Prediction of Stock Market Price using Hybrid of Wavelet Transform and Artificial Neural Network

Chandar, Sarath; Sumathi, M.; Sivanandam, S. N.

doi:10.17485/ijst/2016/v9i8/87905

Cited by 42 publications

(26 citation statements)

References 10 publications

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“…The performance for stocks exhibiting mean reversion pattern are not known. Chandar et al [61] adopted DWT approach to decompose stock prices of five different companies into approximation and detail components. The decomposed components were combined with other variables (e.g., volume) to serve as input parameters for BPNN.…”

Section: Discrete Wavelet Transform-based Modelsmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.

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Section: Discrete Wavelet Transform-based Modelsmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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“…A combination of a discrete wavelet transform and an ANN was used by Chandar, Sumathi, and Sivanandam (2016) for predicting stock prices. This combination yielded positive results when tested on five data sets.…”

Section: Introductionmentioning

confidence: 99%

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Mohamed

Ahmed

Mehdi

et al. 2021

Intell Sys Acc Fin Mgmt

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Predicting stock price remains one of the challenges for investors' investment strategies. This study helps with accurate prediction and the main factors affecting variations in stock prices. It applies an adaptive neuro-fuzzy model on 58 listed firms from both the Abu Dhabi Securities Exchange and the Dubai Financial Market for the period 2014-2018 to estimate the predictive power of corporate performance measures and their significance. After examining four performance predictors-return on asset (ROA), return on equity (ROE), earning per share (EPS), and profit margin (PM)-the study finds that ROE is the most significant predictor and ROA is the least. EPS is the most influential profitability measure and PM the least.

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“…Even small improvement in predictive performance of stock prices is very beneficial [8]. With development of machine learning algorithms and powerful computers, the longlasting debate on predictability of financial markets is re-invigorated again in the last few years [9].Different methods and models are applied to predict stock market. Technical, fundamental and statistical measures have been proposed and used in financial forecasting such as simple moving average, linear regression, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Market Prediction on High-Frequency Data Using ANN

Nova

Mim

Rowshan

et al. 2021

AJRCoS

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A stock market is a place where company shares are traded to the stockbrokers. Stock price prediction is one of the most challenging problems as a high level of accuracy is the key factor in predicting a stock market. Many methods are used to predict the price in the stock market but none of those methods are proved as a consistently acceptable prediction tool due to its volatile nature. In this paper, we proposed Artificial Neural Network (ANN) technique because ANN can generalize and predict data after learning and analyzing from the initial inputs and their relationships. We used feed forward network and backward propagation algorithm to predict stock prices. In this paper, we introduced a method that can find out the future value of stock prices in a particular day based on some input using ANN back propagation algorithm.

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Prediction of Stock Market Price using Hybrid of Wavelet Transform and Artificial Neural Network

Cited by 42 publications

References 10 publications

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Stock Market Prediction on High-Frequency Data Using ANN

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