Abstract This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network–long short-term memory (CNN–LSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methods—i.e., Boruta, genetic algorithm, and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNN–LSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
Identifying the expected growth in cryptocurrency involves analyzing a combination of market trends, technological advancements, regulatory developments, and economic indicators. Historical performance and adoption rates of major cryptocurrencies provide insight into market trends, while innovations in blockchain technology, such as Ethereum 2.0 and Layer 2 solutions, along with the rise of decentralized finance (DeFi) and non-fungible tokens (NFTs), highlight significant technological advancements. Regulatory developments, including supportive legislation and the involvement of institutional investors through financial products like Bitcoin ETFs, play a crucial role in shaping market confidence and investment. Economic indicators, such as inflation, monetary policies, and global events, also influence interest in cryptocurrencies as alternative assets. Investor sentiment, driven by public perception, media coverage, and social media activity, impacts market dynamics. Additionally, research from financial analysts, market research firms, and academic studies, along with corporate partnerships and the integration of crypto solutions with traditional systems, contribute to growth predictions. Monitoring market capitalization and trading volumes further helps gauge market interest and liquidity. By considering these multifaceted factors, a more comprehensive understanding of the potential growth in the cryptocurrency market can be achieved.
This paper investigates both coin-specific and market-based factors that drive cryptocurrency pump-and-dump schemes. It analyzes a data set comprising 1,457 pump events that occurred from January 3, 2018, to January 2, 2022. Empirical findings, derived from binary cross-sectional regression models, reveal several characteristics that increase the likelihood of cryptocurrencies being pumped. These include lower market capitalization, lower trading volume, greater social media popularity, increased developer activity, and fewer exchanges trading them. Furthermore, the study employs count time-series models to examine market-based factors. The results indicate that periods of higher volatility or uncertainty are associated with an increase in pre-announced pump-and-dump activities. Additionally, the analysis shows that macroeconomic factors and specific time-related effects - such as Sundays, certain months, and the COVID-19 period - are significant in explaining the frequency of pump occurrences. Based on these findings, the article discusses several targeted recommendations.
We analyse the impact of cybercrime, particularly cryptocurrency heists and scams, on dynamic conditional correlations and abnormal returns in cryptocurrency. Our high-frequency, hourly data set covers three years, from January 2020 to December 2022, and our results show that certain hacking events have a negligible negative impact on investors, especially when focusing on less popular tokens or coins. This new perspective has significant implications for the investment community. Existing literature generally assumes that the impact of cybercrime is situational and argues for increased awareness, proactive cybersecurity measures and multi-stakeholder collaboration in traditional financial markets such as equities and currencies. However, the cryptocurrency market – a relatively young and still developing area – has a unique dynamic. Less popular tokens or coins often have lower market integration and liquidity, indicating a lower impact of cybercrime. If such a token or coin already has a limited reputation or investor base, the overall negative sentiment may be further mitigated, lessening the expected negative impact.
This study addresses a critical gap by providing an in-depth examination of how cryptocurrency markets respond to U.S. monetary policy shocks at various price levels. This study contributes significantly to our understanding of the nuanced dynamics governing cryptocurrency markets under diverse monetary policy conditions, thereby enhancing our knowledge of the broader financial ecosystem. Through rigorous quantitative analysis, we utilize monthly time series data spanning from January 2015 to December 2023 and employ models such as Markov-switching dynamic regression, Autoregressive Conditional Heteroskedasticity, and Generalized Autoregressive Conditional Heteroskedasticity. This study reveals that monetary policy shocks result in a decrease in cryptocurrency prices and volatility. Moreover, monetary policy tightening stabilizes the market at low cryptocurrency prices. In higher price states, interest rate increases are associated with reduced cryptocurrency prices and volatility. The findings suggest that changes in interest rates influence the opportunity cost of holding cryptocurrencies, impacting their appeal compared with traditional interest-bearing assets.
Bitcoin has increased rapidly in value since the first day of its integration into today's markets. The increases experienced have directed the interest of global investors to this field over time. In addition to these developments, the increasing popularity of blockchain technology and the increase in the volume of cryptocurrencies have turned these currencies into an important tool for commercial activities. Although there are many studies to measure the international trade balance with exchange rates, no study has been found to examine the relationship between the change in cryptocurrency prices and the trade balance of countries. In this study, the relationship between the trade balance of Nigeria, one of the leading countries in the world in terms of cryptocurrency usage, and cryptocurrencies is analysed using NARDL analysis with coefficient symmetry test (2016/M4-2020/ M12). According to the research results, Bitcoin and Litecoin can have significant long-term impact on Nigeria's trade balance.
One of the financial assets in currency exchange is now cryptocurrency. The public is drawn to cryptocurrency trading because it is considered a lucrative form of investing. For cryptocurrency investors to maximize their earnings, accurate price forecasting is crucial. As price forecasting involves time series analysis, a hybrid deep learning model is suggested to project cryptocurrency prices in the future. Long Short-Term Memory and Gated Recurrent Unit (LSTM-GRU) networks are integrated into the hybrid model. Three cryptocurrency datasets are evaluated using the suggested hybrid model: Ethereum, Ripple, and Bitcoin. According to experimental results, the suggested LSTM-GRU model may provide the lowest MSE and RMSE values on the Bitcoin dataset (0.0611 and 0.2472), the Ethereum dataset (0.0369 and 0.19222), and the Ripple dataset (0.0006 and 0.0247).
This study aims to analyze the impact of internal variables, including total Ethereum, number of transactions, fees per transaction, and number of active wallets, as well as external variables, namely the price of Bitcoin and the price of gold, on global Ethereum prices. The study utilizes daily data covering the period from December 31, 2016, to December 31, 2021. The data analysis employs time series data with the assistance of Eviews 10 and the error correction model (ECM) method. The study's findings indicate that total Ethereum, number of transactions, fees per transaction, number of active wallets, price of Bitcoin, and price of gold collectively exert a significant influence on Ethereum prices. However, when examined individually, total Ethereum demonstrates a negative impact and lacks statistical significance on Ethereum prices. Similarly, the number of transactions exhibits a negative and significant effect on Ethereum prices. Conversely, transaction fees, number of active wallets, and the price of Bitcoin have a positive and significant impact on Ethereum prices. Meanwhile, global gold prices do not exhibit any influence on Ethereum prices.
Yu Yvette Zhang, Zichen Li, Tao Yan, Qianyu Liu · 6 authors
Cyclic arbitrage chances exist abundantly among decentralized exchanges (DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or practitioners usually choose a specific token, such as Ether (the cryptocurrency from Ethereum) as input, and optimize their input amount to get the net maximal amount of the specific token as arbitrage profit without considering the tokens’ market price from the centralized markets (CEXs). By considering the tokens’ prices from CEXs in this paper, the new arbitrage profit will be quantified as the product of the net number of a specific token we got from the arbitrage loop and its corresponding price in CEXs. The new arbitrage profit will be called monetized arbitrage profit in this paper. Based on this concept, we put forward three different strategies to maximize the monetized arbitrage profit for each arbitrage loop. The first strategy is called the MaxPrice strategy. Under this strategy, arbitrageurs start arbitrage only from the token with the highest CEX price. The second strategy is called the MaxMax strategy. Under this strategy, we calculate the monetized arbitrage profit for each token as input in turn in the arbitrage loop. Then, we pick up the most maximal monetized arbitrage profit among them as the monetized arbitrage profit of the MaxMax strategy. It is easy to prove that this strategy can bring more profit than the MaxPrice strategy. The third one is called the Convex Optimization strategy. In this strategy, we mapped the MaxMax strategy to a convex optimization problem and proved that the Convex Optimization strategy could get more profit in theory than the MaxMax strategy, which is proved again in a given example. We also proved that if no arbitrage profit exists according to the MaxMax strategy, then the Convex Optimization strategy can not detect any arbitrage profit, either. However, the empirical data analysis denotes that the profitability of the Convex Optimization strategy is almost equal to that of the MaxMax strategy, and the MaxPrice strategy is not reliable in getting the maximal monetized arbitrage profit compared to the MaxMax strategy.
Nektarios Aslanidis, Aurelio F. Bariviera, Christos S. Savva
This paper adopts a versatile conditional correlation approach to explore daily seasonality in the major cryptocurrencies. Given the lack of clear fundamental value in this market and the active online profile of investors, the study also relates cryptocurrency cross-correlations to online market attention and sentiment. Our results highlight that while investor attention has a positive effect, sentiment has a much stronger negative impact on the correlations. These findings can offer interesting insights for investors and regulators, as the influence of market attention and sentiment on the correlations has important implications for portfolio diversification and market stability.
Muhammad Mahmudul Karim, Abu Hanifa Md. Noman, M. Kabir Hassan, Asif M. Khan · 5 authors
Purpose This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic. Design/methodology/approach The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons. Findings The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons. Practical implications The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios. Originality/value This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.
Purpose: This study sought to explore cryptocurrency and its role in portfolio diversification. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cryptocurrency and its role in portfolio diversification. Preliminary empirical review revealed that incorporating cryptocurrencies into investment portfolios offered promising diversification benefits due to their low correlation with traditional assets, despite their high volatility and regulatory uncertainties. It highlighted the significant risk management challenges posed by cryptocurrencies' extreme price fluctuations and the evolving regulatory landscape. The study emphasized the importance of careful, limited allocation to cryptocurrencies, robust risk management practices, and continuous market monitoring. Ultimately, it suggested that cryptocurrencies could enhance portfolio performance when strategically used alongside traditional diversification methods. Unique Contribution to Theory, Practice and Policy: The Modern Portfolio Theory, Efficient Market Hypothesis and Behavioural Finance Theory may be used to anchor future studies on portfolio diversification. The study recommended a cautious yet strategic inclusion of cryptocurrencies in investment portfolios to enhance diversification, emphasizing the importance of ongoing research, robust risk management, and proactive monitoring due to their high volatility and regulatory uncertainties. It called for clear and consistent regulatory frameworks to protect investors while fostering market growth, and highlighted the need for collaboration between academia, industry, and regulatory bodies to improve financial literacy and market stability. These recommendations aimed to contribute to theoretical, practical, and policy aspects of cryptocurrency investments.
This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.
The study explores diverse AI methodologies employed in the cryptocurrency domain, focusing on their applications in key areas such as price prediction, sentiment analysis, market trend analysis, volatility prediction, trading strategy optimization, fraud detection, and portfolio management. Various machine learning models, including regression, neural networks, and reinforcement learning, are investigated for their effectiveness in predicting cryptocurrency prices and optimizing trading strategies. The integration of Natural Language Processing (NLP) techniques is discussed in the context of sentiment analysis, where AI algorithms analyze vast amounts of textual data from social media, news articles, and online forums to gauge market sentiment and its potential impact on cryptocurrency prices. Additionally, the paper examines the role of AI in identifying patterns, trends, and anomalies in market data, facilitating effective decision-making for traders and investors. However, the paper emphasizes the need for caution, acknowledging the inherent uncertainties and risks associated with cryptocurrency investments. It concludes by highlighting the potential for continued advancements in AI applications, contributing to a deeper understanding of cryptocurrency market dynamics and aiding in more informed decision-making in this rapidly evolving financial landscape.
Our study investigates the hedging ability of Gold and Bitcoin to hedge against financial market crashes. We also examined the ability of the VIX fear gouge to improve the ability of those financial assets to hedge financial risks. We found a positive dependency between the current daily prices of Gold and Bitcoin with a stronger impact of Gold on Bitcoin than vice versa. We also find that in recent years (2021-2023), Gold price changes are negatively correlated to yesterday's price change of the S&P500 a day before and positively correlated to yesterday's NASDAQ price change.
An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
This paper examines the efficiency and asymmetric multiracial features of NFTs (Mana, Tezos), and traditional assets (EGX30, Oil index) using Asymmetric Multiracial Cross-Correlations Analysis covering the period from January 2020 to May 2021. Considering the full sample with a significant variation among asset classes. (Oil-Tezos)and (Mana-Tezos) is the most efficient.Since their inception, the blockchain-based digital asset classes have received immense interest from investors and portfolio managers as an alternative investment platform. Along with other established traditional cryptocurrencies such as Bitcoin, Litecoin, Ripple, and Ethereum, new blockchain asset classes such as Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) have made a considerable contribution tothe asset market’s recent expansion (Aharon & Demir, 2021; Alam, Chowdhury, Abdullah, & Masih, 2023; Maouchi, Charfeddine, & el Montasser, 2021; Yousaf & Yarovaya, 2022).Fundamentally, NFTs and DeFi differ from traditional cryptocurrencies as they are not virtual currency. Where NFTs are non-transferable cryptographic digital assetscreated by Ethereum smart contracts and can be sold and traded, the interchangeability of NFTs when comparing the other cryptocurrencies is very low (Karim, Lucey, Naeem, & Uddin, 2022; Q. Wang, Li, Wang, & Chen, 2021; Y. Wang, 2022).The NFTs and DeFi are relatively contemporary and unexplored asset classes, but their market capitalization has grown substantially as risk minimizing assets, particularly during the COVID-19 period. In the NFT space, the KeywordsVolatility, NFTs, Traditional Financial Assets, and MGARCH