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How does information asymmetry provide liquidity in HFT?

How does information asymmetry provide liquidity in HFT?

High Frequency Traders (HFT), are they devils or disruptors? According to some they are responsible for market behavior like the 2010 Flash Crash, to others they are the ones that create and seize opportunities in financial markets.

It does seem that HFTs can heavily influence price changes, for example by back running on sell off orders being sold in smaller chunks (Korajczyk & Murphy, 2015). But are they exploiting information asymmetry and because of that disrupting the market in a negative way or do they create liquidity so that more traditional companies can benefit from market volatility? And if so, do their actions create the same sort of liquidity in small-cap markets compared to large-cap markets? The answers to these questions might tell us if HFT is a blessing or the proverbial curse. In 2005 stock exchanges became fully automated. This increased automation reduced the human role and gave way for a new type of electronic market makers, the algorithm traders (AT) (Brogaard, et al., 2013).

In 2009 automated trading was already responsible for 73% of executed orders in the United States. The level of automation made it easier and cheaper to execute (large) orders while the algorithms determine price, quantity and venue routing (Hendershott, et al., 2010). One type of automated trader attracted the most attention: the high-frequency trader (HFT). In the absence of a formal definition most associate HFT with extremely fast computers running algorithms coded by developers. These traders typically do not work at the large sell-side banks, but at privately held firms. They trade with many small positions and normally do not carry positions overnight. HFTs are characterized as a new breed of intermediary, but their position as providers of improved market quality or hurting the market by exploiting information asymmetry is fiercely debated between practitioners and academics (Menkveld, 2016).

The rise of automated trading and in particular HFT made markets more efficient by improving liquidity and lowering transaction costs. HFT plays a double role in the financial markets by acting as a liquidity provider and thus helping the market quality, but also profiting of arbitrage opportunities (Rijper, et al., 2010). This effectively means that automated trading narrows quotes and lowers costs for the market as a whole, but demands an advantage in transaction speed to keep maintaining profitability. Therefore HFT will be focusing even more on those small margins to profit of asymmetry by looking for even faster ways to execution leaving non-HFT with less opportunities. One might say that this is indeed an unfair advantage. When evaluating the percentage of algorithm trades in the market non-HFTs seem to become a dying breed as they will not be able to match the innovations in IT. Given the developments in automation HFT does look like the future of trading by given the market the liquidity it needs to become truly efficient. Quoting prices, lowering the bid-ask spread and delivering market demand will drive informativeness. More information known to the public will result in prices that resemble the fundamental value better as stated in the Efficient Market Hypothesis. Trades revealing information in market microstructures gives HFT the opportunity to build on their advantages towards traditional traders. They will always focus on getting information sooner than others, hence the never ending need for speed improvement (Muhle-Karbe & Webster, 2017). The gathered information gives room for quoting specific prices and possibilities for arbitrage and thus for providing liquidity. HFT essentially creates their own market opportunities. Needed methods to obtain this information will keep giving room for debate. The question if this same techniques would also work in small-cap markets should be researched more thoroughly. It is quite clear that more automation has its benefits on market tradability but it is expected that there are more factors that explain stock liquidity. Expected return is important for investors and the willingness to trade a stock. When an investor would trade a small-cap stock he needs to be sure that this stock can be sold at the right price and generating the required return. The illiquid nature of small-caps demands a premium to bear the risk involved. The main question would then be, how can HFT reduce risk? A possible solution would be to create more demand. As Amihud and Mendelson stated (1986) higher returns are required for high spread stocks, but lower spreads result in higher firm value. Instead of the currently hired designated markets makers to unnaturally support the small-caps other financial incentives could be a trigger to make small-cap markets more interesting for investors. This would mean that the risk-return ratio should become more attractive for investors. Theoretically continuous quoting by HFT could lower spreads and make the trade less risky. Demand could rise if traders were more confident that they can buy, but also sell the asset when the opportunity arises. A possible side-effect could be that more demand creates higher market prices as shown in theory (Wang & Zhang, 2015) but the market price would not reflect book value affecting the book-to-market ratio used by Fama and French (1996). The relative large bid-ask variances currently in the small-cap market may give a difference in the use of the Efficient Market Theory compared to the actual value. At some point in time, when price differences are arbitraged out by the market, a natural equilibrium will possibly emerge, but till that moment trading opportunities will be available. HFT can be a catalyst to improve liquidity, even in small-caps, but it seems that more market factors need to be taken into account. As they are all related to each other (return, liquidity, asset pricing) additional research would be necessary.

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