Predicting investment fluxes from implicit lead-lag investor networks
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1 Predicting investment fluxes from implicit lead-lag investor networks D. Challet 1,2 M. Lallouache 1 R. Chicheportiche 3,4 1 CentraleSupélec 2 Encelade Capital SA 3 CFM 4 previously at Swissquote Bank SA September 8, 2015
2 Broker internal order matching versus
3 Internal order matching...?
4 Broker s gain G T = T I t r t = t=1 T t=1 (I t 1 + δi t )r t Classic optimization problem 1 Fix T (1 day) 2 Assume random δi t and r t 3 Constraints 4 Minimize cost function
5 Broker s gain G T = T I t r t = t=1 T t=1 (I t 1 + δi t )r t Prediction problem 1 predict flux 2 predict price return 3 PROFIT!
6 Prediction problem= Science + Engineering Science: flux 1 cluster clients 2 lead-lag networks 3 machine learning 4 ENJOY! Engineering 1 Predict price returns 2 Inventory constraints 3 PROFIT!
7 Investor classification Supervised Individual vs institutional (Odean, Barber, etc.)... Unsupervised Similarity measure categories
8 Supervised classification
9 Real networks D. Challet, M. Lallouache, R. Chicheportiche Predicting investment fluxes from implicit lead-lag investor networks
10 Unsupervised learning
11 Unsupervised learning
12 FX traders cumsum(strat) cumsum(strat) yday yday cumsum(strat) cumsum(strat) yday yday
13 Unsupervised classification I Spanish brokers Lillo et al. (2008) Daily inventory change V i (t) Correlation matrix C E(V i V j ) Principal Component Analysis + Random Matrix theory
14 Unsupervised classification I Equities, Spanish brokers Lillo et al. (2008)
15 Unsupervised classification II Equities, Chinese investors Zhou et al. (2012)
16 Unsupervised classification III: FX, individual investors, daily p(λ) Data Shuffled RMT C SR log(n) λ
17 Unsupervised classification IV: FX, individual investors, hourly λ p(λ) Data Shuffled RMT log(n) C SR
18 Unsupervised classification Statistically validated networks (SVNs) Tumminello et al. (2011a) 1 Agent i, state i (t) {1,,S} s i (t) = sign Agents i and j: measure frequency Compute p-values Buy(t) Sell(t) { 1,0,1,2 = /0} Buy(t) + Sell(t) s i (t) = s && s j (t) = s O(N 2 ) pairs multiple hypothesis correction NETWORK
19 Origin of trade synchronicity 1 Explicit communication 2 Implicit communication 1 same news 2 same strategy: MA(CD) 3 same parameters 4 Master in Finance
20 Trader-trader communication: explicit
21 Trader-trader communication: explicit
22 Trader-trader communication: explicit
23 Do traders look at prices?
24 Implicit AND explicit communication
25 Implicit communication: same price analysis
26 Implicit communication: same price analysis
27 Implicit communication: same price analysis
28 Example: MACD (1970) Parameters 12, 26, and 9 days Trading week then: 6 days Now: 5 days
29 Investor SVNs group=disconnected subgraphs Tumminello et al. (2011b): daily Finish investment fluxes
30 Datasets 1 Swissquote (SQ): individual traders 2 Large Bank: institutional traders P>(N) 10-2 P>(N) N N Number of transactions
31 Step 1: Implicit network + community, daily Challet et al. (2015) EUR.USD SQ Links
32 Implicit network + community, hourly Challet et al. (2015) EUR.USD SQ Links
33 Implicit network + community detection, LB, hourly Challet et al. (2015) GBP.USD EUR.USD Links Links
34 Cluster stability 1_1 2_1 1_2 1_3 1_15 1_24 1_16 1_17 1_26 1_29 2_37 1_4 2_2 2_3 1_31 1_7 2_8 1_10 2_9 1_8 2_5 2_21 2_10 1_21 1_23 2_27 1_11 2_12 1_22 2_26 2_19 2_36 2_22 2_25 1_20 1_27 2_32 1_30 2_34 2_24 2_28 1_28 2_33 2_29 2_31 1_0 2_17 2_18 2_20 2_23 2_0 3_1 3_2 3_3 3_5 3_7 3_13 3_29 3_10 3_20 3_30 3_14 3_34 3_21 3_27 3_39 3_15 3_19 3_40 3_31 3_37 3_16 3_42 3_32 3_38 3_26 3_28 3_35 3_12 3_25 3_24 3_22 3_36 4_1 4_3 4_2 4_6 4_9 4_13 4_22 4_15 4_25 4_7 4_34 4_14 4_23 4_24 4_30 4_19 4_28 4_33 4_37 4_27 4_11 4_32 4_35 4_36 4_31 4_17 4_21 4_26 4_0 5_1 5_2 5_3 5_7 5_16 5_11 5_30 5_8 5_34 5_10 5_36 5_32 5_29 5_14 5_42 5_47 5_21 5_40 5_31 5_26 5_27 5_22 5_24 5_44 5_48 5_35 5_28 5_37 5_15 5_18 5_45 5_12 5_19 5_49 5_38 5_50 5_46 5_39 5_41 5_20 5_0 6_1 6_2 6_3 6_15 6_18 6_32 6_6 6_30 6_28 6_39 6_33 6_36 6_34 6_13 6_7 6_10 6_16 6_47 6_29 6_45 6_37 6_41 6_19 6_35 6_43 6_38 6_27 6_44 6_31 6_21 6_8 6_42 6_25 6_14 6_23 6_46 6_26 6_20 6_0 7_1 7_2 7_3 7_11 7_4 7_10 7_31 7_27 7_14 7_16 7_36 7_20 7_29 7_32 7_33 7_6 7_17 7_21 7_38 7_37 7_42 7_41 7_35 7_28 7_19 7_30 7_25 7_40 7_24 7_26 7_39 7_23 7_22 7_0 8_1 8_14 8_2 8_5 8_7 8_20 8_15 8_24 8_6 8_12 8_25 8_9 8_27 8_23 8_30 8_21 8_28 8_19 8_31 8_29 8_33 8_32 8_26 8_10 8_22 8_0 9_1 9_6 9_8 9_38 9_37 9_22 9_14 9_23 9_15 9_25 9_10 9_12 9_21 9_19 9_30 9_33 9_27 9_34 9_26 9_28 9_40 9_31 9_35 9_36 9_29 9_24 9_5 9_41 9_39 9_32 9_16 9_18 9_0 10_1 10_9 10_7 10_11 10_5 10_21 10_20 10_16 10_4 10_23 10_22 10_15 10_26 10_30 10_24 10_25 10_29 10_31 10_27 10_32 10_34 10_10 10_18 10_28 10_35 10_33 10_0 11_1 11_4 11_6 11_12 11_10 11_16 11_30 11_25 11_27 11_11 11_17 11_7 11_33 11_32 11_29 11_28 11_18 11_39 11_36 11_34 11_26 11_37 11_38 11_20 11_14 11_35 11_31 11_19 11_2 11_21 11_0 12_1 12_2 12_21 12_3 12_13 12_6 12_12 12_8 12_26 12_9 12_15 12_17 12_24 12_23 12_28 12_22 12_30 12_25 12_20 12_29 12_27 12_14 13_1 13_2 13_4 13_8 13_3 13_13 13_27 13_21 13_5 13_19 13_22 13_24 13_23 13_9 13_17 13_36 13_30 13_34 13_26 13_32 13_28 13_31 13_14 13_18 13_33 13_29 13_35 14_1 14_2 14_4 14_3 14_5 14_8 14_30 14_11 14_18 14_21 14_10 14_22 14_27 14_23 14_19 14_24 14_28 14_25 14_29 14_26 14_0 movie GBP.USD 2014 movie EUR.USD 2014
35 Step 2: lead-lag SVNs determine groups group lead-lag? SNVs: p-values of s g (t) s g (t + 1)
36 Intertemporal interaction networks (FX LB) Compute p-value of {s g (t),s g (t + 1)} 4 [ 1 > 1 ] [ 1 > 1 ] [ 2 > 1 ] 5 [ 1 > 1 ] [ 1 > 1 ] 4 [ 0 > 0 ] [ 1 > 1 ] 27 [ 0 > 1 ] 25 [ 1 > 1 ] 23 [ 2 > 1 ] [ 1 > 0 ] [ 0 > 0 ] [ 1 > 0 ] [ 1 > 1 ] 12 [ 0 > 1 ] [ 1 > 1 ] [ 2 > 1 ] [ 1 > 2 ] 24 [ 1 > 2 ] [ 2 > 2 ]
37 EUR.USD (LB) [ 1 > 1 ] [ 0 > 1 ] [ 1 > 1 ] [ 2 > 1 ] [ 1 > 0 ] [ 0 > 0 ] [ 1 > 0 ] 37 27
38 Origin of lead-lag 1 Explicit communication 2 Delayed reaction 1 faster news 2 same strategy, faster parameters MA(CD) 12, 26, 5 10, 25, 4: 3 Master in Finance +...
39 Lead-lag: number of links vs time 20 #Links #Links SQ LB EUR.USD
40 Lead-lag between groups: inter-temporal networks Goal: predict global flux sign (B S) out of sample 1 Sliding in-out sample periods 2 Determine groups in-sample 3 Calibrate machine learning in-sample 4 Predict next SIGN
41 Predictors+predictee F 1,t F 2,t F 1,t 1 F 2,t 1 hour 3-2 NA 0 1 NA NA NA NA 3 sign (B S) +1-1????
42 Machine learning: random forest Random forest = collection of random trees Classification tree: P predictors, T data points each Each tree: bootstrap of data points Each node: cut according to criterion e.g. predictor i > 2
43 Prediction results 12 weeks in-sample 24 hours out-of-sample Predict sign of flux Predictors: group actions {-1,0,1,NA} Random Forests
44 Prediction results 12 weeks in-sample 24 hours out-of-sample Predict sign of flux Predictors: group actions {-1,0,1,NA} Random Forests
45 Prediction results: SQ 12 weeks in-sample 24 hours out-of-sample Predict sign of flux Predictors: group actions {-1,0,1,NA} Random Forests cumsum(s t φ t ) in billions obliquerf RF H 0 2 H Time (hours)
46 Out-of-sample performance by hour
47 Origins of performance peaks
48 Summary Method Clustering communities lead-lag machine learning prediction Todo Find algorithmic strategy of groups Other fields fund ownership recommendations systems...
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