The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.
This study investigates the interconnectedness of the Islamic stock market, Bullion, and Bitcoin as diversifiers for portfolios, exploring their role as hedges and safe havens. The analysis covers the period from January 2015 to December 2022, with a particular focus on the influence of the COVID-19 pandemic and the Russia-Ukraine War on the MSCI World Islamic Index, bullions (Gold, Silver, Platinum, Nickel, Palladium, and Aluminium), and Bitcoin, employing a time-varying parameter vector autoregression (TVP-VAR) model. During crisis periods, our findings reveal that the transmission and reception of shocks among these assets varied, with a heightened level of co-movement observed during the pandemic and war periods. These results emphasise the importance of considering the dynamic nature of financial assets' connectedness in asset investment decisions, particularly in times of crisis. Furthermore, the findings suggest that Bullion can serve as a hedge for both Bitcoin and the Islamic stock market. The study also explores the optimal diversification of investment portfolios and highlights the importance of adhering to Islamic principles in portfolio diversification. By integrating Islamic rules into the diversification process, investors can enhance the effectiveness and relevance of their investment strategies.
Central banks worldwide are developing, piloting and launching new central bank digital currencies (CBDCs). As the hub for the central banking community, the Bank for International Settlements (BIS) promotes a curiously botanical CBDC imaginary. From ‘money flowers’ to ‘tree trunks’ and a ‘strong canopy’, This helps to naturalize CBDC without clarifying its sociopolitical implications or envisioned monetary future, such as geopolitical tensions and financial fragmentation, new modes of financial interaction or the strengthened role of central banks. While omitting the paradoxes and ambivalences of CBDC, the imaginary of the BIS structures the enfolding discourses and allows the bank to function as a think tank for financial policy‐making.
<title>Abstract</title> In this paper, we try to examine the relationship between the Bitcoin price, social media metrics and the intensity of Covid-19 pandemic. We also attempt to investigate the behavior of Bitcoin volatility during such pandemic. For this end, we use the error correction model, Co-integration processing tool and vector error correction model to detect potential transmission mechanisms among different variables and the dynamic coupling between them. We also apply the GARCH-type models to better apprehend the behavior of Bitcoin volatility. Our results clearly display the short- and long term evidences of the relationshipbetween the Bitcoin price, severity of the Covid-19 health crisis and social media metrics. Moreover, there is strong evidence related to the information content of social media during turbulent phases. We also report some distinctive and salient features of Bitcoin volatility. The information spillover from pandemic-related news to the Bitcoin prices is well-documented. Using the Covid-19 deaths and confirmed cases can be considered as measure of pandemic severity. As well, the information transmission mechanism is well-documented through social media which seems to have an added value during the stressful periods. Such analysis could have insightful implications for investors in crypto-currency market.
Markus Frohmann, Manuel Karner, Said Khudoyan, Robert F. Wagner · 5 authors
Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.
Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transaction outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.
Historically, gold and silver have played distinct roles in tra- ditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has com- monly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Lite- coin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transac- tion outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we’ve devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing re- sults not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee’s metaphor, underscoring Bitcoin’s superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we’ve made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.
Türkiye, Brezilya, Hindistan, Güney Afrika ve Endonezya'nın ekonomik büyümelerini finanse etmek için istikrarsız yabancı yatırımlara olan yüksek bağımlılıkları nedeniyle, bu ülkeler “Kırılgan Beşli” ülke olarak adlandırılmıştır. Aynı zamanda Global Crypto Adoption Index'e göre, bu ülkeler kripto para birimlerine yatırım yapma konusunda oldukça aktiflerdir. Bu çalışmada “Kırılgan Beşli” ülkeler dikkate alınarak Bitcoin ve finansal varlıklar arasındaki uzun dönemli asimetrik ilişki Ağustos 2010 - Temmuz 2022 dönemine ait aylık veriler baz alınarak ARDL ve NARDL yöntemleri ile incelenmiştir. Pozitif ve negatif Bitcoin şoklarından kaynaklanan dinamik çarpanların doğrusal kombinasyonu, beş ülkenin tümü için NARDL üzerinden Dinamik çarpan testine başvurarak grafikleri çizilmiştir. Sonuçlar, Bitcoin'in tüm borsa endekslerine olumlu bir etkisi olmasına rağmen, yalnızca Türkiye ve Hindistan'daki değişkenlerin eş bütünleşik olduğunu göstermektedir. Bitcoin'in olumsuz şoklarının Türkiye'de daha derin ve baskın etkiye sahip olduğu anlaşılmıştır. Ancak, Bitcoin’in olumlu şoklarının Hindistan'da daha baskın olduğu sonucuna rastlanmıştır.
This study aims to identify the sources of spillovers affecting tourism tokens and classify the type of assets to which they correspond. Using daily data for different asset classes from June 2018 through November 2022, we employ a TVP-VAR methodology to test the connectedness between two tourism tokens, two leading travel equity indices, and the two dominant cryptocurrencies, namely, Bitcoin and Ethereum. The findings show that tourism tokens are relatively independent of fluctuations in the traditional sources affecting the travel and leisure sector, such as the U.S. dollar, the price of oil, or travel equity indices. These results hint that tourism tokens are more closely related to cryptocurrencies rather than pure travel goods. The results may help decision-makers in the travel and hospitality industries considering the use of tourism tokens identify the potential forces impacting them.
In recent years, Bitcoin and other cryptocurrencies like Ethereum and Dogecoin have emerged as important asset classes in general, and diversification and hedging instruments in particular. The recent COVID-19 pandemic has provided the chance to examine and assess cryptocurrencies’ behavior during extremely stressful times. The methodology of this study is based on an estimate using the ARDL model from 22 January 2020 to 12 March 2021, allowing us to analyze the long-term and short-term relationship between cryptocurrencies and COVID-19. Our results demonstrate that there is cointegration between the chosen cryptocurrencies in the market and COVID-19. The results indicate that Bitcoin, ETH, and DOGE prices were affected by COVID-19, which means that the pandemic seriously affected the three cryptocurrency prices.
In the burgeoning field of bitcoin research, a cohesive understanding of how knowledge and insights have evolved over time is lacking. This study aims to address this gap through an exploration of 4123 academic articles pertaining to bitcoin. Utilizing co-word analysis and main path analysis (MPA), it uncovers key themes and seminal works that have substantially influenced the field’s progression. The identified clusters, including safe haven, internet of things (IoT), proof of work (PoW), market efficiency, sentiment analysis, digital currency, and privacy, shed light on the multifaceted discourse surrounding bitcoin. The MPA, incorporating both forward and backward local paths, traces an evolving narrative, starting from an in-depth exploration of bitcoin’s structure, anonymity, and contrasts against traditional financial assets. It tracks the shift in focus to broader market dynamics, volatility, speculative nature, and reactions to economic policy fluctuations. The analysis underscores the transformation of bitcoin research, from its beginnings as a decentralized, privacy-oriented currency to its role in global economics and green financing, revealing a complex narrative of an innovative financial instrument to a multifaceted entity. Implications drawn from this analysis include the need for further research on the potential integration of bitcoin within emerging technologies like AI and cybersecurity, the implications of bitcoin’s interplay with traditional financial systems, and the environmental impacts of bitcoin and blockchain utilization. Overall, the current study not only enhances our understanding of the bitcoin field but also charts its dynamic evolution and stimulates further academic inquiry.
Abstract Bitcoin enthusiasts argue that it is free from central banks decisions and it is a hedge against inflation. Using high-frequency monetary surprises associated with decisions made by the Fed and the ECB, I show that these claims are not supported by the data. Bitcoin systemically reacts to monetary and central bank information shocks. I find that these reactions vary over time: not only by changing the magnitude but sometimes sign of reaction. Fed’s disinflationary shocks increase Bitcoin price, while the ECB’s decrease, hence providing little support for it as an inflation hedge.
Abstract This article examines the causal relationship between stock indices and cryptocurrencies during the current war between Russia and Ukraine. The econometric investigation runs from February 24, 2022 to April 12, 2023, focusing on seven stock market indices (S&P500, DAX, CAC40, Nikkei, TSX, MOEX and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litcoin, Dash, Ripple, DigiByte and XEM). In this article, we try to understand how investors react to fluctuations in financial assets to seek safe havens in crypto currencies. We used dynamic causality in the Granger (1969) sense to detect a possible causal relationship in the short term, and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. The causal relationship between financial market indexes and cryptocurrency coins in the short run indicate that three famous cryptocurrencies (BITCOIN, ETHEREUM, RIPPLE) and the two digital asset with minor popularity (XEM, Digibyte) are impacted by the German, Russian and Ukrainian stock markets. In the long-run we found a positive and significate effect of the American, Canadian, French and Ukrainian stock market indexes on Bitcoin. Thus, the stability of the traditional financial markets during the current war period can be explained on the one hand by investors’ fears of an unstable business climate, and on the other hand, by speculators’ sentiment towards new electronic products which are perceived as hedging instruments and a safe haven in the face of the conflict between Ukraine and Russia. JEL Classifcation: C5 · C22 · G1
Open access
Environmental and Biological Research in Conflict Zones
In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.