Improving Returns on Stock Investment through Neural Network Selection

Quah, Tong-Seng

doi:10.4018/978-1-59140-670-9.ch009

Cited by 20 publications

(19 citation statements)

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“…The results indicate that predictions can be produced to a high level of accuracy, in a readily understandable format. Quah [7] and Srinivasan uncover the intricate relationships between the performance of stocks and the related financial and technical variables by neural network. Experimental results obtained this far have been very encouraging.…”

Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

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Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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“…Lee and Chen (2007) Wu and Xu (2006) found that fundamental analysis can be powerful to predict stock price, especially with the aid of neural networks approach and rough set theory. Atiya et al (1997), Quah and Srinivasan (1999), Raposo and Cruz (2002) also roped in fundamental indicators to forecast stock prices using Neural Networks. Schumaker and Chen (2006) tried to find out influence of news article on stock price using Support Vector Machine (SVM).…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

Chandwani¹,

Saluja²

2014

IJCA

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Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map nonlinear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.

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“…The learning process includes storing the presented data. All instances correspond to points in an n-dimensional space and the nearest neighbors of a given query that already defined regarding the standard Euclidean distance [8]. The probability of a query q belongs to a class c that can be calculated as follows:…”

Section: K-nn Based Classifiersmentioning

confidence: 99%

Real Time Business Analytics for Buying or Selling Transaction on Commodity Warehouse Receipt System

Djatna

Teniwut²,

Hairiyah³

et al. 2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. The requirement for smooth information such as buying and selling is essential for commodity warehouse receipt system such as dried seaweed and their stakeholders to transact for an operational transaction. Transactions of buying or selling a commodity warehouse receipt system are a risky process due to the fluctuations in dynamic commodity prices. An integrated system to determine the condition of the real time was needed to make a decision-making transaction by the owner or prospective buyer. The primary motivation of this study is to propose computational methods to trace market tendency for either buying or selling processes. The empirical results reveal that feature selection gain ratio and k-NN outperforms other forecasting models, implying that the proposed approach is a promising alternative to the stock market tendency of warehouse receipt document exploration with accurate level rate is 95.03 %.

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Improving Returns on Stock Investment through Neural Network Selection

Cited by 20 publications

References 0 publications

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

Real Time Business Analytics for Buying or Selling Transaction on Commodity Warehouse Receipt System

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