Eugene Krel
sunmulA
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as silly as it sounds, it's their job to make markets.
Clamping Down on Rapid Trades in Stock Market
- Thread starter Tom Maloney
- Start date
http://www.ft.com/intl/cms/s/0/4863d592-f424-11e0-bdea-00144feab49a.html#axzz1aWQMYbXo
what do you guys think?should there be different regulations between market-makers and market takers?sounds naive but I think it should be possible..
what do you guys think?should there be different regulations between market-makers and market takers?sounds naive but I think it should be possible..
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Arguably, it's not their job to go broke doing so, because someone else is distorting the market.
Eugene Krel
sunmulA
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try to take your statement out of a financial context and re-think it again.
Why not limit no of trades in a stock in a minute? Or disallow selling the stock for 2 minutes after a purchase? Or....
Come on.......
Yike Lu
Finder of biased coins.
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Why not limit number of trades? You aren't limiting number of quotes.Why not limit no of trades in a stock in a minute? Or disallow selling the stock for 2 minutes after a purchase? Or....
Come on.......
Why disallow selling stock for 2 minutes? Why not 1 minute, or 5 minutes then? And do you think market makers will really go for it?
I just gave a hypothetical example to emphasize my point that over-regulation kills free markets.
Yike Lu
Finder of biased coins.
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But what constitutes over-regulation? In what way, for example, does what I mentioned constitute over-regulation?
I agree that it's a slippery slope, but having no regulation tends to lead to abusive and predatory behavior.
I think limiting number of trades or trades to quotes ratio is an over kill and is more generic, e.g. it could hamper trading in and out large portfolios in a fast moving market, and could ultimately affect price discovery. We should look at specific instances of abusive behavior and address them. For example, some exchanges employ rules to prevent front running which has minimal impact on overall trading.
Yike Lu
Finder of biased coins.
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Obviously limiting number of trades is bad. But limiting quotes to trade ratio is largely innocuous. If you aren't getting trades, then your quote engine is either ineffective (and would signal your algo is not performing correctly) or specifically designed not to trade most of the time (which would constitute abuse).
Specific rules are good, but finding each possible abuse and addressing them individually is trying to use a bandaid on an open wound. At some point, you have to refactor and eliminate large classes of abuse with one sweeping rule.
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A reader response to the article
http://www.nytimes.com/2011/10/20/opinion/automated-stock-trading.html?_r=1
http://www.nytimes.com/2011/10/20/opinion/automated-stock-trading.html?_r=1
See Abrstract and Summary of Does Algorithmic Trading Improve Liquidity?
Full article from The Journal of Finance and I believe can be accessed for free here: http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2010.01624.x/full
Abstract
The authors analyze whether algorithmic trading has increased the efficiency and liquidity of the NYSE. They examine the introduction of Autoquote to determine the causal effect of algorithmic trading. They find that the increase in algorithmic trading appears to reduce spreads, lower adverse selection among liquidity demanders, and increase the amount of information incorporated into prices without trades being executed.
In recent years, technology has significantly altered the manner in which financial markets operate by reducing the layers and concurrent costs involved in trading stocks. Algorithmic trading (AT), introduced in the mid-1990s, is an example of such technological change and is now a factor in an estimated 70 percent or more of U.S. trading volume. Algorithms are used in active and passive strategies by those that demand liquidity and those that supply it and to assess market conditions, track benchmarks, break large orders into smaller pieces, and generally pursue a desired position. The authors explore the execution aspect of algorithms by using data on actual orders submitted and cancelled.
They investigate the relationship between AT and liquidity through a proxy for AT: the NYSE electronic message traffic normalized by trading volume. To start, the authors look at AT and liquidity in a sample of NYSE stocks from 2001, when decimalization was introduced, through 2005, before major structural changes took place in the exchange. After they remove stocks with an average share price of less than $5 or more than $1,000 along with stocks that are not present in the trade and quote and CRSP databases throughout the entire sample period, their sample includes monthly observations for 943 common stocks. The stocks are then arranged into quintiles based on market capitalization, with Quintile 1 holding the largest-cap stocks and Quintile 5 the smallest-cap stocks. As AT increases, the measures of liquidity tracked by the authors—quoted half-spread, quoted depth, and effective spread—also generally improve, with a few spikes of illiquidity during instances of severe market decline.
But the question is whether a causal relationship exists between the increases in AT and liquidity. To answer that question, the authors next focus on a change in NYSE structure that resulted in an exogenous increase in AT. The NYSE introduced and phased in Autoquote software beginning with six actively traded large-cap stocks on 29 January 2003. It gradually phased in more than 200 other issues on various dates and finished on 27 May 2003 with the remaining stocks listed on the exchange.
Autoquote made it possible for algorithmic traders to quickly integrate new information about potential terms of trade and quickly respond as liquidity suppliers or demanders. The rollout sequence was determined months in advance; this factor, along with other factors, leads the authors to conclude that the staggered introduction of Autoquote serves as a valid instrument to assess AT’s impact on liquidity. The shorter time frame for this sample results in a larger balanced panel of 1,082 stocks, which are similarly sorted into quintiles based on market capitalization.
The results show that the introduction of Autoquote significantly increased the amount of message traffic among the Autoquoted stocks compared with non-Autoquoted stocks. The effects are strongest among the larger-cap stocks, Quintiles 1 and 2, which is consistent with the general impression of the use of AT. In those quintiles, both the quoted and the effective spread narrow with an increase in AT as measured by the Autoquote instrument. Because quoted depth declines with Autoquote, the authors assess whether Autoquote is associated with narrower effective spreads after sorting trades based on size and find that AT significantly narrows the effective spread for trade sizes below 5,000 shares.
To explore whether AT alters the nature of price discovery, the authors use a vector autoregression that separates price moves into trade-related and trade-unrelated components through the use of two equations. The results suggest that the availability of Autoquote, leading to increased use of AT, allows for a larger amount of information to be incorporated into stock prices without trades being executed. As with the narrowing of spreads, the most reliable effects are found for the larger-cap quintiles.
The authors speculate about how algorithmic traders are changing the nature of price discovery. They suggest that algorithms allow traders to easily and with almost no marginal cost monitor common factor price information and adjust their orders and quotes accordingly. Also, algorithms can be designed to identify and respond to other algorithms as well as recognize order flow and other information patterns in data that can be used in adjusting trades or quotes.
A couple of caveats are included with regard to the authors’ analysis. They observe that their sample period was characterized by generally increasing stock prices and that the introduction of Autoquote came at a relatively stable time. To determine whether AT and algorithmic liquidity supply are also beneficial in market climates that are more volatile or characterized by generally declining stock prices, they suggest that analyzing data from 2007 and 2008 would be valuable. With regard to the decline in trading depth to which AT has contributed, the authors point out that such technological innovations as “dark pools” can potentially allow traders with large orders to electronically search for counterparties without revealing their trading interest.
Full article from The Journal of Finance and I believe can be accessed for free here: http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2010.01624.x/full
Abstract
The authors analyze whether algorithmic trading has increased the efficiency and liquidity of the NYSE. They examine the introduction of Autoquote to determine the causal effect of algorithmic trading. They find that the increase in algorithmic trading appears to reduce spreads, lower adverse selection among liquidity demanders, and increase the amount of information incorporated into prices without trades being executed.
In recent years, technology has significantly altered the manner in which financial markets operate by reducing the layers and concurrent costs involved in trading stocks. Algorithmic trading (AT), introduced in the mid-1990s, is an example of such technological change and is now a factor in an estimated 70 percent or more of U.S. trading volume. Algorithms are used in active and passive strategies by those that demand liquidity and those that supply it and to assess market conditions, track benchmarks, break large orders into smaller pieces, and generally pursue a desired position. The authors explore the execution aspect of algorithms by using data on actual orders submitted and cancelled.
They investigate the relationship between AT and liquidity through a proxy for AT: the NYSE electronic message traffic normalized by trading volume. To start, the authors look at AT and liquidity in a sample of NYSE stocks from 2001, when decimalization was introduced, through 2005, before major structural changes took place in the exchange. After they remove stocks with an average share price of less than $5 or more than $1,000 along with stocks that are not present in the trade and quote and CRSP databases throughout the entire sample period, their sample includes monthly observations for 943 common stocks. The stocks are then arranged into quintiles based on market capitalization, with Quintile 1 holding the largest-cap stocks and Quintile 5 the smallest-cap stocks. As AT increases, the measures of liquidity tracked by the authors—quoted half-spread, quoted depth, and effective spread—also generally improve, with a few spikes of illiquidity during instances of severe market decline.
But the question is whether a causal relationship exists between the increases in AT and liquidity. To answer that question, the authors next focus on a change in NYSE structure that resulted in an exogenous increase in AT. The NYSE introduced and phased in Autoquote software beginning with six actively traded large-cap stocks on 29 January 2003. It gradually phased in more than 200 other issues on various dates and finished on 27 May 2003 with the remaining stocks listed on the exchange.
Autoquote made it possible for algorithmic traders to quickly integrate new information about potential terms of trade and quickly respond as liquidity suppliers or demanders. The rollout sequence was determined months in advance; this factor, along with other factors, leads the authors to conclude that the staggered introduction of Autoquote serves as a valid instrument to assess AT’s impact on liquidity. The shorter time frame for this sample results in a larger balanced panel of 1,082 stocks, which are similarly sorted into quintiles based on market capitalization.
The results show that the introduction of Autoquote significantly increased the amount of message traffic among the Autoquoted stocks compared with non-Autoquoted stocks. The effects are strongest among the larger-cap stocks, Quintiles 1 and 2, which is consistent with the general impression of the use of AT. In those quintiles, both the quoted and the effective spread narrow with an increase in AT as measured by the Autoquote instrument. Because quoted depth declines with Autoquote, the authors assess whether Autoquote is associated with narrower effective spreads after sorting trades based on size and find that AT significantly narrows the effective spread for trade sizes below 5,000 shares.
To explore whether AT alters the nature of price discovery, the authors use a vector autoregression that separates price moves into trade-related and trade-unrelated components through the use of two equations. The results suggest that the availability of Autoquote, leading to increased use of AT, allows for a larger amount of information to be incorporated into stock prices without trades being executed. As with the narrowing of spreads, the most reliable effects are found for the larger-cap quintiles.
The authors speculate about how algorithmic traders are changing the nature of price discovery. They suggest that algorithms allow traders to easily and with almost no marginal cost monitor common factor price information and adjust their orders and quotes accordingly. Also, algorithms can be designed to identify and respond to other algorithms as well as recognize order flow and other information patterns in data that can be used in adjusting trades or quotes.
A couple of caveats are included with regard to the authors’ analysis. They observe that their sample period was characterized by generally increasing stock prices and that the introduction of Autoquote came at a relatively stable time. To determine whether AT and algorithmic liquidity supply are also beneficial in market climates that are more volatile or characterized by generally declining stock prices, they suggest that analyzing data from 2007 and 2008 would be valuable. With regard to the decline in trading depth to which AT has contributed, the authors point out that such technological innovations as “dark pools” can potentially allow traders with large orders to electronically search for counterparties without revealing their trading interest.
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Is this not going to hamper technological innovation once you limit the number of trades ?
http://video.ft.com/v/1235709818001/Algorithms-will-defeat-regulators
http://video.ft.com/v/1235709818001/Algorithms-will-defeat-regulators
