abhishek gupta et al, int.j.computer technology ... survey on stock market prediction using various...

A Survey on Stock Market Prediction Using Various

Algorithms

Abhishek Gupta,

Dr. Samidha D Sharma

Department of Information Technology, Department of Information Technology

NRI Institute of Information Science and Technology, Bhopal (M.P.) NRI Institute of Information Science and Technology, Bhopal (M.P.)

[email protected] [email protected]

Abstract

Stock market prediction is a technique of predicting the future value of the stock markets on the basis of the current and the

previous information available in the market.Stock market

prediction is an important issue for investigating in academic and

financial research. There are various techniques available for the prediction of the stock market value. Here in this paper a survey

of all the techniques and schemes for stock market prediction are

discussed and analysed.

Keywords: Stock Market, Prediction, Analysis.

Introduction

The prediction of stock market movement is an important

area of financial forecasting. Notwithstanding years of

study and the newest technology, it seems that no technique

has been exposed that constantly works. Essential analysis

frequently works best over longer periods of time, where

technical analysis is more appropriate for short term

trading. Researchers have made many attempts to predict

the performance of financial market .Many models or

approaches like artificial intelligence techniques such as

[1]Neural Network and Fuzzy Systems had proposed. However it is difficult to interpret their results .They are

unable to view the nature of interactions between technical

indicators and stock market fluctuations.

The difficulty with technical analysis is that a

complete pattern is required to make an accurate prediction

on the stock movement. Preferably, such a forecast should

be made before the pattern was completed to facilitate

prediction. For this task execute a Time-Delay Artificial

Neural Network known as Midas. Similar to Metis, Midas

receives input data including close, open, low and high

prices per day for a particular stock ticker over a period of

time, along with the equivalent trading quantity for every

day. This data is adequate for employing technical analysis

through Metis and is therefore used as the input data for

Midas as well [2].

The financial market unusual from a lot of substantial systems like we identify the weather is that the financial

market is an arrangement of complex feedback system.

What people expect prices to be concerns the prices they

examine and then the prices they examine then influences

how they are going to form their expectations about what

the prices will be in the after that stage. The market is

essentially an unresolved creature or an undecided

institution, it’s an institution where public trade risk,

exchange risk, and that’s why it’s present. And so if it were

probable to forecast it there would be no risk. In

individuals, I believe there cannot be any publicly

accessible system to predict a financial market.

Alternatively, neural networks have been originated useful

in stock price prediction [3-4]. Both feed forward and

recurrent neural networks have been investigated and good

results have been acquired. That means the calculation software would be very useful to support entity in

accomplishing a complete decision. In this paper, assuming

that it is feasible to predict markets, a prediction system is

extended using fuzzy neural networks with a learning

algorithm to predict the expectations stock assessments.

The system consists of numerous neural networks

components. These models are all used to discover the

relationships between unusual technical and economical

indices and the judgment to buy or sell stocks. The inputs

to the networks are practical and financial indices. The

amount produced of the system is the choice to buy and

sell. There are numerous neural network methods for stock

forecast, such as recurrent neural network, Time Series

method and Feed-forward neural network method, etc. [4].

When compared to these techniques, Fuzzy neural network

is a very valuable and efficient method to progression,

which is given in detailed in the later sections.

Stock market prediction is the act of trying to determine the

future value of a company stock or other financial

instrument traded on a financial exchange. The flourishing

calculation of a stock's future price could yield momentous

profit. Some consider that stock price movements are

governed by the random walk hypothesis and thus are

unpredictable. Others oppose and those with this

perspective possess a myriad of methods and technologies

Abhishek Gupta et al, Int.J.Computer Technology & Applications,Vol 5 (2),530-533

IJCTA | March-April 2014 Available [email protected]

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ISSN:2229-6093

mailto:[email protected]

which purportedly allow them to gain future price

information [5].

Fuzzy Neural Networks

According to the mechanism of fuzzy logic control system,

the fuzzy neural network typically has 5 functional layers:

(1) Layer one is the input layer. (2) Layer two is the

fuzzification layer; (3) Layer three is the fuzzy reasoning

layer that may consist of AND layer and OR layer; (4) Layer four is the de-fuzzification layer; (5) Layer five is the

output layer. The design of a fuzzy neural network is

described in Figure.

Figure 1: Architecture of the fuzzy neural network

Usually, the fuzzy neural network maps crisp inputs xi

(I=1, 2… n) to crisp output yi (j=1, 2… m). A fuzzy neural network is constructed layer by layer according to fuzzy IF-

THEN rules, the fuzzy reasoning method, linguistic

variables, and the de-fuzzification scheme of a fuzzy reasoning method and the de-fuzzification scheme of a

fuzzy logic control system.

Each neuron in the fuzzification layer represents

an input membership function of the antecedent of a fuzzy

rule. One familiar method to execute this layer is to express

membership functions as discrete points. Thus for a fuzzy

rule "IF X1 is A1 and X2 is A2 ... THEN Y is B", A's

characterize the possibility distribution of the antecedent clause "X is A". Each of the hidden nodes is defined as a

fuzzy reference point in the input space. The function of

the de-fuzzification layer is for rule assessment. Each

neuron in this layer symbolizes a consequential proposition

"THEN Y is B" and its membership function can be

implemented by combining one or two sigmoid functions and linear functions [6].

Related Work

Nair, Mohandas and Sakthivel [7] proposes a

decision tree rough set hybrid system for stock market

prediction presents the design and performance evaluation of a hybrid decision tree- rough set based system for

predicting the next day’s trend in the Bombay Stock

Exchange (BSESENSEX). This system outperforms both

the neural network based system and the naïve bayes based

trend prediction system.

Lee, Anthony, Lin, Kao, and Chen [8] propose an

effective clustering approach to stock market prediction,

which combines the advantages of K-means and HAC, to

perform stock market prediction. This method consists of

three phases. First, it converts each financial report into a

feature vector and use HAC to divide them into clusters.

Second, for each cluster, it recursively apply K-means to

partition each cluster into sub-clusters so that most feature

vectors in each sub-cluster belong to the same class. Then,

for each sub-cluster, it chooses its centroid as the

representative feature vector. Finally, it employs the

representative feature vectors to predict the stock price movements.

Kim and Han [9] propose genetic algorithms

approach to feature discretization in artificial neural

networks for the prediction of stock price index. In this

Genetic Algorithm is employed not only to improve the

learning algorithm, but also to reduce the complexity in

feature space. GA optimizes simultaneously the connection

weights between layers and the thresholds for feature

discretization. The genetically evolved weights mitigate the

well-known limitations of the gradient descent algorithm.

This genetic based model outperforms the other

conventional models.

Kim [10] proposes artificial neural networks with

evolutionary instance selection for financial forecasting. He

uses a new hybrid model of ANN and genetic algorithms

(GAs) for instance selection. An evolutionary instance

selection algorithm reduces the dimensionality of data and may eliminate noisy and irrelevant instances. In addition, it

searches the connection weights between layers in ANN

through an evolutionary search. The genetically evolved

connection weights mitigate the well-known limitations of

gradient descent algorithm.GA-based learning and the

instance selection algorithm significantly outperforms the

conventional GA-based learning algorithm.

Matsui and Sato [11] propose neighborhood

evaluation in acquiring stock trading strategy using Genetic

algorithms. This method involves evaluation for

neighboring points of a genetic individual in fitness



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landscape as well as itself. It reduces the influence of the

singular points in the training phase and to improve the

profit in the testing phase.

Mahdi, Hamidreza and Homa [12] propose stock

market value prediction using neural networks. In this, two

kinds of neural networks, a feed forward multi layer

perception (MLP) and an Elman recurrent network, are

used to predict a company’s stock value based on its stock

share value history. The application of MLP neural network

is more promising in predicting stock value changes rather

than Elman recurrent network and linear regression

method. However, based on the standard measures Elman

recurrent network and linear regression can predict the

direction of the changes of the stock value better than the

MLP.

Shweta, Rekha and Vineet [13] propose predicting

future trends in stock market by decision tree rough-set

based hybrid system with HHMM. It presents a hybrid

system based on decision tree rough set, for predicting the

trends in the Bombay Stock Exchange (BSESENSEX) with the combination of Hierarchical Hidden Markov Model. It

also presents future trends on the bases of price earnings

and dividend. The data on accounting earnings when

averaged over many years help to predict the present value

of future dividends.

Sam Mahfoud and Ganesh Mani [14] propose

financial forecasting using genetic algorithms a new system

that utilizes genetic algorithms (GAs) to predict the future

performances of individual stocks. More generally, the

system extends GAs from their traditional domain of

optimization to inductive machine learning or

classification. The overall learning system incorporates a

GA, a niching method and several other components.

Chenoweth, Zoran and Lee [15] propose

embedding technical analysis into neural network based

trading systems. it compare an NN-based trading system

using a threshold-based pattern filtering technique to a system using a more sophisticated preprocessing technique

utilizing the ADX indicator to identify trends in the S&P

500 index. The results indicate that the ADX-based

directional filter used for preprocessing works better with

smaller ADX smoothing parameter values. However, the

simple threshold-based filtering technique still outperforms

the ADX based filtering technique with the rule-based

integration strategy.

Kuang and Jane [16] propose a hybrid model for

stock market forecasting and portfolio selection based on

ARX, grey system and RS theories. In this approach,

financial data are collected automatically every quarter and

are input to an ARX prediction model to forecast the future

trends of the collected data over the next quarter or half-

year period. The forecast data is then reduced using a

GM(1,N) model, clustered using a K-means clustering

algorithm and then supplied to a RS classification module which selects appropriate investment stocks by applying a

set of decision-making rules. Finally, a grey relational

analysis technique is employed to specify an appropriate

weighting of the selected stocks such that the portfolio’s

rate of return is maximized.

Chen, Leung and Daouk [17] propose application of neural networks to an emerging financial market:

forecasting and trading the Taiwan Stock Index. This

model predicts the direction of return on market index of

the Taiwan Stock Exchange, one of the fastest growing

financial exchanges in developing Asian countries.

Probabilistic neural network (PNN) is used to forecast the

direction of index return after it is trained by historical data.

Statistical performance of the PNN forecasts are measured

and compared with that of the generalized methods of

moments (GMM) with Kalman filter. Moreover, the

forecasts are applied to various index trading strategies, of

which the performances are compared with those generated

by the buy-and-hold strategy as well as the investment

strategies guided by forecasts estimated by the random

walk model and the parametric GMM models.

Connor, Niall and Madden [18] propose a neural

network approach to predicting stock exchange movements using external factors. In this substantial dataset has been

compiled with daily values of the DJIA, derived technical

indicators, and external indicators and profit-based

evaluation procedure for financial prediction systems is

proposed, based on simulations with a simple trading

strategy, it is more meaningful than evaluating systems

based on the error between predicted and actual values, as

is sometimes done. A neural network approach is shown to

be successful in predicting movements of the DJIA,

provided that external factors are considered. These results

may be used as a baseline against which to compare other

prediction techniques in the future.

Yang, Min and Lin [19] propose the application of

fuzzy neural networks in stock price forecasting based on

Genetic Algorithm discovering fuzzy rules. This paper

provides a method to improve black box model considering

problems existed in its application. The improvement is achieved mainly by applying GA (Genetic Algorithm) in

fuzzy systems to discover rules, eliminate errors or invalid

rules caused by noisy data, and thus form valid sets of

rules. Evaluation of the rule sets, as that of the whole

prediction model, is performed through known knowledge

and theories. At last, fuzzy reasoning approach is used

based on the rule sets to predict price trend of stock market.

Conclusion

Although there are various techniques implemented for the

prediction of stock market. Here in this paper a complete

survey of all the stock market prediction techniques are

given. On the basis of existing technique used for the future

prediction of stock market a new technique for the

prediction is proposed which provides close prediction of

stock market.



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