Comparative study of stock market forecasting using different functional link artificial neural networks

Bebarta, Dwiti Krishna; Biswal, Birendra; Dash, P.K.

doi:10.1504/ijdats.2012.050407

Cited by 21 publications

(5 citation statements)

References 16 publications

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“…In this article, we have used the cloud computing environment provided by Microsoft Azure. Several machine learning tools provided by Azure such as boosted decision tree, decision forest tree, linear regression and neural network regression have been used to predict the future value of US dollar [18][19][20]. In this experiment, we have considered several performance metrics for future prediction of US dollar, and the results have been presented in Table 1.…”

Section: Results Analysismentioning

confidence: 99%

Prediction of Exchange Rate in a Cloud Computing Environment Using Machine Learning Tools

Pandey

Priya

Jena

2020

Smart Innovation, Systems and Technologies

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In this ever-changing world, cloud computing will definitely play a vital role in our daily life as everything is going to be mapped on it. In this paper, we have considered the cloud environment provided by Microsoft Azure to use machine learning tools for the prediction of exchange rate. We have also considered the exchange rates of forty-two different countries to design our data set for the experimental purpose. We have used various machine learning tools provided by Microsoft Azure and predict the future values of dollar in terms of Indian rupees (US/INR). The experiments are performed in the cloud computing environment, and it has been analyzed that neural network regression model is performing better than the other models considered in this paper. It has also been observed that the performance of the machine learning tools provided by Microsoft Azure are very much competitive with respect to the traditional machine learning approach.

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Section: Results Analysismentioning

confidence: 99%

Prediction of Exchange Rate in a Cloud Computing Environment Using Machine Learning Tools

Pandey

Priya

Jena

2020

Smart Innovation, Systems and Technologies

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“…Artificial neural networks (ANN) Abraham et al ( , 2003, , Bebarta et al (2012), Chen et al (2005), Egeli et al (2003), Emir et al (2012, Gupta and Sharma (2014), Hargreaves and Hao (2013), Kara et al (2011), Khashei and Bijari (2010), Kuo et al (2001), Mizuno et al (1998), Nayak et al (2014), Rodriguez et al (2000), Saeedmanesh et al (2010), Trippi and Desieno (1991), Trinkle (2006), Tsai and Wang (2009), Vaisla and Bhatt (2010), Vanstone et al (2010), Yao and Poh (1995) Back propagation neural network (BPNN) Arasu et al (2014), Chun and Kim (2004), Chen et al (2006), Dai et al (2012), Hammad et al (2009), Kumar and Thenmozhi (2006), Majumder and Hussian (2007), Oh and Kim (2002), Roman and Jameel (1996), Tjung et al (2010), Tay and Cao (2001), Yao et al (1999), Zhang and Wu (2009) Recurrent neural networks (RNN) Diaconescu (2008), Hsieh et al (2011), Kwon and Moon (2007), Roman and Jameel (1996), Versace et al (2004), Yumlu et al (2005), Wunsch et al (1998), Wang and Leu (1996)<...…”

Section: Type Of Neural Network Employed Referencesmentioning

confidence: 99%

Application and performance of data mining techniques in stock market: A review

Kaur

Dharni

2022

Intell Sys Acc Fin Mgmt

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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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“…Values between 0 and 1 are assigned to weight vector randomly to train the network. Further, it is updated in the perspective of the negative gradient of the performance function [33,34].…”

Section: Recurrent Functional Link Artificial Neural Network (Rflann)mentioning

confidence: 99%

An Intelligent Hybrid System for Forecasting Stock and Forex Trading Signals using Optimized Recurrent FLANN and Case-Based Reasoning

Bebarta¹,

Das²,

Chowdhary³

et al. 2021

IJCIS

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A precise prediction of future market trends is inevitable for traders in stock and foreign exchange market. However, it is a challenging task to have a profound understanding of technical indicators, including market-dominant factors and inherent process mechanism. In order to expedite the trading decision-making process, an intelligent stock trading system is proposed blending dynamic time windows coupled with case-based reasoning (CBR), and recurrent function link artificial neural network (FLANN) optimized with firefly algorithm. The idea of using CBR module is to offer a dynamic window search to assist the recurrent FLANN architecture for superior fine-tuning the trading operations. This integrated stock trading system is intended to pick the buy/sell window of target stock to maximize the profit. To demonstrate the applicability of the projected system, the time-series data from IBM, Oracle stock data, and in currency Euro to INR and USD to INR exchange data on daily closing stock prices are used for simulation. The assessment of performance of the proposed model is realized using error measures such as mean absolute error and mean absolute percent error. Furthermore, the experimental results obtained with and without using CBR has been exhibited for different stock and forex trading.

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Comparative study of stock market forecasting using different functional link artificial neural networks

Cited by 21 publications

References 16 publications

Prediction of Exchange Rate in a Cloud Computing Environment Using Machine Learning Tools

Prediction of Exchange Rate in a Cloud Computing Environment Using Machine Learning Tools

Application and performance of data mining techniques in stock market: A review

An Intelligent Hybrid System for Forecasting Stock and Forex Trading Signals using Optimized Recurrent FLANN and Case-Based Reasoning

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