Presentation on financial world data science problems.

1. TITLE: Financial World-Data
Science Problems
By
NIKITA KAPIL
Internship Carried out on
Career Launcher Data School

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3. DOMAIN

4. MACHINE LEARNING
Machine learning is the scientific
study of algorithms and statistical
models that computer systems use
to perform a specific task without
using explicit instructions, relying
on patterns and inference instead.
It is seen as a subset of artificial
intelligence

5. STOCKS
• Small Cap: In India, normally a
company below market capitalization
of Rs.5000 crores is classified as small
cap company.
• Mid Cap: A company with market
capitalization above Rs.5000 crores
and less than Rs.20000 crores.
• Large Cap: In India, normally
companies with the market
capitalization higher than Rs.20,000
crores are considered as Large cap
companies.

6. Technology

8. PANDAS
Pandas is an opensource
library that allows to you
perform data manipulation
in Python.
Pandas library is built on
top of NumPy,
meaning Pandas needs
NumPy to operate.
Pandas provide an easy
way to create, manipulate
and wrangle the data.
Pandas is also an elegant
solution for time series
data.

9. DATA EXPLORATION
USING PANDAS

10. The Basic Requirements
• Reading Data From CSV
• Formatting, cleaning and filtering Data Frames
• Group-by and Merge
• Libraries: Pandas, numpy.
• 495 instances.

11. DATA VISUALIZATION
AND TECHNICAL
ANALYSIS

12. • Data visualization is the discipline of trying to understand
data by placing it in a visual context so that patterns,
trends and correlations that might not otherwise be
detected can be exposed.
• Python offers multiple great graphing libraries that come
packed with lots of different features at might not
otherwise be detected can be exposed.
• Plotting in Financial Markets.
• A picture speaks a thousand words has never been truer
in financial markets.
• Libraries: matplotlib, seaborn.

13. Matplotlib
• Matplotlib is the most popular
python plotting library. It is a low-
level library with a Matlab like
interface which offers lots of
freedom at the cost of having to
write more code.
• Matplotlib is specifically good for
creating basic graphs like line
charts, bar charts, histograms and
many more.

14. Seaborn
• Seaborn is a Python data
visualization library based on
Matplotlib. It provides a high-
level interface for creating
attractive graphs.
• Seaborn has a lot to offer. You
can create graphs in one line that
would take you multiple tens of
lines in Matplotlib. Its standard
designs are awesome and it also
has a nice interface for working
with pandas data frames.

15. FUNDAMENTAL
ANALYSIS USING
REGRESSION

16. • Regression is basically a statistical approach to find the
relationship between variables.
• This used to predict the outcome of an event based on the
relationship between variables obtained from the dataset.
• Beta Calculation using regression.
• Beta of a publicly traded company can be calculated
using the Market Model Regression (Slope).

17. • In this method, we regress the company’s stock returns (ri) against
the market’s returns (rm). The beta (β) is represented by the slope
of the regression line.
Where,
ri is the stock’s return
α represents the intercept
β is the stock’s beta
rm is the market returns

18. Trade Call Prediction using Classification
TRADE CALL
PREDICTION
USING
CLASSIFICATION
• 'Buy' if the stock price is below
the lower Bollinger band.
• 'Hold Buy/ Liquidate Short' if the
stock price is between the lower
and middle Bollinger band.
• 'Hold Short/ Liquidate Buy' if the
stock price is between the middle
and upper Bollinger band.
• 'Short' if the stock price is above
the upper Bollinger band.

19. Modern Portfolio Theory

20. • Modern portfolio theory (MPT) is a theory on how risk-averse
investors can construct portfolios to optimize or maximize expected
return based on a given level of market risk, emphasizing that risk is
an inherent part of higher reward. According to the theory, it's
possible to construct an "efficient frontier" of optimal portfolios
offering the maximum possible expected return for a given level of
risk.

21. CLUSTERING FOR
DIVERSIFICATION
ANALYSIS
• Clustering is a Machine Learning
technique that involves the grouping
of data points.
• Given a set of data points, we can
use a clustering algorithm to
classify each data point into a
specific group
• Data points that are in the same
group should have similar properties
and/or features, while data points in
different groups should have highly
dissimilar properties and/or features.
• Clustering is a method of
unsupervised learning and is a
common technique for statistical
data analysis used in many fields

22. • In financial Markets, Cluster analysis is a technique used to group
sets of objects that share similar characteristics.
• It is common in statistics, but investors will use the approach to build
a diversified portfolio.
• Stocks that exhibit high correlations in returns fall into one basket,
those slightly less correlated in another, and so on, until each stock is
placed into a category.

24. APPLICATIONS
• Web Search Engine
• Social Media Services
• Spam Detector
• Predictions while Commuting
• Product Recommendations

25. REFERENCES
• https://www.investopedia.com/art
icles/technical/102201.asp
• https://medium.com/app-
affairs/9-applications-of-
machine-learning-from-day-to-
day-life-112a47a429d0
• https://intellipaat.com/blog/super
vised-learning-vs-unsupervised-
learning-vs-reinforcement-
learning/
• https://www.investopedia.com/ter
ms/m/modernportfoliotheory.asp

27. THANK YOU