Showing papers on "Algorithmic trading published in 2008"

Manipulation and the Allocational Role of Prices

Abstract: It is commonly believed that prices in secondary financial markets play an important allocational role because they contain information that facilitates the efficient allocation of resources. This paper identifies a limitation inherent in this role of prices. It shows that the presence of a feedback effect from the financial market to the real value of a firm creates an incentive for an uninformed trader to sell the firm’s stock. When this happens the informativeness of the stock price decreases, and the beneficial allocational role of the financial market weakens. The trader profits from this trading strategy, partly because his trading distorts the firm’s investment. We therefore refer to this strategy as manipulation. We show that trading without information is profitable only with sell orders, driving a wedge between the allocational implications of buyer and seller initiated speculation, and providing justification for restrictions on short sales.

Corporate Governance and Liquidity

Abstract: We investigate the empirical relation between corporate governance and stock market liquidity. We find that firms with better corporate governance have narrower spreads, higher market quality index, smaller price impact of trades, and lower probability of information-based trading. In addition, we show that changes in our liquidity measures are significantly related to changes in the governance index over time. These results suggest that firms may alleviate information-based trading and improve stock market liquidity by adopting corporate governance standards that mitigate informational asymmetries. Our results are remarkably robust to alternative model specifications, across exchanges, and different measures of liquidity.

Macroeconomic news, order flows and exchange rates

Abstract: Under rational expectations and efficient markets, the news contained in public information announcements is directly impounded into prices with there being no role for trades in this process of information assimilation. This paper directly tests this assertion using transaction level exchange rate data and a sample of scheduled macroeconomic announcements. The main result of the paper is that even information that is publicly and simultaneously released to all market participants is largely impounded into prices via the key micro-level price determinant — order flow. We quantify the role that order flow plays and find that between a half and two thirds of price relevant information is incorporated into prices via the trading process.

Correlated Trading and Returns

Abstract: A German broker's clients place similar speculative trades and therefore tend to be on the same side of the market in a given stock during a given day, week, month, and quarter. Aggregate liquidity effects, short sale constraints, the systematic execution of limit orders (coordinated through price movements) or the correlated trading of other investors who pick off retail limit orders, do not fully explain why retail investors trade similarly. Correlated market orders lead returns, presumably due to persistent speculative price pressure. Correlated limit orders also predict subsequent returns, consistent with executed limit orders being compensated for accommodating liquidity demands.

Investor Sentiment, Trading Behavior and Informational Efficiency in Index Futures Markets

Abstract: This paper shows that traders in index futures markets are positive feedback traders—they buy when prices increase and sell when prices decline. Positive feedback trading appears to be more active in periods of high investor sentiment. This finding is consistent with the notion that feedback trading is driven by expectations of noise traders. Consistent with the noise trading hypothesis, order flow in index futures markets is less informative when investors are optimistic. Transitory volatility measured at high frequencies also appears to decline in periods of bullish sentiment, suggesting that sentiment-driven trading increases market liquidity.

Where Is the Market? Evidence from Cross-Listings in the United States

Abstract: We analyze the location of stock trading for firms with a US cross-listing. The fraction of trading that occurs in the United States tends to be larger for companies from countries that are geographically close to the United States and feature low financial development and poor insider trading protection. For companies based in developed countries, trading volume in the United States is larger if the company is small, volatile, and technology-oriented, while this does not apply to emerging country firms. The domestic turnover rate increases in the cross-listing year and remains higher for firms based in developed markets, but not for emerging market firms. Domestic trading volume actually declines for companies from countries with poor enforcement of insider trading regulation.

Informed trading in the index option market: The case of KOSPI 200 options

Abstract: This study examines if informed trading is present in the index option market by analyzing the KOSPI 200 options, the most actively traded derivative product in the world. The spread decomposition model developed by Madhavan, Richardson, and Roomans (1997) is utilized and the adverse-selection cost component of the spread estimated by the model is then used as a proxy for the degree of informed trading. We find that adverse-selection costs constitute a nontrivial portion of the transaction costs in index options trading. Approximately one-third of the spread can be accounted for by information asymmetry costs. A further analysis indicates that adverse-selection costs are positively related with option delta. Our regression analysis shows that option-related variables are significantly associated with estimated information asymmetry costs, even when controlling for proxies for informed trading in the index futures market. Finally, we find the evidence that foreign investors are better informed compared to domestic investors

Option Trading and Individual Investor Performance

Abstract: This paper examines the impact of option trading on individual investor performance. The results show that most investors incur substantial losses on their option investments, which are much larger than the losses from equity trading. We attribute the detrimental impact of option trading on investor performance to poor market timing that results from overreaction to past stock market returns. High trading costs further contribute to the poor returns on option investments. Gambling and entertainment appear to be the most important motivations for trading options while hedging motives only play a minor role. We also provide strong evidence of performance persistence among option traders.

System and method for securities liquidity flow tracking, display and trading

Abstract: A method, system and computer program that receives, processes, and displays level one, level two, and time and sales securities data Through a variety of charts, the data is analyzed to identify liquidity trade imbalances and trends in trading liquidity A logic based trading algorithm utilizes the current market maker activity information and the historical liquidity tiers to execute trades automatically

Block Ownership, Trading Activity, and Market Liquidity

Abstract: We examine the impact of block ownership on the firm's trading activity and secondary-market liquidity. Our empirical results show that block ownership takes potential trading activity off the table relative to a diffuse ownership structure and impairs the firm's market liquidity. These adverse liquidity effects disappear, however, once we control for trading activity. Our findings suggest that block ownership is detrimental to the firm's market liquidity because of its adverse impact on trading activity - a real friction effect. After controlling for this real friction effect, we find little evidence that block ownership has a negative impact on informational friction. Our results suggest that the relative lack of trading, and not the threat of informed trading, explains the inverse relation between block ownership and market liquidity.

Convertible Bond Arbitrage, Liquidity Externalities and Stock Prices

Abstract: In the context of convertible bond issuance, we examine the impact of arbitrage activity on underlying equity markets. In particular, we use changes in equity short interest following convertible bond issuance to identify convertible bond arbitrage activity and analyze its impact on stock market liquidity and prices for the period 1993 to 2006. There is considerable evidence of arbitrage-induced short selling resulting from issuance. Moreover, we find strong evidence that this activity is systematically related to liquidity improvements in the stock. These results are robust to controlling for the potential endogeneity of arbitrage activity.

Price-Volume Correlation in the Housing Market: Causality and Co-Movements

Abstract: Housing market cycles are featured by a positive correlation of prices and trading volume, which is conventionally attributed to a causal relationship between prices and volume. This paper analyzes the housing markets in 114 metropolitan statistical areas in the United States from 1990 to 2002, treats both prices and volume as endogenous variables, and studies whether and how exogenous shocks cause co-movements of prices and volume. At quarterly frequency, we find that, first, both home prices and trading volume are affected by conditions in labor markets, the mortgage market, and the stock market, and the effects differ between markets with low and high supply elasticity. Second, home prices Granger cause trading volume, but the effects are asymmetric - decreases in prices reduce trading volume, and increases in prices have no effect. Third, trading volume also Granger causes home prices, but only in markets with inelastic supply. Finally, we find a statistically significant positive price-volume correlation; which, however, is mainly explained by co-movements of prices and volume caused by exogenous shocks, instead of the Granger causality between prices and volume.

Statistical Arbitrage in the U.S. Equities Market

Abstract: We study model-driven statistical arbitrage strategies in U.S. equities. Trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic components of stock returns, and model them as a mean-reverting process, which leads naturally to "contrarian'' trading signals.The main contribution of the paper is the back-testing and comparison of market-neutral PCA- and ETF- based strategies over the broad universe of U.S. equities. Back-testing shows that, after accounting for transaction costs, PCA-based strategies have an average annual Sharpe ratio of 1.44 over the period 1997 to 2007, with a much stronger performances prior to 2003: during 2003-2007, the average Sharpe ratio of PCA-based strategies was only 0.9. On the other hand, strategies based on ETFs achieved a Sharpe ratio of 1.1 from 1997 to 2007, but experience a similar degradation of performance after 2002. We introduce a method to take into account daily trading volume information in the signals (using "trading time'' as opposed to calendar time), and observe significant improvements in performance in the case of ETF-based signals. ETF strategies which use volume information achieve a Sharpe ratio of 1.51 from 2003 to 2007.The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. In particular, we study in some detail the performance of the strategies during the liquidity crisis of the summer of 2007. We obtain results which are consistent with Khandani and Lo (2007) and validate their "unwinding'' theory for the quant fund drawndown of August 2007.

The Risk and Return of Arbitrage in Dual-Listed Companies

Abstract: This paper evaluates investment strategies that exploit the deviations from theoretical price parity in a sample of 12 dual-listed companies (DLCs) in the period 1980-2002. We show that simple trading rules produce abnormal returns of up to almost 10% per annum adjusted for systematic risk, transaction costs, and margin requirements. However, arbitrageurs face uncertainty about the horizon at which prices will converge and deviations from parity are very volatile. As a result, DLC arbitrage is characterized by substantial idiosyncratic return volatility and a high incidence of large negative returns, which are likely to impede arbitrage.

Exchange Traded Funds: History, Trading, and Research

Abstract: One of the most spectacular successes in financial innovation since the advent of financial futures is probably the creation of exchange traded funds (ETFs). As index funds, they aim at replicating the performance of their benchmark indices as closely as possible. Contrary to conventional mutual funds, however, ETFs are listed on an exchange and can be traded intradaily. Issuers and exchanges set forth the diversification opportunities they provide to all types of investors at a lower cost, but also highlight their tax efficiency, transparency, and low management fees. All of these features rely on a specific “in-kind” creation and redemption principle: New shares can continuously be created by depositing a portfolio of stocks that closely approximates the holdings of the fund; similarly, investors can redeem outstanding ETF shares and receive the basket portfolio in return. Holdings are transparent since fund portfolios are disclosed at the end of the trading day. ETFs were introduced to U.S. and Canadian exchanges in the early 1990s. In the first several years, they represented a small fraction of the assets under management in index funds. However, the 132% average annual growth rate of ETF assets from 1995 through 2001 (Gastineau, 2002) illustrates the increasing importance of these instruments. The launching of Cubes in 1999 was accompanied by a spectacular growth in trading volume, making the major ETFs the most actively traded equity securities on the U.S. stock exchanges. Since then, ETF markets have continued to grow, not only in the number and variety of products, but also in terms of assets and market value. Initially, they aimed at replicating broad-based stock indices; new ETFs extended their fields to sectors, international markets, fixed-income instruments, and, lately, commodities. By the end of 2005, 453 ETFs were listed around the world, for assets worth $343 billion. In the United States, overall ETF assets totaled $296.02 billion, compared to $8.9 trillion in mutual funds.

News Analytics for Financial Decision Support

Abstract: textThis PhD thesis contributes to the newly emerged, growing body of scientific work on the use of News Analytics in Finance. Regarded as the next significant development in Automated Trading, News Analytics extends trading algorithms to incorporate information extracted from textual messages, by translating it into actionable, valuable knowledge. The thesis addresses one main theme: the incorporation of news into trading algorithms. This relates to three main tasks: i) the extraction of the information contained in news, ii) the representation of the information contained in news, and iii) the aggregation of this information into actionable knowledge. We validate our approach by designing and implementing three semantic systems: a system for the computational content analysis of European Central Bank statements, a system for incorporating news in stock trading strategies, and a time-aware system for trading based on analyst recommendations. The approach we choose for addressing these tasks is an interdisciplinary one. For the extraction of information from news we rely on approaches borrowed from Computer Science and Linguistics. The representation of the information contained in news is realized by using, and extending, the state-of-the-art in Semantic Web technology. We do this by bringing together insights from Logics, Metaphysics, and Computational Semantics. The aggregation of information is done by using techniques and results from Computational Intelligence and Finance

The Evaluation and Optimization of Trading Strategies

Abstract: Foreword. Preface. Acknowledgments. Introduction. Chapter 1. On Trading Strategies. Why This Book Was Written. Who Will Benefit from this Book? The Goals of this Book. The Lay of the Land. Chapter 2. The Systematic Trading "Edge". Discretionary Trading. Raising the Bar. Verification. Quantification. Risk and Reward. The Performance Profile. Objectivity. Consistency. Extensibility. The Benefits of the Historical Simulation. Positive Expectancy. The Likelihood of Future Profit. The Performance Profile. Proper Capitalization. A Measure of Real-Time Trading Performance. The Benefits of Optimization. The Benefits of the Walk-Forward Analysis. The Advantages of a Thorough Understanding. Confidence. Strategy Refinement. Chapter 3. The Trading Strategy Development Process. Two Philosophical Approaches to Strategy Development. The Scientific Approach. The Path of Empirical Development. An Overview of the Trading Strategy Design Process. Step 1: Formulate the Trading Strategy. Step 2: Translate the Rules into a Definitive Form. Step 3: Preliminary Testing. Step 4: Optimize the Trading strategy. Step 5: the Walk Forward Analysis(t). Step 6: Trade the System. Step 7: Evaluate Real-Time Performance. Step 8: Improving the System. Chapter 4. The Strategy Development Platform. The Scripting Language. Diagnostics. Reporting. Optimization. The Objective Function. Speed. Automation. Walk Forward Analysis(t). Portfolio Analysis. In Conclusion. Chapter 5. The Elements of Strategy Design. The Three Principle Components of a Strategy. Entry and Exit. Risk Management. Position Sizing. An Overview of a Typical Trading Strategy. A Trade Equals an Entry and an Exit. Entry Filters. The Management of Risk. Trade Risk. Strategy Risk. Portfolio Risk. The Management of Profit. The Trailing Stop. Profit Targets. Position Sizing. Advanced Strategies. Summary. Chapter 6. The Historical Simulation. The Essential Reports. The Performance Summary. The Trade List. The Equity Curve. Performance by Period. The Importance of Accuracy. Software Limitations. Rounding Issues. Phantom Trades. Price Orders. Realistic Assumptions. Price and Trade Slippage. Opening Gap Slippage. Opening and Closing Range Slippage. Slippage Due to Size. The Significance of Slippage. Limit Moves. Major Events and Dates. Historical Data. Stock Prices. Cash Markets. Futures Markets. The Continuous Contract. The Perpetual Contract. Adjusted Continuous Contracts. The Size of the Test Window. Statistical Requirements. Sample Size and Statistical Error. How Many Trades? Stability. Degrees of Freedom. Frequency of Trading. Types of Markets. The Bull Market. The Bear Market. The Cyclic Market. The Congested Market. Efficient Markets. The Life Cycle of a Trading Strategy. Window Size and Model Life. Chapter 7. Formulation and Specification. Formulate the Trading Strategy. Specification - "Translate" The Idea Into A Testable Strategy. Make a Vague Idea Precise. Chapter 8. Preliminary Testing. Verification of Calculations and Trades. Calculations. Trading Rules. In Summary. Theoretical Expectations. Preliminary Profitability. The Multi-Market and Multi-Period Test. Selecting the Basket. Determining the Length of the Test Period. Segmenting the Data. The Test. The Results of the Test. Chapter 9. Search and Judgment. Search Methods. The Grid Search. The Prioritized Step Search. Hill Climbing Search Algorithms. Multi-Point Hill Climbing Search. Advanced Search Methods. Simulated Annealing. Genetic Algorithms. Particle Swarm Optimization. General Problems with Search Methods. The Objective Function. A Review of a Variety of Evaluation Methods. Multiple Evaluation Types. Chapter 10. Optimization. Optimization contra Overfitting. A Simple Optimization. The Optimization Framework. The Parameters. The Scan Range. The Historical Sample. The Objective Function. The Optimization Evaluation. A Multi-Market and Multi-Period Optimization. The Evaluation of the Optimization. The Robust Trading Strategy. The Robust Optimization. The Statistically Significant Optimization Profile. The Distribution of the Optimization Profile. The Shape of the Optimization Profile. How Does the Strategy Respond to Optimization? Does the Strategy Deserve Further Development? Chapter 11. Walk-Forward Analysis. Is the Trading Strategy Robust? Robustness and Walk-Forward Efficiency. The Cure for Overfitting. A More Reliable Measure of Risk and Return. Assessing the Impact of Market Changes. The Best Parameter Set for Trading. The Theory of Relevant Data. Peak Performance. Statistical Rigor. Shifting Markets. The Varieties of Market Conditions. The Walk-Forward. The Role of the Walk Forward. Setting Up a Walk-Forward. An Example of a Walk-Forward Test. The Walk-Forward Analysis. The Purpose of the Walk-Forward Analysis. An Example of a Walk-Forward Analysis. Is the Strategy Robust? What Rate of Profit Should We Expect? What Is the Risk? Walk-Forward Analysis and the Portfolio. Chapter 12. The Evaluation of Performance. The Trading Strategy as an Investment. The Dimension of Risk. Compare the Strategy to the Alternatives. Maximum Drawdown and Trading Risk. Maximum Drawdown in Context. Maximum Drawdown and the Trader. Maximum Run up and the Trader. Trading Capital and Risk. Risk Adjusted Return. Reward to Risk Ratio. Model Efficiency. Consistency. Patterns of Profit and Loss. Chapter 13. The Many Faces of Overfitting. What Is Overfitting? The Abuse of Hindsight. The Case of the Overfit Forecasting Model. The Case of the Overfit Trading Model. The Symptoms of an Overfit Trading Model. The Causes of Overfitting. Degrees of Freedom. Measuring Degrees of Freedom. Degrees of Freedom, Sample Size and Startup Overhead. Trade Sample Size. Optimization Error #1 - Over Parameterization. Optimization Error #2 -Over Scanning. The Big Fish in a Small Pond Syndrome. The Walk-Forward Test. Chapter 14. Trading the Strategy. The Mental Aspects of Trading. Return on Investment. Poor Strategy. Market Contraction. Unseen Market Conditions. In Conclusion. Maximum Risk. Real-time and Evaluation Performance. Comparing the Evaluation and Trade Profile. Understanding the Test Profile Performance Quirks. The Windfall Profit. The Losing Run. Flat Production. In Conclusion. Notes. Index.

Information, Trading, and Product Market Interactions: Cross-sectional Implications of Informed Trading

Abstract: I present a simple model of informed trading in which asset values are derived from imperfectly competitive product markets and private information events occur at individual firms. The model predicts that informed traders may have incentives to make information-based trades in the stocks of competitors, especially when events occur at firms with large market shares. In the context of 759 earnings announcements, I use intraday transactions data to test the hypothesis that net order flow and returns in the stocks of nonannouncing competitors have information content for announcing firms. HOW DOES AN INFORMED TRADER’S PROPENSITY to trade on inside information in a given company’s stock vary with industry and firm characteristics? Using a simple model of informed trading in which asset values are derived from imperfectly competitive product markets and private information events occur at individual firms, I examine the question of where informed traders choose to transact. 1 This paper adds to existing research by explicitly linking informed trading in stock markets to the structure of competition in product markets. It also provides new evidence on the process of information diffusion across stocks as we vary the location at which private information is observed. Given privately observed information at a particular location, informed traders may choose to trade in the stocks of related firms (in the same industry, for example). ∗Heather E. Tookes is at the Yale School of Management. This paper is based on part of my dis

Strategies for Wind Power Trading in Competitive Electricity Markets

Abstract: The emergence of independent generators and new technologies over past decades has led to the competition in the electricity sector to encourage development of a more market-based electricity industry. The success of wind energy under new market structures will depend, to a large degree, on the market rules on technical and financial aspects. This paper proposes a suitable trading option for wind power in the emerging electricity market for its sustainable development. Market clearing price with and without wind power has been analyzed in both supply side and demand side bidding scenarios for linear bid and block bid trading models. Several other aspects of wind power in electricity market have also been highlighted. This paper could be a guideline for the policy makers and market operators to promote the wind power with system reliability and security.

Technical market indicators optimization using evolutionary algorithms

Abstract: Real world stock markets predictions such as stock prices, unpredictability, and stock selection for portfolios, are challenging problems. Technical indicators are applied to interpret stock market trending and investing decision. The main difficulty of an indicator usage is deciding its appropriate parameter values, as number of days of the periods or quantity and kind of indicators. Each stock index, price or volatility series is different among the rest. In this work, Evolutionary Algorithms are proposed to discover correct indicator parameters in trading. In order to check this proposal the Moving Average Convergence-Divergence (MACD) technical indicator has been selected. Preliminary results show that this technique could work well on stock index trending. Indexes are smoother and easier to predict than stock prices. Required future works should include several indicators and additional parameters.

The impact of futures trading on volatility of the underlying asset in the Turkish stock market

Abstract: This paper examines the impact of the introduction of stock index futures on the volatility of the Istanbul Stock Exchange (ISE), using asymmetric GARCH model, for the period July 2002–October 2007. The results from EGARCH model indicate that the introduction of futures trading reduced the conditional volatility of ISE-30 index. Results further indicate that there is a long-run relationship between spot and future prices. The results also suggest that the direction of both long- and short-run causality is from spot prices to future prices. These findings are consistent with those theories stating that futures markets enhance the efficiency of the corresponding spot markets.

Empirical evidence on feedback trading in mature and emerging stock markets

Abstract: We investigate the hypothesis that some participants in mature and emerging stock markets engage in feedback trading. The analysis is based on the Shiller–Sentana–Wadhwani model, which has the attractive property that it yields testable implications about the presence of positive and negative feedback traders in stock markets. In addition, the Shiller–Sentana–Wadhwani model is particularly well-suited to investigate whether momentum type behaviour might be present during periods of large stock market downturns. This theoretical framework, together with asymmetric GARCH-type models, allows us to draw conclusions whether differences exist between mature and emerging stock markets in terms of the degree of feedback trading as well as the behaviour of traders during stock market crashes.

Liquidity and Price Discovery in the European CO2 Futures Market: An Intraday Analysis

Abstract: European Union CO2 allowances (EUAs) are traded on several markets with increasing intensity. We provide an intraday data analysis of the EUA futures market for the complete first trading period 2005-2007 (Phase I). To investigate the trading process in this young market, we compare the two main trading platforms, ECX and Nord Pool, with respect to price discovery and liquidity. Both are of high relevance to traders. We compare liquidity by estimating traded bid-ask spreads following the approach of Madhavan et al. (1997) and study price discovery using the VECM framework of Engle & Granger (1987). We find that while estimated transaction costs are always lower on the larger exchange ECX, the less liquid platform Nord Pool also contributes to price discovery, especially during the first months of trading. Overall, results indicate that from 2005 to 2007 liquidity in the European CO2 futures market has markedly increased and according to microstructural criteria the trading process has flowed smoothly.

Emerging Market Liquidity and Crises

Abstract: Whereas conventional wisdom argues that markets shut down during crises, with sellers struggling to find buyers, we find that markets continue to operate during financial turmoil, even in narrow and volatile emerging economies. Simple event studies indicate that both trading volume and trading costs increase in crisis times. Prices change more with each dollar transacted (pushing the Amihud illiquidity measure up) and bid-ask spreads widen. More generally, econometric estimates show that large price downturns, typical of crises, are associated with higher trading activity and increased trading costs, with trading activity declining only later as crises progress. Thus, while trading activity tends to be negatively related to trading costs during tranquil times (and across securities), this relation appears to break down during crises. These results are consistent with the analytical literature on portfolio rebalancing by heterogeneous agents in times of crises.

How does the call market method affect price efficiency? Evidence from the Singapore Stock Market

Abstract: On August 21, 2000, the Singapore Exchange (SGX) adopted the call market method to open and close the market while the remainder of the day’s trading continued to rely on the continuous auction method. The call method significantly improved the price discovery process and market quality. A positive spillover effect is observed from the opening and closing calls. Day-end price manipulation also declined after the introduction of the call market method. However, the beneficial impact from the call market method is asymmetric, benefiting liquid stocks more than illiquid stocks.