Deep learning for stock market prediction from financial news articles

Vargas, Manuel; Lima, Beatriz Souza Leite Pires de; Evsukoff, Alexandre G.

doi:10.1109/civemsa.2017.7995302

Cited by 167 publications

(107 citation statements)

References 23 publications

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“…Other studies were aimed at predicting stock prices given textual information from the financial news [44][45][46]. For instance, Akita et al [44] converted newspaper articles into distributed representations via paragraph vectors and modeled the temporal effects of past events with an LSTM on predicting opening prices of stocks on the Tokyo Stock Exchange.…”

Section: Feature Selection For Stock Price Predictionmentioning

confidence: 99%

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Nguyen

Yoon

2019

Applied Sciences

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Stock price prediction has always been an important application in time series predictions. Recently, deep neural networks have been employed extensively for financial time series tasks. The network typically requires a large amount of training samples to achieve high accuracy. However, in the stock market, the number of data points collected on a daily basis is limited in one year, which leads to insufficient training samples and accordingly results in an overfitting problem. Moreover, predicting stock price movement is affected by various factors in the stock market. Therefore, choosing appropriate input features for prediction models should be taken into account. To address these problems, this paper proposes a novel framework, named deep transfer with related stock information (DTRSI), which takes advantage of a deep neural network and transfer learning. First, a base model using long short-term memory (LSTM) cells is pre-trained based on a large amount of data, which are obtained from a number of different stocks, to optimize initial training parameters. Second, the base model is fine-tuned by using a small amount data from a target stock and different types of input features (constructed based on the relationship between stocks) in order to enhance performance. Experiments are conducted with data from top-five companies in the Korean market and the United States (US) market from 2012 to 2018 in terms of the highest market capitalization. Experimental results demonstrate the effectiveness of transfer learning and using stock relationship information in helping to improve model performance, and the proposed approach shows remarkable performance (compared to other baselines) in terms of prediction accuracy.

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Section: Feature Selection For Stock Price Predictionmentioning

confidence: 99%

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Nguyen

Yoon

2019

Applied Sciences

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“…Configuration of GRU network used in this research work was 14×50×50×50×50×1. Vargas et al (2017) proposed an RCNN model. The results showed that sentence embedding is better than word embedding, RCNN is better than CNN, and the influence of technical indicators leads to better performance.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange

Saud¹,

Shakya²

2019

J. Inst. Sci. Tech.

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The stock price is the cost of purchasing a security or stock in a stock exchange. The stock price prediction has been the aim of investors since the beginning of the stock market. It is the act of forecasting the future price of a company's stock. Nowadays, deep learning techniques are widely used for identifying the stock trends from large amounts of past data. This research has experimented two big and robust commercial banks listed in the Nepal Stock Exchange (NEPSE) and compared stock price prediction performance of GRU with three widely used gradient descent optimization techniques: Momentum, RMSProp, and Adam. GRU with Adam is more accurate and consistent approach for predicting stock prices from the present study.

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“…Unfortunately only few studies have undertaken this solution. Vargas et al [26] employed Deep learning algorithm for stock market prediction with the help of news articles. This model utilizes Convolutional Neural Networks (CNN) and RNN deep learning algorithms to improve the stock prediction.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

Bhuvaneshwari,

Beena

2020

IJEAT

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Analyzing and forecasting the future trends in stock market is challenging due to the ever increasing size of stock data. Modern techniques extract the stock indicators from the web data to forecast the stock movements. However, most previous studies were based on single source of data for extracting these indicators. This might not be effective in obtaining all the possible diverse factors that influence the market movements. Multi-source data has been rarely applied for stock prediction and even those techniques have limitations in handling larger data. In an attempt to utilize multi-source data more effectively for extracting stock indicators and improve the forecasting accuracy of stock movements, this paper developed a stock market forecasting model using Tolerance based Multi-Agent Deep Reinforcement Learning (TMA-DRL) model. The TMA-DRL model effectively combines the quantitative stock data with the indicators i.e. the events extracted from news data and sentiments extracted from tweets. This forecasting model utilizes Random forests to extract the twitter opinions and Restricted Boltzmann Machine (RBM) for event extraction from news data. Combining these indicators, the TMA-DRL model leads to improved data learning and provides highly accurate prediction of future stock trends. Datasets for evaluation were collected from three sources namely Twitter, Market News and Stock exchange, for 12 months period. Evaluation results illustrate the effectiveness of the proposed TMA-DRL stock market forecasting model which makes predictions with high accuracy and less time complexity.

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Deep learning for stock market prediction from financial news articles

Cited by 167 publications

References 23 publications

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

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