A novel approach to predict stock market price using radial basis function network

Kumar, Rajesh; Srivastava, Shivangi; Dass, Anuli; Srivastava, Smriti

doi:10.1007/s41870-019-00382-y

Cited by 14 publications

(5 citation statements)

References 23 publications

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“…Let us use the symbols 𝑥 for the hidden layer's input, c for the radial basis function's center, and for the width. The hidden layer's 𝑗 𝑡ℎ neuron, designated as ℎ 𝑗 , is activated as follows [27]:…”

Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Ali,

Alakkari,

Abotaleb

et al. 2024

Mesopotamian Journal of Big Data

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In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

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Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Ali,

Alakkari,

Abotaleb

et al. 2024

Mesopotamian Journal of Big Data

View full text Add to dashboard Cite

show abstract

“…There exists a good number of ML and DL techniques handling TS forecasting problems. Kumar et al [8] proposed a prediction model based on Radial Basis Function Network (RBFN). They developed a learning algorithm based on back propagated technique to tune the parameters of the network.…”

Section: Related Workmentioning

confidence: 99%

Bogaba: A Novel Hybrid Deep Learning Model for Time Series Forecasting Using Sentiment Analysis

Mandal¹,

Chakraborty²,

Mahato³

et al. 2023

INDJCSE

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In this work, a multi-layer hybrid model named BoGaBa (formed by Boosting, Gated RNN and Bagging) has been proposed which takes the advantage of both of the Machine Learning(ML) and Deep Learning(DL) techniques and uses Sentiment Analysis to perform future prediction based on Time-Series(TS) data. An amalgamation of Support Vector Regression(SVR), Multiple Linear Regression(MLR) and Ridge Regression from the ML domain and Gated Recurrent Unit(GRU) Networks from the DL domain has been made to form a single hybrid model that makes it more robust in predicting patterns hidden in historical TS data. There exist some TS problems, in which future values are influenced by user sentiments. The proposed work analyses such sentiments based on NEWS, published in four different mediums, quantified as numerical data and predicts future values. To justify the usefulness of the proposedmodel, it is tested with the popular TS problem of Stock Price Forecasting to predict next-day Adjusted Closing prices of S&P 500 Stock Index. The results of the proposed work have been compared with two techniques proposed by Xinyi Li et al. [1] and Yang Li et al [2]; both worked on the same data set. The proposed model reduces Mean Square Error(MSE) by 15% and 9.3% respectively from the works under comparison and the Accuracy Score is improved by 30% than Yang Li et al [2].

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“…𝐹1 − 𝑠𝑐𝑜𝑟𝑒 = 2 * 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 * 𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙 (20) The F1 score should be used when accuracy or recall are more important than the other because it includes both. Avoiding false negatives may be more important than false positives.…”

Section: 𝑅𝑒𝑐𝑎𝑙𝑙 =mentioning

confidence: 99%

Experimental Analysis of Heart Disease Prediction Using Machine Learning with Emphasis on Hyper Parameter Tuning and Recursive Feature Elimination

2023

IJIES

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Cardiovascular disease is any illness that makes the heart work less well. Researchers are working on making smart systems that can correctly detect heart conditions from electronic health data using machine learning algorithms. This is because heart conditions can be very serious. In this study, data from patients and key clinical factors are used to identify cardiovascular disease using machine learning. The main goal of the suggested model is to improve the accuracy and reliability of predicting cardiac disease by focusing on parameter tuning, ensemble methods, and recursive feature removal approaches. Our methods for making predictions included logistic regression, decision trees, K-nearest neighbour (KNN), support vector machine (SVM), naive bayes (NB) machine learning (ML) approaches, ensemble technique approaches, and artificial neural networks (ANN) with stress on regularisation. Compared to the other ways, it was found that using a KNN model gave the most accurate results for the model. A number of factors, such as accuracy, precision, memory, and F1-score, were used to judge the models. The KNN model is the most accurate, at 97.8%.

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A novel approach to predict stock market price using radial basis function network

Cited by 14 publications

References 23 publications

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Bogaba: A Novel Hybrid Deep Learning Model for Time Series Forecasting Using Sentiment Analysis

Experimental Analysis of Heart Disease Prediction Using Machine Learning with Emphasis on Hyper Parameter Tuning and Recursive Feature Elimination

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