[PDF] from annalsofrscb.ro Predictive Analytics for Sentiment Classification of Social Media Data Using Deep Neural Network

A huge amount of user-generated data in the form of tweets or reviews on social media can be collected and analyzed for making informed decisions. This paper uses the novel deep learning model, namely the Elite Opposition-based Bat Algorithm for Deep Neural Network (EOBA-DNN) for performing polarity classification of the social media data. The proposed method includes three major steps, such as preprocessing, term weighting, and sentiment classification for identifying the polarity of the data. The results show that the EOBA-DNN outperforms other existing algorithms with improved accuracy for Sentiment Classification

Analysis of the relationship between the sentiment of retail investors and the performance of the chinese stock market

Mestrado em FinançasAo contrário dos mercados de ações em países desenvolvidos, os mercados de ações chineses são principalmente composto por investidores de varejo. O comportamento do investimento no varejo é suscetível a emoções, que pode afetar o desempenho dos mercados de ações. Ao estudar a relação entre o dois tipos de mercados de ações, os investidores de varejo podem aumentar sua consciência do risco e investimento racional e a regulamentação dos mercados de capitais chineses também podem ser desenvolvidos de forma mais científica e saudável. Neste artigo, o método de computação afetiva é usado para quantificar o sentimento dos investidores de varejo registrados na Bolsa de Valores de Xangai. Então, a série temporal de sentimento de varejo, o preço de fechamento dos Valores de Xangai Índice Composto e o volume total de negociação da Bolsa de Valores de Xangai são organizado para análise e avaliado por meio de três métodos de análise, o modelo VAR, Correlação de Pearson e TLCC. As conclusões tiradas deste estudo são as seguintes: (i) Não há relação causal entre o sentimento dos investidores de varejo e o fechamento preço do Shanghai Securities Composite Index. (ii) Existe uma relação causal entre o sentimento do investidor de varejo e o volume total de negociação das Ações de Xangai Troca. (Iii) Há uma influência de defasagem mútua e forte correlação entre o sentimento dos investidores de varejo e a taxa de mudança do Shanghai Securities Composite Índice.Unlike stock markets in developed countries, Chinese stock markets are mainly composed of retail investors. Retail investment behavior is susceptible to emotions, which can affect the performance of stock markets. By studying the relationship between the two types of stock markets, retail investors can increase their awareness of risk and rational investment, and the regulation of Chinese capital markets can also be developed more scientifically and healthily. In this paper, the affective computing method is used to quantify the sentiment of retail investors registered on the Shanghai Stock Exchange. Then, the retail sentiment time series, the closing price of the Shanghai Securities Composite Index, and the total trading volume of the Shanghai Stock Exchange are organized for analysis and assessed through three analysis methods, the VAR model, Pearson correlation, and TLCC. The conclusions drawn from this study are as follows: (i) There is no causal relationship between the sentiment of retail investors and the closing price of the Shanghai Securities Composite Index. (ii) There is a causal relationship between retail investor sentiment and the total trading volume of the Shanghai Stock Exchange. (iii) There is a mutual lag influence and strong correlation between the sentiment of retail investors and the changing rate of the Shanghai Securities Composite Index.info:eu-repo/semantics/publishedVersio

SOCIAL MEDIA, TRADING VOLUME, VOLATILITY AND STOCK PRICES

This study investigates the impact of social media events on the behavior of six mega-cap technology stocks. Analyzing both positive and negative events, we find that negative events correlate with higher VIX Betas and trading volumes, while positive events are linked to higher stock prices. These effects vary across individual stocks, and unexpected associations emerge, such as increased trading volume for certain positive events. Our findings offer insights into the intricate interplay between social media events and market dynamics, highlighting the nuanced influences on volatility, trading volume, and stock prices for specific stocks. This study contributes to our understanding of the complex relationship between social media and financial markets, emphasizing the importance of considering stock-specific dynamics in investment strategies

An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach

El índice de sentimiento en las redes sociales y su impacto en los rendimientos del S&P 500

El estudio de la construcción y el análisis de índices de sentimiento en redes sociales es una técnica reciente que ha captado interés por su capacidad para identificar tendencias en los precios de las acciones. Además, la aplicación de inteligencia artificial para analizar rápidamente grandes volúmenes de datos de diversas fuentes de información ha creado una nueva forma de evaluar información masiva de redes sociales. El procesamiento del lenguaje natural (NLP, por sus siglas en inglés) es el método preferido que se sigue en la investigación. Originado en los años cincuenta, el NLP surgió de la intersección entre la inteligencia artificial y la lingüística. En un comienzo se empleó para recuperar información textual, con métodos basados en estadísticas para indexar y buscar de manera eficaz en grandes secciones de texto

Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research

Comparing traditional news and social media with stock price movements; which comes first, the news or the price change?

Twitter has been responsible for some major stock market news in the recent past, from rogue CEOs damaging their company to very active world leaders asking for brand boycotts, but despite its impact Twitter has still not been as impactful on markets as traditional news sources. In this paper we examine whether daily news sentiment of several companies and Twitter sentiment from their CEOs have an impact on their market performance and whether traditional news sources and Twitter activity of heads of government impact the benchmark indexes of major world economies over a period spanning the outbreak of the SAR-COV-2 pandemic. Our results indicate that there is very limited correlation between Twitter sentiment and price movements and that this does not change much when returns are taken relative to the market or when the market is calm or turbulent. There is almost no correlation under any circumstances between non-financial news sources and price movements, however there is some correlation between financial news sentiment and stock price movements. We also find this correlation gets stronger when returns are taken relative to the market. There are fewer companies correlated in both turbulent and calm economic times. There is no clear pattern to the direction and strength of the correlation, with some being strongly negatively correlated and others being strongly positively correlated, but in general the size of the correlation tends to indicate that price movement is driving sentiment, except in the turbulent economic times of the SARS-COV-2 pandemic in 2020

Customer and Employee Social Media Comments/Feedback and Stock Purchasing Decisions Enhanced by Sentiment Analysis

The U.S. Securities and Exchange Commission (SEC) warns professional investors that sentiment analysis tools may lead to impulsive investment decision-making. This warning comes despite evidence showing that aided social sentiment investment decision tools can increase accurate investment decision-making by 18%. Using Fama\u27s theory of efficient market hypothesis, the purpose of this quantitative correlational study was to examine whether customer Twitter comments and employee Glassdoor feedback sentiment predicted successful investing decisions measured by business stock prices. Two thousand records from 3 archival U.S. public NASDAQ 100 datasets from March 28, 2016, to June 15, 2016 (79 days) of 53 companies with over 100 comments were analyzed using multiple linear regression. The multiple regression analysis results indicated no significant predictability for successful investing decisions, F(10, 2993) = .295, p = .982, R2 = .001. The results indicated that the sentiment from both Twitter and Glassdoor was not necessarily an indicator for investors to make successful investment decisions for the 79 days in 2016. The knowledge about Artificial Intelligence (AI) sentiment usage may help professional investors gain profit or prevent losses. A recommendation to investors is to heed warnings from the SEC about tools for sentiment analysis investment decisions. Implications for positive social change include preventing an investor from using a risky sentiment tool for investment decision-making that may lead to losing capital