Deep learning for stock prediction using numerical and textual information

Akita, Ryo; Yoshihara, Akira; Matsubara, Takashi; Uehara, Kuniaki

doi:10.1109/icis.2016.7550882

Cited by 292 publications

(173 citation statements)

References 12 publications

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“…al. [103] used textual information and stock prices through Paragraph Vector + LSTM for forecasting the prices and the comparisons were provided with different classifiers. Ozbayoglu [104] used technical indicators along with the stock data on a Jordan-Elman network for price prediction.…”

Section: Stock Price Forecastingmentioning

confidence: 99%

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

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

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: Stock Price Forecastingmentioning

confidence: 99%

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

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

View full text Add to dashboard Cite

show abstract

“…Automobile The difference between the predicted and actual price of the stock for a weekly time period is given below. The accuracy of prediction is measured for Last 1 year for every month beginning and end price and the result is below From the results, it can been that the accuracy of the proposed solution is better than solution [7]. The reason for higher accuracy is consideration of multiple features and parameter tuning to select the best features and window sampling period.…”

Section: Performance Analysismentioning

confidence: 99%

“…Based on this knowledge they formalized Rules Based Short Term Reversal strategy. In [7] authors have proposed an innovative application of Paragraph Vector, LSTM and Deep Learning models to time series forecasting of stock prices. Traders emotions about the market is driven by the news hence they make decision based on the factors such as Price Earnings (PE) ratio, consumer price index and other political or financial news.…”

Section: Guruprasad S H Chandramoulimentioning

confidence: 99%

See 1 more Smart Citation

CNN based Stock Market Prediction

Guruprasad¹,

Chandramouli²

2020

IJEAT

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Abstract: Indian Stock market is highly dynamic and especially after globalization stock market modeling has become even more complex due to influence of multiple parameters. In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. This work models the dynamics of stock market in terms of Convolutional Neural Networks and multiple parametersimpacting the stock trend. The proposed solution is implemented for Indian stock market for stocks in different sectors to prove its prediction accuracy.

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“…Hogenboom et al [22] give an overview of event extraction methods. Akita et al [3] use Paragraph Vector to convert newspaper articles into distributed representations and apply LSTM to model the temporal effects of past events on opening prices of stocks in Tokyo Stock Exchange. Nguyen et al [32] formulate a temporal sentiment index function to extract significant events and then analyzed the corresponding blog posts using topic modeling to understand the contents.…”

Section: Related Workmentioning

confidence: 99%

Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

Zhang

Wang

et al. 2018

Knowl Inf Syst

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Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the fluctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods.

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Deep learning for stock prediction using numerical and textual information

Cited by 292 publications

References 12 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

CNN based Stock Market Prediction

Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

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