Effect of Twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies

CONFIDENCE MATTERS: A SIMULATION-BASED STABILITY ANALYSIS OF ALGORITHMIC STABLECOINS

The crash of various stablecoins led to continuous adjustments of their design, most recently by backing algorithmic stablecoins with cryptocurrency pools. However, as this seems to be more of a trial-and- error process, the aim of this work is to support design decisions on their peg stability mechanism by an agent-based simulation model as a base to forecast the probability of a stablecoin run dependent on market participants’ confidence levels. Our model is tailored to the algorithmic stablecoin USDD which is i.a. pegged to the Fiat-backed stablecoin USDT and hence to the USD. As main result of our numerical study, stability depends on the price and volatility assessment of market participants and a stablecoin run can’t be prevented for sure. Methodologically, this work belongs to design science research, even though empirical market data is used to calibrate the simulation model, which can be used as base for design recommendations

АНАЛІЗ МЕТОДІВ ТА МОДЕЛЕЙ ПРОГНОЗУВАННЯ РИНКУ ЦИФРОВИХ КРИПТОВАЛЮТ

With the development of financial institutions, this application software and related information technologies are used not only by specialists, but also by ordinary citizens to solve tasks that a few years ago seemed to be within the competence of only mathematicians specializing, for example, in building forecasting models. It can be noted that the collaboration of IT with application software, as well as with the mathematical apparatus most typical for forecasting tasks, gives good results. In particular, this applies to the Central Bank market. The study is devoted to the problem of approaches to the selection of methods and strategies for analysis and forecasting of the central bank markets, which is an urgent issue today. Far from all possible methods and strategies have sufficient coverage in the scientific information space, which prompts the need to analyze and systematize already existing information in this field. Accordingly, basically. the purpose of the study is to analyze and systematize the theoretical foundations of existing approaches to forecasting the CCV market. An analysis and systematization of the theoretical foundations of existing approaches to forecasting the CCV market was carried out. Generalized advantages and disadvantages of structural methods and models used for making market forecasts were outlined. A comparative analysis of ANN models was carried out in terms of their use for market analysis tasks. Among the analyzed ANN models are the following: CNN-2l, CNN-3l, LSTM, sLSTM, BiLSTM, GRU, CLSTM, MLP and RFBNN. The analysis and testing of existing models provided results that provide a wide scope for further research and study.З розвитком фінансових інститутів дане прикладне ПЗ та супутні інформаційні технології використовуються не лише фахівцями, а й простими громадянами для вирішення завдань, які ще кілька років тому здавалися під силу лише фахівцям математикам, що спеціалізуються, наприклад, на побудові моделей прогнозування. Можна відзначити, що колаборація ІТ з прикладним програмним забезпеченням, а також з математичним апаратом, найбільш типовим для завдань прогнозування дає хороші результати. Зокрема, це стосується і ринку ЦКВ. Дослідження присвячено питанню проблематики підходів до вибору методів та стратегій аналізу та прогнозування ринків ЦКВ є актуальним питанням сьогодення. Далеко не всі можливі методи та стратегії мають достатнє висвітлення у науковому інфопросторі, що спонукає до необхідності аналізу та систематизації уже існуючої інформації в даній сфері. Відповідно, основною метою дослідження є аналіз та систематизація теоретичних засад існуючих підходів до прогнозування ринку ЦКВ. Було проведено аналіз та систематизація теоретичних засад існуючих підходів до прогнозування ринку ЦКВ. Було окреслено узагальнені переваги та недоліки структурних методів та моделей, що використовуються для складання прогнозів на ринку. Було проведено порівняльний аналіз моделей ШНМ в розрізі використання їх для задач аналізу ринку.  Серед проаналізованих моделей ШНМ є такі: CNN-2l, CNN-3l, LSTM, sLSTM, BiLSTM, GRU, CLSTM, MLP та RFBNN. Проведений аналіз та тестування існуючих моделей надав результати, що надають широкий простір для подальшого дослідження та вивчення

A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e1/t1−1/γ scaling law, where γ\u3e0 role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3eγ\u3e0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values

Trading on Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors

Driven by innovative information technologies, the financial industry is facing a recent disruptive fintech revolution. One emerging technology within this field is cryptocurrency, aiming to change the future means of payment. In this paper, we study Bitcoin exchange trading and examine what factors influence the behavior of different cryptocurrency investor types. To answer this question, market bids are considered in form of investors\u27 offers and orders as a proxy for their trading behavior. First, an unsupervised clustering technique is applied in order to group different types of investors based on similarities in trading behavior. Second, a supervised classification mechanism is used on social media news to measure the sentiment influencing trading decisions. Among other indicators this bullishness is integrated in an autoregressive distributed lag (ARDL) model to identify the factors influencing the trading behavior of investor types. Besides large investors, foreign traders and speculators, cryptocurrency-specific market participants are characterized in the form of miners. With identifying indicators driving investors\u27 actions (i.e., macro-financial fundamentals, technical trading indicators, technological measures and market sentiment), this study contributes to recent research by explaining the trading behavior on cryptocurrency markets and its impact on exchange rates

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Text-mining in macroeconomics: the wealth of words

The coming to life of the Royal Society in 1660 surely represented an important milestone in the history of science, not least in Economics. Yet, its founding motto, ``Nullius in verba'', could be somewhat misleading. Words in fact may play an important role in Economics. In order to extract relevant information that words provide, this thesis relies on state-of-the-art methods from the information retrieval and computer science communities. Chapter 1 shows how policy uncertainty indices can be constructed via unsupervised machine learning models. Using unsupervised algorithms proves useful in terms of the time and resources needed to compute these indices. The unsupervised machine learning algorithm, called Latent Dirichlet Allocation (LDA), allows obtaining the different themes in documents without any prior information about their context. Given that this algorithm is widely used throughout this thesis, this chapter offers a detailed while intuitive description of its underlying mechanics. Chapter 2 uses the LDA algorithm to categorize the political uncertainty embedded in the Scottish media. In particular, it models the uncertainty regarding Brexit and the Scottish referendum for independence. These referendum-related indices are compared with the Google search queries ``Scottish independence'' and ``Brexit'', showing strong similarities. The second part of the chapter examines the relationship of these indices on investment in a longitudinal panel dataset of 2,589 Scottish firms over the period 2008-2017. It presents evidence of greater sensitivity for firms that are financially constrained or whose investment is to a greater degree irreversible. Additionally, it is found that Scottish companies located on the border with England have a stronger negative correlation with Scottish political uncertainty than those operating in the rest of the country. Contrary to expectations, we notice that investment coming from manufacturing companies appears less sensitive to political uncertainty. Chapter 3 builds eight different policy-related uncertainty indicators for the four largest euro area countries using press-media in German, French, Italian and Spanish from January 2000 until May 2019. This is done in two steps. Firstly, a continuous bag of word model is used to obtain semantically similar words to ``economy'' and ``uncertainty'' across the four languages and contexts. This allows for the retrieval of all news-articles relevant to economic uncertainty. Secondly, LDA is again employed to model the different sources of uncertainty for each country, highlighting how easily LDA can adapt to different languages and contexts. Using a Bayesian Structural Vector Autoregressive set up (BSVAR) a strong heterogeneity in the relationship between uncertainty and investment in machinery and equipment is then documented. For example, while investment in France, Italy and Spain reacts heavily to political uncertainty shocks, in Germany it is more sensitive to trade uncertainty shocks. Finally, Chapter 4 analyses English language media from Europe, India and the United States, augmented by a sentiment analysis to study how different narratives concerning cryptocurrencies influence their prices. The time span ranges from April 2013 to December 2018 a period where cryptocurrency prices experienced a parabolic behaviour. In addition, this case study is motivated by Shiller's belief that narratives around cryptocurrencies might have led to this price behaviour. Nonetheless, the relationship between narratives and prices ought to be driven by complex interactions. For example, articles written in the media about a specific phenomenon will attract or detract new investors depending on their content and tone (sentiment). Moreover, the press might also react to price changes by increasing the coverage of a given topic. For this reason, a recent causal model, Convergent Cross Mapping (CCM), suited to discovering causal relationships in complex dynamical ecosystems is used. I find bidirectional causal relationships between narratives concerning investment and regulation while a mild unidirectional causal association exists in narratives that relate technology and security to prices

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice