Algorithmic trading (AT) is trading conducted via electronic platforms where buy and sell orders are automatically generated by quantitative models with little human intervention. AT strategies include execution algorithms like VWAP and TWAP that minimize market impact, and alpha generating algorithms like arbitrage and trend following that exploit short-term price anomalies. While AT increases market liquidity and price discovery, it can also increase short-term volatility. Experts note that high-frequency trading puts less privileged traders at a disadvantage due to its high costs and speed, though it benefits the market overall through greater liquidity.

1. Impact of Algorithmic Trading
Group 5

2. Overview
Definition What is Algorithmic Trading?
Definition, characteristics and evolution
Strategies How is it done?
Various AT strategies, about High Frequency Trading
Impact How does AT impact the markets?
Analysis of stock market volatilities, increase in liquidity
Opinion Expert opinion on AT
Interview with Mr. Sanket Kapse (D E Shaw & Co.)

3. What is Algorithmic Trading?
• Trading conducted via Electronic Platforms
• Buy or sell order of a defined quantity into a
quantitative model
• Timing and size of the order are automatically
generated
• Decisions are based on goals specified by the
parameters and constraints of the algorithm
• Little or no human intervention

4. Evolution and Background
• Computerization of order flow began in the early
1970s
• Landmark: introduction of the NYSE’s “designated
order turnaround” system (DOT)
• DOT routed orders electronically to the proper
trading post, which executed them manually
• Program trading became widely used in S&P500
equity and futures markets by 1980s
• By 2009, High Frequency Trading firms accounted
for as much as 73% of all US equity trading volume

5. Algorithmic Trading Strategies
Algorithms can be broadly categorized into the following two “families”
EXECUTION ALGORITHMS ALPHA GENERATING ALGORITHMS
These programs execute stock These algorithms actively try to make
market trades in such a manner that money. They track historical
the prices aren’t influenced by relationships between securities,
momentary swings in the market. assets or markets and then exploit
minor deviations for quick gains.
Two of the common execution Examples: Arbitrage, Scalping &
algorithms are the VWAP & TWAP Trend Following Algorithms

6. VWAP
Volume Weighted Average Price
• Calculated by weighting a stock’s price quotes
through the trading session with volumes traded at
each price
• Algorithm’s objective is to execute the order at a
price that is as close as possible to this weighted
average
• If the price of a buy trade is lower than the VWAP, it
is a good trade and bad if the price is higher than
the VWAP

7. TWAP
Time Weighted Average Price
• This strategy simply breaks up a large order into
equal parts and then dribbles buy or sell orders into
the market evenly over the trading session
• This is also referred to as "iceberging“
• This ensures that the price at which the investor
buys or sells is not distorted by momentary blips in
the market

8. Arbitrage Algorithms
• These algorithms earn a spread from trading on
anomalies between securities, trading venues or
asset classes
• For example, simple arbitrage algorithms may earn a
‘spread’ by buying a stock at Rs. 100 on the BSE and
selling it at Rs. 100.50 on the NSE
• The transactions must occur simultaneously to avoid
exposure to market risk

9. Trend Following
• These are commonly used by technical analysts to
identify a reversal in trends
• They then piggyback on it at an early stage to
benefit from the momentum
• Track technical indicators such as the 50 or 200-day
moving averages or relative strength index, to bet
on stocks on the verge of breaking out or breaking
down

10. High Frequency Trading
Special class of AT in which computers initiate orders
based on information that is received
electronically, before human traders are capable of
processing the information they observe
• Highly quantitative
• Positions are held only for brief periods
• NO investment position at the day’s end
• Sensitive to latency and processing speed
• Mostly employed by large firms

11. Analyzing the Impact
• Pros:
 Increases Liquidity
 Leads to better price discovery
• Cons:
 Leads to market Volatility
 Puts the less privileged traders at a disadvantage

12. Increase in Liquidity
Analysis of 2002-2006 data from S&P500
Source: The Journal of Finance

13. Increase in Liquidity
Analysis of 2002-2006 data from S&P500
Source: The Journal of Finance

14. Market Volatility
• 1987: was caused in part by
dynamic portfolio insurance (a 2010:
way of protecting losses in the Flash
market) Crash
• 2010: The “Flash Crash”
occurred as a result of HF
Traders rapidly changing
positions in a market void of
Fundamental Buyers Market Volatility
• 2012: Knight Capital’s trading 1987: 2012:
system flooded the market Stock Knight
with erroneous trades Market Capital
Crash Case

15. Expert View
Interview with Mr. Sanket Kapse (DE Shaw & Co.)
• Mr. Sanket Kapse works as Finance and Operations
Generalist at DE Shaw and Company where he takes
care of the back office functions for the AT Portfolio
• According to him, AT is just a way of trading which is
faster and smarter than how human traders can
trade
• He believes that AT, being inherently expensive to
implement, puts less privileged traders at a
disadvantage
• However, he also agrees that AT, especially HFT
leads to more liquidity and better price discovery

16. THANK YOU