Algorithmic trading, which emerged in the 1970s, uses automated logic to execute trades, significantly impacting the Indian stock market since its introduction in 2008, with nearly 50% of trades being algorithmic by 2016. It improves market liquidity and efficiency while reducing volatility, but raises concerns like potential unethical practices and negative impacts on capital formation. Despite these concerns, algorithmic trading is seen as an inevitable evolution of the securities markets with the ability to enhance investment rewards.

1. Author
CMA K.R. Ramprakash,
TA- LIBA.

2. What is Algorithmic Trading?
• Stock market is influenced by diverse forces and factors which even the most
brilliant investors fail to comprehend.
• The quant revolution, started in early 1970s in US, believe that it is possible
to capture the relationship among these forces and factors using reliable
mathematical models.
• This paves way to the birth of algorithmic trading – any order generated
using automated executed logic (SEBI, 2012).

3. How trading algorithms are working?
Monitor
financial
markets
Discover
overlooked patters
and anomalies
Buy/Sell
signals
Allocation of
money
Trading
strategies

4. High Frequency Trading
• Only half the game is lucrative trade ideas.
• The trader who moves fast in the market often holds a competitive edge.
• High Frequency Trading is a type of algorithmic trading which is latency sensitive and is
characterized by a high daily portfolio turnover and high order to trade ratio.
• Average holding time of a stock – 22 seconds (Yan Ohayan)
• HFTs are trading at a speed of 64 millionth of a second (the average processing speed of
human brain is 9/10 of a second).

6. Algorithmic Trading in India
• In 2008, SEBI introduced algorithmic trading in India by
allowing Direct Market Access Facility to institutional
investors.
• DMA allows brokers to provide their infrastructure to
their clients, thereby they can access exchange trading
system without broker’s intervention, leads to lower cost
per transaction.
• Indian stock market adopts so quickly to algorithmic
trading. In 2016, nearly 50% trading in India are
algorithmic and there was 8 times jump in the action of
leading HFT firms in the year 2019 in India (Kriplani,
2019).

7. Co-Location Facility
• In 2010, NSE started co-location facility by
providing 54 server racks to clients.
• Co-location involves placing the servers of
HFT firms in the same floor where the
exchange server is located, which enables to
trade in microseconds.

8. India Flash Crash
• AT attracts more and more interest in the
academic community, after the India Flash Crash
on October 5, 2012.
• Because of the Flash Crash, the NIFTY index
dropped as much as 15.6 percent within minutes
and then experience a speedy V-shared recovery
after a brief trading pause (Dalko & Wang,
2019).
• Johnson et. al (2013), reported a sharp rise in so-
called ultra-fast extreme events. They noted the
occurrence of 18520 crashes and spikes between
the years of 2006-11.

9. What is a Flash Crash?

10. Impact of AT in India
This paper aims to provide a compendium of existing researches on AT in
India, which is very few in fact, in order to answer the following two important
questions:
1) What is the impact of Algo Trading on the quality of Indian Stock
Market?
2) What are the areas of concern in using Algorithmic Trading in India?

11. Algorithmic Trading & Market Quality
Gomer et. al (2011) postulates that stock market quality is mainly determined
by three parameters viz.,
1) liquidity
2) volatility
3) efficiency

12. Impact of AT on Market Liquidity
• After CLT, Latency dropped from 30 microseconds to 2 microseconds
(Aggarwal, 2013).
• Securities with higher algorithmic trading have lower liquidity costs and order
imbalance thus reduces the risk of intraday liquidity. (Thomas, 2014).
• Due to CLT large number of shares are available for trade and a reduced
imbalance is found between the number of share available to buy and sell.
(Syamala & wadhwa, 2020).

13. Impact of AT on Market Volatility
• There is a sharp drop in the volatility of prices and the volatility of
transaction costs after the increase in AT (Aggarwal, 2013; Thomas, 2014).
• Volatility has significantly reduced for the post AT period in comparison to
pre AT period in NSE (Iyer et. al, 2019).
• The speed of information adjustment in the stock market has improved a
lot, which had led to more intensive data analysis by the investors which has
led to a significant reduction in arbitrage opportunities, thereby reducing
order imbalance and volatility. (Jain, 2020)

14. Impact of AT on Market Efficiency
• Jawed & Chakrabarti (2018) examined whether increased AT intensity caused by the introduction of
co-location trading facilities improve the productive efficiency of the Indian stock indicies by
measuring the change in speed of information adjustment and change of persistence before and after
the introduction of co-location for Indian Indices.
• Their study reveals that, on the whole, the speed of information adjustment into the prices increased
while the persistence of older information decreased, for NSE after the introduction of the exogenous
event of CLT.
• There is an improvement in the overall productive efficiency of the leading Indian Indices, Midcap
and Smallcap indices being the prominent beneficiaries.
• Syamala & Wadhwa (2020) also proved in their study that AT improve overall price efficiency.

15. Areas of Concern in Using AT
• Lack of visibility, algorithms action on other algorithms, choice of algorithm
to use and absence of intuition are the areas of concern in using AT in India.
(Kumar & Puttan, 2015).
• AT imposes serious costs on the major function that securities markets
perform: allocating capital efficiently and productively across the real
economy. While quantitative models and advanced programming bring
considerable computational power to markets, they also generate risk of
information loss at significant cost to allocative efficiency. (Yadav, 2015)

16. Areas of Concern in Using AT
• AT traders might involve in unethical practices to get more benefits and
hence may seriously distort the process of price discovery (in reality, stock
prices are not discovered; they are made, they are fabricated). (Dubey et. al
2017).
• AT doesn’t facilitate capital formation. India’s capital formation rate has been
consistently declining at a CAGR -4.10% in the last 9 years, whereas the
volume in AT in last 7 years has been growing at the rate of 5.11%. (Mehra
& Vajpayee, 2018)

17. Conclusion
• This paper examined several studies which have proved the causal impact of AT on
the reduction in the overall transaction costs, order imbalance and order volatility.
• Despite its growing popularity and acceptance in India, one of the important issues
addressed by academic researchers is its negative impact on price discovery and
capital formulation, which are the fundamental functions of a capital market.
• Having said that, there is nothing to fear about AT as it is a natural evolution of the
securities markets. (Gomar et. al, 2011)
• Like all other technologies, AT enables sophisticated market participant to achieve
legitimate rewards on their investments – especially in technology – and
compensation for their market, counterparty and operational risk exposures.

18. Thank You…