Predicting Stock Price Movement Using Financial News Sentiment

2021 ◽  
pp. 503-517
Author(s):  
Jiaying Gong ◽  
Bradley Paye ◽  
Gregory Kadlec ◽  
Hoda Eldardiry
Keyword(s):  
Stock Price ◽  
Price Movement ◽  
Financial News ◽  
News Sentiment

scholarly journals Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

2020 ◽  
Author(s):  
Wei Li ◽  
Ruihan Bao ◽  
Keiko Harimoto ◽  
Deli Chen ◽  
Jingjing Xu ◽  
...  
Keyword(s):  
Correlation Matrix ◽  
Stock Price ◽  
Daily Basis ◽  
Market Impact ◽  
Convolutional Network ◽  
Price Movement ◽  
Extensive Experiment ◽  
Exact Time ◽  
Financial News ◽  
Movement Prediction

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.

Predicting Stock Price Movement from Financial News Articles

2011 ◽  
pp. 96-128
Author(s):  
Robert P. Schumaker ◽  
Hsinchun Chen
Keyword(s):  
Mathematical Models ◽  
Stock Prices ◽  
Stock Price ◽  
Financial Data ◽  
Price Movement ◽  
Computational Approaches ◽  
Stock Recommendations ◽  
Fund Managers ◽  
Financial News ◽  
Computational Systems

However, using computational approaches to predict stock prices using financial data is not unique. In recent years, interest has increased in Quantitative funds, or Quants, that automatically sift through numeric financial data and issue stock recommendations. While these systems are based on proprietary technology, they do differ in the amount of trading control they have, ranging from simple stock recommenders to trade executors. Using historical market data and complex mathematical models, these methods are constrained to make assessments within the scope of existing information. This weakness means that they are unable to react to unexpected events falling outside of historical norms. However, this disadvantage has not stopped fund managers at Federated, Janus, Schwab, and Vanguard from trusting billions of dollars of assets to the decisions of these computational systems.

scholarly journals Sentiment correlation in financial news networks and associated market movements

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xingchen Wan ◽  
Jie Yang ◽  
Slavi Marinov ◽  
Jan-Peter Calliess ◽  
Stefan Zohren ◽  
...  
Keyword(s):  
Market News ◽  
Stock Price ◽  
Global Market ◽  
Financial Network ◽  
Financial News ◽  
Or Groups ◽  
News Networks ◽  
And Performance ◽  
Media Sentiment ◽  
News Sentiment

AbstractIn an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period of 7 years. We investigate the propagation of such sentiment in company networks and evaluate the associated market movements in terms of stock price and volatility. Our results suggest that, in certain sectors, strong media sentiment towards one company may indicate a significant change in media sentiment towards related companies measured as neighbours in a financial network constructed from news co-occurrence. Furthermore, there exists a weak but statistically significant association between strong media sentiment and abnormal market return as well as volatility. Such an association is more significant at the level of individual companies, but nevertheless remains visible at the level of sectors or groups of companies.

scholarly journals Predicting stock price movement using effective Thai financial probabilistic lexicon

2021 ◽  
Vol 11 (5) ◽  
pp. 4313
Author(s):  
Surinthip Sakphoowadon ◽  
Nawaporn Wisitpongphan ◽  
Choochart Haruechaiyasak
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Numerical Data ◽  
Prediction Performance ◽  
Research Papers ◽  
Related Factors ◽  
Price Movement ◽  
Financial News ◽  
Proposed Model

Predicting stock price fluctuation during critical events remains a big challenge for many researchers because the stock market is extremely vulnerable and sensitive during such time. Most existing works rely on various numerical data of related factors which can impact the stock price direction. However, very few research papers analyzed the effect of information appearing in financial news articles. In this paper, a novel probabilistic lexicon based stock market prediction (PLSP) algorithm is proposed to predict the direction of stock price movement. Our approach used the proposed thai financial probabilistic lexicon (ThaiFinLex) derived from Thai financial news and stock market historical prices. The PLSP development consists of three steps. Firstly, we constructed ThaiFinLex by extracting event terms from news articles and calculating their associated probability of increasing/decreasing values of stock prices. Then, event terms with bad prediction performance were filtered out. Finally, the stock price directions were predicted using the PLSP and the remaining effective event terms. Our results indicated that the proposed model can be used for predicting stock price movement. The performance is as high as 83.33% when PLSP is used to predict stocks from the financial sector.

scholarly journals Stock Price Forecasting with Deep Learning: A Comparative Study

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1441
Author(s):  
Tej Bahadur Shahi ◽  
Ashish Shrestha ◽  
Arjun Neupane ◽  
William Guo
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Comparative Study ◽  
Stock Price ◽  
Short Term Memory ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Financial News ◽  
Stock Market Forecasting ◽  
News Sentiment

The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the cooperative deep-learning architecture proposed by us. Our experiments show that: (1) both LSTM and GRU are circumstantial in stock forecasting if only the stock market features are used; (2) the performance of LSTM and GRU for stock price forecasting can be significantly improved by incorporating the financial news sentiments with the stock features as the input; (3) both the LSTM-News and GRU-News models are able to produce better forecasting in stock price equally; (4) the cooperative deep-learning architecture proposed in this study could be modified as an expert system incorporating both the LSTM-News and GRU-News models to recommend the best possible forecasting whichever model can produce dynamically.

scholarly journals The Geometric Brownian Motion of Indosat Telecommunications Daily Stock Price During the Covid-19 Pandemic in Indonesia

2021 ◽  
Vol 2084 (1) ◽  
pp. 012012
Author(s):  
Tiara Shofi Edriani ◽  
Udjianna Sekteria Pasaribu ◽  
Yuli Sri Afrianti ◽  
Ni Nyoman Wahyu Astute
Keyword(s):  
Brownian Motion ◽  
Stock Price ◽  
Service Providers ◽  
Main Idea ◽  
Geometric Brownian Motion ◽  
Government Policies ◽  
Percentage Error ◽  
Price Movement ◽  
Data Movement ◽  
Total Data

Abstract One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as R t 1 * , t 1 * = 1 , 2 , 3 , … , 40 . The main idea of this study is adding datum one by one as much as 10% – 15% of the total data R t 1 * , starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in R t 1 * , so t 1 * = − 4. − 3 , − 2 , … , 40 and form five other data groups, R t 2 * , … , R t 6 * . Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, R t 6 * is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic.

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

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