ND-SMPF: A Noisy Deep Neural Network Fusion Framework for Stock Price Movement Prediction

Ronaghi, Farnoush; Salimibeni, Mohammad; Naderkhani, Farnoosh; Mohammadi, Arash

doi:10.23919/fusion45008.2020.9190453

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…DNN [7] is a Deep Neural Network prediction work in which researchers used five models and took different kinds of data in all five models for HAN'S model they took News Information from Twitter and in this case, the accuracy was 47.8%. They have used the ND-SMPF Model and took historical price as well as Twitter data and because of this, they have improved the accuracy by 58.63%.…”

Section: Related Workmentioning

confidence: 99%

“…A Deep Learning-based Long Short-Term Memory (LSTM) Algorithm [8] In some research, the researchers used the Linear Regression Model, the same can be applied when the repetition of errors or error variance is constant [9]. When stock / Index price data is nonlinear, and the error frequency has no variance across the time XGBoost [10] and DNN [7] are more useful methods i.e., for the nonlinear data. XGBoost is a sensible work for stock price forecasting with accuracy, feature importance analysis, and the ability to handle complex feature-outcome relationships.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

Sisodia,

Nayak,

Boghey

2024

J. Appl. Artif. Intell.

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As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

Sisodia,

Nayak,

Boghey

2024

J. Appl. Artif. Intell.

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“…However, stock price data is nonlinear, and the error term is not constant over time. XGBoost [13] and DNN [44] are more popular methods when the type of data is nonlinear. Therefore, we have considered XGBoost and DNN methods.…”

Section: E Stock Crisis Prediction Using Xgboost Regression Techniquementioning

confidence: 99%

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

Naik

Mohan

2021

IEEE Access

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A stock market crash is a drop in stock prices more than 10% across the major indices. Stock crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell-offs are due to various reasons such as company earnings, geopolitical tension, financial crisis, and pandemic situations. Crisis prediction is a challenging task for researchers and investors. We proposed a stock crisis prediction model based on the Hybrid Feature Selection (HFS) technique. First, we proposed the HFS algorithm to removes the irrelevant financial parameters features of stock. The second is the Naive Bayes method is considered to classify the strong fundamental stock. The third is we have used the Relative Strength Index (RSI) method to find a bubble in stock price. The fourth is we have used moving average statistics to identify the crisis point in stock prices. The fifth is stock crisis prediction based on Extreme Gradient Boosting (XGBoost) and Deep Neural Network(DNN) regression method. The performance of the model is evaluated based on Mean Squared Error(MSE), Mean Absolute Error(MAE), and Root Mean Square Error(RMSE). HFS based XGBoost method was performed better than HFS based DNN method for predicting the stock crisis. The experiments considered the Indian datasets to carried out the task. In the future, the researchers can explore other technical indicators to predict the crisis point. There is more scope to improve and finetune the XGBoost method with a different optimizer.

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“…Based on the constructed COVID19 PRIMO dataset, the paper proposes a data-driven (DNNbased) COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP) that uses information fusion to combine COVID-19 related Twitter data with extended horizon market historical data. More specifically, in contrary to the existing data-driven movement prediction models, where a single DL model is used (Ronaghi et al, 2017), the proposed COVID19-HPSMP is a hybrid framework with two parallel paths, i.e., one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM). The former path is incorporated within the COVID19-HPSMP framework to extract temporal features, while the latter path is used to extract spatial features.…”

Section: Contributionsmentioning

confidence: 99%

“…Literature Review: Stock market movement prediction is a key and challenging problem in financial econometrics as such has attracted extensive recent research focus (Frankel, 1995;Ronaghi et al, 2017;Mohammadi et al, 2017;Edwards et al, 2007;Bollen & H. Mao, 2011;Jiang, 2020;Hu et al, 2019;Koshiyama et al, 2020;Schumaker & Chen, 2009). It is widely acknowledged that investors need high-quality data to make informed and accurate decisions.…”

Section: Introductionmentioning

confidence: 99%

COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction

Ronaghi¹,

Salimibeni²,

Naderkhani³

et al. 2021

Preprint

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The novel of coronavirus has suddenly and abruptly changed the world as we knew at the start of the 3 rd decade of the 21 st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion centre that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

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ND-SMPF: A Noisy Deep Neural Network Fusion Framework for Stock Price Movement Prediction

Cited by 5 publications

References 27 publications

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction

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