Market microstructure and asset pricing

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Jaksa Cvitanic, Andrei A. Do high frequency traders affect transaction prices? In this paper we derive distributions of transaction prices in limit order markets populated by low frequency traders humans before and after the entrance of a high frequency trader machine.

We find that the presence of a machine is likely to change the average transaction price, even in the absence of new information. We also find that in a market with a high frequency trader, the distribution of transaction prices has more mass around the center and thinner far tails. With a machine, mean intertrade duration decreases in proportion to the increase in high frequency traders and asset prices ratio of the human order arrival rates with and without the presence of the machine; trading volume goes up by the same rate.

This explains the shape of the transaction price density. In fact, we show that in a special case, the faster humans submit and vary their orders, the more profits the machine makes. Machines are assumed to be strategic uninformed liquidity providers.

They have only one advantage over the humans — the speed with which they can submit or cancel their orders. Because of this advantage, machines dominate the trading within each period by undercutting slow humans at the front of the book.

This is only one of the strategies used by actual high-frequency traders in real markets, and the only one we focus on. The guts of the paper is: We also find that in the presence of a machine, the shape of the transaction price density remains the same in the middle, between the bid and the ask of the machine, the far tails of the density get thinner, while the parts of the tails closer to the bid and the ask of the machine get fatter.

In the presence of the machine, mean intertrade duration decreases in proportion to high frequency traders and asset prices increase in the ratio of the human order arrival rates with and without the presence of the machine. Trading volume goes up by the same rate. In other words, if the humans submit orders ten times faster when the machine is present, intertrade duration falls and trading volume increases by a factor of ten.

Second, we compute the optimal bid and ask prices for the machine that optimizes expected high frequency traders and asset prices subject to an inventory constraint. The high frequency traders and asset prices constraint prevents the machine from carrying a significant open position to the next intra-human-trade period.

The optimal bid and the ask for the machine are close to being symmetric around the mean value of the human orders, with the distance from the middle value being determined by the inventory constraint — the less concerned the machine is about the size of the remaining inventory, the closer its bid and the ask prices are to each other. High frequency traders and asset prices expected profit of an optimizing machine is increasing in both the variance and the arrival frequency of human orders.

In fact, in a special case, the faster humans submit and vary their orders, the more profits the machine makes. The conclusion of interest is: We also find that a machine that optimizes expected profits subject to an inventory constraint submits orders that are essentially symmetric around the mean value of the human orders.

The expected profit of an optimizing machine increases in both the variance and the arrival frequency of human orders. I analyze a unique dataset to study the strategies utilized by high frequency traders HFTstheir profitability, and their relationship high frequency traders and asset prices characteristics of the overall market, including liquidity, price discovery, and volatility.

The 26 HFT firms in the dataset participate in I find the following key results: He provides a good discussion of pinging: I begin by testing a variety of potentially important variables in an ordered logistic regression analysis. The results show the importance of past returns. I carry out a logistic regression analysis distinguishing the dependent variables based on whether the HFTr is buying or selling and whether the HFTr is providing liquidity or taking liquidity.

Finally, I include order imbalance in the logit analysis and find that the interaction between past order imbalance and past returns drives HFTr activity and is consistent with HFTs engaging in a short term price reversal strategy. Anticipatory trading is not itself an illegal activity. In my analysis, as HFTs are propriety trading firms, they do not have clients and so the anticipatory trading they may be conducting would likely not be illegal. Where HFT and anticipatory trading may be problematic is if market manipulation is occurring that is used to detect orders.

Trillium was fined for the following layering strategy: Market participants would see this new influx of sell orders, update their priors, and lower their bid and offer prices. By me, that just shows that most traders are little girls with no conception whatsoever of fundamental value and FINRA panders high frequency traders and asset prices them.

This Figure analyzes the depth of the order book and how much depth different types of traders provide by analyzing the high frequency traders and asset prices impact of a share trade hitting the order book with and without different types of traders.

There are three graphs. The first, Price Impact of a Share Trade, examines the total price impact a share trade would have with all available liquidity accessible.

The daily dollar price impact value is calculated giving equal weight to each stock. High frequency traders and asset prices order book data is available during 10 5-day windows. The X-axis identifies the first day in the 5-day window. That is, The observation is followed by observations on January 8th, 9th, 10th, and 11th of The next observation is for April 7, and is followed by the next four consecutive trading days. To separate the 5-day windows I enter a zero-impact trade, creating the evenly spaced troughs.

Also of interest was the effect of the short-selling ban on market participation by HFT: In the HFT dataset, 13 stocks are in the ban. The first graph reports the fraction of dollar-volume where HFTs supplied liquidity. The second reports the fraction where HFTs demanded liquidity. The two vertical lines represent the first and last day of the short-sale ban. This entry was posted on Sunday, January 9th, at You can follow any responses to this entry through the RSS 2.

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