Algorithmic Trading: Setting the record straight
Automated trading impacts markets positively by bringing in greater liquidity, efficient price discovery, efficient order execution due to greater objectivity than human emotion, reduced scope for human errors and most importantly â€“ lesser information leakage.
Over the years, stock exchanges around the world have been increasingly leveraging technology to build more sophisticated, faster and smarter trading platforms. As an example, BSE and NSE have dramatically scaled up their platforms to support high volume of trades executed at lowest latencies, while observing comprehensive risk management practices. Investments in the financial services sector is likely to spur more such innovations, revolutionizing the way financial technology is conceptualized, designed and consumed.
One such area in the Indian context where financial technology has gained prominence is Algorithmic Trading, a system of trading which facilitates order execution based on an array of pre-programmed logic ranging from simple to complex. There has been a substantial amount of hue and cry about algorithmic trading post the market crash of May 6th 2015, when the BSE Sensex and NSE Nifty fell more than 2 per cent to their lowest in four months, back then. Market experts were quick to attribute this abnormal market movement to algorithmic trading. A more detailed investigation would have in fact revealed that FIIs who wanted to sell positions of their large basket orders only used algorithms for better execution of those orders. Had the trades been executed manually, they would have still had the same impact on the markets, albeit the time taken would have been much longer to sell the positions.
Concerns around the risks involved with algorithmic trading made a quick comeback in the latter part of June with the Reserve Bank of India’s Financial Stability Report of 2015. The Central Bank raised an alarm about the threat of systemic risks accrued to the financial markets on account of the increased use of algorithmic trading as a proportion to the overall trades being undertaken in the Indian equity and derivatives markets. Algo and High Frequency Trading volumes have increased substantially in the cash segment of the Indian equity market from 17 per cent on the NSE and 11 percent on the BSE in 2011, to around 40 per cent of total trades in both the exchanges in March 2015.
While the RBI is undeniably right in its observation, it only highlights the philosophical truth of every technological innovation has facets of ‘Ying-Yang’ or a ‘Vice-Virtue’ to it. Hence the need of the hour is to be looking at this fintech innovation from a prism of objectivity.
On one hand, such automated trading impacts markets positively by bringing in greater liquidity, efficient price discovery, efficient order execution due to greater objectivity than human emotion, reduced scope for human errors and most importantly – lesser information leakage. On the other hand, rogue algo programmes pose the threat of destabilizing the markets by increased volatility resulting in a ‘roller coaster’ market, or worse, a scenario like a “flash crash”. RBI’s call giving a leg up to the monitoring and surveillance procedures is indeed welcome, for it reinforces the need for sound risk management systems to mitigate the cons of algo trading.
With respect to the central bank’s observation of the share of cancelled algo orders constituting around 90 per cent of the total number of cancelled orders, posing a risk to the markets, it is my studied opinion that introducing disincentivizing pricing mechanisms could prove to be useful. There is already a penalty clause in place to charge for orders that are cancelled beyond a certain order –to- trade ratio.
Other than this, there are 2 key areas of risk management that have been put in place by the market regulator SEBI, as well as Indian exchanges that I would like to focus on. The first being a comprehensive risk approval process for algo trades. This involves sharing the algo logic along with risk measures; testing all algos in simulated environments and following it up with exchange mock trading days. This is then validated by a certified auditor and finally by the exchange undertaking a comprehensive demo for newer algos. Secondly, to achieve lower latency for exponentially high trading volumes, exchanges have leveraged technology for more efficient price discovery and order execution by overhauling their trading platforms with inherent risk management features such as ‘Anti-Wash feature’, and accepting orders within the range of the last traded price to prevent Flash Crash scenarios.
Measures undertaken by SEBI in terms of tightening controls on its circuit breaker mechanism (implementing market-wide circuit breakers at 10, 15 and 20 per cent of index movements) as well as initiating messages to stop matching of executable order and acceptance of fresh orders if the limits are breached indicate that the regulator is indeed proactive and, to use a phrase, ‘On the Ball’.
All in all, the extant regulatory and risk management systems adopted by SEBI and stock exchanges are robust enough to handle complex algo trading strategies.
Regarding RBI’s concern for the possibility of market manipulations as observed in the FSR, I see limited scope for this to take place if stronger surveillance systems are implemented. The surveillance done at the broker level, exchange level, and regulator level may become more comprehensive compared to where it is today (which is mostly at the end of day and checking for wash trades, turnover level limits etc.). In an algo driven world, surveillance needs to be at trade time, order book level and should be able to identify patterns which are missed on T+1 surveillance process. The one thing which may be added in the algo approval process is a back-testing of all algo logic against extreme market movements in a simulated environment such as index drop of x% within a few seconds etc. While back-testing is already being used by a few players, it has not become a mandatory procedure yet.
To stop algo trading due to its perceived threats is akin to stopping air travel to avoid aviation accidents. Air travel is regarded as the most efficient form of transportation. Similar is the case of algo usage to executing transactions. Needless to say, risk management and passenger safety across the two industries need to constantly improve with industry consultations. An industry wide consultation on how to further minimize the risks may lead to much better outcomes.
Until then, happy trading!
Author is co-founder and CEO with uTrade Solutions