This document summarizes various techniques that have been used to predict stock market performance, including data mining, artificial neural networks, hidden Markov models, neuro-fuzzy systems, and rough set data modeling. It reviews several studies that have applied these techniques to predict movements in stock market indices. Specifically, it discusses research that used support vector machines and neural networks to predict changes in the Hang Seng Index, and that proposed a hybrid decision tree and neuro-fuzzy system to predict trends in four major international stock markets. The document concludes that while various techniques have been implemented, fusion models combining hidden Markov models, neural networks, and genetic algorithms may help control and monitor stock price behavior and fluctuations.

1. STOCK MARKET PREDICTION :A SURVEY
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Presented by
Rajshekhar Patil
PG Student BMSIT
rjpatil058@gmail.com
Bangalore.
Under the guidance of
Guru Prasad S
Asst. Professor BMSIT
Bangalore.

2. outline
Abstract
Introdution
Stock market prediction analysis
 Literature Review
Conclusion
References
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3. Abstract
• Stock market is a widely used investment scheme promising high
returns but it has some risks.
• Stock market variation –demand & Supply strategy.
• An intelligent stock prediction model would be necessary.
• Stock market prediction is a act to forecast the future value of
the stock market.
• There are various techniques available for the prediction of the stock
market value .
• Few are: Neural Network (NN), Data Mining, Hidden
Markov Model (HMM), Neuro Fuzzy system etc.
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4. Introduction
• Stock market plays a vital role in the economic performance---
concludes particular nation.
• Prediction of stock markets –challenging task.
Because, its randomness in nature.
• Using only technical analysis is
Very difficult to anticipate.
• Researchers have made several attempts
to predict financial market values using
various techniques.
• Successful stock market prediction –
Achieve Best results
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5. PREDICTION ANALYSIS
• Analysis needed for access the knowledge to guide investors
in terms of when to buy or sell or hold the shares;
• Stock market prediction is mainly based on Two Analysis:
1.Fundamental Analysis:-
&
2. Technical Analysis:-
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6. Technical analysis:
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7. LITERATURE REVIEW:
• Phichhang Ou & Wang :
• Data mining to predicted stock market movements.
• Applied 10 different data mining techniques to anticipate price
variation of Hang Seng index of Hong Kong stock market.
• LS-SVM and SVM generate high ranking predictive performance.
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8. 8
M Suresh babu & et al.
•Different Data mining to discover pattern and forecast the future trends
and behavior .
• Author proposed an algorithm to accommodate flexible and dynamic
pattern matching task in time series analysis.

9. Continued…
• Binoy B Nair et al. they used hybrid decision tree &
neuro fuzzy system methods for forecasting the stock market.
they proposed auto-stock market trend anticipation system.
• They used two techniques such as
1.Technical analysis --feature extraction.
2.decision tree -- feature selection.
• dataset obtained by these two is fed as input, to train and test the
adaptive neuro- fuzzy system for next day stock prediction
• They tested their proposed system on 4 major international stock
market data.
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10. Fig: Block diagram of neuro-
fuzzy system.
Their experimental results clearly
showed that the proposed hybrid system
produces much higher accuracy when
compared to stand-alone decision tree
based system and Adaptive Neuro Fuzzy
Inference System (ANFIS).
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11. Md. Rafiul Hassan et al. deployed a fusion model by
combining HMM, NN and GA to anticipate financial market prediction.
• This model consist of two phases:
Phase 1: Optimizations of HMM.
Phase 2: Using weighted average method to
obtain the forecast.
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12. 12
Fig : Block diagram for fusion model.

13. • A.E Hassanien et al. proposed a generic rough set model
using the data set consisting of daily variations of a stock traded by
gulf-bank of Kuwait.
• Objective Modifying the existing rough set and build new model
that reduce the number of decision rules.
• They created an information table contains set of market indicator
like closing price, high price, low price, trade, value, average & roc
etc.
• These indicators acts as conditional attributes to predict stock price.
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14. Conclusion
 Although there are various techniques implemented for the
prediction of stock market.
 Here we surveyed some important stock market prediction
technique Such as Data mining, ANN,HMM, GA, Neuro Fuzzy
system and Rough set data model.
 This paper also highlights the fusion model by merging the HMM
Artificial NN and GA.
 These approaches are used to control and monitor the entire the
market price behavior as well as fluctuation.
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15. References
• S.Arun , Joe Babulo, B. Janaki, C. Jeeva, “Stock Market Indices Prediction
with Various Neural Network Models”, International Journal of Computer
Science and Mobile Applications, Vol. 2, Issue 3, .pp32-35 march 2014.
• http://www.learnartificialneuralnetworks.com/stockmarketprediction.ht
ml.
• Phichhang Ou and Hengshan Wang “Prediction of Stock Market Index
Movement by Ten Data Mining Techniques”, Canadian Center of Science
and Education, Vol. 3 no 12 December, 2009.
• M. Suresh babu, N.Geethanjali and B. Sathyanarayana, “Forecasting of
Indian Stock Market Index Using Data Mining & Artificial Neural Network”,
International journal of advance engineering & application, Vol. 3 Issue.4,
.pp 312-316 may 2011.
• Binoy B. Nair, N. Mohana Dharini, V.P. Mohandas, “A Stock Market Trend
Prediction System Using a Hybrid Decision Tree-Neuro-Fuzzy System”,
International Conference on Advances in Recent Technologies in
Communication and Computing on , .pp 381-385 June 2010.
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16. End of Presentation.
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