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 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 A financial econometric analysis of E-Commerce stock price predictability

2012 ◽  
Vol 9 (2) ◽  
pp. 59
Author(s):  
Kok-Boon Oh ◽  
Sardar M N Islam
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Financial Data ◽  
Policy Implications ◽  
Australian Study ◽  
Equity Prices ◽  
Contentious Issue ◽  
Long Period ◽  
Equity Value ◽  
Stationarity Tests

The predictability of stock price changes has been a contentious issue in finance for a long period of time. Using the Australian e-commerce financial data for determining the equity value of e-commerce firms, this paper provides an empirical analysis of the issue of predictability of stock prices. The factors contributing to the predictability of equity prices in the e-commerce markets are identified, analyzed and the issues and implications are discussed and explained. This paper presents new approaches to econometric specification, estimation and testing in relation to e-commerce stock predictability including stationarity tests, co-integration modeling and analyses. The policy implications of the empirical findings are stated. The empirical findings of the Australian study are extrapolated and inferences are made for other countries.

scholarly journals Applying LSTM for Stock Price Prediction with Sentiment Analysis

2021 ◽  
Author(s):  
Alexandre Heiden ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Market Volatility ◽  
Analysis Model ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Social Media Platforms

Financial news has been proven to be valuable source of information for the evaluation of stock market volatility. Most of the attention has been given to social media platforms, while news from vehicles such as newspapers are not as widely explored. Newspapers provide, although in a smaller volume, more reliable information than social media platforms. In this context, this research aims to examine the influence of financial news within the stock price prediction problem, by using the VADER sentiment analysis model to process the news and feed the sentiments as a feature into a LSTM-based stock price prediction model, along with the historical data of the assets. Experiments indicate that the model has better results when the news’ sentiments are considered, and the model demonstrates potential to accurately predict stock prices up to around 60 days into the future.

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.

scholarly journals Analisis Teknikal Harga Saham Waskita Karya Tahun 2017 dengan Metode ARIMA

2018 ◽  
Vol 7 (1) ◽  
pp. 122-126
Author(s):  
Wahyuni Windasari
Keyword(s):  
Key Words ◽  
Stock Prices ◽  
Stock Price ◽  
Historical Data ◽  
Arima Model ◽  
Security Analysis ◽  
Technical Analysis ◽  
Price Movement

AbstractAs an investor needs to do an analysis before making a decision either in selling or buyingshares. Security analysis consist of two types of analysis, namely tecnical analysis andfundamental analysis. Technical analysis to test wheater historical data will predict stock pricesas a consideration to buy or sell an investment's instrument. One type of technical analysis isthe ARIMA method. In this research uses daily stock price of WSKT Tbk during 1 Januari–10Oktober 2017 to predict stock prices the few days. The best ARIMA model to describe WSKTstock price movement is MA(4), with MAE predict data is 480.25.Key words : forecasting, ARIMA, technical analysis, stock prices.

SOCIAL NETWORK NEWS SENTIMENTS AND STOCK PRICE MOVEMENT: A CORRELATION ANALYSIS

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Anupong Sukprasert ◽  
Kasturi Kanchymalay ◽  
Naomie Salim ◽  
Atif Khan
Keyword(s):  
Real Time ◽  
Stock Prices ◽  
Stock Price ◽  
R Package ◽  
Online News ◽  
Streaming Data ◽  
Price Movement ◽  
News Sources ◽  
R Programming ◽  
The Relationship

The stock market prediction is one of the most important issues extensively investigated in the existing academic literatures. Researchers have discovered that real–time news has much bearing on the movement of stock prices. Analysts now have to deal with vast amounts of real time, unstructured streaming data due to the advent of electronic and online news sources. This paper aims to investigate the relationship between online news and actual stock price movement.  R programming together with R package are applied to capture and analyze the online news data from Yahoo Financial. The data are plotted into graphs to analyze the relationship between the two variables. In addition, to ensure the levels of the relationship, the Pearson’s correlation and Spearman’s Rank are applied to test whether there is a statistical association between these two variables. This initial analysis of dynamic online news based on sentimental words is relatively constructive.

STOCK EVALUATION USING FUZZY LOGIC

2001 ◽  
Vol 04 (04) ◽  
pp. 585-602 ◽  
Author(s):  
HUSSEIN DOURRA ◽  
PEPE SIY
Keyword(s):  
Fuzzy Logic ◽  
Human Behavior ◽  
Stock Prices ◽  
Stock Price ◽  
Evaluation Method ◽  
Technical Analysis ◽  
Price Movement ◽  
Analysis Theory ◽  
System Output ◽  
Logic System

We use fuzzy logic engineering tools to detect human behavior in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. This system can create an optimum computerized model to evaluate stock price movement. This method relies on human psychology to predict human behavior when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis field. The flexibility of the system is also demonstrated.

scholarly journals Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 800
Author(s):  
Xiaodong Zhang ◽  
Suhui Liu ◽  
Xin Zheng
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Data Representation ◽  
Attention Mechanism ◽  
Supplementary Information ◽  
Series Data ◽  
Price Movement ◽  
Factorization Machine

The prediction of stock price movement is a popular area of research in academic and industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock prices. In this paper, we constructed a convolutional neural network model based on a deep factorization machine and attention mechanism (FA-CNN) to improve the prediction accuracy of stock price movement via enhanced feature learning. Unlike most previous studies, which focus only on the temporal features of financial time series data, our model also extracts intraday interactions among input features. Further, in data representation, we used the sub-industry index as supplementary information for the current state of the stock, since there exists stock price co-movement between individual stocks and their industry index. The experiments were carried on the individual stocks in three industries. The results showed that the additional inputs of (a) the intraday interactions among input features and (b) the sub-industry index information effectively improved the prediction accuracy. The highest prediction accuracy of the proposed FA-CNN model is 64.81%. It is 7.38% higher than that of traditional LSTM, and 3.71% higher than that of the model without sub-industry index as additional input features.

scholarly journals Technical Analysis and Malaysian Banking Sector during COVID-19 Pandemic

2020 ◽  
Vol 4 (1) ◽  
pp. 41-46
Author(s):  
Kelvin Yong Ming Lee
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Banking Sector ◽  
Moving Average ◽  
Investment Decision ◽  
Movement Control ◽  
Technical Analysis ◽  
Price Movement ◽  
Technical Indicator ◽  
Control Order

The announcements of Movement Control Order and Loan Moratorium caused a significant impact on the stock prices of Malaysian banks during the COVID-19 pandemic. This study aims to investigate the effectiveness of technical analysis in predicting the stock price movement and the ability of the technical analysis in generating returns. In doing so, six moving average rules used as the proxy of technical analysis and tested in this study. Majority of the MA rules shown positive returns before the various announcements dates. Specifically, this study revealed that MA rules of (2,5) and (2,10) were among the best performing MA rules during the COVID-19 pandemic. This study also recommends the investors to use the signals emitted by the technical indicator as the reference for their investment decision in the banks’ stock.

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