description

1. Study on the relationship between stock price and
emotions in tweets
5315076

2. ………………..…3
……………….….5
…………………..6
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…………………12
…………14
…18
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2

3. • SNS
• SNS 77%
•
3
•
•
•
• =>

4. •
=> 45
•
4
ダウ平均株価
26485 ドル 25715 ドル
27766 ドル 27875 ドル
-2.9%
0.4%

5. •
•
• =>
•
5
ダウ平均株価
26485 ドル 25715 ドル
27766 ドル 27875 ドル
-2.9%
0.4%

6. • Twitter mood predicts the stock market
• 2008/2/28~2008/12/19
• POMS GPOMS OPINION FINDER 1
• SOFNN( )
6

7. • Calm( )
• SOFNN 73.3% Calm 86.7%
7

8. • 2017/1/20 2018/12/31
•
• 1 2
8

9. •
• Vader
• Emotion Recognition on Twitter 6
( : Depression, : Confusion, : Fatigue, : Anger, : Vigour, : Tension)
• 7
9
,

10. 10
•

11. (vader )
11

12. •
•
• 0.1 =>
12
相関係数 怒り 憂鬱 緊張 疲労 混乱 活力 Positive Negative
株価 ( ツイート
後 )
0.050806 0.007154 -0.028658 -0.031818 0.024938 -0.027203 -0.064583 0.045539
変動率 0.005419 0.033395 -0.029623 -0.025225 -0.025725 0.006399 -0.002984 -0.017395

13. • =>
• =>
• =>
13

14. •
•
•
• A B A (AR) A B
(VAR) VAR
14

15. , ,
• F
• Confusion( ) Lag=1~3( ~1 ) Depression( ) Lag=4~9(2
4 ) Tension( ) Lag=8~14(4 ~1 ) p < 0.1
• Depression( ) Lag=4 P
15
Anger Confusion Depressio Fatigue Tension Vigour
Lag F p F p F p F p F p F p
1 0.41282798 0.52062996 3.10906483 0.07804911 1.62870012 0.20199875 0.40064336 0.52684792 2.3755593 0.22845145 1.79422 0.18060367
2 0.9249089 0.39678053 2.79385884 0.06148468 0.59498913 0.55164494 0.43554553 0.64698872 1.30657302 0.3964241 1.44352593 0.23640269
3
4 0.94926446 0.4344727 2.0502528 0.08503052 2.45509241 0.04383734 0.93038087 0.44529516 2.2647244 0.12784817 0.83660072 0.50183662
5
6
7 1.25699659 0.26820597 1.45874668 0.17782052 1.88832652 0.06745187 1.3745835 0.21189452 2.72543054 0.06864401 0.8721278 0.52780323
8 1.17256992 0.31200919 1.35250806 0.2130793 1.68440891 0.09710885 1.37881126 0.20096671 2.36130641 0.0859893 1.3958169 0.1934373
9 1.04548503 0.40100775 1.22102667 0.2774022 1.69465434 0.08488212 1.27186345 0.24734055 2.27929598 0.08617515 1.25721508 0.25573892
10 1.06946097 0.38254277 1.14867938 0.32179366 1.50405043 0.13134607 1.2984086 0.22571607 2.1131056 0.09570648 1.17014564 0.30650026
11 1.16527594 0.3064308 1.04956243 0.3995229 1.39307236 0.16896753 1.12522834 0.33689746 2.00365009 0.1047785 1.09908968 0.35780065
12 1.1451506
13 1.07528914 0.37627948 0.92256769 0.52849917 1.19322816 0.27729383 1.36811867 0.16735862 2.0251835 0.08645716 1.21408133 0.26238848
14 1.27302745 0.2165464 0.88801805 0.571770251.33232882 0.17965167 1.55448501 0.08509853 2.06527591 0.08469627 1.17516713 0.28788541
15
16 1.17928338 0.27709291 0.817088 0.66709924 1.26012112 0.21409745 1.39593105 0.13451857 1.86399248 0.14058487 1.2064283 0.25490524
17
18
19 1.08339075 0.36157424 0.81741526 0.68783106 1.41204275 0.10996602 1.21457891 0.23593705 1.6997964 0.28134402 1.08683947 0.35784587
20 1.03398041 0.41727675 0.78628249 0.73279021 1.33659989 0.14434056 1.1635309 0.27758758 1.72970104 [8]0.247924541.18652421 0.25634399

16. • 2 =>
•
•
16 16

17. • =>
• =>
• =>
17

18. • LSTM
•
• ×
• ×
• ×
•
• 3
•
18

19. •
• Mean Directional Accuracy
•
19

20. • 2017/1/20~2018/12/31
• 2019/1/1~2019/3/1
20

21. Accuracy: 51%
21
: 71 : 69
2019/1/1~3/1 140

22. Accuracy: 58%
22
: 81 : 59
2019/1/1~3/1 140

23. Accuracy: 58%
23
2019/1/1~3/1 140
: 81 : 59

24. Accuracy: 60%
24
2019/1/1~3/1 140
: 84 : 56

25. • 51% 58~60%
=> 7~9%
• MAPE( )
25

26. • LSTM
=>
• =>
• =>
26

27. • Vader POMS
•
•
• 51%
7~9%
•
27

28. •
• p
=> I am I feel
• 1
28

29. 29

31. 31

32. Mean Directional Accuracy
32

33. LSTM
33

34. •
=>
• :
•
• : , : , : n (n > 2)
34
3