We examine how Web3-specific education and AI-generated investment guidance affect retail investor performance in crypto markets. In a twelve-month randomized controlled trial with 3,948 participants trading real tokens on a simulated CEX, investors were assigned to a control group, Web3 education, AI recommendations, or both. Measured by raw return, alpha, and portfolio diversification, both interventions improved performance, with the combined treatment producing the largest gains. Education effects accumulated over time, AI effects were immediate, and benefits were greatest for less experienced investors and on high-complexity news days. A portion of gains persisted after support was withdrawn, especially for education-based treatments, suggesting lasting benefits from knowledge acquisition alongside real-time decision support.
Nowadays, financial markets are becoming more and more complex, and new portfolios need to be built to cope with them. This paper aims to build a Markowitz model for portfolio research based on new calibrations for nine different industries. Firstly, the weights and minimum variance combinations are calculated by using valid information such as mean, standard deviation, variance, and covariance. Second, this paper aims to maximize the return of the portfolio, diversify the investment risk of the selected portfolio, and finally determine the optimal portfolio. The portfolio can be adjusted to reduce risk or increase return by adjusting the percentage of Bitcoin. This paper further explores the portfolio using Bitcoin as a variable. This paper derives the volatility and return of the least risky portfolio to be 11.04% and -0.46%, respectively, when the portfolio is calibrated without Bitcoin, and the volatility and return of its Sharpe optimal portfolio are 14.61% and 7.11%, respectively. When the portfolio contains Bitcoin, the volatility and return of its risk-minimal portfolio are 9.45% and 0.6%, respectively, and the volatility and return of its Sharpe-optimal portfolio are 16.31% and 37.35%, respectively. Ultimately, it is concluded that Bitcoin has some risk-reducing and return-enhancing effects.
We examine whether disagreement in social media discussions related to financial markets affects subsequent volatility and abnormal trading volume. We also compare how traditional and digital asset markets differ by comparing stocks and Bitcoin. We show that social media disagreement is positively associated with future market volatility and abnormal trading volume in the stock market. The effect of disagreement is more pronounced at the individual stock level than at the index level. A higher level of social media disagreement also increases the probability of extremely negative stock market returns. In contrast, disagreement in Bitcoin-related social media weakly affects subsequent volatility but does not affect trading volume or extremely negative returns. Our findings also reveal that market activity impacts the disagreement in the stock market and Bitcoin communities differently.
Santiago CarbĂł-Valverde, Pedro J. CuadrosâSolas, Francisco RodrĂguez FernĂĄndez
Acknowledging the potential threats posed to financial stability by owning cryptoassets combined with a lack of financial literacy, this paper investigates the relationship between financial literacy and cryptocurrency ownership using machine learning methods. Analyzing 2121 survey responses, it shows that financial literacy emerges as a crucial factor in cryptocurrency ownership, even when accounting for other factors such as age, income, and digital activity. A neural network model reveals that a unit increase in financial literacy reduces the probability of cryptocurrency ownership by 0.2. Causal forest analysis indicates that financial literacy bias positively impacts ownership likelihood (a point estimate of 75.30 %). However, the bias-corrected financial literacy measure has a negative effect of â25.40 % on ownership likelihood. This reveals that cognitive biases, particularly overconfidence, as a significant influence on cryptocurrency ownership. These results show that individuals with more financial literacy and with less biased self-assessments are less likely to hold cryptocurrencies.
This study analyses the effect of cryptocurrency on the Nigerian economy. The development of crypto-currency as a means of exchange without legal backing and invisibility of the identity of operators has posed peculiar challenges, such as illicit financial flow and terrorism, amongst others, to the country. This study, therefore, sought to examine the effect of crypto-currency on the Nigerian economy. The study hinged on social exchange theory. Secondary data were obtained from the CBN statistical bulletin and Global Financial Integrity Report for a period of six years from 2015 to 2020. The data were analyzed using a simple regression model. The result shows that R is 7.9%, which means that there is a low positive relationship between crypto-currency and the level of economic development in Nigeria. It further shows an adjusted R square of -38.4 which depicts that crypto-currency has a low inverse effect on the level of economic development in Nigeria. In conclusion, the computed p-value of 0.945, which is higher than the set p-value of 0.05, shows that crypto-currency does not have a significant effect on the level of economic development in Nigeria. Hence, it is recommended that, in order to sustain economic development from the activities of crypto-currency in Nigeria, the CBN needs to ensure that laws and mechanisms are put in place to capture the activities of crypto-currency in the country adequately.
This study investigates the efficiency of cryptocurrency markets by examining the presence and exploitability of arbitrage opportunities. Using high-frequency data from the Binance Exchange, we implement a triangular arbitrage strategy, considering Bitcoin, Litecoin, and the U.S. Dollar. We find 4,879 possible arbitrage opportunities. Although these findings suggest potential inefficiencies, transaction costs and limited trading volumes in the order book eliminate their profitability. Consequently, centralized cryptocurrency markets exhibit a high degree of efficiency. Moreover, our results suggest that the mere number of triangular arbitrage opportunities is not a reliable indicator of market inefficiency. ⢠Triangular arbitrage opportunities at cryptocurrency exchanges do exist. ⢠Transaction costs, potential slippage and limited trading volumes in the order book eliminate their profitability. ⢠Centralized cryptocurrency markets exhibit a high degree of efficiency. ⢠The mere number of triangular arbitrage opportunities is not a reliable indicator for market efficiency.
This study delves into the impact of reversals and investor attention on cryptocurrency returns before and during the COVID-19 pandemic. We employ the Two Stages Least Squares to analyze a sample of the top 20 cryptocurrencies from January 2016 to April 2021. Our results reveal that investor attention positively influences bitcoin returns in both periods, with a more pronounced effect during the pandemic. Conversely, reversals demonstrate a positive correlation with cryptocurrency returns before the outbreak but a negative relationship during the pandemic. Our robustness test further indicates that investor attention positively affects the returns of small and medium-cap cryptocurrencies, while reversals only exhibit positive consequences for small-cap cryptocurrencies. Additionally, our findings highlight stablecoins as a safe haven during the epidemic. The results suggest that investor attention has little influence on the returns of stablecoins, indicating that these coins are primarily resistant to market sentiment due to their inherent stability. The negative impact of the pandemic on the crypto market demonstrates a downward trend through each wave. Despite aligning with attention-induced price pressure and behavioral finance hypotheses, our results do not support efficient market theory or the notion of heterogeneity among investors. This research provides valuable insights for investors and policymakers in devising effective strategies for the cryptocurrency market.
ABSTRACT This paper provides a first economic analysis of liquid staking tokens, which are derivatives representing a share of staked tokens in ProofâofâStake blockchains. We document substantial timeâvariation in the âliquid staking basisâ as given by the price difference between a derivative staking token and its underlying cryptocurrency. We find evidence that staking rewards, concentration risks, limits to arbitrage, and behavioral factors influence this basis. The liquid staking basis is wider when the yields offered by the liquid staking protocol are low relative to the alternative of staking directly, when cryptocurrency returns are more volatile, and when secondary market liquidity is low. In contrast, it is smaller when investors pay more attention to liquid staking and when investor sentiment is positive. Furthermore, liquid staking tokens contribute a significant and overall growing amount to price discovery in the underlying cryptocurrencies.
Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.
We investigate how exchange default risk and liquidity affect Bitcoin cross-exchange arbitrage opportunities. Analyzing minute-level data from 16 cryptocurrency exchanges (April 2013âApril 2024), we find arbitrage opportunities last longer when higher-risk exchanges have higher prices, as traders are cautious of default risks. There is a strong positive relation between capital flows from high-risk to low-risk exchanges and arbitrage opportunities, showing a preference for safer exchanges. Liquidity accelerates arbitrage by enabling faster execution, but high transaction fees and blockchain congestion slow capital transfers. The paper highlights exchange risk, liquidity, and transaction costs as key factors in Bitcoin market efficiency. ⢠Exchange default risk significantly impacts Bitcoin cross-exchange arbitrage behaviour. ⢠Arbitrage is more persistent when high-risk exchanges have higher prices. ⢠Higher liquidity enhances the effect of net flows on Bitcoin arbitrage opportunities. ⢠Blockchain congestion and fees hinder capital movement, slowing arbitrage execution.
Mai H. Bui, Huy Pham, Binh Nguyen Thanh, Aviral Kumar Tiwari
Cryptocurrencies have emerged as a new financial asset class, and the literature in this area is increasing rapidly. This study examines the determinants and proposes a new approach to capture the scarcity effect of proof-of-work cryptocurrency return. We find that the scarcity effect is one of the major determinants of excess return. Besides the scarcity effect, our results indicate that market risk premium, momentum effect, size effect, investor attention, and mining costs effect are significant determinants of proof-of-work cryptocurrency excess return. In addition, we compare the effectiveness of three mimicking portfolios: size effect, momentum effect, and scarcity effect to their background factors. The findings show that compared to their background factors, size effect and scarcity effect mimicking portfolios have better-explaining power.
Huned Materwala, Shraddha M. Naik, Ali S. Taha, Tala Abdulrahman Abed ¡ 5 authors
Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.
Maria Grith, Caio Almeida, Ratmir Miftachov, Zijin Wang
We analyze the first and second moment risk premia in the Bitcoin market based on options and realized returns and contrast them to the premia embedded in the main US stock index market. First, Bitcoin is much more volatile and has a higher variance risk premium than the S&P 500. By decomposing the return premium into different regions of the return state space, we find that while most of the S&P 500 equity premium comes from mildly negative returns, the corresponding negative Bitcoin returns (between three and one standard deviations) account for only one-third of the total Bitcoin premium (BP). Further, applying a novel clustering algorithm to a collection of estimated Bitcoin option-implied risk-neutral densities, we find that risk premia vary over time as a function of two distinct market volatility regimes. The low-volatility regime implies a relatively high share of BP attributable to positive returns and a high Bitcoin Variance Risk Premium (BVRP). In high-volatility states, the BP attributable to positive and negative returns is more balanced, and the BVRP is lower. These results suggest Bitcoin investors are more concerned about variance and upside risk in a low-volatility regime.
Krzysztof Gogol, Manvir Schneider, Tessone, Claudio, Livshits, Benjamin
Layer-2 (L2) blockchains inherit Ethereums security guarantees while reducing gas fees. As a result, they are gaining traction among traders at Automated Market Makers (AMMs), sparking debate over whether they contribute to liquidity fragmentation of Ethereum. Our research suggests that such fragmentation is not currently occurring. However, it could emerge in the future, particularly if Liquidity Providers (LPs) recognize the higher returns available on L2s. Using Lagrangian optimization, we develop a model for optimal liquidity allocation across AMMs on Ethereum and its L2s, using staking as a benchmark. We show that, in equilibrium, AMM liquidity provision returns converge to this reference rate. Additionally, we measure the elasticity of trading volume with respect to Total Value Locked (TVL) in AMMs and find that, on well-established blockchains, an increase in TVL does not necessarily lead to higher trading volume. Finally, our empirical findings reveal that Ethereums liquidity pools are oversubscribed compared to those on L2s and often yield lower returns than staking Ether. LPs could maximize their rewards by reallocating more than two-thirds of their liquidity to L2s and staking.
Decentralized finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for concentrated liquidity market makers (CLMM). The focus is on leveraging the liquidity distribution approximated using a parametric model, to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap v3 for pairs of altcoins, stablecoins and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating LP's revenues as part of the pool rewards, as focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for liquidity providers (LP). By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges. ⢠Develop a methodology for backtesting liquidity provision in a CFMM. ⢠Enhance CFMM backtesting by leveraging GPU acceleration for faster computation. ⢠Showcase the practicality of CFMM backtesting using actual Uniswap pool data.
Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such prices is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as \(R^{2}\) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.
As technology has improved in the last decade, financial institutions have developed new technologies, including quantitative trading and cryptocurrency, to enhance their financial products and services. This paper first provides a brief background of quantitative trading and argues for the transactional efficiency of quantitative trading over traditional trading practices; it characterizes quantitative trading as fast and precise. Meanwhile, the study also accounts for the regulatory concernsâincluding data leakage and platform securityâthat quantitative trading firms may encounter. This study then establishes a distinction between cryptocurrency and quantitative tradingâthe former is money-driven, and the latter is data-driven. This paper then discusses the speculative nature of cryptocurrency and addresses its financial concerns citing the FTX collapse. Overall, this paper establishes the argument that quantitative trading supported by technological experts and facilitators offers more advantages than disadvantages compared to cryptocurrency trading. This research concludes that since quantitative trading and cryptocurrency trading are conducted without consideration for international boundaries, they offer bold financial potential as alternatives to traditional banking practices, as long as specific international financial laws are complied with.
B. M. Sowunmi, Sunday Mlanga, Anderson Emmanuel Oriakpono
This study investigated the effect of distributed ledger technology (DLT) factors on eliminating financial reporting errors (FREs) in quoted Nigerian banks. Using an exploratory survey design, data was collected from 300 employees of 14 quoted banks involved in financial reporting. DLT factors of public, private, hybrid, and blockchain were examined as independent variables affecting the dependent variable of FRE elimination. Descriptive analysis showed that all DLT types were perceived as highly effective for error reduction. Correlation analysis revealed strong positive relationships between DLT factors and FRE mitigation. Regression modeling found that hybrid DLT had the largest impact on error elimination, followed by private, public, and blockchain DLT. Together, the DLT factors explained 98.1% of the variance in FRE reduction. The results statistically established the significant positive effects of DLT factors on eliminating prevalent FREs like principle, omission, entry, disclosure, and reversal errors. Key contributions include providing robust empirical evidence that leveraging DLT, especially hybrid DLT, can eliminate common financial reporting errors in Nigerian banks. The pioneering study expands conceptualizations, theories, and literature regarding DLT's potential to comprehensively transform financial reporting accuracy. It offers important implications for policy, practice, and research on regulating, adopting, and studying DLT solutions to address persistent financial statement errors undermining stakeholder trust in Nigeria's banking sector. The study concludes by strongly recommending for policy and, in practice, the regulation and full adoption of DLT for the elimination of FREs in Nigeria.
Popular methods to value Bitcoin include the stock-to-flow model, Metcalfeâs Law, technical analysis, and sentiment-related measures. Within this paper, I test whether such models and variables are predictive of Bitcoinâs returns. I find that the stock-to-flow model predictions and Metcalfeâs Law help to explain Bitcoinâs returns in-sample but have limited to no ability to predict Bitcoinâs returns out-of-sample. In contrast, Bitcoin market sentiment and technical analysis measures are generally unrelated to Bitcoinâs returns in-sample and are poor predictors of Bitcoinâs returns out-of-sample. Despite the poor performance of Bitcoin return predictors within out-of-sample regressions, I demonstrate that a very successful out-of-sample Bitcoin tactical allocation or âmarket timingâ strategy is formed via blending out-of-sample univariate model predictions. This OOS-blended model trading strategy, which algorithmically allocates between Bitcoin and cash (USD), significantly outperforms buying-and-holding or âHODLâing Bitcoin, boosting CAPM alpha by almost 1300 basis points while also increasing portfolio Sharpe Ratio and Sortino Ratio and dramatically reducing portfolio maximum drawdown relative to buying-and-holding Bitcoin.
This study proposes an Automatic Cryptocurrency Trading System using Deep Reinforcement Learning (DRL). Six popular cryptocurrencies were used: Bitcoin, Ethereum, BinanceCoin, DogeCoin, Cardano, and WAVES. Development of the trading system started with building three timeseries models â Temporal Convolutional Neural Network (TCNN), Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit Network (GRU) â to predict future prices. Then, cryptocurrency sentiment data was scraped using the Alternative.me API. Data on historical prices, predicted future prices, cryptocurrency sentiment index, technical indicators, and trading account information was fed as input states to three DRL Agents â Deep Q Network (DQN), Advantage Actor Critic (A2C), and Recurrent Proximal Policy Optimization (RPPO) â which were trained using a custom-developed trading environment. Each agent was given $1000 initial capital for all six cryptocurrencies to trade using three possible actions â Buy, Sell and Hold â and were back-tested on one year of unseen data. Our DQN model had the highest overall return on investment (ROI) of $740, an average 12.3% ROI across all six cryptocurrencies, with an ROI of 63.98% achieved for BinanceCoin. However, A2C and RPPO both had negative ROI.
This paper examines the effect of investor attention on the cross-section of cryptocurrency returns and trading activities. We find that cryptocurrencies associated with higher abnormal Google search volume subsequently exhibit higher returns, higher volatility, and higher trading volume. The results are robust to alternative sample periods and alternative search keywords, providing concrete support to the attention-induced price pressure hypothesis and consistent with prior studies on the equity market. The effect is more pronounced among larger cryptocurrencies. Only a partial reversal after the initial return increase is observed, implying that investor attention permanently impacts cryptocurrency prices.
Muhammad Mahmudul Karim, Mohamed Eskandar Shah Mohd Rasid, Abu Hanifa Md. Noman, Larisa Yarovaya
This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders. ⢠We analyze 6.5 million observations to unveil intraday asymmetric return-volatility dynamics in Bitcoin and Ethereum. ⢠The Model-Free Implied Volatility, Quantile Regression Model, and Wavelet Coherence are employed. ⢠We found that asymmetry in these relationships intensifies at lower frequencies and high quantiles. ⢠Findings contribute to cryptocurrency literature using high-frequency data across different intervals.
Mar Grande, F. Borondo, Juan Carlos Losada, J. Borondo
Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include H<0.5 as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market.