economically relevant potential of Smart Order Routing to improve the trading process on a gross basis. After the inclusion of switching costs (net basis), the realisability of this value potential depends on whether the user can directly access post-trading infrastructure of foreign markets or has to make use of intermediaries’ services.
... Though our approach is algorithmic, we are not concerned with volume-weighted algorithmic trading. See [4], [5] and [11] for a review of the literature and for insights into the study of automated trading. ...
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Large market participants (LMPs) must often execute trades while keeping their intentions secret. Sometimes secrecy is required before trades are completed to prevent other traders from anticipating (and exploiting) the price impact of their trades. This is known as “front-running”. In other cases, LMPs with proprietary trading strategies wish to keep their positions secret even after trading because their strategies and positions contain valuable information. LMPs include hedge funds, mutual funds, and other specialized market players.
... For example, Engle, Russell, and Ferstenberg (2007) use execution data from Morgan Stanley algorithms to study the effects on trading costs of changing algorithm aggressiveness. Domowitz and Yegerman (2005) study execution costs of ITG buy-side clients, comparing results from different algorithm providers. Chaboud et al. (2009) study AT in the foreign exchange market and focus on its relation to volatility, ...
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Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions, cancellations, and executions) as a proxy for algorithmic trading, and we trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity.
... Engle, Russell, and Ferstenberg (2007) use execution data from Morgan Stanley algorithms to study the tradeoffs between algorithm aggressiveness and the mean and dispersion of execution cost. Domowitz and Yegerman (2005) study execution costs of ITG buy-side clients, comparing results from different algorithm providers. ...
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A seismic shift is taking place in the United States securities markets. The fault lines have been present for quite some time; however, it is only now, in the last few years that the ramifications of these displacements have been felt. The traditional approach to investing has gone from a focus on investing – namely examining companies to determine whether they will be a good long-term investment – to examining the markets as a whole. Nowhere is this shift more apparent than in the rise and increasing prevalence of quantitative trading models. As a result, there is now a disconnect between the markets themselves and the companies that are traded on the markets. Oftentimes, what a company does or does not do matters very little to whether that company’s stock should be bought or sold. Instead, whether that company’s stock is a good “buy” amounts more to how that stock is doing and how the market is behaving. This shift has broad implications for retail and institutional investor behavior, regulatory structures and the role of government in oversight and, if unchecked, the global economy at large. The ever-changing advances in computer technology have fostered a new breed of trading that is much more reliant on quantitative mathematics than on corporate analysis. This article explores algorithmic trading and assesses the impact of its dominance on regulation of the securities markets and their stability in the global economy.
... Their findings indicate that AT improves liquidity and enhances the informativeness of quotes". Similarly, [12] found AT to be a cost-effective technique for large orders. ...
Conference Paper
Algorithmic trading (AT) strategies aim at executing large orders discretely, in order to minimize the order's impact, whilst also hiding the traders' intentions. Most AT evaluation methods range from running the AT strategies against historical data (back testing) to evaluating them on simulated markets. The contribution of the work presented in this paper is twofold. First we investigated different types of agent-based market simulations and suggested how to identify the most suitable market simulation type, based on the specific market model to be investigated. Then we proposed an extended model of the Bayesian execution strategy. We implemented and assessed this model using our tool AlTraSimBa (Algorithmic Trading Simulation Back testing) against the standard Bayesian execution strategy and naive execution strategies, for momentum markets and random markets. The results revealed useful insights on the trade-offs between the frequency of decision making and more complex decision criteria, on one side, and the negative outcome of lost trading on the agents' side due to them not participating actively in the market for some of the execution steps.
... e to the difficulty in identifying AT, most existing research directly addressing AT has used data from brokers who sell AT products to institutional clients. Engle, Russell, and Ferstenberg (2007) use execution data from Morgan Stanley algorithms to study the tradeoffs between algorithm aggressiveness and the mean and dispersion of execution cost. Domowitz and Yegerman (2005) study execution costs of ITG buy-side clients, comparing results from different algorithm providers. Several recent studies use comprehensive data on AT. Chaboud et al. (2009) study the development of AT in the foreign exchange market on the electronic broking system (EBS) in three currency pairs euro-dollar, dollar-yen, and euro-yen. T ...
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We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is cheap and supply liquidity when it is expensive. AT contribute more to the efficient price by placing more efficient quotes and AT demanding liquidity to move the prices towards the efficient price.
... Our main objective is to introduce and empirically examine a new measure of realised volatility that includes the volume associated with the price of each trade, namely the volume weighted volatility (VWV or ˆV WV σ ), or alternatively demand-based volatility. 4 The related volume weighted average price (VWAP) has been popular with institutional traders for a number of years as a benchmark for trading success over the day, with the objective of generating an average buying price for the daily trading below the VWAP, or an average selling price above the VWAP (see Madhavan, 2002;Bessembinder, 2003;Kissell and Malamut, 2005;Hobson, 2006;Domowitz and Yegerman, 2006;Sofianos, 2006;Hu, 2007). Moreover, Ting (2006) shows that the VWAP is less noisy than using the closing price, 5 thereby yielding a better approximation of the unobservable efficient price. ...
... The widespread reliance on such solutions engendered new empirical patterns observable across major financial markets [24]. Dealing with pre-specified tasks at hand, automated trading systems have been leveraging execution speed while simultaneously attempting to optimize objective function be it the minimization of price volatility or transaction costs [25]. ...
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The paper postulates that enhanced informational efficiency and signal processing capacity, which have characterized the evolution of commodity markets’ architecture during the last two decades, have rendered commodity prices more robust with respect to external shocks. Our econometric analysis of times series over 2001–2015 revealed a persistent decline in the responsiveness of crude oil prices to inflows of information concerning potentially supply-disruptive events. International news on terrorist attacks involving damage to oil infrastructure including those occurring in proximity to oil extraction sites, political unrest, and conflicts of rivaling factions are all documented to exercise a decreasing impact on oil price volatility both over short and medium observation spans. The previously observed spikes in oil prices accompanying similar disruptive events in OPEC countries are also shown to flatten over time as price sensitivity to information shocks declines. The discovered weakening of market response becomes more pronounced from the mid-2000s, which corresponds to the period of rapid algorithmization of commodity trading.
... For order sizes up to 10% of daily average trading volume, algorithmic trading has been found to be a cost-effective method (Domowitz and Yegerman, 2005). The auto quote power of algorithms as a competitive advantage disappears when new entrants come with better models. ...
Conference Paper
Algorithmic trading has made a paradigm shift in Indian stock market. Popularity of algorithmic trading is gaining momentum among Indian traders and investors due to technological advancement. The purpose of this paper is to apply Interpretive Structural Modeling to develop a hierarchical structure among the key barriers of algorithmic trading in India. Eleven barriers have been identified through the literature reviews which are then validated for significance, using a structured questionnaire, from the domain experts. Interpretive structural modeling (ISM) approach has been utilized to rank the barriers and analyse their mutual interactions. Subsequently, MICMAC analysis was conducted to elucidate dependence and driving power of these barriers. MICMAC analysis also elicits the relative importance and interdependence between these barriers from Indian context. For Practitioners, a list of relevant barriers is indications to take a decision to adopt Algorithmic trading. The study has importance from Indian context due to scarcity of research in this area. For Researchers, this methodology facilitates to further carry out exploratory studies by identifying the factors and focus on their interactions through hierarchical structures. The proposed model developed through qualitative ISM modeling technique has been accomplished from the perspectives of capital market experts and brokers in algorithmic trading in India. Because of the novelty of the research we presume this may contribute significantly to the Literature.
... A number of studies have suggested that AT and HFT could follow VWAP strategies to optimize the timing of their trades (e.g. Domowitz and Yegerman, 2005;Hendershott et al., 2011;Easley, Lopez de Prado, and O'Hara, 2012). Carrion (2013) uses end-of-day VWAP metrics to show that, ex post, HFT times the market successfully. ...
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Does Algorithmic Trading (AT) exacerbate price swings in turbulent markets? We find that stocks with high AT experience less price drops (surges) on days when the market declines (increases) for more than 2%. This result is consistent with the view that AT minimizes price pressures and mitigates transitory pricing errors. Further analyses show that the net imbalances of AT liquidity demand and supply orders have smaller price impacts compared to non-AT net order imbalances and algorithmic traders reduce their price pressure by executing their trades based on the prevailing volume-weighted average prices.
... However, AT is insufficient to make a profit on every decision at every trading moment because the financial market is highly complicated ( Domowitz & Yegerman, 2006;Hu et al., 2015;Yadav, 2015 ). In their review of its limitations, Hu et al. (2015) find that AT poses a challenge in predicting future market trends. ...
Article
We study trading systems using reinforcement learning with three newly proposed methods to maximize total profits and reflect real financial market situations while overcoming the limitations of financial data. First, we propose a trading system that can predict the number of shares to trade. Specifically, we design an automated system that predicts the number of shares by adding a deep neural network (DNN) regressor to a deep Q-network, thereby combining reinforcement learning and a DNN. Second, we study various action strategies that use Q-values to analyze which action strategies are beneficial for profits in a confused market. Finally, we propose transfer learning approaches to prevent overfitting from insufficient financial data. We use four different stock indices—the S&P500, KOSPI, HSI, and EuroStoxx50—to experimentally verify our proposed methods and then conduct extensive research. The proposed automated trading system, which enables us to predict the number of shares with the DNN regressor, increases total profits by four times in S&P500, five times in KOSPI, 12 times in HSI, and six times in EuroStoxx50 compared with the fixed-number trading system. When the market situation is confused, delaying the decision to buy or sell increases total profits by 18% in S&P500, 24% in KOSPI, and 49% in EuroStoxx50. Further, transfer learning increases total profits by twofold in S&P500, 3 times in KOSPI, twofold in HSI, and 2.5 times in EuroStoxx50. The trading system with all three proposed methods increases total profits by 13 times in S&P500, 24 times in KOSPI, 30 times in HSI, and 18 times in EuroStoxx50, outperforming the market and the reinforcement learning model.
... Our main objective is to introduce and empirically examine a new measure of realised volatility that includes the volume associated with the price of each trade, namely the volume weighted volatility (VWV or ˆV WV σ ), or alternatively demand-based volatility. 4 The related volume weighted average price (VWAP) has been popular with institutional traders for a number of years as a benchmark for trading success over the day, with the objective of generating an average buying price for the daily trading below the VWAP, or an average selling price above the VWAP (see Madhavan, 2002;Bessembinder, 2003;Kissell and Malamut, 2005;Hobson, 2006;Domowitz and Yegerman, 2006;Sofianos, 2006;Hu, 2007). Moreover, Ting (2006) shows that the VWAP is less noisy than using the closing price, 5 thereby yielding a better approximation of the unobservable efficient price. ...
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We introduce a new conceptually superior realised volatility estimator, volume weighted volatility (VWV), which effectively measures demand-based volatility, rather than only measuring the variability of a price series. We compare the VWV to other return-And range-based measures using the stock index futures, with our results supporting the empirical uniqueness of VWV. First, regressions show that the VWV provides unique information. Second, VWV is (only) weakly associated with other volatility measures for the smallest four volatility quintiles. Third, correlograms illustrate that the VWV is less persistent than the other measures, leading to more unique volatility values. Finally, the VWV most closely approximates the normal distribution.
... Engle, Russell, and Ferstenberg (2012) use execution data from Morgan Stanley to study the trade-offs between algorithm aggressiveness and the mean and dispersion of execution costs. Domowitz and Yegerman (2006) Chaboud, Chiquoine, Hjalmarsson, and Vega (2014) study ATs in the foreign exchange markets on the electronic broking system (EBS) in 3 major currency pairs: euro-dollars, dollar-yen, euroyen. ...
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Prediction of stock prices using various computer programs is on rise. Popularly known in the field of finance as algorithmic trading, a radical transformation has taken place in the field of stock markets for decision making through automated decision making agents. Machine learning techniques can be applied for predicting stock prices. This paper attempts to study the various stock market forecasting processes available in the forecasting plugin of the WEKA tool. Twenty experiments have been conducted on twenty different stocks to analyse the prediction capacity of the tool.
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Grossman, Randy, " The Search for the Ultimate Trade: Market Players in
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Tabb, Larry, " Institutional Equity Trading in America: A Buy-Side Perspective, " consulting report, The Tabb Group, April 2004.
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Giraud, Jean-Renẻ, " Best Execution for Buy-Side Firms: A Challenging Issue, A Promising Debate, A Regulatory Challenge, " consulting report, Edhec-Risk Advisory, June 2004.
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QSG, " The implementation Costs of Algorithmic Trading, " consulting report, Quantitative Services Group LLC., December 2004.