Cryptocurrencies market capitalization has surpassed $4 trillion in 2025, attracting individual and institutional traders seeking investment and speculation. However, volatility of cryptocurrencies prices makes profitable strategies a huge challenge, especially with respect to the variance of returns. In this context, this paper presents an innovative strategy based on the counterintuitive concept from Game Theory called Parrondoâs Paradox. The presented strategy results in improved capital gains (returns) when compared to traditional buy & hold. Also, the strategy is proven to work in daily, weekly and minute-by-minute timeframes. With the empirical results shown in this paper, the Parrondoâs Paradox framework can be used as a trading strategy by either individual or institutional investors.
The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework.
PrzemysĹaw GrÄ dzki, Piotr WĂłjcik, Stefan Lessmann
Abstract This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architecturesâincluding the vanilla Transformer encoder, FEDformer, and Autoformerâalongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance.
The aim of this dissertation is to test the applicability of two strategies â Dollar Cost Average (DCA) and Lump-Sum (LS) â in the context of the crypto market. We tested these strategies on three assets, namely Bitcoin, Ethereum and Ripple. We developed a simulation using daily historical data recorded over a period of nine years. We then calculated performance ratios and created an AR-GARCH model to analyse their properties and predictive capacity more effectively. Our empirical results show that all assets are highly volatile and exhibit heavy tails and asymmetry. Additionally, they are moderately to highly correlated with each other. We also presented proof of higher Sharpe and Sortino ratios for DCA strategies, with Bitcoin performing better than the other two assets. The results also show that Bitcoin has low-to-moderate shock sensitivity and high persistence; Ethereum has low shock sensitivity and high persistence; and Ripple has both high shock sensitivity and persistence. Furthermore, we observed the impact of strategy choice on volatility. When compared to DCA, LS lowered shock sensitivity in Bitcoin and Ripple, enhancing persistence, while having an insignificant effect on Ethereum. Finally, we demonstrate that our model exhibits superior predictive capacity with regard to Ripple compared to Bitcoin and Ethereum, and that all three assets are inefficient. These findings contribute to previous literature by providing novel empirical data and attesting to the attributes of cryptocurrencies. Furthermore, this thesis improves financial awareness and provides investors with valuable information.
We investigate the predictability of cryptocurrency returns using a comprehensive set of macroeconomic and cryptocurrency-specific factors and a set of 12 machine learning models. To enhance interpretability, we employ SHAP analysis to quantify the marginal contribution of each factor to model outputs. We further assess the economic value of predictive signals by constructing long-short and long-only portfolios. Empirically, tree-based methods, particularly random forests, deliver the highest predictive accuracy and outperform neural network and linear benchmarks, with predictability substantially stronger than that documented in equity markets. Across models, the market-to-realized-value ratio, new addresses, and active addresses consistently emerge as the most influential predictors, with higher values associated with higher expected returns. Portfolio results show that neural network-based strategies achieve the highest cumulative performance, indicating meaningful investment gains. Overall, our findings demonstrate the value of machine learning for return forecasting in the cryptocurrency market and provide practical insights for investors and financial analysts operating in highly volatile and evolving cryptocurrency environments.
Crypto analysts have to deal with a variety of challenges, with the most important area being the price volatility of cryptocurrencies. Due to uncertain market trends, many studies have been conducted on forecasting techniques, and some of these techniques have been integrated with advanced analytical tools, including machine learning (ML) techniques. Making reliable predictions of the speculative behavior of financial assets, especially in non-stationary and highly volatile environments such as the cryptocurrency market, is a challenging task. In this study, ML techniques are used to identify influential features that affect the prices of cryptocurrencies, especially for Bitcoins. In addition, multivariate control charts are utilized for signal detection, allowing for a structured approach to develop trading strategies for seasonal market conditions. Unlike other studies that do not take seasonality into serious consideration when analyzing market fluctuations, the proposed approach explicitly accounts for it. The developed strategy is tested across various market conditions, including the final days of each year from 2019 to 2024, and demonstrates strong and consistent performance in all cases. By systematically identifying key on-chain features and analyzing them by means of control charts, this study develops a structured approach to anomaly-based trading strategies in Bitcoins. These discoveries address an extensive discussion on automated trading systems, demonstrating that feature selection, technical indicators, market seasonality, and halving impacts are important components in hinting at successful cryptocurrency exchange strategies.
This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoinâs daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = â0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volumeâpriceâvolatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.
Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.
This dissertation examines the evolving market microstructure of digital assets, focusing on transaction costs, liquidity provision returns, and the development of innovative exchange mechanisms. In three essays, the research provides empirical evidence on digital asset trading in both traditional and emerging decentralized market architectures. Each essay addresses previously unresolved questions, offering valuable insights for researchers, practitioners, and regulators to better understand and manage the benefits, costs, and risks of trading in digital asset markets.The first essay examines the cost of trading across digital assets in traditional centralized limit-order-book exchanges and a nascent, decentralized market architecture: the Automated Market Maker. By employing a novel methodology the study extends prior research that relies on less detailed, low-frequency information. The findings reveal transaction cost advantages for Automated Market Makers with remarkable stability across varying levels of market volatility, trading volume, and market capitalization. These results offer practical insights into execution venue selection and market design considerations.The second essay explores the evolution of Automated Market Makers, using the introduction of a new generation of these exchange architectures as a case study. In addition to documenting their technical advancements, the research shows that asset pairs migrate to the new Automated-Market-Maker models based on asset-specific fundamentals. The study makes key contributions through two experimental setups, demonstrating that reductions in inventory costs and the introduction of flexible fee tiers deliver welfare benefits for both liquidity demanders and providers. These findings enrich the broader discussion on market design and highlight the potential for innovative mechanisms to enhance efficiency in both decentralized and traditional financial systems.The third essay sheds light on liquidity provision in Automated Market Makers. Leveraging granular profitability data, the study finds that a small subset of liquidity providers dominate liquidity provision. These sophisticated agents achieve significantly higher absolute and relative profits compared to retail participants, while demonstrating a high level of skill. The emergence of these de-facto intermediaries challenges the decentralized finance ethos of disintermediation, highlighting that liquidity provision, even in decentralized markets, remains dominated by specialists. Understanding the composition of participants in these nascent markets is not only crucial for practitioners but also regulators, enabling them to develop targeted and effective policies that promote fair and competitive market environments.
We investigate a multi-class machine learning (ML) framework to generate daily Bitcoin trading signalsâBuy, Sell, or Hold. Three algorithmsâXGBoost, LightGBM, and Random Forestâare compared with a naive buy-and-hold strategy. Using BTC/USD daily data (2015â2024), we apply a range of technical indicators across trend, momentum, volatility, and volume, later pruned by correlation analysis. A Âą1% threshold defines the "Hold" zone to avoid minor fluctuations. Empirical tests show that LightGBM outperforms other models and even surpasses buy-and-hold in final portfolio value. Our findings support the design of tri-class ML strategies tailored for high-volatility markets like cryptocurrency.
This study explores how integrating cryptocurrencies into traditional financial portfolios can influence investment performance. Focusing on Bitcoin and Ethereum alongside key European stock indices (BUX, DAX, and FTSE), the analysis examines whether blockchain-based assets can enhance diversification and improve the balance between risk and return. Using weekly market data from 2019 to 2023, the research applies Markowitz meanâvariance optimization to identify optimal asset allocations under different objectives such as maximizing the Sharpe ratio, minimizing risk, and maximizing returns. The findings reveal that cryptocurrencies show weak correlations with European stock indices, suggesting meaningful diversification potential. When included in portfolios, Bitcoin and Ethereum can significantly boost returns, though they also increase volatility. Portfolios optimized for risk reduction favored traditional indices, while those targeting higher returns relied predominantly on cryptocurrencies. Overall, combining digital and conventional assets produced a more balanced performance, with the Sharpe-ratioâmaximized portfolio demonstrating the best tradeâoff between stability and profitability. These results indicate that cryptocurrencies can play a valuable complementary role in modern portfolio construction. They are most suitable for investors willing to accept higher risk in exchange for potentially greater rewards, while more riskâaverse investors may benefit from maintaining a stronger focus on traditional equity indices. The study contributes to understanding how blockchainâdriven assets can expand financial opportunities and supports a broader view of diversification in contemporary investment strategies.
Jeremy EngâTuck Cheah, Thong Dao, Hung Do, Tapas Mishra
ABSTRACT This paper investigates the stability and coâmovement of cryptocurrency assets in Decentralized Finance (DeFi), with a focus on the Speed of Adjustment (SA), the rate at which shocks dissipate, and prices revert to longârun equilibrium. SA provides a critical measure of market efficiency and portfolio allocation in a highly volatile DeFi environment. We extend conventional cointegration analysis by applying a Fractionally Cointegrated Vector Autoregressive framework, which captures slow error corrections. Rolling estimations generate a timeâvarying series of SA, allowing examination of its evolution and crossâasset spillovers. The results reveal multiple cointegrating relationships, heterogeneous adjustment speeds, and strong contagion effects among DeFi assets. For instance, RPL exhibits rapid yet volatile adjustment, while LDO, BAL, and SNX revert more slowly, reflecting distinct riskâreturn tradeâoffs. Spillover analysis highlights high systemic interconnectedness, underscoring challenges for diversification and contagion management. Overall, dynamic SA emerges as a valuable forwardâlooking indicator of stability in digital asset markets.
Cryptocurrency markets are characterized by high volatility and complex patterns, creating both challenges and opportunities for traders and investors. This study introduces a machine learning framework for cryptocurrency trading optimization that leverages advanced analytical techniques to enhance trading decisions. We extracted historical data for 30 cryptocurrencies over a four-year period from Yahoo Finance. After preprocessing, we applied Principal Component Analysis (PCA) and K-means clustering to select representative coins. Four machine learning models (Gradient Boosting, XGBoost, Support Vector Regression, and Long Short-Term Memory networks) were trained to predict cryptocurrency price movements. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ). Gradient Boosting and XGBoost consistently outperformed SVR and LSTM models across all cryptocurrencies, with R 2 values of approximately 0.98 for most coins. The framework successfully identified trading signals through both moving average strategies and machine learning predictions, providing actionable insights for cryptocurrency traders. Our analysis demonstrates that ensemble-based models offer superior performance for cryptocurrency price prediction compared to neural network approaches. The integration of advanced visualization tools and trading signal generation creates a comprehensive system for data-driven cryptocurrency trading decisions.
This research paper presents the design and development of a Cryptocurrency Dashboard that applies data analytics techniques to the financial technology sector. The goal of this project is to visualize historical cryptocurrency data such as market capitalization, trading volume, and price fluctuations through an interactive and user-friendly interface. Using tools such as Python and Power BI, data was collected, cleaned, analyzed, and visualized to provide dynamic insights for investors and analysts. The dashboard enables efficient decision-making by simplifying complex financial data into clear and interpretable visuals. The study demonstrates how data analytics enhances understanding of cryptocurrency trends and contributes to evidence-based financial analysis in the digital economy.
This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalizationâBitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAIâagainst a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.
Cryptocurrency price prediction poses significant challenges due to the inherent volatility and nonlineardynamics of the market. This study introduces a hybrid stacked modeling framework that integrates machine learning (ML) and deep learning (DL) techniques, capitalizing on their complementary strengths-ML models are effective at capturing nonlinearfeature interactions in structured data, while DL architectures are adept at modeling temporal dependencies in sequential data. The proposed model leverages historical price data, technical indicators, macroeconomic variables, and sentiment metrics, with feature engineering applied to enhance predictive capability. Empirical evaluation was conducted through two experimental setups: (i) short-term, monthly segment analysis and (ii) long-term generalization via five-fold cross-validation. The hybrid model outperformed individual baseline models, achieving up to 18.3% lower RMSE and 6.7% higher directional accuracy. Additionally, it yielded superior risk-adjusted returns, with Sharpe Ratios reaching 0.094 on the Ethereum dataset. Beyond technical improvements, this research offers foresight into digital financial markets, providing a robust tool for investors, institutions, and policymakers navigating the evolving cryptocurrency landscape. The model supports more informed decision-making, enhances market oversight, and contributes to the development of adaptive regulatory frameworks for digital finance.
This paper analyzes the time-varying herding behavior in the non-fungible token (NFTs) and cryptocurrency markets and investigates their interrelationship. Using the daily market data from January 1st, 2020 to April 30th, 2023, our study covers the period characterized by Covid and post-Covid-19 induced global financial market volatility, capturing the dynamics in the global macroeconomic system and the Federal Reserveâs interest rate policy. Based on the rolling window method, our findings show the presence of herding behavior in both markets, where herding behavior in these markets may be influenced by the major events announcements particularly those related to the Federal Reserve's interest rate policy. Vector error correction model (VECM) indicates that the NFT market impacts the price of Ethereum, thereby influencing the broader cryptocurrency market. Such finding contributes to a deeper understanding of the market dynamics. By examining herding behavior, our findings indicate that the NFT market demonstrates relative independence from the volatile prices of the cryptocurrency market, suggesting the potential diversification benefits of incorporating NFTs for investorsâ portfolio construction and risk management.
Purpose This paper reviews the application of game theory in finance, focusing on its role in modeling strategic interactions among market participants. It synthesizes classical models such as Nash equilibrium and signaling games while integrating emerging themes including behavioral finance, sustainability-linked decisions, decentralized finance (DeFi) and artificial intelligence (AI)-driven agents. The study aims to highlight how game-theoretic frameworks inform financial decision-making, market design and governance and to identify conceptual gaps and future research directions. Design/methodology/approach The study employs a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol, complemented by bibliometric mapping using VOSviewer. It analyzes 78 peer-reviewed articles published between 2000 and 2025 across five finance domains: asset pricing, corporate finance, investment strategies, financial markets and behavioral finance. Conceptual frameworks and taxonomies are developed to categorize game-theoretic models by strategic orientation and information structure, offering a structured synthesis of theoretical advancements and practical applications. Findings Game theory enhances understanding of strategic behavior in finance, particularly under conditions of asymmetric information and market complexity. Key findings include the relevance of signaling games in initial public offerings pricing, repeated games in environmental, social and governance commitments and mechanism design in DeFi governance. The review identifies gaps in behavioral integration, empirical validation and modeling of decentralized ecosystems. It proposes future research directions involving multi-agent learning, adaptive mechanism design and sustainability-linked financial strategies. Research limitations/implications The review is limited by its focus on published literature and may exclude emerging models in unpublished or proprietary research. Empirical validation of proposed frameworks remains a future research priority. Practical implications The paper offers actionable insights for regulators, investors and policymakers by applying game-theoretic tools to systemic risk management, portfolio allocation and financial regulation in digitized markets. Originality/value This study provides a novel synthesis of game theoryâs evolution in finance, introducing conceptual frameworks that integrate behavioral, technological and sustainability-linked dimensions.
Alex Brockbank, Charlene M. Kalenkoski, Christopher R. Browning, Michael Guillemette
Do financial advisors recommend cryptocurrency investment within a household portfolio? Cryptocurrencies have emerged in popularity as households seek to maximize returns. Financial advisors are expected to provide beneficial advice for a household in managing financial decisions including investments. The existing literature has examined this relatively new form of investing and found some determinants for cryptocurrency investment but has not sufficiently explored the association between this investment option and the investorâs use of a financial advisor. With data from the 2018 wave of the National Financial Capabilities Study (NFCS), this paper examines the relationship between cryptocurrency investment and the use of a financial advisor for American investors. The results suggest that investors who use a financial advisor are more likely to be invested in cryptocurrencies. Additional determinants seen in previous works are also confirmed in the current study; showing that men, younger investors, married investors, and investors with a higher tolerance for risk are more likely to have cryptocurrency investments.
Abstract Cryptocurrency speculation involves investing in assets with highly volatile price movements in which large sums can be gained or lost in short periods. Although fear of missing out (FOMO) has been positively linked to this type of activity, less is known about the role of regret, such as how people react to actions taken (acts of commission) or not taken (acts of omission). Anticipated regret was investigated in a study involving 403 investors ( M = 325, F = 73, Other = 5) recruited from an online panel and presented with meme coin scenarios that manipulated omission (not buying) or commission (sold early) while also examining the roles of social comparison and temporal framing. Scenarios were arranged in a 2 Ă 2 Ă 2 factorial design with FOMO, risk tolerance, impulsivity, financial literacy and problem gambling included as covariates to control for potential individual differences. Acts of commission were associated with greater regret and negative emotion but not with FOMO-based investment decisions. No effects were found for temporal distance or social comparison. At-risk and problem-gambling investors were also found to be more vulnerable to negative emotions and risky intention decision-making than non-risk gamblers. FOMO and risk tolerance were related to making decisions based on FOMO, whereas cryptocurrency literacy appeared to mitigate this tendency. These findings underscore the potential value of consumer education in raising awareness of psychological biases that are likely to lead to riskier speculative decisions.
Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the meanâvariance model introduced by Markowitz. However, this framework relies on restrictive assumptionsâsuch as normally distributed returns and symmetric risk preferencesâthat often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the MeanâDeviationâEntropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are consideredâShannon entropy, Tsallis entropy, and cumulative residual SharmaâTanejaâMittal entropy (CR-STME)âto explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical meanâvariance and meanâMAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.
We formulate automated market maker (AMM) \emph{rebalancing} as a binary detection problem and study a hybrid quantum--classical self-attention block, \textbf{Quantum Adaptive Self-Attention (QASA)}. QASA constructs quantum queries/keys/values via variational quantum circuits (VQCs) and applies standard softmax attention over Pauli-$Z$ expectation vectors, yielding a drop-in attention module for financial time-series decision making. Using daily data for \textbf{BTCUSDC} over \textbf{Jan-2024--Jan-2025} with a 70/15/15 time-series split, we compare QASA against classical ensembles, a transformer, and pure quantum baselines under Return, Sharpe, and Max Drawdown. The \textbf{QASA-Sequence} variant attains the \emph{best single-model risk-adjusted performance} (\textbf{13.99\%} return; \textbf{Sharpe 1.76}), while hybrid models average \textbf{11.2\%} return (vs.\ 9.8\% classical; 4.4\% pure quantum), indicating a favorable performance--stability--cost trade-off.
ABSTRACT Cryptocurrency markets are known for their wide price fluctuations, lack of central control, and fastâpaced development. These characteristics present serious challenges to traditional theories about how markets work and how prices reflect available information. Understanding how information is processed in these markets is essential for investors, policy makers, and academic researchers. This paper offers a thorough review on the extent to which cryptocurrency markets reflect information, based on 977 peerâreviewed articles published between 2015 and 2024 and indexed in Scopus. Using a combined method of bibliometric analysis and thematic review, the study identifies key research directions and common methods used to explore how information affects cryptocurrency prices. The review goes beyond the Efficient Market Hypothesis (EMH) and includes related topics such as volatility modelling, behavioral dynamics, spillovers, liquidity, and institutional influences. It presents a detailed overview of the most influential publications and organises the literature into six thematic research clusters, highlighting conceptual tensions and new methodological approaches. Finally, the paper outlines a future research agenda that connects market efficiency with changing regulatory environments, innovations in market structure, and the increasing role of institutional actors in the cryptocurrency space.