Indian financial markets have seen a major growth in the current decade in terms of technological advancements. This has enabled growth in algo trading volumes, leading to Indian exchanges featuring in the world’s largest exchanges by volumes. Technology has helped in
– faster dissemination of market data and other relevant information by exchanges and trading participants
– capturing and consolidation of market data from various exchanges and other sources of relevant information
– making complex calculations on live and historic data, leading to trade decisions
– trading and managing risk
– lowering the latency and cost of trading
– various post-trade processes
The exchanges started offering co-location to its members, which means that member servers can be hosted in proximity to the exchange matching engines, achieving faster market data access, as well as orders and executions. Exchange co-location is paramount in for low latency algo trading. The round trips for orders executions reduced from 10s of milliseconds to 100s of microseconds in 2014. The cross exchange co-location based trading will also enable members faster algo access across the exchanges. This means that algorithms become more relevant than ever, because the trading opportunities within microseconds are executable only by the algorithms, and not by humans. Various kinds of algo traders participate in these trades, including, high frequency traders (who trade in huge volumes aggregating small profits in as many trades as possible, this can include arbitrageurs), market makers (that provide bids and offers in the market so they can gain the spread, while deepening the liquidity in the market), institutional execution algo traders (these buy side or sell side traders execute larger order quantities into smaller slices across a defined time period), quant algo traders (who use quantitative models to predict the future movements) etc.
Algo trading is on the rise. In 2015, more than 30-45% of all trading on Indian exchanges happens through algorithms. The percentage of algo trading is going to rise and may reach 75%+ by the end of this decade, which is close to where algo trading volume percentage in US / UK is today. More than 80% of the Indian institutional trading flow (from both FIIs and Domestic Fund Managers) goes through algorithmic execution currently. The reason is simple, once you build highways, faster transportation vehicles come, the bullock carts don’t offer efficient transportation mechanism for most business needs. Barring a few incidents, there had been no major issues faced due to technology adoption.
Risk management framework from SEBI and the Exchanges has been fairly comprehensive. –Exchanges approve each new algorithm before it goes live, and the approval process includes algo logic as well as risk validation. Regular mock trading sessions and audits are conducted to test the exchange systems, algos, etc. Advanced risk measures like trade execution range (i.e. the trade cannot happen too far away from the last trade price), circuit limits (cut off trading at exchange when a financial security breaches max allowed price movement in a day), self trade prevention checks (i.e. same trader / algos / member cannot trade against itself on the exchange), etc. have kept various flash crash scenarios at bay. There has been some backlash against algo trading.
To have a perspective, we should also consider what happens when we do not use algorithms for trading:
Errors due to fat finger order punching, mishearing or misinterpreting client instructions or missing some smaller unfavourable market movements are more probable in manual trading.
Leakage of information
If the buy side fund managers do not use algorithms, they rely on sales traders and traders to execute their orders. Its very hard to track the information leakage through manual sources. In fact, this is the reason why buy side cross their flow in Dark Pools which are permitted in the developed markets like UK, US etc.
High Cost of execution
Execution cost in manual trading scales with the volume of trades, as humans can only process so much information in live markets. Also, the cost of technology per trade comes down with higher volumes of trading. Execution quality is also better in algorithms as they can execute smarter faster logics by processing lots of information, which is even unavailable to the human traders (example tick by tick data)
Manual trading is typically prone to emotional trade decisions which may lead to deviate from logical trade instructions, which algos tend to follow much better. Looking from retail investors perspective, technology has helped with surge in Online and Mobile trading too. Even though retail participation in India is still limited (around 1% of the total population), but trading technologies are available for retail consumption via web, mobile and desktop versions. These platforms are well supported by telecom and internet networks and are accessible in most urban cities. There needs to be some more policy improvement to entice retail into trading as long term investment diversification (instead of only gold, fixed deposits, or real estate which tops the Indian retail investor’s portfolio today). Over time retail investors will also directly benefit from algo trading as more basic algos will become commoditized.
One other initiative, which is likely to deepen the capital markets in India is the Gift city initiative. If the Indians trading overseas and Foreign investors trading India is opened up (slowly with time and if INR currency conversion is managed efficiently somehow), that is likely to expand the Indian capital markets liquidity. The technology shall make it seamless and efficient.
If the core technology and growth is there, What more could be done to avoid major market wide technology driven breakdowns? Maybe further risk mitigation could be achieved in the following areas, Cyber security – given that most of the trading now happens through online, mobile or other infrastructure driven digital messages, the security of the communication channels could be strengthened further. uTrade Solutions had hired ethical hackers to find out where our software(s) are vulnerable to external cyber threats and we got good ideas for strengthening our platforms.
Surveillance in an algo driven world
Most of the surveillance tools run end of day or T+1 reports to identify market manipulations. In today’s algo driven world, we need to identify real time manipulations or non-compliant activities by having surveillance systems that behave like an algorithm. By the way, this need exists in the most developed countries and has been a topic of discussion with regulators like FCA in UK.
Stress-test the algorithms against extreme market events
A back-testing framework supported by exchanges may be introduced to conduct more comprehensive algo testing and hence become part of the algo approvals. For example, scenarios like how would an algorithm behave if the index were to drop a few percentage points within a few seconds, would the strategy also sell various assets ignoring stop loss or shut down signs, or will it shut down and let the humans take over the risk management and trade execution. Such stress testing shall help identify algo behavior in extreme events, which can be controlled if needed.
Secondary Risk systems
To avoid reliance of a single risk management system, which is part of the algorithmic platform itself, firms can consider having a secondary risk management system (as a second line of defense), which can manage the risk in case primary risk management system fails. Such practices are common when the algos are run on FPGA cards (hardware cards which make algos and risk management run faster)
To summarize, technology has been the key enabler in the Indian capital markets growth; whether its exchanges matching engines, trading platforms for online or algos, or comprehensive risk management. The technology standards available in India can be benchmarked as fairly close to the best systems available in the world. The risk management framework support by exchanges, regulators and the participants is also fairly comprehensive, reducing any major market-wide crash risks in the market.