Predicting the Effects of News Sentiments on the Stock Market

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.Comment: 4 page

Application of neuro-fuzzy methods for stock market forecasting: a systematic review

Predicting stock prices is a challenging task owing to the market's chaos and uncertainty. Methods based on traditional approaches are unable to provide a solution to the market predictability issue. Thus, contemporary models using accurate neuro-fuzzy systems are found to be the most effective approach to tackling the problem. However, the existing literature lacks a detailed survey of the application of neuro-fuzzy techniques for stock market prediction. This paper presents a systematic literature review of the use of neuro-fuzzy systems for predicting stock market prices and trends.  On this basis, articles issued in various reputed international journals from 2000 to July 2022 were examined, 11 duplicates and 4 non-exclusive articles were removed and, as consequent, 24 eligible studies were retrieved for inclusion. Thus, analysis and discussions were based on two major viewpoints: predictor techniques and accuracy metrics. The review reveals that the researchers, based on their knowledge and research interests, applied a diverse neuro-fuzzy technique and shown stronger preference for certain neuro-fuzzy methods, such as ANFIS. To draw conclusions about the model performance, researchers chose different statistical and non-statistical metrics according to the technique used. It was finally observed that neuro-fuzzy approaches outperform, within its limits, conventional methods. However, each has its own set of constraints regarding the challenges involved in putting it into practice. The complexity of the presented approaches is the most significant potential obstacle that they face. Therefore, stock market prediction is a difficult undertaking, and multiple elements should be considered for accurate prediction. Yet, despite the subject's prominence, there are still promising new frontiers to explore and develop. Keywords: Fuzzy logic, Artificial neural network, Neuro-fuzzy, stock market forecasting JEL Classification: F37 Paper type: Theoretical Research  Predicting stock prices is a challenging task owing to the market's chaos and uncertainty. Methods based on traditional approaches are unable to provide a solution to the market predictability issue. Thus, contemporary models using accurate neuro-fuzzy systems are found to be the most effective approach to tackling the problem. However, the existing literature lacks a detailed survey of the application of neuro-fuzzy techniques for stock market prediction. This paper presents a systematic literature review of the use of neuro-fuzzy systems for predicting stock market prices and trends.  On this basis, articles issued in various reputed international journals from 2000 to July 2022 were examined, 11 duplicates and 4 non-exclusive articles were removed and, as consequent, 24 eligible studies were retrieved for inclusion. Thus, analysis and discussions were based on two major viewpoints: predictor techniques and accuracy metrics. The review reveals that the researchers, based on their knowledge and research interests, applied a diverse neuro-fuzzy technique and shown stronger preference for certain neuro-fuzzy methods, such as ANFIS. To draw conclusions about the model performance, researchers chose different statistical and non-statistical metrics according to the technique used. It was finally observed that neuro-fuzzy approaches outperform, within its limits, conventional methods. However, each has its own set of constraints regarding the challenges involved in putting it into practice. The complexity of the presented approaches is the most significant potential obstacle that they face. Therefore, stock market prediction is a difficult undertaking, and multiple elements should be considered for accurate prediction. Yet, despite the subject's prominence, there are still promising new frontiers to explore and develop. Keywords: Fuzzy logic, Artificial neural network, Neuro-fuzzy, stock market forecasting JEL Classification: F37 Paper type: Theoretical Research &nbsp

Financial distress prediction of Tehran Stock Exchange companies using support vector machine

The main objective of this study is to evaluate and to compare the power to predict company financial distress by utilizing the Support Vector Machine (SVM) to the multiple-discriminant analysis and the logistic regression models. Companies approved for acceptance into Tehran Stock Exchange Market between 2007 and 2013 comprise the statistical population for the study. In order to predict financial distress based on financial ratios such as profitability, activity ratio, ratios per share, etc. by using the Support Vector Machine (SVM), the sample data has been divided into two separate groups: the training group and the experimental group. The training set is made up of 540 year-company and the experimental set is comprised of 120 companies in 2013.  Finally, conclusions obtained from SVM, multiple-discriminant analysis and the logistic regression models for predicting financial failure were surveyed and compared. Results of testing hypothesis indicate with a 95% certainty ratio that there is a significant difference in the average prediction accuracy of the three models. Consequently among the three, the SVM model has the highest accuracy level for predicting company financial failure and the multiple-discriminant analysis model has the lowest

Financial distress prediction of Tehran Stock Exchange companies using support vector machine

The main objective of this study is to evaluate and to compare the power to predict company financial distress by utilizing the Support Vector Machine (SVM) to the multiple-discriminant analysis and the logistic regression models. Companies approved for acceptance into Tehran Stock Exchange Market between 2007 and 2013 comprise the statistical population for the study. In order to predict financial distress based on financial ratios such as profitability, activity ratio, ratios per share, etc. by using the Support Vector Machine (SVM), the sample data has been divided into two separate groups: the training group and the experimental group. The training set is made up of 540 year-company and the experimental set is comprised of 120 companies in 2013.  Finally, conclusions obtained from SVM, multiple-discriminant analysis and the logistic regression models for predicting financial failure were surveyed and compared. Results of testing hypothesis indicate with a 95% certainty ratio that there is a significant difference in the average prediction accuracy of the three models. Consequently among the three, the SVM model has the highest accuracy level for predicting company financial failure and the multiple-discriminant analysis model has the lowest

Exploring Investor Attention in Financial Models

The purpose of this study is to investigate whether stock prices are influenced by investor attention and how this, in turn, can be used to better advise the financial decisions of the everyday investor. Using weekly adjusted close data, weekly traded volumes, and weekly company searches using Google Trends, I tested my hypothesis that including the frequency of company searches, found through consumers using Google, in financial models will help better predict stock returns. Using S&P 500 company data from February 2012 to February 2017, frequency is a better predictor of price in comparison to trading volumes. But, to maximize predictability, both frequency and volume should be used to predict price. Further investigation revealed that the Health Care and Energy sectors tend to have the strongest correlation between frequency and volume, compared to the Consumer Staples and Utilities sectors, which tend to attract individual investors

Stock Price Change Rate Prediction by Utilizing Social Network Activities

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques