Investigating the Effects of Dimension-Specific Sentiments on Product Sales: The Perspective of Sentiment Preferences

While the literature has reached a consensus on the awareness effect of online word-of-mouth (eWOM), this paper studies its persuasive effect—specifically, dimension-specific sentiment effects on product sales.We examine the sentiment information in eWOM along different product dimensions and reveal different persuasive effects on consumers’ purchase decisions based on consumers’ sentiment preference, which is defined as the relative importance that consumers place on various dimension-specific sentiments. We use an aspect-level sentiment analysis to derive dimension-specific sentiment and PVAR (panel vector auto-regression) models, and estimate their effects on product sales using a movie panel dataset. The findings show that three dimension-specific sentiments (star, genre, and plot) are positively related to movie sales.Regarding consumers’ sentiment preferences, we find a positive relationship to movie sales that is stronger for plot sentiment, relative to star sentiment for low-budget movies. For high-budget movies, we find a positive relationship to movie sales that is stronger for star sentiment, relative to plot or genre sentiment

Pengaruh Interest Rate, Investor Sentiment, Financial Distress Terhadap Stock Return

The purpose of this research is to know the effect of interest rate, investor sentiment, and financial distress of stock return on manufacturing company listed on the Indonesia Stock Exchange from 2014-2017. The sampling method used in this research used 49 manufacturing company that were selected using purposive sampling method. Data used for this study is obtained from financial statement for the year ended December 31st during 2014-2017. Analysis tool that will be used to analyze the hypothesis with multiple linier regression model is software IBM SPSS (Statistical Product and Service Solutions) version 23.0 for Windows. The result for this research showed that interest rate and investor sentiment have positive and significant effect on stock return, while financial distress has negative and significant effect on stock return

Stock Prediction Analyzing Investor Sentiments

We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stocks. The dissertation work leverages social data from Twitter and seeks the sentiment of certain investors. Once the sentiment of the tweets is calculated using an advanced sentiment analyzer, it is used as an additional attribute to the other fundamental properties of the stock. This dissertation demonstrates how incorporating the sentiments improves forecasting accuracy of predicting stock valuation. In addition, various experimental analysis on regression based statistical models are considered which show statistical measures to consider for effectively predicting the closing price of the stock. The Efficient Market Hypothesis (EMH) states that stock market prices are largely driven by additional information and follow a random walk pattern [7, 8, 37, 39, 41]. Though this hypothesis is widely accepted by the research community as a central paradigm governing the markets in general, several people have attempted to extract patterns in the way stock markets behave and respond to external stimuli. We test a hypothesis based on the premise of behavioral economics, that the emotions and moods of individuals basically the sentiments affect their decision-making process, thus, leading to a direct correlation between ?public sentiment? and ?market sentiment? [42, 43, 44, 45]. We first select key investors from Twitter [27, 28] whose sentiments matter and do sentiment analysis on the tweets pertaining to stock related information. Once we retrieve the sentiment for every stock, we combine this information with the other fundamental information about stocks and build different regression-based prediction models to predict their closing price

The Impact of Financial Performance Towards Annual Report Readability: Analysis of Mediating Impact of Disclosure’s Sentiment

Several literatures show the existence of opportunistic behavior of the company’s management that intends to cover up the company’s performance through the information disclosures using particular strategies. This study examines the direct effect of financial performance on the annual report readability. Furthermore, this study investigates the mediation effect of annual report sentiment on the influence between the financial performance and the annual report readability. This study is a quantitative research using secondary data in the form of financial reports and Management and Discussion Analysis in the company’s annual report. The population comprises manufacturing companies listed on the Indonesia Stock Exchange (IDX) during 2016-2021. Based on the purposive sampling method, this study uses 85 companies as the sample. This study uses ROA as an indicator of financial performance and the Gunning Fog index as an indicator of the annual report readability. The sentiment in the text of the annual report is measured using the Valence Aware Dictionary and Sentiment Reasoner (VADER) method with the help of the Orange Data Mining application. The analyses used to test the hypotheses are the regression analysis and the sobel test. The result of this study indicates that financial performance does not have a direct effect on the annual report readability. However, this study finds that financial performance has an indirect effect on the annual report readability through the mediating role of the annual report sentiment

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Non-Parametric Causality Detection: An Application to Social Media and Financial Data

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about the statistical relationships among the variables of the study and can effectively control a large number of factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201

Studi Moderasi Berita Pada Pengaruh Efek Disposisi Terhadap Sentimen Investor yang di Moderasi Kembali Oleh Tingkat Kesepakatan Investor Pasar Modal di Indonesia

Investors assume that the news or information obtained is accurate so that every investment decision taken tends to produce a bias. This deviation or bias shows that investors are not completely rational, where an investor does not only use his common sense in making investment decisions but is very likely to be influenced by the emotions felt at that time. Behavior finance is an area of research that refutes the efficient market hypothesis theory that has developed over the last three decades that assume that investors are rational. Investor sentiment is one of the research areas in behavioral finance theory that analyzes investor behavior and how it affects stock market activity. With the advancement of information technology in recent years, the trend of using social media has greatly increased in all circles, including investors who are actively seeking news or information and also investment advice given by expert investors. The ease and disclosure of such information lead to greater deviations or biases in investment decisions and also in the way investors form a belief or sentiment. Twitter is a social media platform that is designed as a forum for sharing information, tweets, or ideas between investors, traders, and expert. For this reason, quantifying investor sentiment can be investigated through microblogs tweets using social media data mining methods for the analysis of public sentiment. So, in this research, a study of news moderation and investor agreement level on the effect of disposition effect bias on investor sentiment in the capital market in Indonesia is conducted. This research using data from investors who are active on the twitter platform based on daily tweets with the hashtag symbol 60 company shares listed on the Indonesian stock exchange in the period January 2015 - May 2020 with 10,533 data and 558 users. With the Regression test approach moderated moderation analysis, the results of this study are that underreaction or overreaction to news of stock price changes and negative news moderates the effect of the disposition effect on investor sentiment where the magnitude of the effect will depend on the level of investor agreement. Disposition effect, level agreement of investors, monetary news, and information quality from investors alpha have a conditional effect on investor sentiment. Meanwhile, the conditional interaction between the moderating variable and the disposition effect simultaneously influences investor sentiment, except for its relationship with negative news