migration motif: a spatial-temporal pattern mining approach for financial markets xiaoxi du, ruoming...
TRANSCRIPT
![Page 1: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/1.jpg)
Migration Motif: A Spatial-Temporal Pattern Mining
Approach for Financial MarketsXiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr
Presented by: Xiaoxi Du
Department of Computer ScienceKent State University
![Page 2: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/2.jpg)
Do we yet fully understand financial market risks?
To describe frequent behaviors
of individual companies
To describethe relationships
between stock market change over time and
stock return
![Page 3: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/3.jpg)
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8
P/B
SIZE
9 10
SBUX
2
24
3
GT3
2
SJM
6
3
5
WEC
PU
2
2
2
4
2
5
SJM:SMUCKER
J M CO
GT:GOODYEAR
YIRE&
RUBRCO
SBUX:STARBUCKS
CORP
PU:PULLMAN
INC
WEC:WISCONSIN
ENERGYCORP
Example: Trajectories on a Financial Grid
Financial Grid
SIZEmarket captalization
= (share price×number of shares)
P/BPrice-to-book ratio
= (Current price per share / book value per share)
Company Trajectory
Compact Trajectory
![Page 4: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/4.jpg)
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
T2
T1
10
10
Spatial and Temporal Constraint
SIZE
P/BSpatial Constraint:
To guaranteedto follow
a bounded pathU
Temporal Constraint:An upper boundtime constraint
(short-term)ε
![Page 5: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/5.jpg)
Migration Motif A migration motif (pattern) corresponds to a
collection of sub-trajectories which follow similar path.
properties: pair-wise similarity: distance ≤ ε Maximal: add one other sub-trajectory violate pair-
wise similarity Frequent: sub-trajectories → at least θ different
trajectories
![Page 6: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/6.jpg)
AlgorithmGoal:To Extract
Migration Motifsefficiently
Trajectories(company)
2-LengthSub-Trajectories
Similarity GraphFrequent 2-Length
Migration Motif
FrequentK-Length
Migration Motif
AprioriProperty
CompactTrajectory
Patternrepresentation Graph
theoretical
MaximalClique
![Page 7: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/7.jpg)
Characteristics of the Datasets
Data Source The Center for Research in Security Prices
(CRSP) and Compustat Databases
Time Period 1964 to 2007
Parameters Temporal Constraint
U = {3,4,5}
Spatial Constraint ε = {0,1,2}
Minimum Support Level
θ = {10,15,20}
Grid Dimensions g = {10×10, 20x20, 50x50, 100x100}
Stock Exchanges
andDescription
NYSE 1717 (relatively large)
NASDAQ 2675 (smaller)
AMEX 825 (mostly smaller)
![Page 8: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/8.jpg)
Motif Sensitivity to Parameters
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8
P/B
SIZE
M6-1
M5-59
M5-37
M5-58
M5-45
M5-25
9 10
NYSE Motifs: (10g/U3/ε1/θ10)
17
P/B
SIZE
M5-6
20
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1 191817161514131211
1
2
M5-13
M5-10
M5-16 M5-2
3 4 5 6 7 8 9 10
18
19
20
M3-42
M4-186
M3-433
M4-184
M4-101
M3-115
NYSE Motifs: (20g/U3/ε1/θ10)
Result: NYSE
![Page 9: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/9.jpg)
Motif Sensitivity to Parameters
50
P/B
SIZE
21
...
4
3
2
1 1916151211
1
... ... ... ... ... 23 ... 25 ... 28 ... 49M6-2M5-17
M5-3
5
6
M3-170M3-304
M3-172
50
M3-22
M4-50
NASDAQ Motifs: (50g/U3/ε1/θ10)
Result: NASDAQ
![Page 10: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/10.jpg)
The randomized data contains many 2-length
motif (M2),
Statistical Significance of Motifs
However, random motifs
longer than 2 are quite rare
Risk factor migration in the stock market is not random,
And should not be
neglected
![Page 11: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/11.jpg)
Oscillation Motif Patterns
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8
P/B
SIZE
M6-1
M5-59
M5-37
M5-58
M5-45
M5-25
9 10
NYSE Motifs: (10g/U3/ε1/θ10)
Value oscillation(horizontal)
size oscillation(vertical)
![Page 12: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/12.jpg)
Distribution of Motifs
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8
P/B
SIZE
M6-1
M5-59
M5-37
M5-58
M5-45
M5-25
9 10
NYSE Motifs: (10g/U3/ε1/θ10)
50
P/B
SIZE
21
...
4
3
2
1 1916151211
1
... ... ... ... ... 23 ... 25 ... 28 ... 49M6-2M5-17
M5-3
5
6
M3-170M3-304
M3-172
50
M3-22
M4-50
NASDAQ Motifs: (50g/U3/ε1/θ10)
![Page 13: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/13.jpg)
Motif Timing
2 2 23 3 3 44 4 55 5 66 60
5
10
15
20
25
NYSE10×10 NASDAQ50×50 AMEX50×50
Motifs by Length and by Market
Ave
rage
Sta
rtin
g Y
ear
- Average Starting Time - the point at which its migration pattern is first captured by a motif - Maturity
- Average Staying Time - Long term vs Short term
- Loser and Winners Portfolios
![Page 14: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/14.jpg)
Motif Company Time Span
-To list Membership information for typical motifs.
-To provide each company’s ticker and time span
- M5-45 time spans are highly concentrated for value oscillation path
- M6-1 significant jumps
- M4-50 no clear clustering of starting years for vertical oscillation path
![Page 15: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/15.jpg)
Conclusion
We introduce two new algorithms to discover migration motifs in the financial grid
Our work is the first attempt to find multi-year migration patterns in financial datasets
We are the first to find long oscillation patterns in P/B value
![Page 16: Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets Xiaoxi Du, Ruoming Jin, Liang Ding, Victor E. Lee, John H.Thornton Jr](https://reader036.vdocuments.us/reader036/viewer/2022081519/56649ed15503460f94be0c1c/html5/thumbnails/16.jpg)