Algorithmic Tradiging and High Frequency Trading

Trading Using Bots

1. Algorithmic & High Frequency Trading in India 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?

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).

5. 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).

6. 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.

7. 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.

8. What is a Flash Crash?

9. 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?

10. 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

11. 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).

12. 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)

13. 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.

14. 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)

15. 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)

16. 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.

17. Thank You…