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

Twitter alloy steel disambiguation and user relevance via one-class and two-class news titles classifiers

This paper addresses the nontrivial task of Twitter financial disam- biguation (TFD), which is relevant to filter financial domain tweets (e.g., alloy steel or coffee prices) when no unique identifiers (e.g., cashtags) are adopted. To automate TFD, we propose a transfer learning approach that uses freely labeled news titles to train diverse one-class and two-class classification methods. These include different text handling transforms, adaptations of statistical measures and modern machine learning methods, including support vector machines (SVM), deep autoencoders and multilayer perceptrons. As a case study, we analyzed the domain of alloy steel prices, collecting a recent Twitter dataset. Overall, the best results were achieved by a two-class SVM fed with TFD statistical measures and topic model features, obtaining an 80% and 71% discrimination level when tested with 11,081 and 3,000 manually labeled tweets. The best one-class performance (78% and 69% for the same test tweets) was obtained by a term frequency-inverse document frequency classifier (TF-IDFC). These models were further used to gen- erate a Financial User Relevance rank (FUR) score, aiming to filter relevant users. The SVM and TF-IDFC FUR models obtained a predictive user discrimination level of 80% and 75% when tested with a manually labeled test sample of 418 users. These results confirm the proposed joint TFD-FUR approach as a valuable tool for the selection of Twitter texts and users for financial social media analytics (e.g., sentiment analysis, detection of influential users).Research carried out with the support of resources of Big and Open Data Innovation Laboratory (BODaI-Lab), University of Brescia, granted by Fondazione Cariplo and Regione Lombardia

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Google search volume’s ability to explain market activity of precious metal ETFs in the US market

Investor attention and its reflection to the stock market is a popular research topic in finance. One of the proxies for measuring this attention among retail investors is Google search volume, or more specifically a standardized figure provided by Google, a Google search volume index (GSVI). Unique features of GSVI include its direct nature and the popularity of Google among the common public. The GSVI’s ability to capture retail investor attention has been excessively studied in the stock market, but the research has not reached the commodity market to a large extent. Precious metals represent a commodity class, especially interesting during the COVID-19 pandemic due to the safe haven nature associated with them. Literature concerning GSVI in relation to precious metal exchange traded funds (ETFs) has so far been non existing. This thesis contributes to the existing literature by studying Google search volume index’s ability to explain market activity within the US precious metal ETF market, thus extending the GSVI literature to precious metal ETFs. More specifically, it is tested if GSVI has explanatory power over trading volume and total return index of selected precious metal ETFs. In addition, gold and silver ETFs are studied separately to spot differences between the metals. Special focus is given to silver ETFs due to the GME short squeeze in the beginning of 2021 and its attempted extension to the silver market. The research sample consist of 19 precious metal ETFs studied between 19.8.2018–31.5.2021 and analyzed with panel regressions. To summarize the results, it is found out that GSVI has very high statistically significant explanatory power over both, trading volume and total return index. In addition, GSVI has a stronger effect on silver ETFs’ trading volume, but a lower effect on silver ETFs’ return index compared with the results obtained with all studied precious metal ETFs. This study opens the research on GSVI’s explanatory power over precious metal ETFs in the US market, leaving multiple avenues for extended research in the future

Twitter financial community sentiment and its predictive relationship to stock market movement

Twitter, one of the several major social media platforms, has been identified as an influential factor for financial markets by multiple academic and professional publications in recent years. The motivation of this study hinges on the growing popularity of the use of Twitter and the increasing prevalence of its influence among the financial investment community. This paper presents empirical evidence of the existence of a financial community on Twitter in which users’ interests align with financial market-related topics. We establish a methodology to identify relevant Twitter users who form the financial community, and we also present the empirical findings of network characteristics of the financial community. We observe that this financial community behaves similarly to a small-world network, and we further identify groups of critical nodes and analyse their influence within the financial community based on several network centrality measures. Using a novel sentiment analysis algorithm, we construct a weighted sentiment measure using tweet messages from these critical nodes, and we discover that it is significantly correlated with the returns of the major financial market indices. By forming a financial community within the Twitter universe, we argue that the influential Twitter users within the financial community provide a proxy for the relationship between social sentiment and financial market movement. Hence, we conclude that the weighted sentiment constructed from these critical nodes within the financial community provides a more robust predictor of financial markets than the general social sentiment

Predicting Forex Currency Fluctuations Using a Novel Bio-inspired Modular Neural Network

This thesis explores the intricate interplay of rational choice theory (RCT), brain modularity, and artificial neural networks (ANNs) for modelling and forecasting hourly rate fluctuations in the foreign exchange (Forex) market. While RCT traditionally models human decision-making by emphasising self-interest and rational choices, this study extends its scope to encompass emotions, recognising their significant impact on investor decisions. Recent advances in neuro- science, particularly in understanding the cognitive and emotional processes associated with decision-making, have inspired computational methods to emulate these processes. ANNs, in particular, have shown promise in simulating neuroscience findings and translating them into effective models for financial market dynamics. However, their monolithic architectures of ANNs, characterised by fixed struc- tures, pose challenges in adaptability and flexibility when faced with data perturbations, limiting overall performance. To address these limitations, this thesis proposes a Modular Convolutional orthogonal Recurrent Neural Net- work with Monte Carlo dropout-ANN (MCoRNNMCD-ANN) inspired by recent neuroscience findings. A comprehensive literature review contextualises the challenges associated with monolithic architectures, leading to the identification of neural network structures that could enhance predictions of Forex price fluctuations, such as in the most prominently traded currencies, the EUR/GBP pairing. The proposed MCoRNNMCD-ANN is thoroughly evaluated through a detailed comparative analysis against state-of-the-art techniques, such as BiCuDNNL- STM, CNN–LSTM, LSTM–GRU, CLSTM, and ensemble modelling and single- monolithic CNN and RNN models. Results indicate that the MCoRNNMCD- ANN outperforms competitors. For instance, reducing prediction errors in test sets from 19.70% to an impressive 195.51%, measured by objective evaluation metrics like a mean square error. This innovative neurobiologically-inspired model not only capitalises on modularity but also integrates partial transfer learning to improve forecasting ac- curacy in anticipating Forex price fluctuations when less data occurs in the EUR/USD currency pair. The proposed bio-inspired modular approach, incorporating transfer learning in a similar task, brings advantages such as robust forecasts and enhanced generalisation performance, especially valuable in domains where prior knowledge guides modular learning processes. The proposed model presents a promising avenue for advancing predictive modelling in Forex predictions by incorporating transfer learning principles