a study of hybrid competitive genetic algorithm models in stock exchange expectation
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©IJCERT 61 | P a g e
ISSN (Online): 2349-7084
International Journal of Computer Engineering In Research Trends (IJCERT)
Volume 1, Issue 1, July 2014, PP 61-69
www.ijcert.org
A Study of Hybrid Competitive Genetic Algorithm
Models
In Stock Exchange Expectation
Rayapu.Swathi1, M.Tech Research Scholar,
N.Parashuram2, Assistant Professor,
Dr.S.Prem Kumar3, Head of the Department
Department of CSE, G.Pullaiah College of Engineering and Technology
JNTU Anatapur, Andhra Pradesh, India
Abstract: Stock exchange expectation (SEE) assumes an imperative part in the advanced time for
any economy which is on the improvement stage. Genetic Algorithm (GA) is an evolutionary
algorithm that is helpful for taking care of issues which are excessively difficult in nature. This
paper reviews distinctive GA shows that have been tested in securities exchange expectation with
exceptional upgrade procedures utilized with them to enhance the forecast precision. The order
is made regarding GA with two layer, multilayer, neural system variations and altered
evolutionary calculations. Through the reviewed paper it is demonstrated that the execution of
GA exceeds expectations when incorporated with other machine learning algorithms.
Keywords-classification, enhancement technique, genetic algorithm, prediction accuracy, Stock
exchange expectation
1. INTRODUCTION
Stock markets are one among the key players in determinant the economy that is that the backbone of any nation and
for that matter evens the world economy. There’s continually some risk concerned in investment within the
exchange owing to its extremely unpredictable behavior. Since exchange is actually non-parametric, dynamic, time-
variant and chaotic in nature, exchange prediction could be a difficult task [1]. Additionally to the present exchange
is littered with several macro economical factors like political events, general economic conditions, firm’s policies,
investors’ expectations, institutional investors’ selections, movement of alternative stock markets and also the
science of investors etc [2]
GA plays a vital role in predicting the exchange costs accurately. Varied researches on the applying of GA in
prediction issues have verified its benefits over alternative applied mathematics and constant quantity techniques.
A Study of Hybrid Competitive Genetic Algorithm Models
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GA has the benefits like simple to grasp, simple to implement, extremely configurable, doesn't suffer from the curse
of spatial property, flexible, applicable to any downside wherever a fitness perform is outlined, simply
parallelizable, output are often was AN actual program and might be run to hurry in real time, and might be wont to
optimize additional formal models. GA additionally inherits bound limitations like stagnation of population, fast
predominance of bound people over the remainder of the population, over-fitting, give the foremost work resolution
that was evolved not essentially the best resolution, laborious to see best parameters. the restrictions of ancient GA
are often engulfed by hybrid superimposed GA in predicting the exchange behavior that inculcates the options like 2
superimposed GA, multilayered GA, neural network variants with GA; modified GA. These further benefits of GA
give prediction of exchange costs at a speedier rate with high prediction accuracy compared to ancient GA.The
purpose of this work is to review and classify the hybrid GA models to exchange prediction. The results area unit
conferred in four completely different classes. The primary class lists 2 layer hybrid GA model, description and their
model comparisons. The second lists 3 layer hybrid GA model for exchange prediction. The third presents stock
markets predictions by GA with neural network variants. The last one summarizes the changed versions of easy
genetic algorithms for exchange predictions. This work focuses on the applying of accessible GA to predict
exchange indexes.GA could also be applied to various markets to forecast the exchange indexes.
II. COMPARISON OF HYBRID GA
Improving SEE by two layer hybrid GA
In [3], a hybrid neurogenetic system for stock prognostication was planned. This hybrid system used RNN trained
by a back propagation primarily based formula, to predict obtain and hold strategy of thirty six corporations in stock
market & National Association of Securities Dealers Automated Quotations for thirteen years from 1992 to
2004. Since back propagation formula is susceptible to stand still in native minima and extremely depends on the
initial weights, the GA was accustomed optimize the NN’s weight underneath a 2-D cryptography and crossover
mechanisms. The GA is parallelized on a UNIX operating system cluster system victimization message passing
interface to scale back the time in process the mass information. The experimental results showed that the hybrid
model predicts higher (34 corporations among thirty six companies) than each GA & SVM alone in obtain and
hold strategy. In [4], a hybrid GA-SVM system was developed for predicting the longer term directions of the stock
value. a group of technical indicators that exhibits high correlation were used as input options. The GA was
accustomed choose the set of most informative input options among all the technical indicators. The chosen options
were used as inputs to SVM.
SVM minimize associate degree higher bond of generalization error. The predictions performance was measured
supported the hit magnitude relation. The results were compared with the stand alone SVM. The GA-SVM hybrid
model considerably outperformed the SVM.A hybrid machine learning system supported GA-TSA model for stock
exchange prognostication was developed [5]. The success of a commerce rate depends on selecting the most
effective parameter combination .This can be done by victimization GA .The GA sets the sub domain of the
parameters and finds close to optimum worth within the sub domain with the statistic analysis during a} very cheap
Rayapu.Swathi,N.Parashuram, Dr.S. Prem Kumar
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time. This hybrid model outperformed the time model alone in terms of accuracy. The accuracy is still improved by
considering varied political & economical factors that affects the stock exchange and incorporating market
specific domain information in to the system. The stock exchange indices were foretold victimization hybrid
GAPSO with perturbation term galvanized by the passive congregation biological mechanism that allows all
particles within the swarm to perform the worldwide search within the whole search house [6]. The GA is employed
to extend the range and provides particles to fly within the new regions in search house. This hybrid model will
increase the prediction accuracy for each short term & long run stock exchange indices compared to GA/PSO
model alone. This model is utilized in alternative applications like pattern classification recognition and optimization
downside. A completely unique technique supported hybrid combination of GAARMA was applied for statistic
prognostication [7]. This hybrid model edges from the capabilities of ARMA to spot linear trends yet as GA’s ability
to get models that capture on non linear patterns from information. The GA evolves models adopting the info with
none restrictions with reference to the shape of models or coefficients. The empirical result with real stock
information confirmed that the hybrid approach to be a good rival over pure ARMA & GA. The accuracy is
still improved by hair care modification purpose detection technique & modeling techniques. In [8], a hybrid
model supported GA-RST foretold the stock value. During this novel technique multi-technical indicators were
accustomed predict stock value trends. This technique has utilized a RST formula to extract linguistic rules from the
linguistic technical indicator dataset and utilize GA to outline the extracted rules to urge higher prognostication
accuracy and stock come. The effectiveness of the planned model was verified with 2 styles of performance
evaluations, accuracy and stock come for 6 year amount of the Taiwan securities market capitalization weighted
index because the experiment dataset.
B.Improving SEE by three layer hybrid GA
An intelligent call network that measures all the qualitative events additionally to quantitative factors which will
influence the securities market were developed [9]. This novel methodology consists of three elements particularly
factors identification, qualitative model and call integration. The fuzzy city methodology was used to capture the
stock expert’s information and remodel it to the suitable format of GFNN. The ANN that considers solely
quantitative factors is outperformed by the projected system within the learning accuracy, buy-sell clarity and buy-
sell performance. Real-number committal to writing approach may be applied additionally to binary committal to
writing approach. A fusion model by combining HMM, ANN and GA to forecast money behavior was developed
[10]. These will be used for in-depth analysis of the securities market. Victimization the ANN the daily stock costs
were reworked to freelance sets of value. The GA is employed to search out the optimum initial parameters for the
HMM given the reworked observation sequences. This fusion model found variety of other information things from
the historical information, that exhibit similar securities market trends. The trained HMM is employed to spot and
find similar patterns within the historical information. The performance of the fusion model is best than the essential
model wherever solely one HMM is employed. It conjointly outperforms the popular applied math foretelling tool.
The hybrid VAR-NN-GA framework has machine-driven the choice method of prediction [11]. Power unit explore
for the correlate stocks and indicators mechanically. Input file like commerce volume, economic process rate and
A Study of Hybrid Competitive Genetic Algorithm Models
In Stock Exchange Expectation
©IJCERT 64 | P a g e
also the currency rate tested with power unit analysis. The NN forecast from the relevant inputs was set by the
power unit analysis. The GA is employed to regulate the weights of every NN model. The VAR-NNGA hybrid
system outperformed the complete neural network in prediction accuracy. A novel approach GA-CPSO-SVR was
used to predict the money returns of Shanghai composite index [12]. GA is employed for feature choice and CPSO
is employed for optimizing the parameter of Foreign Intelligence Service model. CPSO methodology combines PSO
with accommodative inertia weight issue and chaotic linear search. The accuracy of the prediction may be improved
by considering different advanced looking techniques to see appropriate parameters and also the range of input file.
This hybrid model outperforms BPNN, ARIMA, Foreign Intelligence Service and CPSO-SVR in foretelling money
returns.
Fig 1. Genetic Algorithm layers
C.Improving SEE by GA with neural network variants
TDNN & GA were used along to urge additional correct forecast of European choice costs of groovy index
[13]. Since back propagation algorithmic rule is at risk of bog down in native minima and specifying the design at
the beginning of the algorithmic rule might not be optimum, the GA is employed to optimize the TDNN. This
approach has mounted design and coaching of TDNN was through with GA. The experimental results showed that
the hybrid model predicts higher than easy feed forward neural network. A GMDHNN and genetic algorithmic rule
was developed for stock prediction of cement sector in Iranian capital stock market [14]. Mistreatment GMDHNN a
model might be depicted as a collection of neurons within which completely different pairs of them in every layer
were connected through a multinomial and thus manufacture new neurons within the next layer. In GMDHNN the
identification method used the appropriate optimization technique GA to seek out the simplest specification. The
complete design of the GMDHNN is intended with the assistance of GA. It provides the optimum variety of
neuronal in every hidden layer and their connectivity configuration to seek out the optimum set of appropriate
coefficients of quadratic expressions to model stock costs.GMDHNN outperforms ancient statistic technique and
regression based mostly models in prediction accuracy. In [15], the author has investigated the effectiveness of a
hybrid approach supported the ATNN and therefore the TDNN with the GA in a very temporal pattern for exchange
prediction tasks.TDNN permits solely weights to be adopted however time delays are enclosed however are
Rayapu.Swathi,N.Parashuram, Dr.S. Prem Kumar
International Journal of Computer Engineering In Research Trends (IJCERT ) 65 | P a g e
mounted. The ATNN network adapts its time delay and weights throughout coaching to rise accommodate dynamic
temporal patterns and supply additional flexibility for optimization tasks.GA supports optimization of the quantity of
your time delay and network study factors at the same time for the ATNN and TDNN model. The result showed that
the accuracy of this approach is over that of the quality ATNN, TDNN and RNN.
D.Improving SEE by modified GA
TIMTAEF method, which performs an evolutionary search for the minimum dimension in determining the
characteristic phase space that generates the financial time series was presented [16].This hybrid model composed of
modular morphological neural network with a modified genetic algorithm(MGA).The modified genetic algorithm
was used to improve search convergence. The prediction of the proposed model obtained a performance much better
in terms of evaluation function than the TAEF & MRLTAEF model To increase prediction accuracy and reduce
search space and time for achieving the optimal solution, the combination of WNN with fuzzy knowledge was used
[17]. The proposed FWNN structure is trained with differential evaluation (DE) algorithm. DE includes evolution
strategies and conventional GA. DE is used for minimizing non-linear and no differentiable continuous space
function. Training FWNN system by DE is much more faster than the traditional GA.The result, demonstrated that
FWNN with DE has better performance than FWNN with BP, FFNN and ANFIS.In [18], the author has investigated
the chaotic behavior of NASDAQ and S&P CNX NIFTY stock markets using EDA based LLWNN.The LLWNN
system was optimized using estimation of distribution algorithm which is a new class of evolutionary
algorithms.EDA explicitly extract global statistical information from the selected solutions. The major issues in
EDAs are selecting parents and building probability distribution model. The author used truncation selection and
Gaussian distribution with diagonal covariance matrix. The result showed that the accuracy of this approach is
higher than that of the standard WNN. Table I lists the main features of hybrid GA.An evolutionary neural network
model was proposed to improve the performance of ANN in time series forecasting [19]. A modified GA was used
to select best input features, optimize the slope of hidden modes activation function, learning parameters and also
the number of hidden layer nodes. To evaluate the effectiveness of the proposed model FOREX rate prediction acts
as a benchmark application. The result showed that the accuracy of this approach is higher than that of hybrid RNN-
ARIMA, fuzzy system-ANN and ANN.
TABLE I. MAIN FEATURES OF HYBRID GA
Hybrid GA Main features
GA-FS
reduce the processing time without loss of performance, , reduce computational cost using message
passing interface, improves the
generalizations, avoids the curse of dimensionality, avoids local minima
GA-SVM resistance to over fitting problem, optimal selection of most informative input features from the
technical indicator
A Study of Hybrid Competitive Genetic Algorithm Models
In Stock Exchange Expectation
©IJCERT 66 | P a g e
GA-TSA finds best parameter combination which is the key for successful trading
GA-PSO
fast convergence ,robustness
GA-ARMA more robust, accurate model of time series
GA-RST
extract rules effectively from past stock data, improve classification accuracy and forecasts profit
,produces more reasonable &Understandable rules, provide objective suggestions
GA-FS-NN X GA-FS-NN Captures the stock expert’s knowledge, decreases the training time and avoids local
minima
ATNN
&TDNNGA
supports the optimization of the number of time delays & network architectural factors
simultaneously, adapts both time-delay and weights to accommodate changing temporal patterns
and provides more flexibility
MGA based
MMNN
Overcomes the random walk dilemma for stock market prediction, does not discard any possible
correlation that exist among the time series parameters, even higher order correlations does not
make any prior assumption
DE based
FWNN
Combines the strengths of wavelet theory, fuzzy logic and neural networks, fast training , ability to
analyze non-stationary signals to discover their local details, self learning characteristic that
increases the accuracy of the prediction, less time for parameter updating
EDA based
LLWNN
Learning efficiency, structure efficiency, provides more parsimonious interpolation in high
dimension spaces when modeling samples are sparse
MGA based
evolutionary
NN
Selects best input features,optimizes the slope of the hidden nodes activation function,learning
parameters and the number of hidden layer nodes.
III. CONCLUSION
This study has surveyed articles that have applied hybrid GA model to predict stock exchange values. This study has
focused on the outline of hybrid GA, model comparison and its increased options. Ancient GA has its own
drawbacks thereby limiting its performance to a precise level of accuracy. The limitation of ancient GA is overcome
by hybrid stratified GA in predicting the stock exchange behavior that inculcates options like 2 layer, multilayer
& changed organic process algorithms.
APPENDIX : ABBREVIATION
ANFIS : Adaptive neuro fuzzy inference system
ANN Artificial neural network
ARMA Auto regressive moving average
ATNN Adaptive time-delay neural network
CPSO Chaotic particle swarm optimization
Rayapu.Swathi,N.Parashuram, Dr.S. Prem Kumar
International Journal of Computer Engineering In Research Trends (IJCERT ) 67 | P a g e
DE Differential evolution
EDA Estimation of distribution algorithms
FFNN Feed forward neural network
FWNN Fuzzy wavelet neural network
GFNN Genetic fuzzy neural network
GMDHNN Group method of data handlingneural network
HMM Hidden markov model
LLWNN Local linear wavelet neural network
MGA Modified genetic algorithm
MRLTAEF morphological rank linear time delay added evolutionary forecasting
PSO Particle swarm optimizitation
RNN Recurrent neural network
RST Rough set theory
SVM Support vector machine
SVR Support vector regression
TAEF Time-delay added evolutionary forecasting
TDNN Time delay neural network
TIMTAEF Translation invariant morphological time-added evolutionary forecasting
TSA Time series analysis
VAR Vector auto regression
WNN Wavelet neural network
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International Journal of Computer Engineering In Research Trends (IJCERT ) 69 | P a g e
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