Leveraging social media news to predict stock index movement using RNN-boost

Chen, Weiling; Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu-Sung

doi:10.1016/j.datak.2018.08.003

Cited by 84 publications

(67 citation statements)

References 23 publications

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“…The authors of [130] used RNN models, Recurrent Computationally Efficient Functional Link Neural Network (RCEFLANN) and Functional Link Neural network (FLANN), with their weights optimized using various EA like Particle Swarm Optimization (PSO), HMRPSO and PSO for time series forecasting. The authors of [154] used social media news to predict the index price and index direction with RNN-Boost with Latent Dirichlet Allocation (LDA) features.…”

Section: Index Forecastingmentioning

confidence: 99%

“…The results of the proposed method were remarkably stationary. The authors of [154] used social media news, LDA features and RNN model to predict the trend of the index price. The authors of [218] proposed a novel method that used expert recommendations (Buy, Hold or Sell), emsemble of GRU and LSTM to predict the trend of the stocks prices.…”

Section: Trend Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

720

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Index Forecastingmentioning

confidence: 99%

Section: Trend Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

720

294

View full text Add to dashboard Cite

show abstract

“…Chen et al [198] focused on Sina Weibo, a Chinese social media platform, to predict stock market volatilities. Moreover, they used the deep recurrent neural network [198].…”

Section: Other Social Media Platformsmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

182

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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“…were used by many researchers for predicting stock prices (Henrique et al, 2018;Hu et al, 2013;Jabin, 2014;Kumar et al, 2011). However, due to their inability to remembering context, recurrent neural networks became popular for predicting stock price (Chen et al, 2018;Li et al, 2019). Again, due to the vanishing/exploding gradient problem associated with recurrent neural networks, long short-term memory (LSTM) and gated recurrent unit (GRU) networks became a state-of-art model for predicting stock prices (Lu et al, 2020;Moghar & Hamiche, 2020;Shen et al, 2018;Zhao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction

Saud

Shakya

2021

J. Inst. Sci. Tech.

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Nowadays stock price prediction is an active area of research among machine learning researchers. One of the main problems with machine learning models is overfitting. Regularization techniques are widely used approaches to avoid over-fitted models. L2 regularization is one of the most popular and widely used regularization techniques. Regularization hyperparameter (ʎ) is one key parameter to be optimized for a well-generalized machine learning model. Hyperparameters can’t be learned by machine learning models during the learning process. We need to find their optimal value through experiments. This research work analyzed the L2 regularization hyperparameter used with a gated recurrent unit (GRU) network for stock price prediction. We experimented with five stocks from the Nepal Stock Exchange (NEPSE) and observed that stock price can be predicted with lower mean squared errors (MSEs) when the value of ʎ was around 0.0005. Therefore, this research paper recommended using ʎ=0.0005 with L2 regularization for stock price prediction.

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Leveraging social media news to predict stock index movement using RNN-boost

Cited by 84 publications

References 23 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Text Mining in Big Data Analytics

Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction

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