The Impact of Algorithmic Trading

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Studying the positive and negative impacts of algo trading on the markets.

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The Impact of Algorithmic Trading

  1. 1. Impact of Algorithmic TradingGroup 5
  2. 2. OverviewDefinition What is Algorithmic Trading? Definition, characteristics and evolutionStrategies 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. 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. 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. 5. Algorithmic Trading Strategies Algorithms can be broadly categorized into the following two “families”EXECUTION ALGORITHMS ALPHA GENERATING ALGORITHMSThese programs execute stock These algorithms actively try to makemarket trades in such a manner that money. They track historicalthe 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. 6. VWAPVolume 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. 7. TWAPTime 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. 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. 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. 10. High Frequency TradingSpecial class of AT in which computers initiate ordersbased on information that is receivedelectronically, before human traders are capable ofprocessing 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. 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. 12. Increase in LiquidityAnalysis of 2002-2006 data from S&P500 Source: The Journal of Finance
  13. 13. Increase in LiquidityAnalysis of 2002-2006 data from S&P500 Source: The Journal of Finance
  14. 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. 15. Expert ViewInterview 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. 16. THANK YOU

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