Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis

Manjunath, C R; Christ, Emanuel; Marimuthu, Balamurugan; Ghosh, Bikramaditya

doi:10.22266/ijies2021.1031.13

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…The reset gate had the task of forgetting information from the previous state. The update gate had to control how much information was added to the current state [22]. GRU Implementation is shown in (8) to (11).…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

Haryono,

Sarno,

Sungkono

2024

IJECE

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In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.

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Section: Gated Recurrent Unitmentioning

confidence: 99%

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

Haryono,

Sarno,

Sungkono

2024

IJECE

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“…GRU has different means than long short-term memory (LSTM), including the simpler architecture by eliminating forget gates to improve computational efficiency [28]. The update and reset gates, the primary gates of GRU, are presented to analyze the data flow within the unit, authorizing it to keep and discard data adaptively over various periods [29].…”

Section: Grumentioning

confidence: 99%

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Aulia,

Sarno,

Hidayati

et al. 2024

IJEECS

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Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.

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“…Technical indicators (TI) are well-known for predicting the equity market and these indicators are simple mathematical models based on open and closing prices. This article applied eight technical indicators based on the previous studies [25] and informed them about deterministic drift values [19] before inputting them into the proposed hybrid prediction model. These TI are illustrated in Table 1.…”

Section: Research Data and Data Labelingmentioning

confidence: 99%

Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance

Manjunath

Marimuthu

Ghosh

2023

IJECE

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<span lang="EN-US">Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifer (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.</span>

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Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis

Cited by 7 publications

References 32 publications

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance

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