When the algorithms go rogue it leads to disasters. In 2011, a biology textbook about flies was priced at $23 million on Amazon. The reason was later discovered to be two sellers, who had set up algorithms that would watch each other’s prices and then reset their own. When the algorithms have gone rogue the planes have crashed so the stock markets are no exception. Algorithmic trading is now an inherent aspect of stock market trading.
Defining Algorithmic trading
Algorithmic trading(automated trading, black-box trading, or simply algo trading is the process of trading with the aid of technology. How quickly one executes trades has become a major source of competitive advantage. The whole co-location scam of the National Stock Exchange revolves around the concept of time advantage.
In equity markets, for many players, it is not what you know but how quickly you can act on it. Nanoseconds matter.
By locating servers that route trades ever closer to the market, by building faster computers, market makers can build their market share. Algorithms are deployed that enable market participants to execute trades using preset parameters based on volume levels, price changes, volatility indicators, and so on.
Many of these algorithms are similar so that, given certain prevailing market conditions, firms will start to execute in the same direction at the same time. Most of the time, the impacts of such changes are limited since prices go up and down, for the most part, in a continuous way—down like a waterfall and up like an airplane.
What happens, however, when price discontinuity is introduced into the market when a plane plunges from 20,000 feet to a hundred feet? The consequences are generally unknowable because the number of algorithmic traders is huge and knowing how they will react given a certain set of highly unlikely market conditions is impossible.
In May 2010, when an algorithm burst into a selling frenzy, the consequences could probably have been a lot worse. The rogue algorithm set off a tremendous wave of selling activity, and prices of certain securities dropped significantly. Indeed certain securities, for example, Accenture, went to close to zero value in a fraction of seconds.
In this instance, at least, market losses were minimal, due in no small part to the sensible and prompt actions taken by market regulators. Based on certain criteria, buyers and sellers were put back in the position they were before the crash.
In 2012, Knight Capital made an apparent error in releasing software updates to the live market environment. As a result, Knight Capital was taken on a buying spree of securities it apparently had no wish or had even considered buying. Eventually, the positions added up to a financial obligation that was beyond Knight Capital’s financial means.
The errant program had, evidently, locked the firm into price points that were above the market price. Unlike the 2010 Flash Crash, exiting the positions in this instance led to real losses and the firm’s failure and subsequent sale.
Algorithmic Trading Frauds in India
This instance was reported in the year 2010. However, a similar episode was repeated in India when the co-location fraud emerged on the Indian shores. Algo trading in India has picked up significantly as people are busy with different things but they still want to invest.
Between 2012 and 2014, several Indian stock brokers received preferential access to servers used for high-frequency and algorithmic trading at the National Stock Exchange (NSE).
This special access allowed certain brokers to game the system by placing trades ahead of their competition.