Selection of Related Stocks using Financial Text Mining

1. Selec%on of Related Stocks
using Financial Text Mining
Masanori HIRANO*1, Hiroki SAKAJI*2, Shoko KIMURA*3,
Kiyoshi IZUMI*2, Hiroyasu MATSUSHIMA*2,
Shintaro NAGAO*3, and Atsuo KATO*4
*1 Faculty of Engineering, The University of Tokyo
*2 School of Engineering, The University of Tokyo
*3 QuanKtaKve Investment Department,
Daiwa Asset Management Co. Ltd.
*4 FronKer Technologies Research & ConsulKng Dept.,
Daiwa InsKtute of Research Ltd.
hirano@g.ecc.u-tokyo.ac.jp
hWps://mhirano.jp/

2. Background
• Pick up some stocks &
build up “themed mutual fund”
• Private investors invest in it
• Lots of stocks
• Tokyo: about 3,600!
• Other stock exchanges
all over the world
• Quite difficult to cover all theme
• Stock abroad
• Not familiar theme
• => Support human!
Robot & Technology related fund
--Robotech--
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3. Demo: Fund Building Support System
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Robot
Similarity/Ticker/Name Similarity
Reasoning Sentences in Japanese
Similarity/Ticker/Name Word/Similarity/File
Robo (abbr.)
Mini Robot

4. Usual Case
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Input:
Theme
“AI”
Companies’ Informa4on
IRs
Companies’ HPs
Web Search
Pick up Stocks
Outputs:
Related stocks

5. Our Method Outline
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Input:
Theme
“AI”
Related Words
“expert system”
“machine learning”
“deep learning”
“natural language processing”
Companies’ Informa>on
IRs
Companies’ Official Websites
Word2vec
Word
matching
Extracted & Reasoning
Sentences
“We automa>cally respond to a
two-way interac>ve AI FAQ
system using natural language
processing.”*
Sum up similarity
Outputs:
Related stocks
+ Similarity to the theme + Reasoning Sentences
*hPps://www.soQbank.jp/corp/group/sbm/news/press/2018/20180301_01/
Crawling &
Preprocessing
Feedback

6. Our Method Outline
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Input:
Theme
“AI”
Related Words
“expert system”
“machine learning”
“deep learning”
“natural language processing”
Companies’ Informa>on
IRs
Companies’ Official Websites
Word2vec
Word
matching
Extracted & Reasoning
Sentences
“We automa>cally respond to a
two-way interac>ve AI FAQ
system using natural language
processing.”*
Sum up similarity
Outputs:
Related stocks
+ Similarity to the theme + Reasoning Sentences
*hPps://www.soQbank.jp/corp/group/sbm/news/press/2018/20180301_01/
Crawling &
Preprocessing
Feedback

7. Data
• Data for similarity (word2vec)
1,809,736,365 words w/ 1,147,973 vocabularies
• livedoor news corpus
• Wikipedia Japanese ar3cles (version 21-Jun-2018 22:09)
• Nikkei newspaper ar3cles(1990–2015 and 2017; data
from 2016 were omiHed for technical reasons)
• Companies’ informaMon
• Investors Rela3ons(2012/10/9-2018/5/11; 90,813 files)
• Official Websites(only 2,293,460 pages in all companies)
=> total: 2TB
• We use 5 kinds of data!
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8. Our Method Outline
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Input:
Theme
“AI”
Related Words
“expert system”
“machine learning”
“deep learning”
“natural language processing”
Companies’ Informa>on
IRs
Companies’ Official Websites
Word2vec
Word
matching
Extracted & Reasoning
Sentences
“We automa>cally respond to a
two-way interac>ve AI FAQ
system using natural language
processing.”*
Sum up similarity
Outputs:
Related stocks
+ Similarity to the theme + Reasoning Sentences
*hOps://www.soPbank.jp/corp/group/sbm/news/press/2018/20180301_01/
Crawling &
Preprocessing
Feedback

9. Feedback using Cooccurrence
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The master set for the word “AI”
Test set for the word1 “NLP”
Test set for the word2 “machine learning”
…
TYO: 8207 TYO: 1945
TYO: 2154
TYO: 3861
TYO: 2397
TYO: 3045 TYO: 7816
TYO: 1931 TYO: 9984
TYO: 1925
TYO: 4845
TYO: 1400 TYO: 4288

10. Feedback using Cooccurrence
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The master set for the word “AI”
Test set for the word1 “NLP”
Test set for the word2 “machine learning”
…
TYO: 8207 TYO: 1945
TYO: 2154
TYO: 3861
TYO: 2397
TYO: 3045 TYO: 7816
TYO: 1931 TYO: 9984
TYO: 1925
TYO: 4845
TYO: 1400 TYO: 4288
5 companies

11. Feedback using Cooccurrence
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The master set for the word “AI”
Test set for the word1 “NLP”
Test set for the word2 “machine learning”
…
TYO: 8207 TYO: 1945
TYO: 2154
TYO: 3861
TYO: 2397
TYO: 3045 TYO: 7816
TYO: 1931 TYO: 9984
TYO: 1925
TYO: 4845
TYO: 1400 TYO: 4288
5 companies

12. Feedback using Cooccurrence
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The master set for the word “AI”
Test set for the word1 “NLP”
Test set for the word2 “machine learning”
…
TYO: 8207 TYO: 1945
TYO: 2154
TYO: 3861
TYO: 2397
TYO: 3045 TYO: 7816
TYO: 1931 TYO: 9984
TYO: 1925
TYO: 4845
TYO: 1400 TYO: 4288
5 companies
4 companies
=0.8
Similarity btw “AI” & “NLP”

13. Feedback using Cooccurrence
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The master set for the word “AI”
Test set for the word1 “NLP”
Test set for the word2 “machine learning”
…
TYO: 8207 TYO: 1945
TYO: 2154
TYO: 3861
TYO: 2397
TYO: 3045 TYO: 7816
TYO: 1931 TYO: 9984
TYO: 1925
TYO: 4845
TYO: 1400 TYO: 4288
6 companies
4 companies
=0.66

14. How to calculate similarity
• A: Similari*es using Word2vec:
• Using 9 models with each sets of hyper parameters
• Only using top-100 similar words in each model
=> harmonic average
• B: Similari*es from feedback using cooccurrence
• Take harmonic average
2"#
" + #
• This is final similarity of each words
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15. Calculate COMPANY similarity
• Count the number of appearance of each top-10
similar word & original word
• Sum the count weighed by similari9es
=> company similarity
• Original theme word’s weight: 1
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16. Results
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17. Similar words to “ ”(health)
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Rank Word word2vec feedback final
1 (getting healthy) 0.6696 0.9921 0.7996
2 (lifestyle diseases) 0.5968 0.9879 0.7441
3 (diet) 0.5724 0.9770 0.7219
4 (nutrition) 0.5692 0.9739 0.7185
5 (antiaging) 0.5433 1.0000 0.7040
6 (prevention medicine) 0.5421 1.0000 0.7031
7 (healthy) 0.5402 0.9774 0.6959
8 (condition of health) 0.5347 0.9803 0.6919
9 (diet) 0.5333 0.9745 0.6893
10 (lifestyle habit) 0.5233 0.9835 0.6831

18. Top 11-20 (Not used) similar words
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Rank Word word2vec feedback final
11 (beauty) 0.5259 0.9507 0.6772
12 (mental health) 0.5154 0.9809 0.6758
13 (antifat) 0.5009 1.0000 0.6674
14 (prevention) 0.5243 0.8888 0.6595
15 (dietary education) 0.4914 0.9742 0.6532
16 (care prevention) 0.4929 0.9532 0.6498
17 (health management method) 0.4796 1.0000 0.6483
18 (metabolic syndrome (abbr.)) 0.4810 0.9897 0.6474
19 (diet therapy) 0.4774 1.0000 0.6462
20 (welfare) 0.4973 0.9203 0.6457

19. Results (e-Sports)
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Rank Similarity Ticker Name
1 604.5824 3922 PR TIMES Inc
2 387.4172 6552 GameWith Inc
3 82.69871 9697 CAPCOM CO., LTD.
4 38.20549 9468 Kadokawa Dwango Corp
5 20.53668 9766 Konami Holdings Corp
6 19.26718 3326 Runsystem Co Ltd
7 15.15767 3904 Kayac Inc
8 14.44024 2378 Renaissance Inc
9 13.2471 7860 Avex Inc
10 9.734876 3135 MarketEnterprise Co Ltd

20. Evaluation (new data to this presentation, paper doesn’t contain)
• 4 experienced fund managers provided answer data
based on their knowledge
• With in only 100 stocks from TOPIX 500 (At random)
• 1 or more fund managers mark as related stocks
=> tagged as “Related”
• No fund manager mark as related
=> tagged as “Not related”
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FM1 FM2 FM3 FM4 Answer data
AAA Inc. ✔ ✔ ✔ ✔ Related
BBB Corp ---
CCC LTD ✔ Related

21. Evaluation (new data to this presentation, paper doesn’t contain)
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0.6602
0.8743
0.5565
0.6609
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(beauty) (child-care) (robot) (amusement)
precision
recall
f1

22. Conclusion & Contribution
• We built the system suppor0ng fund managers to
build up themed mutual fund
• We use some types & kinds of data
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Future work
• More RECALL!!!!
• Fix Crawling Problem <= Critical problem for recall
• Tune some hyper-parameters
• Check casus of the difference btw good & bad cases