Financial markets have undergone a dramatic transformation. Traders no longer sit in trading
pits buying and selling stocks with hand signals, today these transactions are executed electronically by computer algorithms. Stock exchanges’ becoming fully automated (Jain (2005)) increased the number of transactions a market executes and this enabled intermediaries to expand their own use of technology. Increased automation reduced the role for traditional human market makers and led to the rise of a new type of electronic intermediary (market- maker or specialist), typically referred to as high frequency traders (HFTs).
This paper examines the role of HFTs in the stock market using transaction level data from NASDAQ that identifies the buying and selling activity of a large group of HFTs. The data used in the study are from 2008-09 for 120 stocks traded on NASDAQ. Of the 120 stocks 60 are listed on the New York Stock Exchange and 60 from NASDAQ. The stocks are also split into three groups based on market capitalization. To understand the impact of HFT on the overall market prices we use national best-bid best-offer prices that represent the best available price for a security across all markets.
The substantial, largely negative media coverage of HFTs and the “flash crash” on May 6, 2010 raise significant interest and concerns about the fairness of markets and HFTs’ role in the stability and price efficiency of markets. Our analysis suggests that HFTs impose adverse selection costs on other investors, by trading with them when they (HFTs) have better information. At the same time, HFTs being informed allows them to play a beneficial role in price efficiency by trading in the opposite direction to transitory pricing errors and in the samedirection as future efficient price movements.
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