About less than 5% of Filipinos invest in the Philippine Stock Exchange. It can be daunting to learn how, when and if to trade at all. Team MCU members Isabel Mañalac, Alexis Canaria and Erika Ureta took on the challenge of helping more Filipinos get into trading and increase their financial literacy. But by being part of the 95%, themselves, the team first learned how traders think, and that the market moves with their sentiment. Using Twitter as one medium for reading the market, tweets and stock quotes from 2012 to 2020 were scraped and modeled together to analyze movement affecting factors.

1. A n a n a l y s i s o f T w i t t e r s e n t i m e n t s
a b o u t m e m b e r s o f t h e P h i l i p p i n e
S t o c k E x c h a n g e
TWEET
TREND
TRADE

2. TWITTER
5%

3. TWITTERTRENDINGTRADING

4. TWITTERTRENDINGTRADING

5. PH Stock Price movements
are affected by the
sentiment of the market
RATIONALE 1

6. b u s i n e s s p r o b l e m
C A N W E U S E P U B L I C L Y
A V A I L A B L E S O U R C E S T O
S H O W U S T H E G E N E R A L
S E N T I M E N T O F
T H E S T O C K M A R K E T?

7. Traders refer to many sources
for news on stocks
RATIONALE 2
Creating a consolidated
reference for a market segment's
sentiment can save traders time
they can repurpose for
portfolio building

8. TWITTERTRENDINGTRADING

9. M A C H I N E L E A R N I N G P R O B L E M
B U I L D A M A C H I N E L E A R N I N G
M O D E L S U P P L E M E N T E D B Y
S E N T I M E N T A N A L Y S I S O N
T W I T T E R A C T I V I T Y T O
P R E D I C T R I S E O R F A L L
M O V E M E N T O F S T O C K S

10. METHODOLOGY
Compile historical stock quotes of PSEi
Scrape tweets with cashtags of companies under PSEi
Cleaning Tweets
Sentiment Analysis on Tweets
XGBoost
Accuracy
DATA COLLECTION
DATA PREPARATION & PREPROCESSING
MODEL BUILDING
MODEL VALIDATION

11. CSV
STOCK
QUOTES
DATA COLLECTION
AND PREPROCESSING
consolidate
high
low
open
close
volume
nfbs

12. PSEI MOVEMENT
(2012-2019)
t r a i n s e t t e s t s e t

13. JFC SM
$JFC $SM$BDO
$MBTshould I buy?
2019 losers
$BEL
PSEi
AC MEG
CSV
DATA COLLECTION
AND PREPROCESSING
consolidate
and classify
sentimentscrape

14. MORE POSITIVE* TWEETS
THAN NEGATIVE
+-

15. nTweets :
posiRatio :
favBoost :
hype :
volume :
nfbs:
FEATURES
Number of tweets
% of Positive sentiments to nTweets
Σ smnt(abs(smnt) + boost(favorites))
Σ abs(favBoost)
number of stocks traded
net foreign buying/selling
AGGREGATED DAILY
TARGET:
RISE OR FALL = LOG RETURN SIGN (+/-)

16. TARGET DISTRIBUTION
IS BALANCED

17. XGBoost
MODEL USED

18. ACCURACY 62%
= right 3 out of 5 times
"PREDICTING SHORT TERM STOCK
MOVEMENT IS LIKE TELLING
A BABY'S GENDER BY LOOKING
AT A PREGNANT WOMAN"
RESULTS

19. FEATURE GAIN IMPORTANCE

20. FEATURE EVALUATION

21. Consider more information on stocks (dividends)
Build dictionaries for Filipino sentiments
Utilize intra-day stock prices if available
Investigate troll/fake/paid tweets
Study the reverse (stocks affecting tweets)
RECOMMENDATIONS FOR
FURTHER STUDIES

22. TWEETS CAN SIGNAL MARKET
i n s i g h t s & o b s e r v a t i o n s
FAVORITES MATTER
NFBS

23. W E CAN M AKE
AN APP O U T OF T HI S

24. AGGREGATION
GLOBE
SECURITY BANK
SAN MIGUEL CORP.
62%
67%
63%
60%*

26. ALEXIS CANARIA ISABEL MANALAC
~
ERIKA URETA
TEAM MCU
TECH LEAD CONNECTOR SCRUMMASTER
COMPUTER
SCIENCE
APPLIED
MATH WITH IT
INDUSTRIAL
ENGINEERING

27. THANK YOU

28. Y: HYPE
COLOR: TARGET ACCURACY
SIZE: TWEET COUNT

29. Y: NFBS
COLOR: TARGET ACCURACY
SIZE: HYPE