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Showing papers on "Algorithmic trading published in 2015"


Journal ArticleDOI
TL;DR: The authors found that high market-wide attention events lead investors to sell their stock holdings dramatically when the level of the stock market is high, which has a negative impact on market prices, reducing market returns by 19 basis points on days following attention-grabbing events.
Abstract: Market-wide attention-grabbing events -- record levels for the Dow and front-page articles about the stock market -- predict the trading behavior of investors and, in turn, market returns. Both aggregate and household-level data reveal that high market-wide attention events lead investors to sell their stock holdings dramatically when the level of the stock market is high. Such aggressive selling has a negative impact on market prices, reducing market returns by 19 basis points on days following attention-grabbing events.

198 citations


Book
07 Oct 2015
TL;DR: In this article, the authors present a primer on the microstructure of financial markets and empirical and statistical evidence - prices and returns of electronic markets and the limit order book.
Abstract: Preface How to read this book Part I. Microstructure and Empirical Facts: 1. Electronic markets and the limit order book 2. A primer on the microstructure of financial markets 3. Empirical and statistical evidence - prices and returns 4. Empirical and statistical evidence - activity and market quality Part II. Mathematical Tools: 5. Stochastic optimal control and stopping Part III. Algorithmic and High-Frequency Trading: 6. Optimal execution with continuous trading I 7. Optimal execution with continuous trading II 8. Optimal execution with limit and market orders 9. Targeting volume 10. Market making 11. Pairs trading and statistical arbitrage strategies 12. Order imbalance Appendix A. Stochastic calculus for finance Bibliography Glossary Subject index.

172 citations


Journal ArticleDOI
TL;DR: In this paper, the authors exploit an optional colocation upgrade at NASDAQ OMX Stockholm to assess how speed affects market liquidity and find that the upgrade is pursued mainly by participants who engage in market making.
Abstract: We exploit an optional colocation upgrade at NASDAQ OMX Stockholm to assess how speed affects market liquidity Liquidity improves for the overall market and even for noncolocated trading entities We find that the upgrade is pursued mainly by participants who engage in market making Those that upgrade use their enhanced speed to reduce their exposure to adverse selection and to relax their inventory constraints In particular, the upgraded trading entities remain competitive at the best bid and offer even when their inventories are in their top decile Our results suggest that increasing the speed of market-making participants benefit market liquidity

146 citations


Journal ArticleDOI
01 Nov 2015
TL;DR: The review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.
Abstract: The first systematic literature review on evolutionary rule discovery in stock algorithmic trading.A clear demonstrate of studies in this field based on a classification framework.A precise analysis of gaps and limitations in existing studies based on detail of evaluation scheme.The most important factors influencing profitability of models are presented in detail.Targeted suggestions for future improvements based on the review are proposed. Despite the wide application of evolutionary computation (EC) techniques to rule discovery in stock algorithmic trading (AT), a comprehensive literature review on this topic is unavailable. Therefore, this paper aims to provide the first systematic literature review on the state-of-the-art application of EC techniques for rule discovery in stock AT. Out of 650 articles published before 2013 (inclusive), 51 relevant articles from 24 journals were confirmed. These papers were reviewed and grouped into three analytical method categories (fundamental analysis, technical analysis, and blending analysis) and three EC technique categories (evolutionary algorithm, swarm intelligence, and hybrid EC techniques). A significant bias toward the applications of genetic algorithm-based (GA) and genetic programming-based (GP) techniques in technical trading rule discovery is observed. Other EC techniques and fundamental analysis lack sufficient study. Furthermore, we summarize the information on the evaluation scheme of selected papers and particularly analyze the researches which compare their models with buy and hold strategy (B&H). We observe an interesting phenomenon where most of the existing techniques perform effectively in the downtrend and poorly in the uptrend, and considering the distribution of research in the classification framework, we suggest that this phenomenon can be attributed to the inclination of factor selections and problem in transaction cost selections. We also observe the significant influence of the transaction cost change on the margins of excess return. Other influenced factors are also presented in detail. The absence of ways for market trend prediction and the selection of transaction cost are two major limitations of the studies reviewed. In addition, the combination of trading rule discovery techniques and portfolio selection is a major research gap. Our review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.

139 citations


Journal ArticleDOI
TL;DR: The results show that the trading rule can beat the market, and the returns provided by the proposed trading rule are higher for the European than for the US index, which highlights the greater inefficiency of the European markets.
Abstract: This work provides empirical evidence which confronts the Efficient Market Hypothesis.This work introduces a new definition of the flag pattern.The results show that the trading rule can beat the market.The European market is more inefficient than the US market. This work presents empirical evidence which confronts the classical Efficient Market Hypothesis, which states that it is not possible to beat the market by developing a strategy based on a historical price series.We propose a risk-adjusted profitable trading rule based on technical analysis and the use of a new definition of the flag pattern. This rule defines when to buy or sell, the profit pursued in each operation, and the maximum bearable loss. In order to untie the results from randomness, we used a database comprised of 91,307 intraday observations from the US Dow Jones index. We parameterized the trading rule by generating 96 different configurations and reported the results of the whole sample over 3 subperiods. In order to widen its validity we also replicated the analysis on two leading European indexes: the German DAX and the British FTSE. The returns provided by the proposed trading rule are higher for the European than for the US index, which highlights the greater inefficiency of the European markets.

139 citations


Journal ArticleDOI
TL;DR: The analysis of Bitcoin reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarized and rising exchange volumes, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.
Abstract: The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.

139 citations


Journal ArticleDOI
TL;DR: In this paper, the authors examined the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross section of securities in the US and found that higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading.

119 citations


Journal ArticleDOI

112 citations


Journal ArticleDOI
TL;DR: In this paper, the authors presented results about US equity market quality and showed that despite many complaints in the national media, various measures of market quality indicate that US markets continue to be very healthy.
Abstract: This paper updates our previous study, "Equity Trading in the 21st Century", which presented results about US equity market quality. Despite many complaints in the national media, various measures of market quality indicate that US markets continue to be very healthy. Trade transaction cost estimates have stayed low and market depth and execution speeds remained high. New findings that measure the total transaction cost of executing very large block orders indicate that improvements in market quality also have benefited large institutional traders. While still high, both the number of quotes per trade and per minute have declined substantially from their peaks in 2008. Intraday volatility is below the levels of the pre-electronic 1990s. Although market quality is quite good, it could be enhanced. We discuss some current concerns about maker/taker pricing, dark pools, high frequency trading, tick sizes, designated dealers, transaction taxes, IPOs, and market stability.

112 citations


Journal ArticleDOI
TL;DR: In this paper, a consistent approach that integrates various datasources in the design of algorithmic traders is presented, which allows them to derive insights into the principles behind the profitability of their trading strategies.
Abstract: The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.

100 citations


Journal ArticleDOI
TL;DR: Evidence from three different samples consistent with investors substituting between playing the lottery and gambling in financial markets is offered, showing the negative relation between trading activity and jackpots is stronger for individuals who are more likely to play the lottery.
Abstract: This paper offers evidence from three different samples consistent with investors substituting between playing the lottery and gambling in financial markets. In the United States, increases in the jackpots of the multistate lotteries Powerball and Mega Millions are associated with significant reductions in small trade participation in the stock market. California-based discount brokerage clients and German discount brokerage clients are significantly less likely to trade during weeks with larger lottery prizes in the California and German lotteries, respectively. Variation in lottery prizes affects speculative trading in more lottery-like securities such as individual stocks and options, but not trading in bonds and mutual funds. Trading that is likely associated with long-term savings motives, such as trading in retirement accounts, does not respond to lottery jackpots, either. The negative relation between trading activity and jackpots is stronger for individuals who are more likely to play the lottery. This paper was accepted by Brad Barber, finance.

Posted Content
TL;DR: The application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition on portfolios of financial data shows that the decomposition is an effective mathematical tool for data-driven discovery of market patterns.
Abstract: We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition (DMD) on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the state of the system into low-rank terms whose temporal coefficients in time are known. By extracting key temporal coherent structures (portfolios) in its sampling window, it provides a regression to a best fit linear dynamical system, allowing for a predictive assessment of the market dynamics and informing an investment strategy. The data-driven analytics capitalizes on stock market patterns, either real or perceived, to inform buy/sell/hold investment decisions. Critical to the method is an associated learning algorithm that optimizes the sampling and prediction windows of the algorithm by discovering trading hot-spots. The underlying mathematical structure of the algorithms is rooted in methods from nonlinear dynamical systems and shows that the decomposition is an effective mathematical tool for data-driven discovery of market patterns.

Journal ArticleDOI
TL;DR: In this article, the authors analyse transaction data from the e-MID market, which is the only electronic interbank market in the Euro Area and US, over a period of 11 years (1999-2009).
Abstract: Interbank markets allow credit institutions to exchange capital for purposes of liquidity management. These markets are among the most liquid markets in the financial system. However, liquidity of interbank markets dropped during the 2007–2008 financial crisis, and such a lack of liquidity influenced the entire economic system. In this paper, we analyse transaction data from the e-MID market which is the only electronic interbank market in the Euro Area and US, over a period of 11 years (1999–2009). We adapt a method developed to detect statistically validated links in a network, in order to reveal preferential trading in a directed network. Preferential trading between banks is detected by comparing empirically observed trading relationships with a null hypothesis that assumes random trading among banks doing a heterogeneous number of transactions. Preferential trading patterns are revealed at time windows of 3-maintenance periods. We show that preferential trading is observed throughout the whole period...

Journal ArticleDOI
TL;DR: This paper developed a consumption-based model of markets in which all institutional traders recognize their impact on prices, and used this model to predict temporary and permanent price effects of supply shocks, order breakup, limits to arbitrage, nonneutrality of trading frequency, and real effects of shocks and announcements in periods other than event dates.
Abstract: Large institutional investors dominate many financial markets. This paper develops a consumption-based model of markets in which all institutional traders recognize their impact on prices. Bilateral (buyer and seller) market power changes efficiency and arbitrage properties of equilibrium. Predictions match temporary and permanent price effects of supply shocks, order breakup, limits to arbitrage, nonneutrality of trading frequency, and real effects of shocks and announcements in periods other than event dates. Maximizing welfare and stabilizing liquidity through disclosure of information about fundamentals presents a trade-off. Equilibrium representation as “trading against price impact” provides a link with the industry's practice

Journal ArticleDOI
TL;DR: Risk metrics are proposed to assess the performance of High Frequency trading strategies that seek to maximize profits from making the realized spread where the holding period is extremely short (fractions of a second, seconds or at most minutes).
Abstract: We propose risk metrics to assess the performance of high-frequency (HF) trading strategies that seek to maximize profits from making the realized spread where the holding period is extremely short (fractions of a second, seconds, or at most minutes). The HF trader maximizes expected terminal wealth and is constrained by both capital and the amount of inventory that she can hold at any time. The risk metrics enable the HF trader to fine tune her strategies by trading off different metrics of inventory risk, which also proxy for capital risk, against expected profits. The dynamics of the midprice of the asset are driven by information flows which are impounded in the midprice by market participants who update their quotes in the limit order book. Furthermore, the midprice also exhibits stochastic jumps as a consequence of the arrival of market orders that have an impact on prices which can give rise to market momentum (expected prices to trend up or down). The HF trader's optimal strategy incorporates a buffer to cover adverse selection costs and manages inventories to maximize the expected gains from market momentum.

Journal ArticleDOI
TL;DR: In this article, the authors propose a model in which allows traders to explicitly choose their trading partners as well as the number of trading links in a dynamic framework and show that traders with higher trading needs optimally choose to match with traders with lower needs for trade and they build fewer links in equilibrium.
Abstract: This paper proposes a theory of intermediation in which intermediaries emerge endogenously as the choice of agents. In contrast to the previous trading models based on random matching or exogenous networks, we allow traders to explicitly choose their trading partners as well as the number of trading links in a dynamic framework. We show that traders with higher trading needs optimally choose to match with traders with lower needs for trade and they build fewer links in equilibrium. As a result, traders with the least trading need turn out to be the most connected and have the highest gross trade volume. The model therefore endogenously generates a core-periphery trading network that we often observe: a financial architecture that involves a small number of large, interconnected institutions. We use this framework to study bid-ask spreads, trading volume, asset allocation and implications on systemic risk.

Journal ArticleDOI
TL;DR: In this article, the performance of a pairs trading system based on various pairs selection methods is analyzed. But the authors focus on the distance method and do not consider the cointegration method.
Abstract: Pairs trading is a popular dollar-neutral trading strategy. This article, using the components of the S&P 500 index, explores the performance of a pairs trading system based on various pairs selection methods. Whereas large empirical applications in the literature focus on the distance method, this article also deals with well-known statistical and econometric techniques such as stationarity and cointegration which make the trading system much more demanding from a computational point of view. Trades are initiated when stocks deviate from their equilibrium. Our results confirm, after controlling for risk and transaction costs, that the distance method generates insignificant excess returns. While a pairs selection following the stationarity criterion leads to a weak performance, this article reveals that cointegration provides a high, stable and robust return.

Journal ArticleDOI
TL;DR: In this article, the authors provide an explicit closed-form strategy for an investor who executes a large order when market order-flow from all agents, including the investor's own trades, has a permanent price impact.
Abstract: We provide an explicit closed-form strategy for an investor who executes a large order when market order-flow from all agents, including the investor's own trades, has a permanent price impact. The strategy is found in closed-form when the permanent and temporary price impacts are linear in the market's and investor's rates of trading. We do this under very general assumptions about the stochastic process followed by the order-flow of the market. The optimal strategy consists of an Almgren-Chriss execution strategy adjusted by a weighted-average of the future expected net order-flow (given by the difference of the market's rate of buy and sell market orders) over the execution trading horizon and proportional to the ratio of permanent to temporary linear impacts. We use historical data to calibrate the model to Nasdaq traded stocks and use simulations to show how the strategy performs.

Journal ArticleDOI
TL;DR: In this paper, a mean-field game framework for a multiple agent optimal execution problem with continuous trading is introduced, where all agents are exposed to temporary price impact and attempt to balance their impact against price uncertainty.
Abstract: We introduce, for the first time, a mean-field game framework for a multiple agent optimal execution problem with continuous trading. This modeling generalizes the classical single agent optimal liquidation problem to a setting with (i) a major agent who is liquidating a large portion of shares, and (ii) a number of minor agents (high-frequency traders (HFTs)) who detect and trade along with the liquidator. As in the classical framework, all agents are exposed to temporary price impact and attempt to balance their impact against price uncertainty. Unlike most other works, we account for the permanent price impact that order-flow from all agents have on the midprice and this induces a distinct cross interaction between major and minor agents. This formulation falls into the realm of stochastic dynamic game problems with mean-field couplings in the dynamics, and we analyze the problem using a mean-field game approach. We obtain a set of decentralized feedback trading strategies for the major and minor agents, and express the solution explicitly in terms of a deterministic fixed point problem. For a finite N population of HFTs, the set of major-minor agent mean-field game strategies is shown to have an epsilon-Nash equilibrium property where epsilon→0 as N→∞.

Journal ArticleDOI
TL;DR: The authors investigated whether and how trading by foreign and domestic institutional investors improves the extent to which firm-specific information is incorporated into stock prices, captured by stock price synchronicity.
Abstract: Using a large sample of firms listed on the Korea Stock Exchange over 1998–2007, this study investigates whether and how trading by foreign and domestic institutional investors improves the extent to which firm-specific information is incorporated into stock prices, captured by stock price synchronicity. We find, first, that stock price synchronicity decreases significantly with the intensity of trading by foreign investors and domestic institutional investors. Second, trading by foreign investors facilitates the incorporation of firm-specific information into stock prices to a greater extent than trading by aggregate domestic institutions. Third, among domestic institutions with differing investment horizons, short-term investing institutions, such as securities and investment trust companies, play a more important role in incorporating firm-specific information into stock prices via their trading activities, compared with long-term investing institutions, such as banks and insurance companies. Finally, we provide evidence suggesting that trading by foreign and domestic short-term institutions reduces the extent of accrual mispricing. Our results are robust to a variety of sensitivity checks.

Posted Content
TL;DR: In this paper, the authors examined the impact of stock exchange trading rules and surveillance on the frequency and severity of suspected insider trading cases in 22 stock exchanges around the world over the period January 2003 through June 2011.
Abstract: We examine the impact of stock exchange trading rules and surveillance on the frequency and severity of suspected insider trading cases in 22 stock exchanges around the world over the period January 2003 through June 2011. Using new indices for market manipulation, insider trading, and broker-agency conflict based on the specific provisions of the trading rules of each stock exchange, along with surveillance to detect non-compliance with such rules, we show that more detailed exchange trading rules and surveillance over time and across markets significantly reduce the number of suspected cases, but increase the profits per suspected case.

Posted Content
TL;DR: The authors argue that Twitter and social media are becoming more powerful forces, not just because they connect people or generate new modes of participation, but because they are connecting human communicative spaces to automated computational spaces in ways that are affectively contagious and highly volatile.
Abstract: '@AP: Breaking: Two Explosions in the White House and Barack Obama is injured’. So read a tweet sent from a hacked Associated Press Twitter account @AP, which affected financial markets, wiping out $136.5 billion of the Standard & Poor’s 500 Index’s value. While the speed of the Associated Press hack crash event and the proprietary nature of the algorithms involved make it difficult to make causal claims about the relationship between social media and trading algorithms, we argue that it helps us to critically examine the volatile connections between social media, financial markets, and third parties offering human and algorithmic analysis. By analyzing the commentaries of this event, we highlight two particular currents: one formed by computational processes that mine and analyze Twitter data, and the other being financial algorithms that make automated trades and steer the stock market. We build on sociology of finance together with media theory and focus on the work of Christian Marazzi, Gabriel Tarde and Tony Sampson to analyze the relationship between social media and financial markets. We argue that Twitter and social media are becoming more powerful forces, not just because they connect people or generate new modes of participation, but because they are connecting human communicative spaces to automated computational spaces in ways that are affectively contagious and highly volatile.

Journal ArticleDOI
TL;DR: A price model is developed that presents the stochastic dynamics of a geometric Brownian motion and incorporates a log-linear effect of the investor's transactions and derives an explicit solution to the optimal execution problem if the time horizon is infinite.
Abstract: We consider the so-called optimal execution problem in algorithmic trading, which is the problem faced by an investor who has a large number of stock shares to sell over a given time horizon and whose actions have an impact on the stock price. In particular, we develop and study a price model that presents the stochastic dynamics of a geometric Brownian motion and incorporates a log-linear effect of the investor's transactions. We then formulate the optimal execution problem as a degenerate singular stochastic control problem. Using both analytic and probabilistic techniques, we establish simple conditions for the market to allow for no arbitrage or price manipulation and develop a detailed characterization of the value function and the optimal strategy. In particular, we derive an explicit solution to the problem if the time horizon is infinite.

Journal ArticleDOI
Lin Tong1
TL;DR: In this article, the authors show that one standard deviation increase in the intensity of high frequency trading activities increases institutional execution shortfall costs by a third, and that high frequency traders are attracted to stocks that have high trading costs.
Abstract: The rapid growth of high frequency trading (HFT) has aroused considerable public attention and policy interests in its impact on institutional investors. Previous studies show that HFT decreases the average bid-ask spread. However, the major component of institutional trading costs is the price impact, as measured by the execution shortfall. Combining data on institutional trades and HFT trades, I find that HFT increases traditional institutional investors' trading costs. Specifically, one standard deviation increase in the intensity of HFT activities increases institutional execution shortfall costs by a third. I also perform various tests to rule out an alternative explanation that high frequency traders are attracted to stocks that have high trading costs. Further analysis suggests that HFT represents a short-lived and expensive source of liquidity provision when demand and supply among institutional investors are imbalanced, and that the impact on institutional trading costs is most pronounced when high frequency traders engage in directional strategies. Additionally, I find that institutional trading skills can alleviate the adverse impact of HFT.

Journal ArticleDOI
TL;DR: In this paper, the authors defined market efficiency in terms of trading profitability, where a zero-profit competitive equilibrium implies market efficiency, and empirically test for market efficiency by assessing the performance of trading strategies from the perspective of virtual traders.
Abstract: The California Independent System Operator (CAISO) has implemented Convergence Bidding (CB) on February 1, 2011 under Federal Energy Regulatory Commission’s September 21, 2006 Market Redesign and Technology Upgrade Order. CB is a financial mechanism that allows market participants, including electricity suppliers, consumers and virtual traders, to arbitrage price differences between the day-ahead (DA) market and the real-time (RT) market without physically consuming or producing energy. In this paper, market efficiency is defined in terms of trading profitability, where a zero-profit competitive equilibrium implies market efficiency (Jensen in, J Financial Econ 6(2):95–101, 1978). We analyze market data in the CAISO electric power markets, and empirically test for market efficiency by assessing the performance of trading strategies from the perspective of virtual traders. By viewing DA–RT spreads as payoffs from a basket of correlated assets, we can formulate a chance constrained portfolio selection problem, where the chance constraint takes two different forms as a value-at-risk constraint and a conditional value-at-risk constraint, to find the optimal trading strategy. A hidden Markov model (HMM) is further proposed to capture the presence of the time-varying forward premium. This is meant to be a contribution to the modeling of regime shifts in the electricity forward premium with unobservable states. Our backtesting results cast doubt on the efficiency of the CAISO electric power markets, as the trading strategy generates consistent profits after the introduction of CB, even in the presence of transaction costs. Nevertheless, by comparing with the performance before the introduction of CB, we find that the profitability decreases significantly, which enables us to identify the efficiency gain brought about by CB. Convincing evidence for the improvement of market efficiency in the presence of CB is further provided by the test for the Bessembinder and Lemmon (J Finance 57(3):1347–1382, 2002) model.

Journal ArticleDOI
TL;DR: In this paper, the authors examined the world's largest carbon exchange, ICE's ECX, by applying Chordia et al.'s (2008) conception of short-horizon return predictability as an inverse indicator of market efficiency.
Abstract: We examine the world’s largest carbon exchange, ICE’s ECX, by applying Chordia et al.’s (2008) conception of short-horizon return predictability as an inverse indicator of market efficiency. We find a strong relationship between liquidity and market efficiency such that when spreads narrow, return predictability diminishes. This is more pronounced for the highest trading carbon futures and during periods of low liquidity. Since the start of trading in Phase II of the EU Emissions Trading Scheme (EU-ETS) prices have continuously moved nearer to unity with, efficient, random walk benchmarks, and this improves from year to year. Overall, our findings suggest trading quality in the EU-ETS has improved markedly and matures over the 2008-2011 compliance years.

Journal ArticleDOI
TL;DR: Two explicit closed-form optimal execution strategies to target volume weighted average price (VWAP) are provided, under very general assumptions about the stochastic process followed by the volume traded in the market, and they account for permanent price impact stemming from order-flow of the agent and all other traders.
Abstract: We provide two explicit closed-form optimal execution strategies to target VWAP. We do this under very general assumptions about the stochastic process followed by the volume traded in the market, and, unlike earlier studies, we account for permanent price impact stemming from order-flow of the agent and all other traders. One of the strategies consists of TWAP adjusted upward by a fraction of instantaneous order-flow and adjusted downward by the average order-flow that is expected over the remaining life of the strategy. The other strategy consists of the Almgren-Chriss execution strategy adjusted by the expected volume and net order-flow during the remaining life of the strategy. We calibrate model parameters to five stocks traded in Nasdaq (FARO, SMH, NTAP, ORCL, INTC) and use simulations to show that the strategies target VWAP very closely and on average outperform the target by between 0.10 and 8 basis points.

Journal ArticleDOI
TL;DR: A new intelligent trading support system based on sentiment prediction is proposed by combining text-mining techniques, feature selection and decision tree algorithms in an effort to analyze and extract semantic terms expressing a particular sentiment from stock-related micro-blogging messages called "StockTwits".
Abstract: Provide linkage between changes in volume semantic terms and subsequent market moves.Sell short at higher prices resulted from decreased appearance of negative words.Buy or take long positions resulted from increased appearance of positive words.StockTwits contains valuable information and precede trading activity in the market. Growing evidence is suggesting that postings on online stock forums affect stock prices, and alter investment decisions in capital markets, either because the postings contain new information or they might have predictive power to manipulate stock prices. In this paper, we propose a new intelligent trading support system based on sentiment prediction by combining text-mining techniques, feature selection and decision tree algorithms in an effort to analyze and extract semantic terms expressing a particular sentiment (sell, buy or hold) from stock-related micro-blogging messages called "StockTwits". An attempt has been made to investigate whether the power of the collective sentiments of StockTwits might be predicted and how the changes in these predicted sentiments inform decisions on whether to sell, buy or hold the Dow Jones Industrial Average (DJIA) Index. In this paper, a filter approach of feature selection is first employed to identify the most relevant terms in tweet postings. The decision tree (DT) model is then built to determine the trading decisions of those terms or, more importantly, combinations of terms based on how they interact. Then a trading strategy based on a predetermined investment hypothesis is constructed to evaluate the profitability of the term trading decisions extracted from the DT model. The experiment results based on 122-tweet term trading (TTT) strategies achieve a promising performance and the (TTT) strategies dramatically outperform random investment strategies. Our findings also confirm that StockTwits postings contain valuable information and lead trading activities in capital markets.

Journal ArticleDOI
TL;DR: The article discusses the relationship between the Merc’s automation and the embodied, deeply social trading practices of the Merc's open-outcry trading pits, and compares how the Merc was mechanized with the quite different—and in a sense more explicitly “social”—project of automation launched by the Mercs rival, the Chicago Board of Trade.
Abstract: This article investigates one important strand in the evolution of today's high-frequency trading or HFT (the fast, automated trading of large numbers of financial securities). That strand is the history of the automation of trading on what has become the world's most prominent futures exchange, the Chicago Mercantile Exchange or Merc. The process of the automation of the Merc was episodic, often driven by responses to perceived external threats, and involved both "local" politics and transnational considerations. The article discusses the relationship between the Merc's automation and the embodied, deeply social trading practices of the Merc's open-outcry trading pits, and compares how the Merc was mechanized with the quite different-and in a sense more explicitly "social"-project of automation launched by the Merc's rival, the Chicago Board of Trade.

Journal ArticleDOI
TL;DR: In the first phase of the European Union's Emissions Trading System (EU ETS), the authors empirically investigated firm trading behavior and the importance of permit trading transaction costs, such as information costs and search costs, and found that firms with smaller number of installations and with less trading experience were less likely to participate in the European emissions trading market and traded the lower quantities of permits.
Abstract: This study is one of the first to empirically investigate firm trading behaviour and the importance of permit trading transaction costs, such as information costs and search costs, in the first phase of the European Union’s Emissions Trading System (EU ETS). The signs and significance of our constructed transaction costs proxy variables indicate for a presence of these costs in the initial years of the EU ETS. In particular, this paper shows that ETS firms with the smaller number of installations and with less trading experience were less likely to participate in the European emissions trading market and traded the lower quantities of permits. Furthermore, these firms chose to trade permits indirectly via third parties. This study also supports the concerns that transaction costs could be excessive for smaller participants and firms operating in the new EU member states.