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.

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outline

Abstract

Introdution

Stock market prediction analysis

Literature Review

Conclusion

References

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, HiddenMarkov Model (HMM), Neuro Fuzzy system etc.

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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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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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Technical analysis:

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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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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.

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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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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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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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Fig : Block diagram for fusion model.

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•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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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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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.html.

 • 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.

End of Presentation.