- Date : 25/01/2019
- Read: 4 mins
Know all about algo trading and how it affects the market.
Algorithmic (‘algo’) trading is a process devised to make trading transactions at great speed with a view to maximising profit. This process is driven by a set of instructions or algorithms and is expected to perform at a speed and efficiency superior to that of any human trader. It is also known as automated trading, black-box trading, or simply algo trading.
Algorithms perform various functions based on the inputs fed into them. Many international companies have embraced the idea of algorithm-driven trades. Usage is expected to further rise as people learn more about financial models, technical indicators, and multi-leg option strategies.
In India, companies have started using algo alerts, which combines technology and human intervention.
Some interesting facts
- The Securities and Exchange Board of India (SEBI) allowed algorithmic trading in India in 2008.
- SEBI reinforced its framework on algorithmic trading recently, making its usage more prevalent.
- Algorithmic trading made its entry into the stock markets in the mid-1980s and has come to constitute nearly 70% of the total trading volumes in present-day developed markets.
- 50% of the volumes exchanged in the futures and options market is driven through algorithms.
- 30% of the volumes exchanged in the cash market is estimated to run through algo trading.
- Key skills required to use algo trading are statistical knowledge, programming knowledge, and understanding of the financial market.
- SEBI is likely to put out guidelines for algo trading for retail investors; presently, algo trading is more popular among institutional investors.
- Algo trading assures trading at the best price, instant and accurate order placement, reduced transaction cost, reduced possibility of mistakes, and completely eliminates emotional and psychological factors.
Algo trading strategies
The basic purpose of applying algo trading is to identify opportunities to increase or maximise profit or reduce cost. Some common algo trading strategies are:
- Trend-following strategies follow trends such as moving averages, channel breakouts, price level movements, and other related technical indicators.
- Arbitrage opportunities aim to earn the price differential of buying a multi-listed stock at a lower price in one market and selling it at a higher price in another.
- Index fund rebalancing capitalises on the trade done just before index fund rebalancing (the rebalancing of assets held to par with their respective indices price).
- Trading range (mean reversion) involves identifying and defining the mean range that the high and low price of an asset reverts to.
- Percentage of volume strategy sees the algorithm sending partial orders based on a defined market participation ratio.
- Implementation shortfall increases the targeted participation rate when the stock price becomes more and more favourable, and decreases it when it doesn’t.
Related: Why long term investments are better
Is algo trading risky?
Algo trading has been under the SEBI radar for some time, particularly after NSE’s algorithmic trading platform identified unfair access to market data and trading systems. Since then, SEBI has been gradually tightening up the framework for algo trading.
Internationally, there have been instances where the risk of using algo trading has been exposed. The Flash Crash of 2010 was the sharpest intra-day fall Dow Jones experienced till then. It was caused by a London-based day trader who spoofed a large volume of fake orders to generate artificial interest in the stocks traded (or rather, fake-traded).
Due to their real-time reaction time, algorithms may make the market more volatile than what it is today. As algo trading works across markets, it can help spread a market’s sentiments. A meltdown in a particular asset class or market can cause a global domino effect, as it did towards the end of 2015 when the Chinese stock market crash impacted global equities.
The magnitude of loss in case of any system error can be quite huge due to the involvement of algo trading. Given the great speed and high volume at which algo trading operates, one faulty program or algorithm can result in millions of losses within a short time. A case in point was the $440 million lost by Knight Capital in a 45-minute spell in August 2012. It was a clear case of amplification of a systemic risk that resulted from trading at unreasonable prices.
So yes, there have been errors noted in algo trading processes that have resulted in losses and adverse influences in the stock market. If you are clueless about investing in stock markets, this article will help you get kick started.