Showing papers on "Algorithmic trading published in 2009"

Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market

Abstract: We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high-frequency data that identify computer-generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high-frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.

Learning By Trading

Abstract: Using a large sample of individual investor records over a nine-year period, we analyze survival rates, the disposition effect and trading performance at the individual level to determine whether and how investors learn from their trading experience. We find evidence of two types of learning: some investors become better at trading with experience, while others stop trading after realizing that their ability is poor. A substantial part of overall learning by trading is explained by the second type. By ignoring investor attrition, the existing literature significantly overestimates how quickly investors become better at trading.

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

Abstract: Acknowledgments. Chapter 1 Introduction. Chapter 2 Evolution of High-Frequency Trading. Financial Markets And Technological Innovation. Evolution Of Trading Methodology. Chapter 3 Overview of the Business of High-Frequency Trading. Comparison With Traditional Approaches to Trading. Market Participants. Operating Model. Economics. Capitalizing a High-Frequency Trading Business. Conclusion. Chapter 4 Financial Markets Suitable for High-Frequency Trading. Financial Markets and Their Suitability for High-Frequency Trading. Conclusion. Chapter 5 Evaluating Performance of High-Frequency Strategies. Basic Return Characteristics. Comparative Ratios. Performance Attribution. Other Considerations in Strategy Evaluation. Conclusion. Chapter 6: Orders, Traders and their Applicability to High-Frequency Trading. Order Types. Order Distributions. Conclusion. Chapter 7: Market Inefficiency and Profit Opportunities at Different Frequencies. Predictability of Price Moves at High Frequencies. Conclusion. Chapter: 8: Searching for High-Frequency Trading Opportunities. Statistical Properties of Returns. Linear Econometric Models. Volatility Modeling. Nonlinear Models. Conclusion. Chapter 9: Working with Tick Data. Properties of Tick Data. Quantity and Quality of Tick Data. Bid-Ask Spreads. Bid-Ask Bounce. Modeling Arrivals of Tick Data. Applying Traditional Econometric Techniques to Tick Data. Conclusion. Chapter 10: Trading on Market Microstructure Inventory Models. Overview of Inventory Trading Strategies. Orders, Traders and Liquidity. Profitable Market Making. Directional Liquidity Provision. Conclusion. Chapter 11: Trading on Market Microstructure Information Models. Measures of Asymmetric Information. Information-Based Trading Models. Conclusion. Chapter 12: Event Arbitrage. Developing Event Arbitrage Trading Strategies. What Constitutes an Event? Forecasting Methodologies. Tradeable News. Application of Event Arbitrage. Conclusion. Chapter 13: Statistical Arbitrage in High Frequency Settings. Mathematical Foundations. Practical Applications of Statistical Arbitrage. Conclusion. Chapter 14: Creating and Managing Portfolios of High-Frequency Strategies. Analytical Foundations of Portfolio Optimization. Effective Portfolio Management Practices. Conclusion. Chapter 15: Back-Testing Trading Models. Evaluating Point Forecasts. Evaluating Directional Forecasts. Conclusion. Chapter 16: Implementing High-Frequency Trading Systems. Model Development Lifecycle. System Implementation. Testing Trading Systems. Conclusion. Chapter 17: Risk Management. Determining Risk Management Goals. Measuring Risk. Managing Risk. Conclusion. Chapter 18: Executing and Monitoring High-Frequency Trading. Executing High-Frequency Trading Systems. Monitoring High-Frequency Execution. Conclusion. Chapter 19: Post-Trade Profitability Analysis. Post-Trade Cost Analysis. Post-Trade Performance Analysis. References. About the Web Site. About The Author. Index.

O/S: The Relative Trading Activity in Options and Stock

Abstract: Relatively little is known about the trading volume in derivatives relative to the volume in underlying stocks. We study time-series properties and the determinants of the options/stock trading volume ratio (O/S) using a comprehensive cross-section and time-series of data on equities and their listed options. O/S is related to many intuitive determinants such as delta and trading costs, and it also varies with institutional holdings, analyst following, and analyst forecast dispersion. O/S is higher around earnings announcements (suggesting increased trading in the options market), and higher O/S predicts lower abnormal returns after the earnings announcement, suggesting that options trading improves market efficiency.

Market Impact and Trading Profile of Hidden Orders in Stock Markets

Abstract: We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5–0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

Algorithmic Trading and Information

Abstract: 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.

A quantitative stock prediction system based on financial news

Abstract: We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText) The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText's predictions against existing quantitative funds and human stock pricing experts We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 01954, predicted Directional Accuracy of 7118% and a Simulated Trading return of 850% (compared to 562% for the S&P 500 index) In direct comparisons to existing market experts and quantitative mutual funds, our system's trading return of 850% outperformed well-known trading experts Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2% higher return than the best performing quant fund

Risk in Dynamic Arbitrage: The Price Effects of Convergence Trading

Abstract: I develop an equilibrium model of convergence trading and its impact on asset prices. Arbitrageurs optimally decide how to allocate their limited capital over time. Their activity reduces price discrepancies, but their activity also generates losses with positive probability, even if the trading opportunity is fundamentally riskless. Moreover, prices of identical assets can diverge even if the constraints faced by arbitrageurs are not binding. Occasionally, total losses are large, making arbitrageurs’ returns negatively skewed, consistent with the empirical evidence. The model also predicts comovement of arbitrageurs’ expected returns and market liquidity. Many hedge funds and some other financial institutions attempt to exploit the relative mispricing of assets. However, from time to time, these institutions (whom I will refer to as convergence traders or arbitrageurs) suffer spectacular losses if the prices of these assets diverge, forcing them to unwind some of their positions. The near-collapse of the Long-Term Capital Management hedge fund in 1998 is frequently cited as an example of this phenomenon. 1 To what extent can these losses be attributed to the actions of arbitrageurs as opposed to unforeseen shocks? Why do other institutions with liquid capital not eliminate the abnormal returns around these events? In this paper, I develop a theoretical model to address these questions. I show that such losses can occur in the absence of any shock and that prices of identical assets can diverge even if the constraints faced by arbitrageurs are not binding.

System and method for analyzing and displaying security trade transactions

Abstract: A system and methods for processing and charting security exchange trading and market information shows security traders if current transactions originated as buy orders or sell orders, and simultaneously indicates traded quantity. Security exchange trading information is received that includes the price, volume and time of each trade. In addition, security exchange market information is received from buyers, specifying bide prices and quantities, and from sellers, specifying asking prices and quantities. The security exchange trading and market information is processed simultaneously and displayed as a continuously updated real-time chart depicting the exchange auction process whereby buyers and sellers agree to trade at specified prices, including details of individual transactions. The chart is formed by plotting each trade at the price traded, and for each plot point shows a distinctive icon indication whether the transaction was initiated by a buyer or seller.

Liquidity Cycles and Make/Take Fees in Electronic Markets

Abstract: We develop a dynamic model of a market with two specialized sides: traders posting quotes ("market makers") and traders hitting quotes ("market takers"). Traders monitor the market to seize profit opportunities, generating high frequency liquidity cycles. Monitoring decisions by market-makers and market-takers are self-reinforcing, generating multiple equilibria with differing liquidity levels and duration clustering. The trading rate is typically maximized when makers and takers are charged different fees or even paid rebates. The model yields several empirical implications regarding the determinants of make/take fees, the trading rate, the bid-ask spread, and the effects of algorithmic trading on liquidity and welfare.

Automated Detection of Financial Events in News Text

Abstract: markdownToday’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news, however, makes manual analysis impossible, and due to the unstructured nature of text, the (semi-)automatic extraction and application of financial events remains a non-trivial task. Therefore, the studies composing this dissertation investigate 1) how to accurately identify financial events in news text, and 2) how to effectively use such extracted events in financial applications. Based on a detailed evaluation of current event extraction systems, this thesis presents a competitive, knowledge-driven, semi-automatic system for financial event extraction from text. A novel pattern language, which makes clever use of the system’s underlying knowledge base, allows for the definition of simple, yet expressive event extraction rules that can be applied to natural language texts. The system’s knowledge-driven internals remain synchronized with the latest market developments through the accompanying event-triggered update language for knowledge bases, enabling the definition of update rules. Additional research covered by this dissertation investigates the practical applicability of extracted events. In automated stock trading experiments, the best performing trading rules do not only make use of traditional numerical signals, but also employ news-based event signals. Moreover, when cleaning stock data from disruptions caused by financial events, financial risk analyses yield more accurate results. These results suggest that events detected in news can be used advantageously as supplementary parameters in financial applications.

Evaluation of Pairs Trading Strategy at the Brazilian Financial Market

Abstract: Pairs trading is a popular trading strategy that tries to take advantage of market inefficiencies in order to obtain profit. The idea is simple: find two stocks that move together and take long/short positions when they diverge abnormally, hoping that the prices will converge in the future. From the academic point of view of weak market efficiency theory, pairs trading strategy shouldn’t present positive performance since, according to it, the actual price of a stock reflects its past trading data, including historical prices. This leaves us with a question, does pairs trading strategy presents positive performance for the Brazilian market? The main objective of this research is to verify the performance and risk of pairs trading in the Brazilian financial market for different frequencies of the database, daily, weekly and monthly prices for the same time period. The main conclusion of this simulation is that pairs trading strategy was a profitable and market neutral strategy at the Brazilian Market. Such profitability was consistent over a region of the strategy’s parameters. The best results were found for the highest frequency (daily), which is an intuitive result.

Do futures lead price discovery in electronic foreign exchange markets

Abstract: Using intraday data, this study investigates the contribution to the price discovery of Euro and Japanese Yen exchange rates in three foreign exchange markets based on electronic trading systems: the CME GLOBEX regular futures, E-mini futures, and the EBS interdealer spot market. Contrary to evidence in equity markets and more recent evidence in foreign exchange markets, the spot market is found to consistently lead the price discovery process for both currencies during the sample period. Furthermore, E-mini futures do not contribute more to the price discovery than the electronically traded regular futures. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 29:137–156, 2009

Some effects of transaction taxes under different microstructures

Abstract: We show that the effectiveness of transaction taxes depends on the market microstructure. Within our model, heterogeneous traders use a blend of technical and fundamental trading strategies to determine their orders. In addition, they may become inactive if the profitability of trading decreases. We find that in a continuous double auction market the imposition of a transaction tax is not likely to stabilize financial markets since a reduction in market liquidity amplifies the average price impact of a given order. In a dealership market, however, abundant liquidity is provided by specialists, and thus a transaction tax may reduce volatility by crowding out speculative orders.

Prebid Run-Ups Ahead of Canadian Takeovers: How Big Is the Problem?

Abstract: Les auteurs examinent la maniere dont evoluent le cours des actions et le volume des transactions aa l'approche d'une offre publique d'achat (OPA); leur echantillon englobe 420 entreprises canadiennes ayant ete l'objet de telles operations entre 1985 et 2002.

Is Speculation Destabilizing

Abstract: The possibility that speculative trading destabilizes or creates a volatile market is frequently debated. To test the hypothesis that speculative trading is destabilizing we employ a unique dataset from the U.S. Commodity Futures Trading Commission (CFTC) on individual positions of speculators. While others have used a more aggregated version of our data, here we test, for the first known time, whether speculators cause, in a forecasting sense, price movements and volatility in futures markets and, therefore, destabilize markets. Our findings provide evidence that speculative trading in futures markets is not destabilizing. In particular, speculative trading activity reduces volatility levels.

Does the law of one price hold in international financial markets? Evidence from tick data

Abstract: This paper investigates the validity of the law of one price (LOP) in international financial markets by examining the frequency, size and duration of inter-market price differentials for borrowing and lending services (‘one-way arbitrage’). Using a unique data set for three major capital and foreign exchange markets that covers a period of more than seven months at tick frequency, we find that the LOP holds on average, but numerous economically significant violations of the LOP arise. The duration of these violations is high enough to make it worthwhile searching for one-way arbitrage opportunities in order to minimize borrowing costs and/or maximize earnings on given funds. We also document that such opportunities decline with the pace of the market and increase with market volatility.

Strategic Cross-Trading in the U.S. Stock Market

Abstract: We provide a theory and novel empirical evidence of cross-price impact -- the permanent impact of informed trades in one asset on the prices of other (either related or fundamentally unrelated) assets -- in the U.S. stock market. To guide our analysis, we develop a parsimonious model of multi-asset trading in the presence of two realistic market frictions -- information heterogeneity and imperfect competition among informed traders -- but in which extant channels of trade and price co-formation in the literature are ruled out by construction. In that setting, we show cross-price impact to be the equilibrium outcome of strategic trading activity of risk-neutral speculators across many assets to mask their information advantage about some other assets. We find strong evidence of cross-asset informational effects in a comprehensive sample of the trading activity in NYSE and NASDAQ stocks between 1993 and 2004: Net order flow in one industry or random stock has a significant, persistent, and robust impact on daily returns of other industries or random stocks. Our empirical analysis further indicates that, consistent with our stylized model, both direct (i.e., an asset's own) and absolute cross-price impact are i) smaller when speculators are more numerous; ii) greater when marketwide dispersion of beliefs is higher; iii) greater among stocks dealt by the same specialist; and iv) smaller when macroeconomic news of good quality is released.

Optimization of the trading rule in foreign exchange using genetic algorithm

Abstract: The generation of profitable trading rules for Foreign Exchange (FX) investments is a difficult but popular problem. The use of Machine Learning in this problem allows us to obtain objective results by using information of the past market behavior. In this paper, we propose a Genetic Algorithm (GA) system to automatically generate trading rules based on Technical Indexes. Unlike related researches in the area, our work focuses on calculating the most appropriate trade timing, instead of predicting the trading prices.

The Carry Trade and Fundamentals: Nothing to Fear But Feer Itself

Abstract: The carry trade is the investment strategy of going long in high-yield target currencies and short in low-yield funding currencies. Recently, this naive trade has seen very high returns for long periods, followed by large crash losses after large depreciations of the target currencies. Based on low Sharpe ratios and negative skew, these trades could appear unattractive, even when diversified across many currencies. But more sophisticated conditional trading strategies exhibit more favorable payoffs. We apply novel (within economics) binary-outcome classification tests to show that our directional trading forecasts are informative, and out-of-sample loss-function analysis to examine trading performance. The critical conditioning variable, we argue, is the fundamental equilibrium exchange rate (FEER). Expected returns are lower, all else equal, when the target currency is overvalued. Like traders, researchers should incorporate this information when evaluating trading strategies. When we do so, some questions are resolved: negative skewness is purged, and market volatility (VIX) is uncorrelated with returns; other puzzles remain: the more sophisticated strategy has a very high Sharpe ratio, suggesting market inefficiency.

A reality check on technical trading rule profits in the U.S. futures markets

Abstract: This article investigates the profitability of technical trading rules in U.S. futures markets during the years 1985–2004. Statistical significance of performance across the trading rules is evaluated using White's Bootstrap Reality Check and Hansen's Superior Predictive Ability tests, which can directly measure the effect of data snooping by testing the performance of the best rule in the context of the full universe of technical trading rules. Results show that the best rules generate statistically significant economic profits for only two of 17 futures markets after correcting for data snooping biases. This evidence suggests that technical trading rules generally have not been profitable in the U.S. futures markets. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:633–659, 2010

Adaptive conceding strategies for automated trading agents in dynamic, open markets

Abstract: One of the crucial issues of automated negotiation in multi-agent systems is how to reach an agreement when a negotiation environment becomes open and dynamic. Even though some strategies have been proposed by researchers, most of them can only work within a static negotiation environment. In this paper, we present a model for designing a strategy for agents that makes adjustable rates of concession by negotiating according to the changes of environments with uncertain and dynamic outside options. This proposal is based on the market-driven agents (MDAs) model, and is guided by four factors in order to determine the degree of concession. These factors are trading opportunity, trading competition, trading time and strategy, and eagerness. The contribution of this paper is extending the MDAs model to an open and dynamic negotiation environment by considering both the current and potential changes of the environment.

Informed Trading around Acquisitions: Evidence from Corporate Bonds

Abstract: This paper examines the prevalence of informed trading in the corporate debt market prior to takeover announcements. Unlike target stocks, target bonds do not always gain in an acquisition. Target bonds rated higher than the acquirer’s stand to lose whereas those rated lower stand to gain. We find significant pre-announcement trading activities and price movements in target bonds, in directions consistent with the nature of pending information. Since selling (buying) target bonds that stand to lose (gain) prior to the public announcement requires information about acquirer characteristics, our evidence is less likely to be due to market anticipation, and is consistent with informed trading. We find that improved transparency in the bond markets achieved by the implementation of the TRACE system reduces the incidence of informed trading. Further, there is some weak evidence that bond dealers affiliated with M&A advisors sell in anticipation of negative news on bonds, pointing to a possible channel of information leakage. Such negative news seems to be incorporated into bond prices no slower than into the target stocks.

Rise of the machines: algorithmic trading in the foreign exchange market

Abstract: We study the impact that algorithmic trading, computers directly interfacing at high frequency with trading platforms, has had on price discovery and volatility in the foreign exchange market. Our dataset represents a majority of global interdealer trading in three major currency pairs in 2006 and 2007. Importantly, it contains precise observations of the size and the direction of the computer-generated and human-generated trades each minute. The empirical analysis provides several important insights. First, we find evidence that algorithmic trades tend to be correlated, suggesting that the algorithmic strategies used in the market are not as diverse as those used by non-algorithmic traders. Second, we find that, despite the apparent correlation of algorithmic trades, there is no evident causal relationship between algorithmic trading and increased exchange rate volatility. If anything, the presence of more algorithmic trading is associated with lower volatility. Third, we show that even though some algorithmic traders appear to restrict their activity in the minute following macroeconomic data releases, algorithmic traders increase their provision of liquidity over the hour following each release. Fourth, we find that non-algorithmic order flow accounts for a larger share of the variance in exchange rate returns than does algorithmic order flow. Fifth, we find evidence that supports the recent literature that proposes to depart from the prevalent assumption that liquidity providers in limit order books are passive.

Liquidation in the Face of Adversity: Stealth vs. Sunshine Trading

Abstract: We consider a multi-player situation in an illiquid market in which one player tries to liquidate a large portfolio in a short time span, while some competitors know of the seller's intention and try to make a profit by trading in this market over a longer time horizon. We show that the liquidity characteristics, the number of competitors in the market and their trading time horizons determine the optimal strategy for the competitors: they either provide liquidity to the seller, or they prey on her by simultaneous selling. Depending on the expected competitor behavior, it might be sensible for the seller to pre-announce a trading intention (sunshine trading) or to keep it secret (stealth trading).

Market Power in Pollution Permit Markets

Abstract: As with other commodity markets, markets for trading pollution permits have not been immune to market power concerns. In this paper, I survey the existing literature on market power in permit trading but also contribute with some new results and ideas. I start the survey with Hahn�s (1984) dominant-firm (static) model that I then extend to the case in which there are two or more strategic firms that may also strategically interact in the output market, to the case in which current permits can be stored for future use (as in most existing and proposed market designs), to the possibility of collusive behavior, and to the case in which permits are auctioned off instead of allocated for free to firms. I finish the paper with a review of empirical evidence on market power, if any, with particular attention to the U.S. sulfur market and the Southern California NOx market.

Measures of Implicit Trading Costs and Buy-Sell Asymmetry

Abstract: This paper shows that the widely documented buy-sell asymmetry in implicit institutional trading cost is mainly driven by mechanical characteristics of a specific class of measures: pre-trade measures. If a post-trade measure is used, the asymmetry is reversed in both rising and falling markets. Both pre-trade and post-trade measures are highly influenced by market movement, while during-trade measures are relatively neutral to market movement. We further show that a pre-trade measure can be decomposed into a market movement component and a during-trade measure, and empirically the market movement component is the dominant component. This paper demonstrates that simple mechanical characteristics of trading cost measures can have important implications for how we interpret empirical results.

A Rational Expectations Equilibrium with Informative Trading Volume

Abstract: Grossman (1976) shows how market prices aggregate private information. In this paper I show how trading volume helps investors to interpret the aggregate information in the price. I construct a model where investors trade for two reasons: private information and risk sharing. When trading volume is high, investors know that private signals are dispersed. They therefore weight the market price heavily relative to their own signals. Conversely, when trading volume is low, investors weight their private signals more heavily. This model ofiers a closed form solution of a rational expectations equilibrium where all investors learn from (1) private signals, (2) market price and (3) aggregate trading volume.