FIN11. Trading and Market Microstructure. Autumn 2018
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1 FIN11 Trading and Market Microstructure Autumn 2018 Lecturer: Klaus R. Schenk-Hoppé
2 Session 13 Automated Trading
3 Themes The rise of the machines Why & How Automated market-making & investment Benefits & Risks
4 Why this session? Machines!
5 Quotes vs trades: Machines!
6 Automated trading Program trading Electronic trading Computer trading Algorithmic trading Algo trades High-frequency trading
7 Automated trading: pressing questions Does it benefit the market? - increase market liquidity - decrease volatility - reduce trading costs Or is it detrimental? - high-frequency trading - flash crash
8
9 Automation of trades Explained by analogy: stopping a car By human By smarter human By computer
10 Lesson Sometimes computers are better
11 Automated trading: basics Using computers to carry out Trades Quantitative strategies Hedges Market making Input data News, fundamentals Market quality measures Portfolio / inventory
12 Automated trading: benefits Low costs (once up and running) Fast & 24/7 attention span Forces you to 100% specify your ideas/goals No emotions Integration with optimization toolboxes sophisticated statistical tools direct data feeds Reporting (back office)
13 Optimal execution using Algorithmic trading Smart, automated trading of blocks Tell software how many shares Give end date Press button (or select more parameters) Computer is doing the rest Examples Goldman Sachs Electronic Trading (GSET) - in Brazil since March Fidessa since 2006
14 Goals in optimal execution Sell/buy such that Trade at best prices Liquidity used optimally AVOID market impact information being revealed non-execution, slippage Mathematical optimization problem: Slice and dice (plus much more!)
15 Automated Hedging June 23, 2011 Goldman Sachs Electronic Trading (GSET) announced new options electronic trading functionality affords our clients the ability to automate the processes involved in delta hedging [ ], (Gupta, VP and head of US-listed options business development, GSET)
16 Automation is as old as the hills
17 Can things go wrong? Yes, but mostly they do not! If algo trade software goes wrong, it is an embarrassment bad for reputation best to be kept a secret Hence - Only some cases come to light - Example follows
18 Knight Capital case How to lose $440 million in 30 minutes
19 Knight Capital - the beginning US market maker specializes in executing trades for retail brokers, handled ~ $20bn of trading per day in August 2012: - new software implemented & started - dormant old software woke up - multiplied outgoing orders by 1,000 - plus other issues First 45 minutes on NYSE stocks affected (volatility )
20 Knight Capital - the disaster 9:41 (17 min) EXC
21 Knight Capital - the disaster 9:46:07 (27 sec)
22 Knight Capital - the disaster 9:46:20.2 (1 sec)
23 Knight Capital - the end Trading loss amounted to $440m CEO s comment to Reuters: more of a networking problem as opposed to using quantitative tools to trade NYSE Canceled trades in 6 stocks, and announced: the decision to not cancel any more trades is "not subject to appeal" Company was bought by Getco end of 2012
24 Lesson What a Knightmare! Humans can show more sense than machines.
25 Adversaries to algo trades Other algo traders! Pattern in order flow means MONEY (to others!) How to profit from 1. Observed large limit order 2. Anticipated trades will be part of the next session.
26 2 6 Your opponents Angler Float Raider Active Iceberg Volare Pilot Spread Trader Shark Volatility Trader RecoilSM Dark List Hunt Dark Probe Shadow Dynamic Scaling AdrenalineSM T-HAWKSM VWAP Pounce Work and Pounce TWAP Dagger Small Order Reload Rapid Cobra Delta Adjusted OptimIS Scaler Stealth City Match Smart Iceberg Sonar Discretion Crossfire Buyback Escalate Exit Pivot Delta Hedge Time Trigger Spotlight Prowler LMX Hydra Spread (Pairs) Trading Market on Close Percent of Volume Midmax Slicer Scaling Piccolo Navigator Priority Implementation Shortfall ESP Cloak Nighthawk Strict Volume Percent Alpha Smart Pegging Synthetic Execution Statistical Arbitrage Swoop PortX Arid Synthetic Close Strategy Fan
27 But Even without interference from trading strategies that are after your money, algos can get you into a tight spot.
28 Good intentions gone bad in automated portfolio management Automated stop-loss orders to protect your wealth when markets are falling How? set a price at which you sell a long-position when the market falls, you sell avoids further losses Aggregate effect If everyone uses stop-loss, be super fast to sell before market goes into tailspin
29 Automated market making
30 Market making Post bid/ask quotes Guarantee minimum volume Selling liquidity Stock markets, e.g. RTS Exchange in Russia (all but 7 stocks are quote-driven) Making deals (OTC) swaps, derivatives, ETF, Market extremely well understood (in theory)!
31
32 Timber Hill case US broker market making for small caps automated trading system In 2007 two Norwegian day traders discovered a flaw in Timber Hill s system made about $68K using 20K trades over 8 months arrested by Norwegian police in 2008
33 Timber Hill case (ctd) What happened? Three very-low turnover stocks (~ 9-20K daily) Hafslund, Wilh. Wilhelmsen, Odfjell
34 3 4 Intraday volatility manipulative behaviour
35 Timber Hill case (ctd) TMB acted as market maker for these stocks posts bid and ask prices, offered volume > 5K Book shows 5k bid at 9.7 and a 5k ask at 10 YOU sell 5,000 shares at 9.7 from TMB Then TMB moves to bid to 9.6 and the ask to 9.9 YOU sell 100 at 9.6 Then TMB moves to bid to 9.5 and the ask to 9.8 [etc] until ask at 9.4 YOU buy 5,500 at 9.4 GAIN: 53,200-51,700 =1,500
36 3 7 Or selling to push quote xxxxxx
37 Timber Hill case (ctd) Discovered by Oslo Stock Exchange 1st verdict market manipulation: using buyand sell-orders that were not intended to be traded on, but to move the prices 2nd verdict acquitted: transparency of trades, common market practice Svend Egil Larsen
38 Timber Hill's presumed thoughts This is business risk
39 Is there an easy fix? Value-weighted adjustment of bid/ask
40 Lesson Sometimes you gain, Sometimes you lose. If you lose more than you gain, move on
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