Stock market sentiment lexicon acquisition using microblogging data and statistical measures

Lexicon acquisition is a key issue for sentiment analysis. This paper presents a novel and fast approach for creating stock market lexicons. The approach is based on statistical measures applied over a vast set of labeled messages from StockTwits, which is a specialized stock market microblog. We compare three adaptations of statistical measures, such as pointwise mutual information (PMI), two new complementary statistics and the use of sentiment scores for affirmative and negated con- texts. Using StockTwits, we show that the new lexicons are competitive for measuring investor sentiment when compared with six popular lexicons. We also applied a lexicon to easily produce Twitter investor sentiment indicators and analyzed their correlation with survey sentiment indexes. The new microblogging indicators have a moderate correlation with popular Investors Intelligence (II) and American Association of Individual Investors (AAII) indicators. Thus, the new microblogging approach can be used alternatively to traditional survey indicators with advantages (e.g., cheaper creation, higher frequencies).This work was supported by FCT - Funda ção para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/201

Can Tweets Predict Intraday Stock Price Movements?

Twitter is a microblogging platform where over 320 million people post about things that matter to them in less than 140 characters. People post about their happiness, sadness, pride, disappointments, love, hate, expectations and other feelings. Nowadays, a popular trend has come up in which data from Twitter, can be gathered and analyzed in real time to see how people react to a particular situation including changes in stock markets. This research focuses on looking into the relationship, if any, between public mood from Twitter and the stock market returns by analyzing the Tweets about four major companies in consumer discretionary industry namely Amazon, Walt Disney, Home Depot and Comcast and their stock price over the duration of about a month. The research showed causal relationship between market sentiment corresponding to a particular stock and their stock returns

Mining the Impact of Investor Sentiment on Stock Market from WeChat

In this study, the CSI 300 Index in China mainland and original articles from authoritative stock WeChat public accounts are investigated regarding their relations. First, a sentence-level sentiment classification approach for analyzing investor sentiment polarities in text corpus is proposed by expanding synonyms. Then, the Granger causality test is utilized to examine the impact of sentiment index on the stock price and volume-values. It shows that the influence of overall investor sentiment on volume-values is more rapid than that on stock price and the impact of positive sentiment is found to be more lasting than the negative in both stock price and volume-values. Furthermore, it is worth noting that there is a dual-stage phenomenon in the impact of positive sentiment on volume-values, which indicates that some investors react to positive information immediately while others may choose to wait and follow the trend

Unfolding the relations between companies and technologies under the Big Data umbrella

Big Data is dominating the landscape as data originated in many sources keeps piling up. Information Technology (IT) business companies are making tremendous efforts to keep the pace with this wave of innovative technologies. This study aims to identify how the different IT companies are aligned with emerging Big Data technologies. The approach consisted in analyzing 11,505 news published between 2013 and 2016 and aggregated through Google News. The companies were categorized according to their position in the 2017 Gartner Magic Quadrant for advanced analytics. A text mining and topic modeling procedure assisted in summarizing the main findings. Leaders dominated a large fraction of the published news. Challengers are making a significant effort in investing in predictive analytics, overlooking other technologies such as those related to data preparation and integration. The results helped to shed light on the emerging field of Big Data from a corporate perspective.info:eu-repo/semantics/acceptedVersio

The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

News-induced style seasonality

This paper posits a new methodological approach to test how specialized media could influence the information transmission channels towards investors. We contribute to the literature on the role of media on investor limited attention, on seasonal effects in market anomalies and on the impact of news on market anomalies. Our approach is somewhat different from the current literature as we determine whether we can detect any seasonality in the news coverage of recommendations, analyses or opinions on investment styles provided by specialized press to institutional investors. Our paper not only contributes to the literature on market anomalies and seasonality effects in financial markets but also aligns itself with a new strand of research involving the application of text mining in finance. First, our text corpus gathers articles from specialized press targeting institutional investors. Such a corpus is unique and has never been investigated. Second, we build our own dictionaries from several statistical methods to extract style information from news flow. The method is innovative and our study is the first to investigate the seasonality in the underlying information channel. At this stage, the paper is mainly methodological and centered on small and large styles. Results will be extended to other investment styles in the near future and completed with statistical test of cyclicality and trend analysis

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Can crude oil serve as a hedging asset for underlying securities? - Research on the heterogenous correlation between crude oil and stock index

In the increasingly frequent global financial turmoil, investors prefer to invest in stable assets to hedge risks. Crude oil naturally has dual use value as a general commodity and as a financial asset, which has attracted wide attention. In this paper, we adopt a wavelet coherence analysis to study the standard of crude oil as a hedging asset and analyze the dynamic correlation of crude oil and stock market price fluctuations in the four economies of the United States, Japan, China and Hong Kong at different frequencies. The empirical evidence shows that crude oil can be conditionally used as a hedging asset for underlying securities. From the perspective of space, crude oil is suitable for investors in China's stock market as a hedging asset, while for stock markets in the US, Japan and Hong Kong, the ability of crude oil to hedge risk has been greatly weakened. From the perspective of investment term, although crude oil cannot be regarded as a hedging asset for long-term investment, it can still play a hedging role in the short term. When the market is in a state of panic, the ability of oil to hedge risk is stronger