Fake News, Investor Attention, and Market Reaction

2020 ◽  
Author(s):  
Jonathan Clarke ◽  
Hailiang Chen ◽  
Ding Du ◽  
Yu Jeffrey Hu
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Control Sample ◽  
Machine Learning Algorithms ◽  
Fake News ◽  
Investor Attention ◽  
Price Reaction ◽  
Stock Price Reaction

Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? The authors use the SEC crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, the authors find that fake news stories generate significantly more attention than a control sample of legitimate articles. The authors find no evidence that article commenters can detect fake news, and they find that Seeking Alpha editors have only modest ability to detect fake news. However, the authors implement several well-known machine learning algorithms based on linguistic characteristics and show that machine learning algorithms can successfully identify fake news. In addition, the stock market appears to price fake news correctly. While abnormal trading volume increases around the release of fake news, the increase is less than that observed for legitimate news. The stock price reaction to fake news is discounted when compared with legitimate news articles.

scholarly journals Stock Price Prediction

2019 ◽  
Vol 9 (2S) ◽  
pp. 425-429
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Company Stock ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Technical And Fundamental Analysis ◽  
Future Value

Stock trading is a very crucial activity in the world of Finance and is a supporting structure for many companies. Predicting the future value of a stock is the main goal of stock price prediction project. In this paper, we have used machine learning algorithms to predict future stock prices of a company. Stock prediction by the stock brokers is mainly done using the time series or the technical and fundamental analysis but as these techniques are very unreliable and limited, we propose making use of intelligent techniques such as machine learning. Python is a programming language which can be used to implement machine learning algorithms with its numerous inbuilt libraries. We propose an approach that uses machine learning algorithms and will be trained on the historical stock data that is available and gain intelligence, later it uses the knowledge acquired for predicting the stock prices accurately. Random Forest Regression is one of the machine learning technique that is used for stock price prediction for small and large capitalizations also in different markets employing both up-to-minute and daily frequencies.

Tebyan: Fake News Detection System (Preprint)

2021 ◽  
Author(s):  
Lamya Alderywsh ◽  
Aseel Aldawood ◽  
Ashwag Alasmari ◽  
Farah Aldeijy ◽  
Ghadah Alqubisy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arab World ◽  
Detection System ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Fake News ◽  
Typical Type ◽  
Performance Results

BACKGROUND There is a serious threat from fake news spreading in technologically advanced societies, including those in the Arab world, via deceptive machine-generated text. In the last decade, Arabic fake news identification has gained increased attention, and numerous detection approaches have revealed some ability to find fake news throughout various data sources. Nevertheless, many existing approaches overlook recent advancements in fake news detection, explicitly to incorporate machine learning algorithms system. OBJECTIVE Tebyan project aims to address the problem of fake news by developing a fake news detection system that employs machine learning algorithms to detect whether the news is fake or real in the context of Arab world. METHODS The project went through numerous phases using an iterative methodology to develop the system. This study analysis incorporated numerous stages using an iterative method to develop the system of misinformation and contextualize fake news regarding society's information. It consists of implementing the machine learning algorithms system using Python to collect genuine and fake news datasets. The study also assesses how information-exchanging behaviors can minimize and find the optimal source of authentication of the emergent news through system testing approaches. RESULTS The study revealed that the main deliverable of this project is the Tebyan system in the community, which allows the user to ensure the credibility of news in Arabic newspapers. It showed that the SVM classifier, on average, exhibited the highest performance results, resulting in 90% in every performance measure of sources. Moreover, the results indicate the second-best algorithm is the linear SVC since it resulted in 90% in performance measure with the societies' typical type of fake information. CONCLUSIONS The study concludes that conducting a system with machine learning algorithms using Python programming language allows the rapid measures of the users' perception to comment and rate the credibility result and subscribing to news email services.

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

Semi-strong Form Market Efficiency in Stock Markets with Low Levels of Trading Activity: Evidence from Stock Price Reaction to Major National and International Events

2017 ◽  
Vol 18 (6) ◽  
pp. 1447-1464
Author(s):  
C. Justin Robinson ◽  
Prosper Bangwayo-Skeete
Keyword(s):  
Market Efficiency ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Strong Form ◽  
Trading Activity ◽  
Price Reaction ◽  
Stock Price Reaction ◽  
Low Levels ◽  
International Events

This study uses the event study methodology to explore semi-strong form market efficiency in the context of low levels of trading activity. Covering six frontier stock markets, it investigates stock price reaction to major national news events that include natural disasters, parliamentary elections and credit rating reviews and the international events such as international terrorist incidents, major events surrounding the 2007/2008 sub-prime mortgage crisis and the United Kingdom’s referendum on membership in the European Union (Brexit). The results of the event studies, which feature a correction for low levels of trading activity, show that in sharp contrast with more actively traded markets, stock prices on markets with relatively low levels of trading activity did not react to the vast majority of major news events, and only tended to react to rare events with major consequences. Usually, where stock prices reacted to a news event, the reaction was significantly delayed, which is inconsistent with semi-strong form market efficiency. The implication is that low levels of trading activity may be associated with semi-strong form inefficiency, and stock prices in such markets may not fully reflect all relevant available information, and may be of limited value to a variety of decision-makers.

scholarly journals Stock price reaction to cash dividend announcements in Vietnam

2017 ◽  
Vol 24 (02) ◽  
pp. 74-89
Author(s):  
Truong Nguyen Xuan ◽  
Huong Dao Mai ◽  
Anh Nguyen Thi Van
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Cash Dividend ◽  
Share Prices ◽  
Price Reaction ◽  
Cumulative Abnormal Returns ◽  
Stock Price Reaction ◽  
The Uk

This study attempts to investigate the stock price reaction to divi-dend announcements using data of Vietnamese listed firms on Hochiminh Stock Exchange (HOSE). Standard event study meth-odology has been employed on a sample of 198 cash dividend an-nouncements made in 2011. The results show that stock prices react significantly and positively to the announcements of cash dividends, including both dividend increasing and dividend decreasing events. It is also plausible that cumulative abnormal returns exhibit an in-creasing trend before announcement yet a decreasing trend after announcement dates. More specifically, we find positively signifi-cant cumulative abnormal returns of around 1.03% on announce-ment dates; other larger windows also demonstrate positive abnor-mal returns of around 1.3%. In addition, cash dividends have differ-ent effects on share prices of firms from different industries. These results support the signaling hypothesis and are also consistent with prior findings of empirical research done on more developed mar-kets, i.e. the US and the UK.

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

scholarly journals ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES

2021 ◽  
pp. 94-100
Author(s):  
V. Serbin ◽  
U. Zhenisserov
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Price Patterns

Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.

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