Showing papers on "Algorithmic trading published in 2014"

High Frequency Quoting, Trading, and the Efficiency of Prices

Abstract: We examine 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 U.S. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk, and a sharp decline in the cost of trading.

Automated trading with performance weighted random forests and seasonality

Abstract: Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.

Have Capital Market Anomalies Attenuated in the Recent Era of High Liquidity and Trading Activity

Abstract: We examine whether the recent regime of increased liquidity and trading activity is associated with attenuation of prominent equity return anomalies due to increased arbitrage. We find that the majority of the anomalies have attenuated, and the average returns from a portfolio strategy based on prominent anomalies have approximately halved after decimalization. We provide evidence that hedge fund assets under management, short interest and aggregate share turnover have led to the decline in anomaly-based trading strategy profits in recent years. Overall, our work indicates that policies to stimulate liquidity and ameliorate trading costs improve capital market efficiency.

Buy Low, Sell High: A High Frequency Trading Perspective

Abstract: We develop a high frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multifactor mutually exc...

Buy Low Sell High: A High Frequency Trading Perspective

Abstract: We develop a High Frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multi-factor mutually-exciting process we allow for feedback effects in market buy and sell orders and the shape of the limit order book (LOB). Our model accounts for arrival of market orders that influence activity, trigger one-sided and two-sided clustering of trades, and induce temporary changes in the shape of the LOB. We also model the impact that market orders have on the short-term drift of the midprice (short-term-alpha). We show that HF traders who do not include predictors of short-term-alpha in their strategies are driven out of the market because they are adversely selected by better informed traders and because they are not able to profit from directional strategies.

Computerized and High-Frequency Trading

Abstract: The use of computers to execute trades, often with very low latency, has increased over time, resulting in a variety of computer algorithms executing electronically targeted trading strategies at high speed. We describe the evolution of increasingly fast automated trading over the past decade and some key features of its associated practices, strategies, and apparent profitability. We also survey and contrast several studies on the impacts of such high-speed trading on the performance of securities markets. Finally, we examine some of the regulatory questions surrounding the need, if any, for safeguards over the fairness and risks of high-speed, computerized trading.

Speed, algorithmic trading, and market quality around macroeconomic news announcements

Abstract: This paper documents that speed is crucially important for high-frequency trading strategies based on U.S. macroeconomic news releases. Using order-level data on the highly liquid S&P 500 ETF traded on NASDAQ from January 6, 2009 to December 12, 2011, we find that a delay of 300 ms or more significantly reduces returns of news-based trading strategies. This reduction is greater for high impact news and on days with high volatility. In addition, we assess the effect of algorithmic trading on market quality around macroeconomic news. In the minute following a macroeconomic news arrival, algorithmic activity increases trading volume and depth at the best quotes, but also increases volatility and leads to a drop in overall depth. Quoted half-spreads decrease (increase) when we measure algorithmic trading over the full (top of the) order book.

HFT and Market Quality

Abstract: We discuss the economics of high-frequency trading (hereafter HFT), survey empirical findings and offer policy recommendations. HFT involves high speed connections to exchanges, computerized trading, and very short-term positions. Beyond these common features, HFT strategies are heterogeneous. They can involve market-making, directional trade, arbitrage, and possibly manipulation. The empirical literature finds that HFT market-making is profitable only because of favorable exchange fees. HFT market orders predict future short-term market movements and correspondingly earn profits, at the expense of other market participants. Yet, there is no empirical evidence of adverse effects of HFT on liquidity. HFT could generate negative externalities, by inducing adverse selection for slower traders, or enhancing the risk of trading firms’ failure waves. To cope with these problems, slow-traders’ friendly market mechanisms should be available, and minimum capital requirements and stress tests should be implemented.

Computerized and High-Frequency Trading

Abstract: The use of computers to execute trades, often with very low latency, has increased over time, resulting in a variety of computer algorithms executing electronically targeted trading strategies at high speed. We describe the evolution of increasingly fast automated trading over the past decade and some key features of its associated practices, strategies, and apparent profitability. We also survey and contrast several studies on the impacts of such high-speed trading on the performance of securities markets. Finally, we examine some of the regulatory questions surrounding the need, if any, for safeguards over the fairness and risks of high-speed, computerized trading.

Informed Options Trading prior to M&A Announcements: Insider Trading?

Abstract: We investigate informed trading activity in equity options prior to the announcement of corporate mergers and acquisitions (M&A), focusing on the target rms. We nd that the probability of informed trading in options during the period just prior to the deal announcement date is much higher than one would expect prior to any randomly chosen date. This is demonstrated by pervasive, unusual option trading volume, implied volatility and liquidity attributes. We document highly positive abnormal trading volumes, excess implied volatility, and higher bid-ask spreads, prior to M&A announcements, with stronger eects for OTM call options on the target company. The probability of option volume on a random day exceeding that of our strongly unusual trading (SUT) sample is trivial - about three in a trillion. These eects are accentuated in the sub-sample of announcements of cash oers for large target rms, particularly in a narrow window before the announcement date. We further document a decrease in the slope of the term structure of implied volatility and an average rise in percentage bid-ask spreads, prior to the announcements. We provide evidence that there is also unusual activity in volatility strategies on the acquirer. We also present a summary analysis of SEC cases involving options trading ahead of M&A announcements, and show that the SEC is more likely to investigate cases where the acquirer is headquartered outside the US, the target is relatively large, and experienced substantial positive abnormal returns after the announcement.

Equilibrium Fast Trading

Abstract: High-speed market connections improve investors' ability to search for attractive quotes in fragmented markets, raising gains from trade. They also enable fast traders to observe market information before slow traders, generating adverse selection, and thus negative externalities. When investing in fast trading technologies, institutions do not internalize these externalities. Accordingly, they overinvest in equilibrium. Completely banning fast trading is dominated by offering two types of markets: one accepting fast traders, the other banning them. However, utilitarian welfare is maximized by having i) a single market type on which fast and slow traders coexist and ii) Pigovian taxes on investment in the fast trading technology.

1980-2008: The Illusion of the Perpetual Money Machine and What It Bodes for the Future

Abstract: We argue that the present crisis and stalling economy that have been ongoing since 2007 are rooted in the delusionary belief in policies based on a "perpetual money machine" type of thinking. We document strong evidence that, since the early 1980s, consumption has been increasingly funded by smaller savings, booming financial profits, wealth extracted from house price appreciation and explosive debt. This is in stark contrast with the productivity-fueled growth that was seen in the 1950s and 1960s. We describe the transition, in gestation in the 1970s, towards the regime of the "illusion of the perpetual money machine", which started at full speed in the early 1980s and developed until 2008. This regime was further supported by a climate of deregulation and a massive growth in financial derivatives designed to spread and diversify the risks globally. The result has been a succession of bubbles and crashes, including the worldwide stock market bubble and great crash of October 1987, the savings and loans crisis of the 1980s, the burst in 1991 of the enormous Japanese real estate and stock market bubbles, the emerging markets bubbles and crashes in 1994 and 1997, the Long-Term Capital Management (LTCM) crisis of 1998, the dotcom bubble bursting in 2000, the recent house price bubbles, the financialization bubble via special investment vehicles, the stock market bubble, the commodity and oil bubbles and the current debt bubble, all developing jointly and feeding on each other until 2008. This situation may be further aggravated in the next decade by an increase in financialization, through exchange-traded-funds (ETFs), speed and automation, through algorithmic trading and public debt, and through growing unfunded liabilities. We conclude that, to get out of this catch 22 situation, we should better manage and understand the incentive structures in our society, we need to focus our efforts on our real economy

Optimal Execution with Limit and Market Orders

Abstract: We develop an optimal execution policy for an investor seeking to execute a large order using limit and market orders. The investor solves the optimal policy considering different restrictions on volume of both types of orders and depth at which limit orders are posted. We show how the execution policies perform when targeting the volume schedule of the Almgren-Chriss execution strategy. The different strategies considered by the investor outperform the Almgren-Chriss price with an average savings per share of about one to two and a half times the spread. This improvement over Almgren-Chriss is due to the strategies benefiting from the optimal mix of limit orders, which earn the spread, and market orders, which keep the investor's inventory schedule on target.

The Provision of Liquidity by High‐Frequency Participants

Abstract: This paper examines the order submission strategies and supply of liquidity by high-frequency participants versus the remainder of participants in the limit order book. The results show that high-frequency participants submit orders at multiple prices in the limit order book, concentrated at or within the quote. This activity translates into the provision of liquidity on an on-going basis, which is robust to fast versus slow and volatile markets, together suggesting that high-frequency participants resolve temporal liquidity imbalances in the limit order book. The evidence is consistent with high-frequency trading (HFT) improving market liquidity, but there remain issues surrounding high-frequency participants’ effect on market depth and the difficulty of trading of non-HFT participants.

An agent-based computational model for China's stock market and stock index futures market

Abstract: This study presents an agent-based computational cross-market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index futures to simulate this structure. This model allows heterogeneous investors to make investment decisions with restrictions including wealth, market trading mechanism, and risk management. Investors' demands and order submissions are endogenously determined. Our model successfully reproduces several key features of the Chinese financial markets including spot-futures basis distribution, bid-ask spread distribution, volatility clustering and long memory in absolute returns. Our model can be applied in cross-market risk control, market mechanism design and arbitrage strategies analysis.

Media-aware quantitative trading based on public Web information

Abstract: Recent studies in behavioral finance discover that emotional impulses of stock investors affect stock prices. The challenge lies in how to quantify such sentiment to predict stock market movements. In this article, we propose a media-aware quantitative trading strategy utilizing sentiment information of Web media. This is achieved by capturing public mood from interactive behaviors of investors in social media and studying the impact of firm-specific news sentiment on stocks along with such public mood. Our experiments on the CSI 100 stocks during a three-month period show that a predictive performance in closeness to the actual future stock price is 0.612 in terms of root mean squared error, the same direction of price movement as the future price is 55.08%, and a simulation trading return is up to 166.11%.

Trading for Status

Abstract: We show that Keeping-up-with-the-Joneses preferences can explain several puzzling retail investor behaviors, including the excessive trading of small local stocks. Status concerns lead households, especially those living in affluent areas, to demand these stocks to track their neighbors' wealth. This demand varies pro-cyclically with the stock market's value and generates household trading. Using Chinese data on local stock turnover, stock message boards and brokerage account trading, we test and confirm this hypothesis by exploiting the uneven rise of affluence across Chinese cities between 1998 and 2012.

The Impact of Dark Trading and Visible Fragmentation on Market Quality

Abstract: Two important characteristics of current equity markets are the large number of competing trading venues with publicly displayed order books and the substantial fraction of dark trading, which takes place outside such visible order books. This paper evaluates the impact on liquidity of dark trading and fragmentation in visible order books. Dark trading has a detrimental effect on liquidity. Visible fragmentation improves liquidity aggregated over all visible trading venues but lowers liquidity at the traditional market, meaning that the benefits of fragmentation are not enjoyed by investors who choose to send orders only to the traditional market.

An empirical examination of the lead–lag relationship between spot and futures markets: Evidence from Thailand ☆

Abstract: This study investigates whether a lead–lag relationship exists between the spot market and the futures market in Thailand during the period 2006 through 2012. In a rational, efficient market, returns on derivative securities and their underlying assets should be perfectly contemporaneously correlated. However, due to market imperfections, one of these two markets may reflect information faster. Using daily data, our results show that there is a price discovery in the Thailand futures market. We find that lagged changes in spot prices lead changes in futures prices. Our results are robust to the use of an alternative equity index. Our results show that the error correction model, which utilizes the traditional linear model, is found to be the best forecasting model. Furthermore, we find that a trading strategy based on this model outperforms the market even after allowing for transaction costs.

High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach

Abstract: In this paper, a high frequency and dynamic pairs trading system is proposed, based on a market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach The proposed pairs trading system was applied to equity trading in US equity markets in any type of market cycle condition to capture statistical mispricing between the prices of each stock pair based on its residuals and to model the stock pairs naturally as a mean-reversion process The proposed pairs trading system was tested for out-of-sample testing periods with high frequency stock data from 2012 and 2013 Our trading strategy yields cumulative returns up to 5658% for portfolios of stock pairs, well exceeding the S&P 500 index performance by 3435% over a 12-month trading period The proposed trading strategy achieved a monthly 267 Sharpe ratio and an annual 925 Sharpe ratio Furthermore, the proposed pairs trading system performed well during the two months in which the S&P 500 index had negative returns Thus, the trading system might be especially more profitable at times when the US stock market performed poorly Therefore, the performance returns of the proposed pairs trading system were relatively market-neutral and were positive regardless of the performance of the S&P 500 index

Gone in Ten Minutes: Intraday Evidence of Announcement Effects in the Electronic Corn Futures Market

Abstract: This article investigates the announcement effects of major USDA reports using intraday Chicago Board of Trade corn futures prices and trading volume from the electronic trading platform for July 2009 to May 2012 Focusing on intraday market reactions, we analyze the extent to which new information impacts and is rapidly reflected in prices Results show that USDA reports contain substantial information for market participants Strongest price reactions to the releases are found immediately after the market opens, and market reactions persist for approximately ten minutes The electronic corn futures market quickly incorporates this new public information, and little evidence exists to support systematic under- or overreactions in prices Other more subtle reactions occur in the last trading session before USDA announcements as traders adjust their market exposure in anticipation of the release

Market Efficiency and Returns to Simple Technical Trading Rules: New Evidence from U.S. Equity Market and Chinese Equity Markets

Abstract: Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in US markets and also compare the excess returns between US market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in US equity index after 1975. During the 1990's break-even costs turned to be negative, -0.06%, even failing to beat a buy-holding strategy in US equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.

On the Determinants of Pairs Trading Profitability

Abstract: We perform a large-scale empirical analysis of pairs trading, a popular relative-value arbitrage approach. We start with a cross-country study of 34 international stock markets and uncover that abnormal returns are a persistent phenomenon. We then construct a comprehensive U.S. data set to explore the sources behind the puzzling profitability in more depth. Our findings indicate that the type of news leading to pair divergence, the dynamics of investor attention as well as the dynamics of limits to arbitrage are important drivers of the strategy's time-varying performance.

A novel approach to dynamic portfolio trading system using multitree genetic programming

Abstract: Dynamic portfolio trading system is used to allocate one's capital to a number of securities through time in a way to maximize the portfolio return and to minimize the portfolio risk. Genetic programming (GP) as an artificial intelligence technique has been used successfully in the financial field, especially for the forecasting tasks in the financial markets. In this paper, GP is used to develop a dynamic portfolio trading system to capture dynamics of stock market prices through time. The proposed approach takes an integrated view on multiple stocks when the GP evolves and generates a rule base for dynamic portfolio trading based on the technical indices. In the present research, a multitree GP forest has been developed to extend the GP structure to extract multiple trading rules from historical data. Furthermore, the consequent part of each trading rule includes a function rather than a constant value. Besides, the transaction cost of trading which plays an important role in the profitability of a dynamic portfolio trading system is taken into account. This model was used to develop dynamic portfolio trading systems. The profitability of the model was examined for both the emerging and the mature markets. The numerical results show that the proposed model significantly outperforms other traditional models of dynamic and static portfolio selection in terms of the portfolio return and risk adjusted return.

An Empirical Analysis of Market Segmentation on U.S. Equities Markets

Abstract: We examine the impact of trading on markets partially exempt from National Market System requirements (‘dark venues’) on equity market quality. We find evidence consistent with the notion that dark venues rely on their special features to segregate order flow based on asymmetric information risk, which results in their transactions being less informed and contributing less to price discovery on the consolidated market. Except for the execution of large transactions and trading in small stocks, the effects of dark venue order segmentation are damaging to overall market quality. Our results have important implications for the regulation of international equity markets.

Order-to-Trade Ratios and Market Liquidity

Abstract: We study the impact on market liquidity of the introduction of a penalty for high order-to-trade ratios (OTRs), implemented by the Italian stock exchange to curtail high-frequency quote submission. We find that the fee is associated with a collapse in the quoted depth of the stocks that make up the bulk of trading in Italian equities and an increase in price impacts of trading across the treated stocks. Spreads do not change, however. Stocks from a pan-European control sample show no such liquidity changes. Thus, the Italian OTR fee had the effect of making Italian stocks markets more shallow and less resilient. Large stocks are more severely affected than midcaps. We also find evidence of a limited decrease in turnover. Consolidated liquidity, constructed by aggregating across all electronic trading venues for these stocks, decreases just like that on the main exchange. Thus, liquidity was not simply diverted from the main exchange, it was reduced in aggregate.

The Behavior of Bid-Ask Spreads in the Electronically-Traded Corn Futures Market

Abstract: This is the first paper to analyze liquidity costs in agricultural futures markets based on the observed bid‐ask spread (BAS) faced by market participants. The results reveal a highly liquid corn market that mostly offers order execution at minimum cost. The BAS responds negatively to volume and positively to price volatility, but also affects volume traded and price volatility. While statistically significant, these responses on a cents/bushel or a percentage basis are generally small. Liquidity costs are also virtually impervious to short‐term changes in demand for spreading and trend‐following trader activity, as well as differences from day‐of‐the‐week changes in market activity. Much larger cents/bushel and percentage changes in BAS occur during commodity index trader roll periods and on USDA report release days. The roll period findings indicate a sunshine trading effect, while announcement effects identify the importance of unexpected information and adverse selection on order execution costs. Overall, our research demonstrates that the transition to electronic trading in the corn futures market has led to low and stable liquidity costs, despite the market turbulence in 2008–2009.