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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.


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Journal ArticleDOI
TL;DR: The US equity market has changed dramatically in recent years Increasing automation and the entry of new trading platforms has resulted in intense competition among trading platforms Despite these changes, traders still face the same challenges as before They seek to minimize the total cost of trading including commissions, bid/ask spreads, and market impact as discussed by the authors.
Abstract: The US equity market changed dramatically in recent years Increasing automation and the entry of new trading platforms has resulted in intense competition among trading platforms Despite these changes, traders still face the same challenges as before They seek to minimize the total cost of trading including commissions, bid/ask spreads, and market impact New technologies allow traders to implement traditional strategies more effectively For example, dark pools and indications of interest are just an updated form of tactics that NYSE floor traders used search for counterparties while minimizing the exposure of their clients’ trading interest to prevent front running Virtually every measurable dimension of US equity market quality has improved Execution speeds and retail commission have fallen Bid-ask spreads have fallen and remain low, although they spiked upward along with volatility during the recent financial crisis Market depth has increased Studies of institutional transactions costs find US costs among the lowest in the world Unlike during the Crash of 1987, the US equity market mechanism handled the increase in trading volume and volatility without disruption However, our markets lack a market-wide risk management system that would deal with computer generated chaos in real time, and our regulators should address this “Make or take” pricing, the charging of access fees to market orders that “take” liquidity and paying rebates to limit orders that “make” liquidity, causes distortions that should be corrected Such charges are not reflected in the quotations used for the measurement of best execution Direct access by non-brokers to trading platforms requires appropriate risk management Front running orders in correlated securities should be banned

231 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide evidence regarding high-frequency traders' performance, trading costs, and effects on market efficiency using a sample of NASDAQ trades and quotes that directly identify HFT participation.
Abstract: This paper provides evidence regarding high-frequency trader (HFT) trading performance, trading costs, and effects on market efficiency using a sample of NASDAQ trades and quotes that directly identifies HFT participation. I find that HFTs engage in successful intra-day market timing, spreads are wider when HFTs provide liquidity and tighter when HFTs take liquidity, and prices incorporate information from order flow and market-wide returns more efficiently on days when HFT participation is high.

230 citations

Journal ArticleDOI
TL;DR: In this article, the effect of algorithmic trading (AT) intensity on short-term volatility and the informational efficiency of stock prices has been investigated using a large sample from 2001-2009 that incorporates 39 exchanges and an average of 12,800 common stocks.
Abstract: We use a large sample from 2001 – 2009 that incorporates 39 exchanges and an average of 12,800 different common stocks to assess the effect of algorithmic trading (AT) intensity on liquidity in the equity market, short-term volatility, and the informational efficiency of stock prices. We exploit the first availability of co-location facilities to identify the direction of causality. We find that, on average, greater AT intensity improves liquidity and informational efficiency, but increases volatility. The volatility increase is robust to a range of different volatility measures and it is not due to more “good” volatility that would arise from faster price discovery. These patterns are widespread and are not limited to a few markets, but they vary in the cross-section of stocks. In contrast to the average effect, more AT reduces liquidity in small stocks; has little effect on the liquidity of low-priced or highvolatility stocks; and leads to greater increases in volatility in these stocks. Finally, during days when market making is difficult, AT provide less liquidity, improve efficiency more, and increase volatility more than on other days.

228 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined three emerging Gulf markets examined in this paper, and found that correction for infrequent trading significantly alters the results of market efficiency and random walk tests, and the Beveridge-Nelson decomposition of index returns is done to estimate the underlying index.
Abstract: Inferences drawn from tests of market efficiency are rendered imprecise in the presence of infrequent trading. As the observed index in thinly traded markets may not represent the true underlying index value, there is a systematic bias toward rejecting the efficient market hypothesis. For the three emerging Gulf markets examined in this paper, correction for infrequent trading significantly alters the results of market efficiency and random walk tests. The Beveridge–Nelson (1981) decomposition of index returns is done to estimate the underlying index.

228 citations

Journal ArticleDOI
TL;DR: In this article, the authors argue that futures trading increases stock market volatility, and do not predict the stock market's future volatility, but do not forecast the future volatility of stocks.
Abstract: (1988). Does Futures Trading Increase Stock Market Volatility? Financial Analysts Journal: Vol. 44, No. 1, pp. 63-69.

227 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202397
2022190
2021144
2020167
2019126
2018160