Predicting investment ﬂuxes from implicit lead-lag investor … · 2019-01-10 · Predicting...

Predicting investment fluxesfrom implicit lead-lag investor networks

D. Challet1,2 M. Lallouache 1 R. Chicheportiche 3,4

1 CentraleSupelec 2 Encelade Capital SA 3CFM 4 previously at Swissquote Bank SA

September 8, 2015

D. Challet, M. Lallouache, R. Chicheportiche Predicting investment fluxes from implicit lead-lag investor networks

Broker internal order matching

versus


Internal order matching

...

?


Broker’s gain

GT =T

∑t=1

Itrt =T

∑t=1

(It−1+δ It)rt

Classic optimization problem1 Fix T (1 day)2 Assume random δ It and rt

3 Constraints4 Minimize cost function


Broker’s gain

GT =T

∑t=1

Itrt =T

∑t=1

(It−1+δ It)rt

Prediction problem1 predict flux2 predict price return3 PROFIT!


Prediction problem= Science + Engineering

Science: flux1 cluster clients2 lead-lag networks3 machine learning4 ENJOY!

Engineering1 Predict price returns2 Inventory constraints3 PROFIT!


Investor classification

SupervisedIndividual vs institutional (Odean, Barber, etc.)

...

UnsupervisedSimilarity measure

→ categories


Supervised classification


Real networks


Unsupervised learning


FX traders

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Unsupervised classification ISpanish brokers

Lillo et al. (2008)

Daily inventory change Vi(t)

Correlation matrixC ∼ E(ViVj)

Principal ComponentAnalysis + Random Matrixtheory


Unsupervised classification IEquities, Spanish brokers

Lillo et al. (2008)


Unsupervised classification IIEquities, Chinese investors

Zhou et al. (2012)


Unsupervised classification III:FX, individual investors, daily

λ

p(λ)

0 2 4 6 8

0.0

0.2

0.4

0.6

0.8

1.0 Data

ShuffledRMT

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500 1000 2000 5000 10000−

0.8

−0.

6−

0.4

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20.

00.

20.

4

log(N)

CS

R


Unsupervised classification IV:FX, individual investors, hourly

λ

p(λ)

0 2 4 6 8 10

0.0

0.2

0.4

0.6

0.8

1.0 Data

ShuffledRMT

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500 1000 2000 5000 10000 20000 50000−

0.6

−0.

4−

0.2

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0.4

log(N)

CS

R


Unsupervised classificationStatistically validated networks (SVNs)

Tumminello et al. (2011a)

1 Agent i, statei(t) ∈ {1, · · · ,S}

si(t) = signBuy(t)−Sell(t)Buy(t)+Sell(t)

∈ {−1,0,1,2 = /0}

Agents i and j: measure frequency

si(t) = s && sj(t) = s′

Compute p-values

O(N2) pairs→ multiple hypothesis correction→NETWORK


Origin of trade synchronicity

1 Explicit communication2 Implicit communication

1 same news2 same strategy: MA(CD)3 same parameters4 Master in Finance


Trader-trader communication: explicit


Do traders look at prices?


Implicit AND explicit communication


Implicit communication: same price analysis


Example: MACD (1970)

Parameters 12, 26, and 9 days

Trading week then: 6 days

Now: 5 days


Investor SVNsgroup=disconnected subgraphs

Tumminello et al. (2011b): daily Finish investment fluxes


Datasets

1 Swissquote (SQ): individual traders2 Large Bank: institutional traders

10-3

10-2

10-1

100

100 101 102 103 104

N

P >( N)

10-4

10-3

10-2

10-1

100

100 101 102 103 104 105

N

P >( N)

Number of transactions


Step 1: Implicit network + community, daily

Challet et al. (2015) EUR.USD SQ

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Links

−1 −11 11 22 2


Implicit network + community, hourly

Challet et al. (2015) EUR.USD SQ

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−1 −11 −12 −1−1 11 12 1−1 21 22 2


Implicit network + community detection, LB, hourly

Challet et al. (2015)

GBP.USD EUR.USD

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1 −11 1−1 −1−1 12 1−1 22 −11 22 2


Cluster stability

1_12_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_222_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_182_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_243_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_314_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_275_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_465_39

5_415_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_146_23

6_46

6_266_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_397_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_319_35

9_36

9_29

9_24

9_5

9_41

9_39

9_32

9_16

9_189_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_3413_26

13_32

13_28

13_31

13_14

13_18

13_33

13_2913_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 2014movie EUR.USD 2014


Step 2: lead-lag SVNs

determine groups

group lead-lag?

SNVs: p-values ofsg(t)→ sg′(t+1)


Intertemporal interaction networks (FX LB)

Compute p-value of {sg(t),sg′(t+1)}

1

3

4[ −1 −> −1 ][ 1 −> −1 ][ 2 −> −1 ]

4

3 1

5[ −1 −> 1 ][ 1 −> 1 ]

1

3

4

5

[ 0 −> 0 ]

9

1430

32

1713

28

5

7

23

11

19

33

10

37

16

4

24

36

3

29

316

1

18

26

8

35

34

21

22

12

15

2527

[ −1 −> −1 ][ 0 −> −1 ][ 1 −> −1 ][ 2 −> −1 ]

7

23

25

14

28

32

96

151

4

5

16

17

18

2 3

810

29

13

11

20

3436

26

35

21

24

30

31

37

19

27

33[ −1 −> 0 ][ 0 −> 0 ][ 1 −> 0 ]

9

1

3

10

18

35

4

6

13

141627

31

32

36

37

7

332428

5

17

8

30

15

34

20

19

21

11

29

26

25

2

12[ −1 −> 1 ][ 0 −> 1 ][ 1 −> 1 ][ 2 −> 1 ]

33

25

35

13

24

15

10

5

11

32

19

6

8

[ −1 −> 2 ][ 1 −> 2 ][ 2 −> 2 ]


EUR.USD (LB)

9

1430

32

1713

28

5

7

23

11

19

33

10

37

16

4

24

36

3

29

316

1

18

26

8

35

34

21

22

12

15

2527

[ −1 −> −1 ][ 0 −> −1 ][ 1 −> −1 ][ 2 −> −1 ]

7

23

25

14

28

32

96

151

4

5

16

17

18

2 3

810

29

13

11

20

3436

26

35

21

24

30

31

37

19

27

33[ −1 −> 0 ][ 0 −> 0 ][ 1 −> 0 ]

9

1

3

10

18

35

4

6

13

141627

31

32

36

37

7

332428

5

17

8

30

15

34

20

19

21

11

29

26

25

2

12[ −1 −> 1 ][ 0 −> 1 ][ 1 −> 1 ][ 2 −> 1 ]

33

25

35

13

24

15

10

5

11

32

19

6

8

[ −1 −> 2 ][ 1 −> 2 ][ 2 −> 2 ]


Origin of lead-lag

1 Explicit communication2 Delayed reaction

1 faster news2 same strategy, faster parameters

MA(CD) 12, 26, 5→ 10, 25, 4:3 Master in Finance + ...


Lead-lag: number of links vs time

20

40

60

05−

2012

06−

2012

07−

2012

08−

2012

#Lin

ks

0

5

10

15

20

04−

2013

07−

2013

10−

2013

01−

2014

04−

2014

07−

2014

10−

2014

#Lin

ks

SQ LBEUR.USD


Lead-lag between groups: inter-temporal networks

Goal: predict global flux sign (B−S) out of sample1 Sliding in-out sample periods2 Determine groups in-sample3 Calibrate machine learning in-sample4 Predict next SIGN


Predictors+predictee

F1,t F2,t F1,t−1 F2,t−1 hour3 -2 NA 0 1

NA NA 3 -2 21 1 NA NA 3

sign (B−S)+1-1

????


Machine learning: random forest

Random forest = collection of random trees

Classification tree: P predictors, T data points eachEach tree: bootstrap of data pointsEach node: cut according to criterion e.g. predictori > 2


Prediction results

12 weeksin-sample

24 hoursout-of-sample

Predict sign offlux

Predictors:group actions{-1,0,1,NA}RandomForests


Prediction results: SQ

12 weeksin-sample

24 hoursout-of-sample

Predict sign offlux

Predictors:group actions{-1,0,1,NA}RandomForests

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Time (hours)

cum

sum

(stφ

t) in

bill

ions

● obliqueRFRFH0

2

H01


Out-of-sample performance by hour


Origins of performance peaks


Summary

Method

Clustering→communities→ lead-lag→ machine learning→prediction

TodoFind algorithmic strategy of groups

Other fields

fund ownership

recommendations systems

...


Predicting investment ﬂuxes from implicit lead-lag investor … · 2019-01-10 · Predicting...

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