Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments

Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We model a set of comprehensive causative relationships over this dataset for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the search behavior of public before any change in the prices/ indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models

A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements

Recently, social media, particularly microblogs, have become highly valuableinformation resources for many investors. Previous studies examined general stockmarket movements, whereas in this paper, USD/TRY currency movements based on thechange in the number of positive, negative and neutral tweets are analyzed. Weinvestigate the relationship between Twitter content categorized as sentiments, such asBuy, Sell and Neutral, with USD/TRY currency movements. The results suggest thatthere exists a relationship between the number of tweets and the change in USD/TRYexchange rate

The contribution of unstructured data from social media for prediction in marketing management

The capacity to obtain market insights is a strategic need for companies to remain competitive. Despite this and the massive volume of data generated by consumers every second, companies rarely have the culture of making marketing decisions based on data and, when they do, rarely use consumer data widely available online, specially on social networks. One reason is that these data (e.g. texts) tend to be “dirty”, disorganized and bulky, a so-called unstructured data. Despite the complexity involved in extracting informational value from this data, companies can gain insights that can improve decision making and result in greater competitive performance. The purpose of this article is to discuss the benefits of new types of data that have become more abundant and accessible in Web 3.0, as well as new methods of analysis, particularly learning methods. For this, an extensive literature review was carried out and a topic modeling was conducted to get an overview of the data and methods. At the end, the article suggests six main marketing challenges that unstructured data analytics can contribute, improving companies’ competitiveness. The capacity to obtain market insights is a strategic need for companies to remain competitive. Despite this and the massive volume of data generated by consumers every second, companies rarely have the culture of making marketing decisions based on data and, when they do, rarely use consumer data widely available online, especially on social networks. One reason is that these data (e.g. texts) tend to be “dirty”, disorganized and bulky, a so-called unstructured data. The purpose of this article is to discuss the benefits of new types of data that have become more abundant and accessible in Web 3.0 through popular social networks, as well as new methods of analysis, particularly learning methods for prediction. For this, an extensive literature review was carried out and a topic modeling was conducted to get an overview of the data and methods. At the end, the article suggests six main marketing challenges that unstructured data analytics can contribute to overcome, improving companies’ competitiveness

Prediction Markets, Social Media and Information Efficiency

We consider the impact of breaking news on market prices. We measure activity on the micro-blogging platform Twitter surrounding a unique, newsworthy and identifiable event and investigate subsequent movements of betting prices on the prominent betting exchange, Bet- fair. The event we use is the Bigotgate scandal, which occurred during the 2010 UK General Election campaign. We use recent developments in time series econometric methods to identify and quantify movements in both Twitter activity and Betfair prices, and compare the timings of the two. We find that the response of market prices appears somewhat sluggish and is indicative of market inefficiency, as Betfair prices adjust with a delay, and there is evidence for post-news drift. This slow movement may be explained by the need for corroborating evidence via more traditional forms of media. Once important Tweeters begin to Tweet, including importantly breaking news Twitter feeds from traditional media sources, prices begin to move

Google Trends e o comportamento do mercado acionário brasileiro

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade e Gestão Pública, Departamento de Ciências Contábeis e Atuariais, 2017.Esta pesquisa tem como propósito investigar se a popularidade das empresas de capital aberto listadas na B3possui relação com as principais variáveis do mercado de ações, como preço de fechamento, volume de negociação, quantidade de títulos e quantidade de negociações. Para isso o presente estudo utiliza duas fontes de dados: a plataforma online Google Trends, afim de mensurar o volume de pesquisas realizadas no Google os dados do mercado acionário brasileiro, obtidos na Economatica. Os dados coletados para o presente estudo tiveram uma frequência semanal para o período de 5 anos. Foram feitos testes para medir as correlações entre os dados e constatou-se que a relação entre as pesquisas no Google e o comportamento do mercado acionário pode ser promissora para a maior parte das empresas que foram pesquisadas, porém não foi possível estabelecer uma relação causal a partir dos valores calculados. Além disso, para alguns casos, onde a correlação foi positiva para determinada variável, houve ausência de correlação ou correlação negativa para as outras, o que impede uma afirmação mais incisiva das relações entre o mercado e o interesse pela empresa. Desta forma, sugere-se que sejam realizadas pesquisas usando informações diárias ou somente trabalhando com os valores extremos. Bem como, trabalhar com série temporal aumentada.This research aims to investigate if the popularity of public equity companies listed on B3 has relation with the company´s performance in the stock market, such as closing price, trading volume, a quantity of bonds and quantity of trades. To reach that goal, this work uses two fonts of data: the online platform, Google Trends, to measure the volume of search queried on Google; and data from the Brazilian stock market, obtained with the Economatica software. The data collected for the present study had a weekly frequency for the period of 5 years. Tests were made to measure correlations between data and it was found that the relationship between how Google searches and the behavior of the stock market can be promising for most of the companies that were surveyed, but it was not possible to establish a causal relationship from the calculated values. In some cases, the correlation was positive for a particular variable, as the closing price, but negative for others. That prevents a more incisive affirmation about the relation between the market and the interest by the company. Thus, it is suggested new researchers using daily information or increased time series

THEORETICAL AND EMPIRICAL ASPECTS OF DETERMINING THE EXPECTATIONS OF ECONOMIC AGENTS BASED ON TEXT ANALYSIS

The Internet is a public source of information, where information can be found at minimum search cost. Social media are becoming increasingly popular among web users trying to find and analyze information about the current economic situation. Web users get the opportunity to exchange views or discuss various issues in the news communities of social networks. This information can be used by economic agents to make decisions. Thus, the study of user behavior in social networks makes it possible to identify the expectations and preferences of economic agents. The goal of this study is to assess the expectations and sentiments of economic agents based on textual analysis of social media data. The study addresses the following objectives: Analysis of the mechanisms of influence of the information dissemination and networking effects on the behavior of economic agents; Systematization of the results of theoretical and empirical analysis of the economic agents’ expectations; An overview of machine learning methods used in text processing; Development of an algorithm for identifying sources of information for web scraping and rules for selecting text information to create a body of posts and comments; Collecting a database and preparing posts and comments for text analysis; Application of topic modeling to the identification of topics and keywords in social media data; Assessment of high-frequency indicators of the public sentiment. The subject of the research is a quantitative assessment of the sentiment of web users based on Russian data. The novelty of the study is the assessment of inflation expectations, sentiments in the foreign exchange market and indices of economic conditions using structured and unstructured internet data. Methods: topic modeling; machine learning methods and econometric methods of time series analysis. The study is based on data for Russia in 2014-2021. The study shows that social media posts, search queries and online news articles can be good proxy variables for the economic agents’ expectations. We construct three types of public confidence indicators based on internet data: inflation expectations; sentiment in the foreign exchange market and index of economic conditions. The results of econometric analysis indicate that the quality of macroeconomic performance models with sentiment indicators is higher than without these indicators. Additionally, indicators based on VK posts, RBC news articles and Google Trends search queries are more informative compared to comments. The main conclusion of the study is that internet data can improve the quality of macroeconomic performance models. In a further study, we plan to expand the list of indicators of the sentiment of economic agents and to evaluate advanced time series model

The economic consequences of financial reporting on Twitter

This thesis examines stock market reaction following financial reporting information on Twitter. The results indicate that larger companies and companies closer to technology are more likely to adopt Twitter for financial reporting. Smaller companies receive greater benefits (reduction of information asymmetry) from this practice, as well as disclosing multiple financial reporting tweets. This study encourages frequent use of Twitter and other new Information Technology, to increase the visibility of small companies. This study provides new evidences to inform regulatory policy and promote ‘best practice’ guidelines for financial reporting on social media