Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. Alain Chaboud, Benjamin Chiquoine, Erik Hjalmarsson, and Clara Vega

Transcription

1 Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market Alain Chaboud, Benjamin Chiquoine, Erik Hjalmarsson, and Clara Vega 1

2 Rise of the Machines Main Question! What role do algorithmic traders (AT) play in the price discovery process? Do ATs make prices more or less informative?! Triangular arbitrage opportunities! Autocorrelation of high frequency returns 2

3 Question (continued)! What is the mechanism? Does the impact depend on whether AT provides liquidity or demands liquidity?! AT measured in five different ways AT participation (VAT) AT liquidity provision (VCm) AT liquidity demand (VCt) AT signed liquidity demand (OFCt) Correlation of AT trading actions (ln(r)) 3

4 Question (continued)! Main problem in answering question: endogeneity (reverse-causality)! Granger causality and Heteroskedasticity identification approach (Rigobon (2003), Rigobon and Sack (2003, 2004)) 4

5 Theory! Excellent review of the literature: Biais and Woolley (2011) and Foucault (2012)! Disagreement on the effect AT may have on price discovery (Foucault (2012))! Positive effect: Oehmke (2009) and Kondor (2009), competition among convergence traders makes prices more informationally efficient 5

6 Theory (continued)! Positive effect: Biais, Foucault, and Moinas (2011) and Martinez and Rosu (2011)! Computers are fast and better informed than other traders! Computers use market orders to exploit their informational advantage! Computers make prices more informationally efficient, but increase adverse selection costs for slow traders! Positive effect: Hoffman (2013) fast computer liquidity providers are better informed. 6

7 Theory (continued)! Negative effect: crowding effect : Kozhan and Wah Tham (2012) and Stein (2009) computers entering the same trade at the same time push prices further away from fundamentals 7

8 Theory (continued)! Negative effect: If computers are noise traders: Delong et al. (1990), Froot, Scharfstein, and Stein (1992)! Positive feedback traders who predictably extrapolate past price trends! Short-term speculators (chartist) herd and put too much emphasis on some (short-term) information and not enough on fundamentals! AT could cause excessive volatility! Foucault (2012): effect may depend on strategy computers specialize on. 8

9 Our Data! EBS (essentially the global site of price discovery in interdealer FX market for several large currencies) records when a trade is placed manually (keyboard) or by a computer interface! Minute-by-minute data from 2003 to 2007! Three currency pairs (EUR-USD, USD-JPY, EUR-JPY) 9

10 Our Data (continued)! Volume and direction of trade breakdown each minute by AT (Computer) and non-at (Human).! We know how much computers take from human makers.! Four possible types of transactions: HH, HC, CH, CC (maker-taker). 10

11 Algorithmic Trading Growth in EBS Participation (Percent) Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 USD/EUR JPY/USD JPY/EUR 11

12 12

13 Our Data (continued)! Five different measures of AT activity! AT participation: Vol(CH+HC+CC)/Vol(CH+HC +CC+HH)! AT liquidity supply: Vol(CH+CC)/Vol(CH+HC+CC+HH)! AT liquidity demand: Vol(HC+CC)/Vol(CH+HC+CC+HH)! AT signed liquidity demand: OF(HC+CC) /( OF(HC+CC) + OF(CH+HH) )! Correlation of AT trading actions: R-measure 13

14 What if algorithmic traders (ATs) all did the same trade at the same time?! Correlated strategies can make prices more informationally efficient ( convergence trades)! Correlated strategies can cause excess volatility 14

15 Do algorithmic trades tend to be correlated?! We do not know strategies, we do not have orders, only completed trades.! Instead: Do computers trade with each other as much as expected, as much as random matching would predict? If computer strategies are correlated, we should observe less trading among computers than expected.! More precisely: Do computers take from humans and computers in the same proportion as humans take from humans and computers? 15

16 ! Prob(HC)/Prob(CC) = computer taker ratio = RC! Prob(HH)/ Prob(CH) = human taker ratio = RH! In a world with more human makers than computer makers (our world), we expect Prob(HC)/Prob(CC) > 1, i.e., computers take more from humans than from other computers. And we expect Prob(HH)/ Prob(CH) > 1, i.e., humans take more from humans than from computers.! However we expect RC/RH=1, i.e., humans take more from humans in a similar proportion that computers take more from humans. 16

17 ! If we find that RC/RH>1, this would indicate that computers do not trade with each other as much as a random matching model predicts.! This finding would be consistent with the trading strategies of computers being correlated, and this is the interpretation that we propose. 17

18 ! We estimate: R= RC/RH= Vol(HC) Vol(CC) / Vol(HH) Vol(CH)! At 1-minute, 5-minute and daily frequency! Report ln(r)! If we find that ln(r)>0, then we conclude that computer trading is more correlated than expected. Note that this is also consistent with human trading being correlated. But in a world with more humans than computers this is unlikely. 18

19 ln(r ) ln(r ) ln(r ) 1- min 5- min Daily EUR/USD Mean 0.222*** 0.367*** 0.531*** (std. err.) (0.0031) (0.0036) (0.0118) Fraction of obs.> No. of non- missing observations Total no. of obs JPY/USD Mean 0.310*** 0.392*** *** (std. err.) (0.0037) (0.0043) (0.0131) Fraction of obs.> No. of non- missing observations Total no. of obs JPY/EUR Mean 0.698*** 0.687*** 0.813*** (std. err.) (0.0049) (0.0051) (0.0173) Fraction of obs.> No. of non- missing observations Total no. of obs

20 Do algorithmic trades tend to be correlated?! Answer: Yes. It seems that computers do not trade with each other as much as random matching would predict. 20

21 Relationship between algorithmic trading activity and triangular arbitrage opportunities! Graphical evidence 21

22 Percent of seconds with triangular arbitrage profit greater than 1 basis point, in 3-11 time interval 22

23 What is the effect of algorithmic trading on triangular arbitrage opportunities?! Endogeneity (reverse causality) problem: Triangular arbitrage must clearly also cause AT! Granger Causality at high frequency (minute-byminute) and! Heteroskedasticity identification 23

24 Structural VAR Estimation A Y t =Φ(L) Y t +Λ X t 1:t 20 +Ψ G t + ε t! Y t =( Arb t, AT t )! Φ(L) Lag-polynomial, 20-lags! X t 1:t 20 Controls for past volatility and liquidity (volume)! G t deterministic intra-daily patterns and time trend 24

25 Test of AT causing triangular arbitrage opp. Test of AT Causing Triangular Arbitrage VAT VCt VCm OFCt ln(r ) Sum of coeffs. on AT lags *** *** *** *** *** Chi- squared (Sum=0) *** *** *** *** 15.39*** p- value Chi- squared (All coeffs. on AT lags=0) *** *** 46.51*** *** 40.99*** p- value Contemporaneous coeff. X σ(at) *** *** *** *** ** No. of obs

26 Test of triangular arbitrage opportunities causing AT Test of Triangular Arbitrage causing AT VAT VCt VCm OFCt ln(r ) Sum of coeffs. on AT lags *** *** *** Chi- squared (Sum=0) *** *** *** 1.69 p- value Chi- squared (All coeffs. on AT lags=0) *** *** 31.00* *** 84.92*** p- value Contemporaneous coeff. X σ(at) *** *** *** *** *** No. of obs

27 Triangular Arbitrage Causality Tests! AT reduces triangular arbitrage opportunities! Predominantly AT acts on posted quotes by other traders that enable the profit opportunity! Increase the speed of price discovery, but increase adverse selection costs of slow traders! Some evidence that algorithmic traders make prices more efficient by posting quotes that reflect new information quickly 27

28 Does algorithmic trading increase or decrease excess volatility: autocorrelation of high frequency returns?! Graphical evidence 28

29 5-second return autocorrelation 29

30 5-second return autocorrelation 30

31 5-second return autocorrelation 31

32 Test of AT participation causing HF return autocorrelation VAT Test of AT Causing HF return Autocorrelation USD/EUR JPY/USD JPY/EUR Sum of coeffs. on AT lags *** *** *** Chi- squared (Sum=0) 33.75*** 25.26*** 14.96*** p- value Chi- squared (All coeffs. on AT lags=0) 43.05*** 45.54*** *** p- value Contemporaneous coeff. X σ(at) *** *** *** No. of obs

33 Test of AT liquidity demand causing HF return autocorrelation VCt Test of AT Causing HF return Autocorrelation USD/EUR JPY/USD JPY/EUR Sum of coeffs. on AT lags *** *** ** Chi- squared (Sum=0) 23.01*** 11.88*** 6.12* p- value Chi- squared (All coeffs. on AT lags=0) 24.79*** 20.87*** 6.65 p- value Contemporaneous coeff. X σ(at) *** *** No. of obs

34 Test of AT liquidity supply causing HF return autocorrelation VCm Test of AT Causing HF return Autocorrelation USD/EUR JPY/USD JPY/EUR Sum of coeffs. on AT lags *** *** *** Chi- squared (Sum=0) 29.72*** 28.84*** 17.88*** p- value Chi- squared (All coeffs. on AT lags=0) 44.16*** 38.86*** 18.90*** p- value Contemporaneous coeff. X σ(at) *** *** *** No. of obs

35 Test of AT correlated actions causing HF return autocorrelation OFCt Test of AT Causing HF return Autocorrelation USD/EUR JPY/USD JPY/EUR Sum of coeffs. on AT lags Chi- squared (Sum=0) p- value Chi- squared (All coeffs. on AT lags=0) p- value Contemporaneous coeff. X σ(at) No. of obs

36 Test of HF return autocorrelation causing AT VAT Test of AT Causing HF return Autocorrelation USD/EUR JPY/USD JPY/EUR Sum of coeffs. on AT lags * * *** Chi- squared (Sum=0) * 3.49** *** p- value Chi- squared (All coeffs. on AT lags=0) 12.87** ** p- value Contemporaneous coeff. X σ(autocorrelation) 0.036* *** No. of obs

37 Conclusion! We find evidence of algorithmic trading improving price efficiency:! Reduces triangular arbitrage opportunities: mainly by acting on the posted quotes of other traders that enable the profit opportunity. Hence it increases adverse selection costs for slower traders.! Reduces HF return autocorrelation: mainly by providing liquidity! Caveat: We do not address, and we do not rule out, the possibility that certain facets of AT may, on rare occasions, contribute to excess volatility. 37