Algo trading: The boon and bane of finance
Months ahead of NSE’s planned IPO, Chitra Ramkrishna shook the financial world by abruptly quitting as the MD & CEO of India’s largest bourse on December 2.
Though the NSE said she quit due to personal reasons, some speculate she had differences with the board.
Moreover, she was under pressure after it appeared that some of the traders at the NSE’s colocation facility got an unfair advantage of around 10-20 milliseconds (1,000 milli seconds = 1 second).
This is a significant time period in high-frequency trading which is done through powerful computers to transact large number of orders at high speed. The computers use complex algorithms to analyse multiple markets and execute orders. In high-frequency trading, brokers that offer the fastest execution speeds usually garner the highest profits.
So how do these algorithms work and why are they employed? An algorithm is a set of clearly defined instructions aimed to carry out a task. Algorithms improve traders’ efficiency and cut down transaction cost. Some of the popular algorithms are as follows:
Mean reversion assumes that the price of an asset will move towards the average price. So in such algorithms, pricing is set on the basis of the mean value of an asset. The algorithm assumes that high and low prices of an asset are temporary phenomenon.
When the current market price is less than the average price, one is advised to purchase the stock, assuming that the price will rise. Moving averages are often reported for 50 and 200 days.
Volume weighted average price
Volume weighted average price is the ratio of the value traded to total volume traded over a particular time horizon (usually one day). In other words, it is the average price at which a stock is traded at a particular time.
Under volume weighted average price strategy, large trades are broken down into smaller ones to minimise market impact costs, which are the adverse effects of the traders’ activities on the price of an asset. Prices above the VWAP reflect a bullish sentiment and prices below it a bearish sentiment.
Time weighted average price
As opposed to VWAP, this strategy is used to execute large orders over a period of time to minimise market impact. High-volume traders use TWAP to sell or buy stocks over a specified period so that they transact at a price close to market price.
It is the difference between the prevailing price (decision price) and the final execution price, including commissions and taxes. In order to maximise profit, investors have tried to keep the implementation shortfall as low as possible. Online trading and access to real-time information has helped reduce this cost.
It is the purchase of a stock in one market for a cheaper price and its simultaneous sale in another market at a higher price. With algorithms, computers can exploit the differences in stock prices around the world in milli seconds before the prices become uniform again.
While, algorithmic trading has made markets more efficient and reduced transaction costs, it has also increased volatility.
A July 2011 report by the International Organisation of Securities Commissions technical committee said that because of the strong inter-linkages between financial markets, such as those in the US, algorithms operating across markets can transmit shocks rapidly from one market to the next, thus amplifying systemic risk. The report pointed to the Flash Crash of May 2010 as a prime example of this risk.
In the Flash Crash, the Dow Jones plunged almost 1,000 points and rebounded in around 36 minutes. The market value was cut by a trillion dollars for a short while.
Moreover, a TCS paper says algorithms can’t capture a trader’s gut feeling. They can’t also compete with the ability of the human brain to react to unanticipated changes and opportunities, says the report.
It seems that more transparency, equal access to information, and prohibiting flash crashes may be the way forward in this age of hi-tech finance.