This document discusses how individual investors could potentially use big data and news sources to make investment decisions and beat the market. It presents two hypotheses: 1) indices with smaller companies would drop more from news about crises due to greater impact, and 2) stock prices would drop on days with more "US Crisis" news articles. The author crawled news data using keywords and collected stock price data from indices. Correlation analyses showed no relationship between news and stock prices, rejecting both hypotheses. The failure analysis notes limitations like using only one news source and keywords capturing irrelevant past events.

1. News Big Data and
Stock Market
Sia Kim | 1712849 | Entrepreneurship

2. Can an individual investors beat the market?

3. Why do individual investors fail?
Lack of information
Lack of Analyzing skills
Information Asymmetry
01
02
03

4. What if individual
investors collect
news source
through using
Big Data and
make decisions?
Individual investors tend to react slower
than institutional investors because they
have to rely on news source to make
decisions. However, their speed of
obtaining information is slower than
institutional investors.

5. Stock price will drop on the days with more
news articles with keyword ‘US Crisis’
Hypothesis 1:
Indices that contains smaller companies will
drop more significantly, since they are more
likely to be effected by outside factors.
Hypothesis 2:
01
02
Method &
Hypothesis
Crawl web news data using
keyword ‘US Crisis’ and ‘Iran Nuclear’
Collect stock closing price
from different indices
: ^DJI , ^GSPC

6. Crawling News Data
601
News
Articles

7. Stock Data : DJI
87
Days
Dow Jones Industrial Average

8. Stock Data : GSPC
87
Days
S&P 500

9. Data Combined

10. Data Analysis: ^DJI - ‘US CRISIS’

11. Data Analysis : Correlation

12. Data Analysis : Correlation
^DJI - ‘US Crisis’
r = - 0.06350401
^GSPC - ‘US Crisis’
r = - 0.01991395
^DJI - ^GSPC
r = 0.98122934

13. Crawling News Data
185
News
Articles

14. Data Analysis : ^DJI - ‘Iran Nuclear’

15. Data Analysis : Correlation

16. Data Analysis : Correlation
^DJI - ‘Iran Nuclear’
r = 0.22555551
^GSPC - ‘Iran Nuclear’
r = 0.26694671
^DJI - ^GSPC
r = 0.98538686

17. Hypothesis accept / reject
^DJI - ‘US Crisis’
r = - 0.06350401
^GSPC - ‘US Crisis’
r = - 0.01991395
^DJI - ^GSPC
r = 0.98122934
^DJI - ‘Iran Nuclear’
r = 0.22555551
^GSPC - ‘Iran Nuclear’
r = 0.26694671
^DJI - ^GSPC
r = 0.98538686
correlation coefficient
is around 0
: reject H1
correlation coefficient
is around 1
: reject H2
correlation coefficient
is around 0
: reject H1
correlation coefficient
is around 1
: reject H2

18. Conclusion
Stock price will drop on the days with more
news articles with keyword ‘US Crisis’
r ≈ 0.
No linear relationship.
It is hard to conclude that the stock price falls
on the days which more news articles
containing keywords ‘us crisis’ and ‘Iran
nuclear’ were published
Hypothesis 1: rejected
Indices that contains smaller companies will
drop more significantly, since they are more
likely to be effected by outside factors.
r ≈ 1.
A strong uphill (positive) linear relationship.
^DJI and ^GSPC’s changes in stock price are
similar. Thus, it is hard to conclude that an
index that contains smaller companies are
more effected by outside factors.
Hypothesis 2: rejected

19. Failure Analysis
Failure
Analysis
News articles data was only from Wall Street Journal.
The number of articles with a keyword ‘US Crisis’ per day varied
from 8 to 10, and which is still a very small number.
Not enough data
Articles with keywords ‘US Crisis’ and ‘Iran Nuclear’ included articles
of analysis of past events which made the data collected irrelevant.
Specification of a keyword

20. Thank you