For algo and high frequency traders (HFT), trading just got costlier as both stock exchanges - BSE and NSE - have imposed charges to curb such trading. Indian exchanges are of the view that HFT and algorithmic trading (AT) are the reasons for wild price fluctuations, as a report in Business Standard states. Nothing can be farther from the truth.
Price fluctuations take place in illiquid markets, or where the order size is very large compared to order outstanding and awaiting execution. If there is one contribution of HFT and AT to the stock markets, it is providing liquidity. In a paper published by The Journal of Finance by Terrence Hendershott, Charles M Jones and Albert J Menkveld titled “Does Algorithmic Trading Improve Liquidity”, the authors have reached the following conclusion: “For large stocks in particular, AT narrows spreads, reduces averse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.”
HFT and AT account for a major portion of transaction volumes in global markets. HFT alone accounts for 50 per cent of world markets volumes, while in the US it has touched 73 per cent of the volume in 2009 (last data available) and in India it is believed to have touched 40 per cent.
HFT traders aim to square off all their positions by the end of the day. A paper on HFT by the University of Heidelberg, Germany, says that an HFT trader holds stocks from a micro-second to 22 seconds. Their strategies are generally classified as liquidity providing strategies, statistical arbitrage strategies and liquidity detection strategies. Such traders use algorithms (mathematical logic) to create software that identifies trading opportunities, as they arise, and also place buy and sell orders on the exchange.
Algorithmic trades are increasingly being used by the buy-side (mutual funds, insurance companies and institutional investors). John Bates and Mark Palmer in an article in Financial Intelligence Guides in 2006 had said that buy-side was demanding for higher anonymity and control on its trades, which has only increased since then. Orders go to the firm adopting the best algos to execute trades.
In a research paper by TCS titled “Algorithmic Trading: Pros and Cons”, author SK Rao says, “Algorithmic trading cuts down transaction costs and allows investment managers to take control of their own trading processes. By breaking large orders into smaller chunks, buy-side institutions are able to disguise their orders and participate in a stock’s trading volume across an entire day or for a few hours.”
Thus, the advantages of AT and HFT far outweigh the disadvantages. By increasing cost of trading, stock exchanges are likely to clamp down on volumes, which would lead to further price fluctuation. The Business Standard article quotes Lokesh Madan, managing director of Algo Trading India llc as saying , “At least 50 per cent of algo trading will be affected by this circular (raising charges on algo trades) and algo volumes may also come down in coming days.”
Algo traders, however, will be thankful that the regulator has been benign to algo trading despite being worried about the rising volumes and the occasional hiccup.
Rather than developing systems that could prevent price disruptions as was witnessed when Infosys dropped by 20 per cent on account of an algo trade, exchanges seem to be choosing the easier way out by putting in financial disincentives. Adequate filters both at the broker’s end and at the stock exchanges could have prevented such a sharp fluctuation. Penalties and financial disincentives are permanent measures which affect the overall structure of the market.
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India anyway, is one of the costliest markets to trade in thanks to various statutory charges as shown in the chart provided by NSE in its paper titled “Cost of Trading in Stock Exchanges: a Perspective”. These curbs introduced by the exchanges have just made trading costlier. One of the factors that institutions consider while deploying money in a country is the impact cost, which can come down only with high liquidity. Markets with lower liquidity do not find many takers.
