Stock price Prediction

1. TRIBHUWAN UNIVERSITY
INSTITUTE OF ENGINEERING
PASHCHIMANCHAL CAMPUS
“ Performance Analysis and Prediction of Stock Market for Investment
Decision using Regression Techniques’’
PRESENTED BY:
HARI K.C.
072/ MSCK /403
Department Of Electronics and Computer Engineering
A Mid Term Defense on
15/4/2018

2. Introduction
 Stock Market
 Time Series Prediction
 Regression Technique
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3. Problem Statement
 Stock Market are evolving and becoming complex.
 Difficult for the investor to get the efficient and reliable stock information of
public companies in less time.
 Problems while making the investment decision .
 This thesis makes prediction based on analysis of Stock market parameters
for effective investment decision .
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4. Objective
 To analyze and forecast the future stock price using Regression techniques.
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5. Motivation and Scope
 Allow Investors to make investments in stock market with less risk.
 Knowledge about Market Stability and Economic Performance.
 Easy visualization of stocks makes life easier.
 Technical analysis of stock is the new trend.
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6. Stock Market
 Share is an indivisible unit of capital that represent the equal proportion of a
company capital.
 The more a share is transacted, the more it is valuable.
 Stock Market is widely used investment scheme promising high returns but it has
some risks.
 Stock Market stock values varies based on “Demand and Supply strategy”.
 Numerous Stock Market exist in different parts of world such as NASDAQ, KSE, LSE,
NEPSE and so on.
 NEPSE belong to the stock exchange of Nepal.
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7. Stock Market
 Stock Market list different Public Companies with variables such as
i) Traded Shares
ii) Volume of Shares
iii) Opening Price
iv) Closing Price
v) Maximum Price
vi) Minimum Price and so on.
 Public Companies belong to different sub indexes such as
i) Hydropower ii) Manufacturing
iii) Banking iv) Insurance
v) Hotels
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8. Stock Market
 The inputs to our model which may effect stock market:-
- Stock history and Present values, information and prices
- Stock company news
- Interest Rate
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9. Time Series Prediction
 Data are indexed in time order.
 Time Series Analysis extract the meaningful statistics and characteristics of data.
 Time Series Forecasting predict the future stock values based on the previous
observed values.
 Moving averages involve averaging the time series over a specified number of
periods.
 Correlation between data over specified number of periods.
 Time series analysis is divided into linear and nonlinear analysis.
 Prediction involves use of new and historical data to forecast future values and trends
in stock market. 95/4/2018

10. Time Series Prediction
 It involves
applying statistical analysis techniques
analytical queries and
automated machine algorithms
to data sets to create predictive model that place a numerical value on the likelihood
of a particular event happening.
 x[t + s] = f ( x[t], x[t − 1], x[t-2]· · ·x[t-n] ) .
 Estimating ‘x’ at some future time ‘s’
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11. Regression Technique
 Regression is fitting the data and forecasting a specific value.
 Regression estimate the relationship among the variables.
 Regression is very popular in predicting stock prices.
 Regression is a form of supervised machine learning in which computer learn
from training sets of data.
 Regression shows the relation between dependent and independent
relationship.
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12. Regression Technique
 Linear regression analyzes two separate variables in order to define a single
relationship.
 Linear regression consists of finding the best-fitting straight line through the
points.
 The best-fitting line is called a regression line.
 Support vector machine gives a solution with lesser computation.
 The function of Support vector machine involves using the Kernels( RBF/
polynomial).
 The Kernel determines how similar the features are with respect to each other. 125/4/2018

13. Methodology
Figure1: A model of a system
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14. Methodology
Linear Regression:
Line of Regression of Y on X
Y(x)=∑mi * Xi + C
Support Vector Regression:
w * u + b ≥ 0
W= max [ 2 / ||w||]
Decision rule: (∑ ai yi xi)* u + b ≥ 0
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15. Methodology
Kernels:
i) RBF: Gaussian distribution of data values
ii) Polynomial: Non –linear polynomial data fitting decision.
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16. Analysis and Output
Main Menu:
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17. Analysis and Output
Agricultural Development Bank Limited(ADBL)
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18. Analysis and Output
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19. Analysis and Output
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20. Analysis and Output
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21. Analysis and Output
21
Deviation
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22. Analysis and Output
22
Moving Average
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23. Analysis and Output
23
Correlation coefficient
Combined Table Output
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24. Analysis and Output
24
ADBL stock news sentiment index
The sentiment index for ADBL is
64.70588235294117 percent
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25. Analysis and Output
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26. Analysis and Output
26
Asian Life Insurance Company Limited.
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27. Analysis and Output
27
ALICL stock price
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28. Analysis and Output
28
ALICL Central Tendency measure
Correlation Measure
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29. Analysis and Output
29
Percentage difference
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30. Analysis and Output
30
Percentage difference
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31. Analysis and Output
31
Moving Average
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32. Analysis and Output
32
Combined Table including St. deviation and moving average
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33. Analysis and Output
33
ALICL stock news sentiment analysis
The sentiment index for alicl is
72.72727272727273 percent
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34. Analysis and Output
34
Data fitting
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35. Work Remaining
a) Implementing Machine learning regression.
b) Time series prediction.
c) Visualizing the actual and forecasted price in graph.
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36. Time Schedule
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37. References
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38. References
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39. References
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40. ***THANK YOU***
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