Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

The Impact of Legal and Political Institutions on Equity Trading Costs: A Cross-Country Analysis

Abstract: We conjecture that macro-level institutions will affect equity trading costs through their impact on information risk and investor participation. The key findings from our study of trading costs for 412 NYSE-listed ADRs from 44 countries are as follows. After controlling for firm-level determinants of trading costs and home country market share, effective spreads and price impact of trades are significantly lower for stocks from countries with better ratings for judicial efficiency, accounting standards, and political stability. Trading costs are significantly higher for stocks from French civil law countries than from common law countries. We confirm the robustness of our results in various analyses. Overall, we conclude that improvements in legal and political institutions will lower the cost of liquidity in stock markets.

Information Leakage and Market Efficiency

Abstract: This article analyzes the effects of information leakage on trading behavior and market efficiency. A trader who receives a noisy signal about a forthcoming public announcement can exploit it twice. First, when he receives it, and second, after the public announcement since he knows best the extent to which his information is already reflected in the pre-announcement price. Given his information he expects the price to overshoot and intends to partially revert his trade. While information leakage makes the price process more informative in the short-run, it reduces its informativeness in the long-run. The analysis supports Securities and Exchange Commission’s Regulation Fair Disclosure. In a perfect world, all investors would receive information pertinent to the value of the stock immediately and simultaneously. In reality, however, some agents like corporate insiders and their favored analysts can receive signals about this information before it is disclosed to the general public. The focus of our analysis is to determine (i) the optimal trading strategy of an early-informed agent and (ii) the implications of this trading behavior for the informational efficiency of the stock market. This knowledge can facilitate the design and evaluation of trading regulations by the Securities and Exchange Commission (SEC). Our model generates several novel insights on insider trading by enriching the information structure typically employed in the prior literature. In our analysis, a trader receives an early imprecise signal about a forthcoming news announcement — possibly in the form of a rumor. The new element is that the stock price reflects unrelated long-run private information held by other traders as well as the early-informed trader’s short-run signal. Given this generalized information structure, we find that the early-informed agent’s trading strategy exhibits three features: (1) he trades based on his private information twice, once before the public

What Do We Know About High-Frequency Trading?

Abstract: This paper reviews recent theoretical and empirical research on high-frequency trading (HFT). Economic theory identifies several ways that HFT could affect liquidity. The main positive is that HFT can intermediate trades at lower cost. However, HFT speed could disadvantage other investors, and the resulting adverse selection could reduce market quality.Over the past decade, HFT has increased sharply, and liquidity has steadily improved. But correlation is not necessarily causation. Empirically, the challenge is to measure the incremental effect of HFT beyond other changes in equity markets. The best papers for this purpose isolate market structure changes that facilitate HFT. Virtually every time a market structure change results in more HFT, liquidity and market quality have improved because liquidity suppliers are better able to adjust their quotes in response to new information.Does HFT make markets more fragile? In the May 6, 2010 Flash Crash, for example, HFT initially stabilized prices but were eventually overwhelmed, and in liquidating their positions, HFT exacerbated the downturn. This appears to be a generic feature of equity markets: similar events have occurred in manual markets, even with affirmative market-maker obligations. Well-crafted individual stock price limits and trading halts have been introduced since. Similarly, kill switches are a sensible response to the Knight trading episode.Many of the regulatory issues associated with HFT are the same issues that arose in more manual markets. Now regulators in the US are appropriately relying on competition to minimize abuses. Other regulation is appropriate if there are market failures. For instance, consolidated order-level audit trails are key to robust enforcement. If excessive messages impose negative externalities on others, fees are appropriate. But a message tax may act like a transaction tax, reducing share prices, increasing volatility, and worsening liquidity. Minimum order exposure times would also severely discourage liquidity provision.

Trading and Returns under Periodic Market Closures

Abstract: This paper studies how market closures affect investors' trading policies and the resulting return-generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U-shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open-to-open returns than close-to-close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs. WE MODEL A COMPETITIVE STOCK MARKET with periodic closures in which investors trade for both allocational and informational reasons. We use the model to study how market closures intrinsically affect investors' trading behavior and the return-generating process. The purpose of this analysis is to increase our understanding of the time variation in security trading and returns that are associated with regular market closures, such as the intraday and intraweek patterns in stock returns, volatility, and trading volume. We consider a stock market in which the exogenous information flow is homogeneous over time and the market closes periodically. When the market is open, investors trade the stock either to rebalance their overall portfolio of assets, which also includes other illiquid assets, or to speculate on future stock payoffs using their private information. In particular, investors adjust their asset portfolio by trading the stock in order to hedge the risk of illiquid assets. We refer to these trades as hedging trades and those motivated by private information as speculative trades. When the market is closed, " Hong is from the Graduate School of Business, Stanford University, and Wang is from the Sloan School of Management, Massachusetts Institute of Technology, and NBER. The authors thank Jennifer Huang for programming assistance and an anonymous referee for many valuable suggestions. They also thank Glenn Ellison, John Heaton, Craig Holden, Andrew Lo, Steve Slezak, Jeremy Stein, Rene Stulz (the editor), the NBER Asset Pricing Lunch Group, and par

High-Frequency Trading, Stock Volatility, and Price Discovery

Abstract: High-frequency trading has become a dominant force in the U.S. capital market, accounting for over 70% of dollar trading volume. This study examines the implication of high-frequency trading for stock price volatility and price discovery. I find that high-frequency trading is positively correlated with stock price volatility after controlling for firm fundamental volatility and other exogenous determinants of volatility. The positive correlation is stronger among the top 3,000 stocks in market capitalization and among stocks with high institutional holdings. The positive correlation is also stronger during periods of high market uncertainty. Furthermore, I find that high-frequency trading is negatively related to the market’s ability to incorporate information about firm fundamentals into asset prices. Stock prices tend to overreact to fundamental news when high-frequency trading is at a high volume. Overall, this paper demonstrates that high-frequency trading may potentially have some harmful effects for the U.S. capital market.