This study constructs a machine learning-driven multi-factor model for Ethereum quantitative trading, combining traditional technical indicators (RSI, MACD), on-chain metrics (gas usage, active addresses), and X platform social sentiment to predict short-term returns. Backtesting from Q4 2021 to Q3 2024, using online learning and genetic algorithms for dynamic factor updates, yields a 97% annualized return, a Sharpe ratio of 2.5, and an information ratio of 1.2, outperforming Ethereum's raw returns. Simulated trading in Q4 2024 (bull market) achieves a 33% quarterly return with an 18% maximum drawdown, while Q1 2025 (bear market) records a -10% quarterly return with a 12% drawdown, confirming robustness. Technical and sentiment factors drive performance, though a 22% maximum drawdown in backtesting highlights volatility risks. An optimal Z-score threshold (±1.0) and 4-hour trading frequency balance profitability and costs. Future enhancements include high-frequency mainnet data integration and advanced risk management to strengthen model resilience in Ethereum's volatile market.
This research investigates the effect of social media sentiment on the cryptocurrency market, particularly focusing on Bitcoin and Ethereum. Using TensorFlow as a machine learning tool, we developed a sentiment index from 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum, collected in 2022. The sentiment scores, ranging from -1 (negative) to 1 (positive), were categorized into positive, neutral, and negative classes and analyzed alongside daily return and volatility metrics for both cryptocurrencies using a Vector Autoregression (VAR) model. Our study identifies significant impacts of social media sentiment on cryptocurrency markets. Specifically, Bitcoinâs returns show a heightened sensitivity to negative sentiment, whereas Ethereumâs returns remain unaffected by any sentiment type. However, the volatility of both cryptocurrencies is affected by neutral sentiment. These findings highlight distinct behavioral patterns across cryptocurrencies and uncover a bidirectional relationship between market dynamics and social media sentiment. This study offers novel insights into how public perception influences digital asset markets, thereby contributing to the behavioral finance literature and providing practical implications for investors and policymakers.
Maksym Lazirko, Deniz Appelbaum, Miklos A. Vasarhelyi
Cryptocurrency exchanges face increasing pressure to demonstrate reserve adequacy following platform failures, yet current Proof of Reserves (PoR) systems suffer from incomplete verification approaches that examine either on-chain or off-chain assets separately. This study introduces the Double-Helix Framework, a verification methodology that integrates on-chain blockchain analysis with off-chain consensus algorithms to provide complete assessment of exchange financial positions. The framework employs parallel verification strands that simultaneously validate blockchain-recorded transactions and off-chain financial information, creating a unified assessment mechanism that addresses the verification gaps in existing PoR systems. The framework's integration of traditional auditing principles with distributed ledger verification creates new possibilities for regulatory compliance and investor protection in digital asset management. This framework has implications for accounting practice, suggesting that comprehensive cryptocurrency audits require verification approaches that extend to on-chain, off-chain, and intersecting transactions that have varying degrees of separation between ledgers.
Ignacio Ariel Del Monte, Juan de Lucio, Miguel Angel Sicilia Urban
This systematic review examines risk of Impermanent Loss (IL) in Automated Market Makers (AMMs) within the Decentralized Finance (DeFi) ecosystem, employing the PRISMA-S methodology. Our comprehensive search across the Web of Science and Scopus databases identified 38 relevant studies published between January 2020 and September 2024. The review reveals a predominant focus on Constant Product Market Makers (CPMMs), which comprise 55.7% of all mentions, underscoring their central role in DeFi markets. There are 9 underlying causes affecting IL risk and the most important ones are price volatility, asset imbalance, and risk/return management. According to our categorization, the most commonly used Mitigation Strategies are Investment Strategies, Decentralized Tools and Technologies, and Design and Management of Liquidity Pool, Hedging Strategies and Context Strategies. IL risk research is calculated equally theoretically and empirically (9 references for each) and there are 6 research papers that calculate in a mixed way. Only 13 research papers employ market data in their reviews and 7.9% of all papers measure IL risk quantitatively. We seek to focus our studies on a more detailed treatment of the risk of IL that will result in improvements to liquidity providers in DeFi.
This paper explores the causal relationship between the U.S. trade policy uncertainty and cryptocurrency returns using the quantile Granger causality test. Unlike traditional approaches that focus on average effects, this method captures asymmetric causal dynamics across the entire conditional distribution. The analysis employs two established indices of trade policy uncertainty developed by Caldara et al. (2020) and by Baker et al. (2016), ensuring robustness and mitigating potential biases from relying on a single measure. The empirical results indicate that changes in cryptocurrency prices consistently Granger cause movements in trade policy uncertainty across most quantiles, suggesting that cryptocurrencies may serve as early indicators of shifts in economic policy sentiment. In contrast, the effect of trade policy uncertainty on cryptocurrency returns is most pronounced in the tails of the distribution, highlighting a stronger influence during periods of extreme market conditions. These findings highlight the importance of accounting for nonlinear and asymmetric effects in assessing the interaction between economic policy uncertainty and cryptocurrency markets.
This study investigates Granger-causality relationships between crypto-assets (Bitcoin and Ethereum) and traditional financial assets (stock indices and exchange rates) in BRICS-T countries over the 2016â2024 period. The findings highlight significant interlinkages: bidirectional causality exists between Bitcoin and Russia's stock market, and between Ethereum and both Brazil's stock market and the USD/INR exchange rate. Unidirectional causality is observed from Bitcoin to the stock markets of Brazil, India, and China, while the USD/TRY exchange rate influences Bitcoin. Similarly, Ethereum affects the stock markets of Russia, India, and South Africa, while the USD/TRY exchange rate also Granger-causes Ethereum. These results indicate a growing synchronization between crypto-assets and conventional financial markets. The presence of both unidirectional and bidirectional causalities emphasizes the increasing integration of global financial systems and highlights the importance for investors to consider cross-market interactions when making decisions. Crypto-assets are no longer isolated but are embedded in broader financial dynamics.
Investment advisory services are now commonly offered by consulting firms with financial experts, typically for a monthly fee. Financial markets require specialized knowledge, but advancements in artificial intelligence have revolutionized this field. Deep learning algorithms, especially Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are widely used to predict asset price trends in nonlinear time-series data. However, they demand large datasets and are prone to overfitting. Recently, combining deep learning with reinforcement learning has shown promise, though it requires intensive research and computational resources. This study introduces the BTC-PDPR (Bitcoin Price Direction Prediction Robot) model, which predicts Bitcoin's daily price direction using the Random Forest Regressor. As an ensemble-based machine learning model, it works effectively with smaller datasets and identifies key technical indicators influencing price trends. The model achieved a 99.20% accuracy rate on data from March 2018 to the present. It runs efficiently in Google Colab (v5e1 configuration), producing results in just 22 seconds. This paper outlines the methodology, reviews relevant studies from 2017 to 2024, highlights gaps in the literature, and emphasizes the studyâs contributions to the field.
This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets.
Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017â2020) and Biden (2021â2024) governments. The 48-week return forecast of the BitcoinâGold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes.
As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The modelâs performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design.
This research examines the dynamic interaction between conventional financial assets, namely the US dollar, the S&P 500 index, gold and crude oil, and ten major green cryptocurrencies, focusing on their spillover linkages and hedging capacities during major global economic and geopolitical shocks. The study analyses daily data to uncover spillover effects using the innovative Quantile-Vector Autoregressive methodology developed by Cunado et al. (2023) . Results indicate that green cryptocurrencies significantly interact with other examined instruments. Algorand, Cardano, IOTA, TRON and Powerledger demonstrate the largest interactive effects, with the latter standing out as a consistent transmitter of influence across both crises, demonstrating that this sub-class of cryptocurrency is exhibiting elevated maturity. Traditional assets predominantly act as receivers of such risk dynamics from more speculative asset classes, with gold identified as an effective absorber of spillovers, especially in bear markets. Conversely, the US dollar and crude oil are identified as large transmitters of spillover impacts, a result found to be particularly influential in periods of geopolitical conflict. The study further reveals that green cryptocurrencies promoting trust, innovation, and renewable energy are more effectively connected with traditional investments than those focusing on financial services or business accessibility, presenting diversification opportunities during crises.
This study aims to analyze the impact of the U.S. Federal Reserve Systemâs monetary policy on major cryptocurrencies. Specifically, it explores whether the effects differ between volatile cryptocurrencies, such as Bitcoin and Ethereum, and the stablecoin Tether. To this end, we utilize an autoregressive distributed lag (ARDL) bounds testing approach, analyzing monthly data from January 2019 to April 2025. The empirical results indicate that the responses of volatile and stable cryptocurrencies to the Fedâs monetary policy differ. In the long term, the prices of Bitcoin and Ethereum tend to react positively to the Fedâs monetary policy changes, whereas Tetherâs prices experience a negative impact. We recommend novel policy implications in this study based on these empirical findings.
The environmental impact of cryptocurrencies has attracted increasing scrutiny, largely due to the high energy consumption of blockchain networks. However, empirical research on the causal relationship between cryptocurrency trading activity and carbon emissions remains scarce. This study addresses this gap by analysing the dynamic interplay between cryptocurrency trading and COâ emissions for Bitcoin, Ethereum, and Binance Coin, using monthly data from January 2015 to September 2024. Employing the Toda-Yamamoto augmented Granger causality approach, we apply logarithmic transformations to ensure data stationarity and address integration and endogeneity concerns. Our results reveal a bidirectional Granger causality between Bitcoin trading and COâ emissions, suggesting a feedback loop between market activity and environmental impact. For Ethereum, we find a similar albeit weaker bidirectional causality from trading to emissions, while no significant causal link is detected for Binance Coin, likely reflecting its more energy-efficient consensus mechanism. These findings highlight the disproportionate environmental burden of proof-of-work cryptocurrencies and underscore the need for targeted regulatory responses. We recommend the adoption of carbon-sensitive crypto policies, such as mandatory energy usage disclosures and incentives for transitioning to sustainable consensus mechanisms. This study advances the environmental finance literature by providing robust empirical evidence on the links between digital asset markets and carbon emissions.
This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression (QQR), wavelet cross-quantilogram (WCQ), and advanced portfolio optimization strategies, our analysis captures asymmetric dependence, tail risks, and time-frequency dynamics from January 2015 to December 2024. Our results show that BTC consistently has strong hedging potential at lower quantiles, particularly during short-term stress, whereas GOLD and GBOND offer greater stability over medium- and long-term horizons. Conditional expected shortfall (CES) and extreme downside correlation (EDC) analyses highlight BTCâs resilience to extreme downside risks, whereas GOLD and GBOND serve primarily as long-term defensive assets. Portfolio optimization confirms BTCâs critical role in diversification under minimum correlation and connectedness strategies, and GBOND dominates variance-minimizing portfolios. These findings offer practical guidance for constructing robust, adaptive portfolios under geopolitical uncertainty.
Abstract ValueâatâRisk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the timeâheterogeneous Student's t autoregressive model outperforms standard models commonly used by practitioners.
Abstract Cryptocurrency markets have evolved into a vital segment of the global financial ecosystem, drawing considerable interest from both investors and regulatory bodies. Yet, their extreme price instability demands innovative strategies for risk mitigation and investment that diverge from conventional financial practices. This research focuses on analyzing the volatility patterns of leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)âby employing GARCH-family models such as GARCH, EGARCH, TGARCH, and CGARCH. Through a comparative evaluation of these models, the study identifies the optimal framework for characterizing cryptocurrency market volatility. Utilizing daily closing prices from Yahoo Finance (January 1, 2019, to January 8, 2025), the analysis reveals that TGARCH outperforms others for BTC, EGARCH for ETH, and CGARCH for BNB, underscoring the critical role of asymmetric volatility in these markets. This work advances existing research by offering a detailed comparison of GARCH-based approaches and practical insights for risk evaluation and portfolio optimization.
Senior Financial Markets Dealer, Nassau, The Bahamas, Vladyslav Yakymashko
This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.
This study aims to demystify the link between Bitcoin pricing and the associated electricity costs, constituting the most significant cost in mining Bitcoin. The article revisits the typical Cost-price (electricity consumption -Bitcoin price) relationship in the context of Bitcoin. The research question is answered using the Nonlinear Autoregressive Distributed Lag (NARDL) Model complemented with Multiple Breakpoints Least Squares Regression (MBLSR). The study analyzes monthly data from various sources from March 2017 to September 2023 and is segregated into four different regimes. The convergence in both techniques provides rigour and robustness to the results. The findings reveal the asymmetric relationship where Bitcoin's energy consumption does not increase significantly with a positive change in Bitcoin Price. This behaviour is counterintuitive given that electricity consumption is expected to increase in a high price period because of more profit margins. The flooding of accumulated Bitcoins by the miners in high price periods may be a contributing reason for no significant increase in the electricity consumption in mining Bitcoins. Conversely, the fall in Bitcoin prices will reduce the energy consumed by the Bitcoin Network conforming to the anticipated pattern. This behaviour is in stark contradiction to the Law of Supply and is well explained by the Bitcoin miners' operational strategy in the Boom and Recession period. Relying on the asymmetric behaviour, investors can revamp their strategy to make profits in the market. In addition, findings suggest policymakers try to limit credit accessibility to miners in the bust to reduce the colossal energy consumption of Bitcoin.
Halilibrahim Gökgöz, Aamir Aijaz Syed, Hind Alnafisah, Ahmed Jeribi
The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoinâs volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions.
This study investigates the dynamic interplay between national currencies of the core BRICS economies and the three strongest monetary assets (US dollar, gold, Bitcoin) in the existing global financial outlook. Using data spanning the inflationary Russia-Ukraine conflict (24 February 2022 to 5 June 2025) and the innovative Quantile-VAR methodology in bear, normal and bull market conditions as expressed by quantiles insights are offered about the potential of transformation of the monetary status quo. Findings reveal that extreme market conditions strengthen the leading potential of Bitcoin and gold in early and later war phases, respectively. This abides by the pseudo-wealth and consumption fluctuations theory of Guzman and Stiglitz (2021) as higher risk-taking appears in turbulent periods for preserving and promoting growth. Shielding from inflation could also work this way. The Brazilian, Chinese and South African currencies gain prominence while the Russian currency acts as a net absorber of shocks. So the US dollar could be partly crowded out. Alterations in monetary asset allocation for investors could serve for better adapting to contemporary financial needs.
Abstract The approval of Bitcoin ETFs by the Securities and Exchange Commission (SEC) on 01/11/2024 was an essential event for both the cryptocurrency market and the traditional financial system. Bitcoin ETFs work as a bridge between digital assets and traditional financial instruments, contributing to increased liquidity and attracting new institutional investors who were reluctant before due to regulatory and security concerns. This study assesses the impact of the approval of Bitcoin ETFs on the stability of the financial system, focusing on the correlations and the volatility spillover effects of Bitcoin and three major financial indices (S&P 500, Dow Jones Industrial Average, and Nasdaq-100). Using Pearson Correlation, Time-Varying Parameter Vector Autoregression (TVP-VAR) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, this research offers a comprehensive analysis of the influence of Bitcoin on the dynamics of market. The results show that, although the correlations between Bitcoin and stock market indices reached a peak in 2021, they dropped later, suggesting a gradual decoupling from traditional financial markets. However, after the launch of Bitcoin ETFs in 2024, the correlations with financial indices â especially with S&P 500 â started to rise again, suggesting a reintegration of Bitcoin into the traditional financial system. Contrary to initial expectations, the results obtained from data covering 90 days before and after the launch of Bitcoin ETFs donât show a significant increase in short-term correlations, which suggest a smooth adaptation of the market to these new financial instruments. In addition, although Bitcoin ETFs contribute to the stabilization of cryptocurrency volatility, they introduced new types of intra-day fluctuations, highlighting the need for an advanced strategy of risk management. The study concludes that, while Bitcoin ETFs contribute to the stability of financial markets, they introduce systemic risks which require continuous surveillance from the regulatory authorities. Long-term implications of the approval of Bitcoin ETFs remain uncertain, hence more research is needed in order to comprehensively assess the impact of these new financial instruments on the global financial stability.
Hanen Ben Ameur, Fouad Jamaani, Mohammed N. Abu-Alfoul
This study investigates the co-movements between prominent financial assets-crude oil, natural gas, gold, and Bitcoin-and uncertainty indices, including the Infectious Disease Equity Market Volatility Tracker (IDEMV) and the Geopolitical Risk Index (GPR), from January 2017 to January 2023. By employing advanced wavelet techniques-Wavelet Power Spectrum (WPS), Bi-Wavelet Coherence (WCA), Multiple Wavelet Coherence (MWC), and Partial Wavelet Coherence (PWC)-we analyze their time- and frequency-dependent responses to market shocks. The results reveal that Bitcoin and WTI exhibit time-varying sensitivity to IDEMV, particularly at short- and medium-term frequencies, highlighting their vulnerability to health-related crises like COVID-19. In contrast, gold and natural gas respond more strongly to GPR, with gold demonstrating a long-term leading role during geopolitical uncertainties, while Bitcoin and WTI lead in health-related shocks. The Russia-Ukraine conflict further amplified GPR's impact on Bitcoin and increased natural gas's vulnerability to geopolitical disruptions. These findings underscore the need for tailored strategies to address health and geopolitical risks. Policymakers should enhance crisis-response frameworks for Bitcoin and crude oil, while investors can reduce uncertainty by diversifying portfolios with resilient assets like gold and natural gas.
Cristina Dima, RÄzvan CÄtÄlin Dobrea, MÄdÄlina Ioana Moncea, Eduard Laurentiu Ion
Abstract For a long time, among the most controversial topics revolves around technology, which encompasses the financial landscape and changes the way we perceive and interact with money. The cause of this transformation is cryptocurrency - a revolutionary innovation that has captured the imagination of individuals and institutions around the world. For the less informed, investing in cryptocurrencies may seem like a game of chance, while for the younger ones, it represents a promising source of income for the future. The reasons for choosing the theme about cryptocurrencies can be motivated by several current factors such as: the topicality and relevance of cryptocurrencies, technological innovation, financial opportunities, regulations and public policies, social and cultural impact, but the main reason is the monetary future, which can become a significant part of the global monetary system.
The popularity of cryptocurrencies as alternative investments has grown in recent years. However, it remains unclear whether cryptocurrency investors behave irrationally in a similar way to emerging market investors. Using a systematic literature review, this study aims to compare the factors related to the presence of behavioural biases in the cryptocurrency and emerging stock markets. This study highlights similarities and differences between cryptocurrency and emerging stock market investor behaviour. Thus, the study's novelty arises from comparing the role of behavioural inclinations in cryptocurrency and emerging stock markets. The findings indicate that the small amount or lack of available information about small-cap emerging stocks or cryptocurrencies may reinforce investor sentiment and herding behaviour. The herding behaviour among investors in both markets may stem from following the most popular investment trends. Investors in cryptocurrency and emerging stock markets also tend to overreact to market sentiment and changes in market conditions. Extreme market conditions may affect the strength of herding behaviour, disposition effect, price clustering, anomalous behaviour, investor sentiment and uncertainty. Thus, cryptocurrency and emerging stock markets are informationally inefficient most of the time, whilst investorsâ irrationality may be more pronounced during certain periods. Furthermore, investorsâ behaviour in the cryptocurrency and emerging stock markets is more consistent with the adaptive market hypothesis than the efficient market hypothesis. This research suggests that cryptocurrency and emerging stock market investors should actively manage investment portfolios. Policymakers should be more concerned about information accessibility and quality, especially in the case of small-cap investment assets. JEL codes: G14;G15;G41