Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.
We investigate the benefits of using intraday realized volatility (RV) commonality, and propose a novel non-parametric framework for forecasting one-day ahead intraday RV (1D-ahead intraday RV). Specifically, we train multiple models using machine learning (ML) techniques under various training settings (single-asset, cluster-driven, and cross-asset), where commonality gradually enters model dynamics as training schemes become more complex. We conclude that models that leverage the cryptocurrency commonality outperform models that do not explicitly account for it, regardless of the market regime considered. The source code of this project is available at: github.com/edjanga/crypto_volatility_commonality.
In this paper, we examine the dynamic relationship between Bitcoin prices and investor sentiment indicators. According to the correlation, the five time series variables of S&P cryptocurrency extensive digital market index, S&P 500 value index, bitcoin trading volume and the original Baidu index were reasonably selected to establish the comprehensive index of investor sentiment. The vector autoregressive VAR model is used to verify the relationship between the Bitcoin price (CLOSE) and the sentiment indicators. According to the Granger causality test and the pulse response function analysis, there is a bidirectional causal relationship between the two. The constructed indicators have important practical significance.
Over the past few years, there has been a notable surge in interest towards cryptocurrency, especially in the context of the crisis, where researchers have been diligently examining the influence of the coronavirus on cryptocurrency returns.Numerous studies have utilized econometric and machine learning techniques to forecast cryptocurrency prices, but most of them have focused solely on the financial domain. This paper introduces a novel approach called ANFPC (Adaptive Neuro Fuzzy Prediction Cryptocurrency), which combines insights from both the financial and health domains to predict the price fluctuations of various cryptocurrencies such as bitcoin, ethereum, cardano, xpr, and dogecoin. The approach relies on the ANFIS Model, a fusion of fuzzy logic and artificial neural network (ANN).The experimental findings demonstrate that ANFPC provides accurate predictions, as measured by metrics like Mae and Mse, outperforming traditional ANN and LSTM methods. This approach proves to be a valuable decision support tool for data analysts in the realm of cryptocurrency prediction.
Vincent Gurgul, Stefan Lessmann, Wolfgang Karl Härdle
We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public . ⢠NLP data from social media improve the accuracy of cryptocurrency forecasting models. ⢠As a target variable, local extrema are a valid alternative to daily price changes. ⢠Deep learning language models substantially outperform dictionary-based methodologies. ⢠Both pre-trained and fine-tuned language models effectively quantify market sentiment.
Purpose: The article aims to investigate the relationship between the returns of the NASDAQ Composite stock index and the Bitcoin cryptocurrency. Theoretical framework: According to the literature, it is obvious that cryptocurrencies are very volatile, especially during the economic instability period. There is a belief that when uncertainty is in the economy, investors prefer alternative investment opportunities. There is a need to prove that. Design/Methodology/Approach: The study employs two different models, the ARMAX and the GARCH, to analyze the data from March 2018 to March 2023. The results of the analysis suggest a significant relationship between the returns of the NASDAQ Composite and Bitcoin. These results have important implications for investors and policymakers. Findings: The findings suggest that investors need to be aware of the potential risks and benefits associated with investing in both assets, particularly in times of economic uncertainty. Policymakers may also need to consider the impact of traditional stock markets and the overall economy on cryptocurrencies. Research, Practical & Social implications: The research suggests that investors should be careful with cryptocurrencies. Originality/Value: The results are based on the time series analysis that makes the research original. Because there are few examples of time series and volatility analysis of cryptocurrencies.
Bikramaditya Ghosh, Mariya Gubareva, Noshaba Zulfiqar, Ahmed Bossman
Purpose The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021â2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authorsâ investigation. Design/methodology/approach The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT). Findings At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant. Originality/value The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authorsâ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.
This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.
Research in recent years has shown that Bitcoin is a virtual asset that is used as a medium of exchange and investment tool other than shares and bonds, the development of the digital era has opened up opportunities for Bitcoin to be chosen as part of an investorâs portfolio. The focus of this study is to examine the impact of nine key determinants on Bitcoin price. The data used in the study are daily data starting from January 1, 2018 to January 1, 2022. The main data source is taken from Investing.com, and the estimation method applied is the Vector Error Correction Model (VECM). The main finding shows that Bitcoin Volume impacts Bitcoin Price negatively, which is in line with the demand theory. Another finding is related to the substitute effect of Ethereum Volume, Litecoin Volume, and Gold Volume, each of which influences Bitcoin Price positively, suggesting that these three commodities are substitutes to Bitcoin. In contrast, whereas Oil Volume has an insignificant effect on Bitcoin price in the short term, it has a negative significant impact in the long term. In addition, LQ45 stock index Volume influences Bitcoin Price positively in the short term, suggesting that LQ45 stock index and Bitcoin substitute for each other. Moreover, Google Trends impacts Bitcoin price positively in the long term. In terms of the income effect, either the Indonesian GDP or US GDP has a strong positive effect on Bitcoin price in both the short and long term.
On 16th March 2022, U.S. Federal Reserve increased the interest rate the first time, and in the whole year, U.S. Federal Reserve made seven increments on interest rate. As this will affect the value of dollar, many American financial assets were also affected by it, including ETH, one of the most famous cryptocurrencies. This paper uses the history data of ETH price from January 2018 to July 2023 and constructs ARIMA model without Federal Reserve increasing the interest rate to compare with the reality in order to comprehend how the increasing interest rate affected the price of ETH, and use the model to predict the trend of Ethereumâs price. With the influence of increasing interest rate, the price of Ethereum should decrease. However, after the U.S. Federal Reserve increased interest rate, the price of Ethereum went up for a while then dropped dramatically. And the reasons why this delay appears are the delay of policy and the first increment of interest rate is not attractive enough for investors to change their strategies. That can bring some inspirations to policymakers. They should acknowledge that there will be a delay in the market after the policy is released and they could give some potential signs or preferences on the new policy to reduce the shock to market. For investors, they could pay more attention on relevant policy and make use of the delay to make more money.
What do we know about the interrelations between economic inequality, ecology and the increased use of Bitcoin? The aim of the paper was to empirically test the relationship between economic and ecological effects related to the increase in Bitcoinâs network hashrate in a selection of countries that have the highest influx of crypto-mining. To test these three types of relationships, I collected a dataset concerning Bitcoin indicators, economic indicators and ecological indicators that were obtained from multiple trustworthy sources: OECD, World Bank, Fred Data, World Inequality Database (WID). Handling the data challenges, I used this unique panel dataset to explore the relationship between Bitcoinâs hashrate and two types of outcomes: (i) economic outcomes (such as the GDP which as we know relates to inequalities through the Kuznets curve) or direct measures of inequality (such as, income inequality (GINI) and the share of people with top 1% of income and 1% of wealth), and (ii) ecological outcomes (such as carbon emissions, carbon footprint and electronic waste). I found that the Bitcoin currency associates with certain redistribution of wealth, but the accumulation of crypto-currency-related wealth itself remains still concentrated in the wealth of the top 1%. Also, there is evidence for certain nonlinearities in the relationships with the ecological degradation, echoing the concept of the Kuznets curve.
The nature of cryptocurrencies is decentralized and they have potential of large returns, due to this nature of crypto currencies, they have increased in approval in form of investment. Due to the erratic and volatile nature of the crypto currency market, it can be difficult to predict their pricing. As a result, reliable price forecasts are essential for investors to make wise investment choices. The proposed LSTM based approach will create machine learning models using open-source libraries like pandas, NumPy, and Scikit-learn. Cross-validation will be utilized to test the working of different models, and the one gives best output will be taken as the final model. The aim of this research is to create a machine learning algorithm that can forecast bitcoin values. Predicting the future price swings of crypto currencies like Bitcoin and Ethereum has become a crucial research subject as their use and popularity have increased. By using regression and deep learning algorithms, the work seeks to use different number of machine learning models to analyze historical bitcoin data and forecast future prices. The end result of this work will be a very effective and accurate model with R2 score of 0.96 for train data and 0.97 test data for forecasting crypto currency values, which traders, investors, and researchers may use to decide wisely on investing in crypto currencies.
Cryptocurrency markets' extreme volatility demands advanced predictive models. The proposed neural network approach utilizes extensive research and real-image exchange data, revealing the Digital Internet of Things' impact. Addressing consumer influence on prices is vital. Our frame working corporate âdoesn't make sense. It should probably be âOur framework incorporates. At the core is an enhanced deep learning framework, integrating autoregressive integrated moving average (ARIMA) with convolutional neural networks (CNNs). This synergy captures intricate price patterns. We integrate sentiment analysis from various sources and block chain data for a holistic market view. Model robustness is bolstered with hyper parameter optimization and cross-validation. Real-time data integration ensures timely predictions. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics are used in performance evaluation. Empirical evidence high lights our model's superiority in predicting cryptocurrency market variations. Compared to traditional methods like ARIMA, it offers substantial improvements, benefiting traders, investors, and decision-makers. Future enhancements include ensemble models, hyper parameter tuning, advanced deep learning, realtime data integration, and model interpretability, empowering stakeholders with precise insights into evolving cryptocurrency markets.
Purpose The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility. Design/methodology/approach This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area. Findings The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with âcryptocurrenciesâ and âbitcoinâ during âCOVID-19.â The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area. Originality/value This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.
This article examines whether cryptocurrency (crypto) hedge funds successfully time the bitcoin market. The author uses a joint market-timing model to assess the bitcoin market returnâ and volatilityâtiming skills of crypto hedge fund managers. For a one-month holding period, the difference in the out-of-sample alpha between the top timers and bottom timers is 11.832% per month. The analysis shows that younger funds with shorter lockup periods tend to have stronger bitcoin market returnâtiming skills. Moreover, funds with lower redemption periods tend to exhibit stronger bitcoin market volatilityâtiming skills. The author also observes that crypto hedge funds demonstrate stronger bitcoin marketâtiming skills during market downturns, likely because of bitcoin being a safe-haven asset in contrast to traditional stock market investments. The findings are important for private investors who are considering crypto hedge funds as an alternative investment.
Purpose This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers. Design/methodology/approach The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation. Findings Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors. Originality/value The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.
Non-fungible tokens (NFTs) are decentralized digital tokens to represent the unique ownership of items. Recently, NFTs have been gaining popularity and at the same time bringing up issues, such as scams, racism, and sexism. Decentralization, a key attribute of NFT, contributes to some of the issues that are easier to regulate under centralized schemes, which are intentionally left out of the NFT marketplace. In this work, we delved into this centralization-decentralization dilemma in the NFT space through mixed quantitative and qualitative methods. Centralization-decentralization dilemma is the dilemma caused by the conflict between the slogan of decentralization and the interests of stakeholders. We first analyzed over 30,000 NFT-related tweets to obtain a high-level understanding of stakeholders' concerns in the NFT space. We then interviewed 15 NFT stakeholders (both creators and collectors) to obtain their in-depth insights into these concerns and potential solutions. Our findings identify concerning issues among users: financial scams, counterfeit NFTs, hacking, and unethical NFTs. We further reflected on the centralization-decentralization dilemma drawing upon the perspectives of the stakeholders in the interviews. Finally, we gave some inferences to solve the centralization-decentralization dilemma in the NFT market and thought about the future of NFT and decentralization.