The current information overload has triggered a race to generate disproportionate opportunities out of an overwhelming amount of data. This means financial advisors need to create unbiased, fast and actionable insights for their clients. Algorithmic trading or algo trading - a computer programme with a set of instructions which, when run in a multitude of scenarios, gives trading signals based on multiple technical, statistical and mathematical rules - is helping advisors, professional traders and institutions generate more profitable trades with the same amount of information. It can be divided into three broad areas:
- Quant-based trading where sophisticated rules are used to identify hidden stock opportunities; this is further strengthened through machine learning principles
- High Frequency Trading, or HFT, where the objective is to execute orders with split second precision and make profit from price anomalies
- Complex arbitrage strategies across exchanges, expiries
Algo trading has been prevalent in developed markets such as the US since the late 1980s. In India, it caught up in 2008 and, since then, has been lapped up by proprietary and institutional investors. Retail participation is not allowed. In global markets like the US, algo trading has led to some significant changes in the market canvas. One can expect these things in India too over a period.
- Lower cost of financial products. The rise of web-based buying/selling in the late 90s to discount broking a few years ago (on the back of sophisticated algorithmic trade execution) has led to a fall in cost and improvement in access to financial products. HFT, where a computer is able to execute thousands of orders in nanoseconds, has become a big source of revenue for most brokerages in the US, leading to further reduction in the cost of trading. HFT accounts for over 70 per cent of all equity trades in the US today.
- Dis-intermediation of the wealth advisor. The launch of robo advisors and rising popularity of index funds and ETFs (which require no people costs) are a big help to the price sensitive investor. Today, an online robo advisor has the capability of managing millions of dollars in a tax effective way for as low as 0.15 per cent a year (compared to 1-2 per cent charged by advisors).
- Better execution for institutional investors. Algorithms allow institutions to buy and sell large quantities of a security without affecting its price. It's also faster and cheaper. Also, institutions can be guaranteed better prices than in a manual trade.
- Algorithms as fund managers. Blackrock, the world's largest fund manager with over $8 trillion assets, has made a bold bet of replacing fund managers with algorithmic strategies. Its algorithmic funds have outperformed 90 per cent active managers over the last decade.
- Co-location facility to entities as opposed to each member setting up and paying for individual facility
- Free of Charge Tick-by-Tick Data (TBT) Feed to enable traders take the benefit of the entire order book and generate opportunities between two snapshots. These changes usually take place frequently (in microseconds), and it is not possible for human beings to observe and analyse these and devise trading strategies
- Order to Trade Ratio (OTR): Algo trading is characterised by high daily portfolio turnover and high OTR. High OTR raises concerns regarding order flooding, clogging of the pipeline, etc. At present, a penalty framework for high OTR is applicable.
- Tagging of Algos: A specific code is attached to algo orders to distinguish them from non-algo orders. This is to establish an audit trail and make surveillance easier
Not only is the process prone to risks due to crash based on a fault in the programme, there have been cases of algos manipulating the market. For example, there was a market crash during the mahurat session in 2011 after an algorithm went into a loop and kept entering repeat trades in futures.
With spurt in volumes and active recognition by regulators and market participants, the future of algos seems bright. We anticipate that machine learning will enable superior self-enhancing algorithms that can adopt to changing market dynamics. We also expect healthy volumes from programmatic trading with emphasis on retail market. We also foresee increasing number of advisors and technology companies offering pointed solutions, thereby making algo trading accessible to a larger group.
Motilal Oswal is Chairman & MD, Motilal Oswal Financial Services Ltd, and Pratik Oswal is Fintech Entrepreneur