This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception.
We study the temporal evolution of the holding-time distribution of bitcoins and find that the average distribution of holding-time is a heavy-tailed power law extending from one day to over at least $200$ weeks with an exponent approximately equal to $0.9$, indicating very long memory effects. We also report significant sample-to-sample variations of the distribution of holding times, which can be best characterized as multiscaling, with power-law exponents varying between $0.3$ and $2.5$ depending on bitcoin price regimes. We document significant differences between the distributions of book-to-market and of realized returns, showing that traders obtain far from optimal performance. We also report strong direct qualitative and quantitative evidence of the disposition effect in the Bitcoin Blockchain data. Defining age-dependent transaction flows as the fraction of bitcoins that are traded at a given time and that were born (last traded) at some specific earlier time, we document that the time-averaged transaction flow fraction has a power law dependence as a function of age, with an exponent close to $-1.5$, a value compatible with priority queuing theory. We document the existence of multifractality on the measure defined as the normalized number of bitcoins exchanged at a given time.
In recent years cryptographic tokens have gained popularity as they can be used as a form of emerging alternative financing and as a means of building platforms. The token markets innovate quickly through technology and decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies must therefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach to resolving these complex issues. However, very little is known about genetic algorithm methods in cryptographic tokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementing selected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlo method. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-Genetic Algorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. The period selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens of Paris Saint-Germain, Manchester City, and Barcelona, leaders in the market. Our results conclude that the Hybrid and Quantum Genetic algorithm display a good execution during the training and testing period. Our study has a major impact on the current decentralized markets and future business opportunities.
In an attempt to assess the appropriateness of the best-practice lexicon-based approaches as opposed to novel learning-based models to extract the sentiment of textual content in the context of the cryptocurrency market, the current study provides further insights into the association between digital activity and price movement of cryptocurrencies. Using a sample of Bitcoin and Ethereum trade data, this study compares the performance of Harvard IV-4 and BERT models in conjunction with the well-known machine learning classifiers. It examines to what extent learning-based sentiment models can enhance the price movement prediction, compared to lexicon-based approaches, and whether the prediction is improved or impaired by introducing different features as input to the classifiers. Results indicate that the contribution of the selected learning-based model varies across the two cryptocurrencies, and predictions are better in the absence of trade volume as an input feature to the classifiers.
Virtual currency assets are an important component of the international investment market. This study applies the Markowitz theory model to the data of the four main virtual currency assets in the market, Bitcoin, Tether, Ethereum, and BNB, in the past year. The Markowitz theory model is used to quantitatively analyze these four virtual currency assets and obtain the short-term data distribution of the investment portfolio when considering investing in these four virtual currency assets at the same time. In the short term, this study recommends investors who are concerned about returns to short Tether, Ethereum, and BNB virtual currencies and invest in Bitcoin and BNB virtual currencies. It is recommended that investors who are concerned about both returns and risks to short Tether, Ethereum, and BNB virtual currencies and invest in Bitcoin or BNB virtual currencies. Risk assets. This study only focuses on short-term data from the past year and provides recommendations. With the development of virtual currencies, market conditions may vary.
The article âSystematic Hedging of the Cryptocurrency Portfolioâ on QuantPedia discusses a strategy for hedging a cryptocurrency portfolio that is stored in cold storage The article suggests that while cold storage has advantages, such as protection against hacking, it also exposes the holder to the price swings of the cryptocurrency market. The article proposes a hypothetical market capitalization-weighted Top 5 cryptocurrency index portfolio (T5) as a proxy for a portfolio that hardcore HODLers may hold. The rule for inclusion in the index is simple: each year, on the first day of the year, select the top 5 coins ranked by market cap for a yearly holding period. Stablecoins are excluded from the index. The article then explores a hedging strategy through BTC derivatives to minimize crypto market beta exposure risk. One approach introduced is a 1:1 (Proportional) Hedge, where the exact amount of $ value corresponding to Bitcoin (BTC) is shorted. The article suggests that this strategy can help mitigate the risk of price swings in the cryptocurrency market, especially when the market is at an all-time high. Please note that this is a high-level summary and for a detailed understanding, you should read the full article.
In this paper, we conduct a portfolio analysis based on the lottery-like characteristics of cryptocurrencies to examine return predictability. Our results show that cryptocurrencies with higher lottery-like characteristics exhibit lower one-month ahead returns. This phenomenon, known as the lottery-like effect, suggests that investors overvalue cryptocurrencies with stronger lottery-like traits, leading to lower future returns. Moreover, the effect persists over longer horizons, and the results remain robust after controlling for other crypto-asset characteristics.
A comparative analysis between 2013-2017 and 2018-2023 reveals a significant transformation in Bitcoin and cryptocurrency investments.In the earlier phase, methods like the Markowitz Model suggested significant allocation to Bitcoin due to its high returns, diversification benefits, and low correlation with other assets.However, with the financialization of Bitcoin in December 2017, the cryptocurrency market underwent a fundamental shift, integrating into the mainstream financial system and increasing its correlation with traditional assets.In the subsequent period from 2018 to 2023, Bitcoin emerged as an average asset class with relatively high risk compared to others.Given these changes and increased institutional interest, our analysis suggests it's prudent to cap allocation to Bitcoin to maximally 2-3% of the portfolio.The analysis highlights the need for caution and realistic expectations when interpreting historical data and extrapolating long-term conclusions.
We examine the impact of a stockâs lottery-likeness on its return comovement with Bitcoin. We find that Bitcoin returns exhibit significantly stronger comovement with lottery-like stocks (LLS). Using firmsâ retail ownership and Robinhood user details to proxy for retail trading, we identify that retail investorsâ preference for speculative, high-risk, and high-reward investments, known as their gambling propensity, is the underlying channel driving the Bitcoin-LLS comovement. Our results are robust across various estimation methods, alternative measures of stock lottery-likeness, and multiple proxies for gambling sentiment including Google search volume, Baker-Wurgler sentiment index, the month of January, and the period around the Chinese Lunar New Year. These findings hold at both daily and monthly intervals and are not confounded by firms in the high-tech industry. Further analysis using Robinhood and Bitcoin usersâ net trading positions yields consistent evidence. Employing a vector autoregressive approach and an exogenous shock to Bitcoin demand, we demonstrate a spillover effect from Bitcoin to LLS. Finally, we demonstrate that Bitcoin provides more effective hedge for LLS than non-LLS.
It has been an explosive start to 2024 in terms of the share market performance. Driven by a wave of enthusiasm for tech heavyweights like Meta and Nvidia, USAâs S&P 500 index of large American firms is up 5% and has crossed the 5,000 mark for the first time ever. On February 22, Japan's Nikkei 225 broke its own record â which it had established in 1989. In Australia, despite some volatility due to speculation on the direction of Reserve Bank interest rates, its share market also has boomed. Given such share market performance, this article asks if it is time to think about investing exclusively in shares. To answer this, the article will consider two fundamental questions that affect investors in capital markets: (1) what is meant by investment risk vs. return, and (2) can investors optimise their risk-return relationship by holding a single asset type (like stocks), or by holding a diversified portfolio of different asset classes? It will also consider a third question:(3) can using cryptocurrencies such as bitcoin help investors to better diversify their portfolio?
We investigate whether investors rely more on technical trading language to rationalise price movements in the absence of substantive information. Compared to equity markets, cryptocurrency markets are characterised by high volatility, often occurring without clear explanation from new information. We apply a machine-learning-based vocabulary of technical trading terms to comments from cryptocurrency- and equity-related subreddits on Reddit.com and analyse how investors use technical talk in different market conditions. We find a U-shaped relationship between technical talk and Bitcoin returns, with higher usage during extreme price movements, while technical talk on equity subreddits is concentrated around median market returns. Technical talk increases in cryptocurrency markets when news is scarce but rises in equity markets alongside greater news availability. Our results suggest that technical talk provides an important communication channel for social media users to describe price variation when information is scarce.
The main theme of this thesis lies in our attempt to contribute to explaining the Home Bias Puzzle (HBP) observed in international financial markets in the presence of Cryptocurrencies, within the framework of FinTech and more specifically in the context of Decentralized Finance (DeFi). Through a meticulous review of recent works on the question of international portfolio diversification, encompassing physico-financial assets such as Cryptocurrencies, technology stocks, classical stocks, currencies, commodities, and oil, we examined the issues of arbitrage and the strategy of choosing investment in domestic assets and/or choosing investment in international portfolio diversification. To empirically test our central issue, we validated four essays formulated as hypotheses: In the first essay on Efficiency and Volatility, we examined, through time series modeling, the impact of integrating cryptocurrencies into the investor's portfolio to verify our first hypothesis, namely the transmission of volatility shocks induced by this asset. The use of ARCH and GARCH modeling shows that the coefficients associated with them are close to unity, thus indicating a permanent effect of shocks on conditional variance. However, during the COVID-19 pandemic, Bitcoin was considered a safe haven asset. Also, relying on the econometric results of EGARCH and TARCH models, similar to Wang (2021), we show the existence of an excessive leverage effect on the volatility of future returns for Bitcoin (+26.50%) and Dogecoin (+65.07%). Furthermore, our study shows the absence of leverage for the other cryptocurrencies in the sample. Our second essay aims to validate the second hypothesis borrowed from industrial economics on the Integration-Segmentation-Diversification (ISD) triptych of asset portfolios, i.e., the relationship between goods and services markets and the capital market. To verify this hypothesis, we used a VAR (Vector AutoRegressive) modeling to analyze the causal time relationship between economic variables (real sphere) and financial variables (financial sphere) through standard tests (AIC) in the first stage and (SC) in the second stage. The results obtained show that price variations in the developed markets of the sample do not follow a common long-term trend. In this context, there would likely be an opportunity for diversification among developed markets, a product of financial liberalization (Attig.N. and al. (2023)). Thirdly, the empirical test of the existence of a Home Bias, our third essay and hypothesis were conducted over the period 2006-2021, with 640 observations. The determinants of the Home Bias Puzzle (HBP) were divided into seven panels: governance variables, macroeconomic variables, market size and microstructure variables, information asymmetry, familiarity and geography, Foreign Trade, and finally geopolitical variables. The econometric results we obtained are consistent with previous findings (Garg, Karmakar, M. and Paul, S., (2023); Lee, J. Lee, K. and Oh, F.D. (2023)). Finally, the last essay, reflecting hypothesis four on the relationship between Cryptocurrencies, Portfolio Diversification, and Behavioral Finance, highlights the superiority of the W. Sharpe (1964) performance index compared to other naive portfolio diversification strategies derived from the Mean-Variance approach by H. Markowitz (1952). Our results corroborate those obtained by Hachicha F., and al. (2023).
The rapid convergence of digital finance, distributed computing, and artificial intelligence has accelerated the global transition toward asset tokenization, redefining how value is represented, exchanged, and governed across financial ecosystems. Asset tokenization enabled by blockchain-based digital representations of real-world or financial assets offers greater liquidity, fractional ownership, transparent auditability, and global accessibility. However, the complexity of multi-asset valuation, interoperability across blockchain networks, and the scalability requirements of high-volume trading environments demand an advanced technological foundation that extends beyond conventional decentralized architectures. At a broader level, the integration of AI-enhanced valuation models with decentralized cloud infrastructure introduces a next-generation approach for developing secure, resilient, and automated end-to-end tokenization systems. Narrowing in focus, this paper proposes a comprehensive framework for designing asset tokenization platforms that leverage distributed cloud networks for computation, storage, and consensus while embedding machine-learning valuation engines at every stage of the asset lifecycle. AI-driven valuation models improve price discovery, dynamic asset classification, risk adjustment, and anomaly detection for tokenized assets spanning real estate, commodities, financial securities, intellectual property, and carbon credits. Smart contracts operationalize these insights by automating minting, compliance checks, investor eligibility, and secondary-market settlement. Decentralized cloud services further enhance scalability by enabling parallelized model inference, distributed identity verification, and state synchronization across multiple chains. Privacy-preserving computation such as secure multiparty learning and encrypted inference ensures that valuation logic remains confidential while maintaining regulatory-grade auditability. By integrating AI, tokenization infrastructures, and decentralized compute layers into a unified architecture, the system supports efficient asset digitization, transparent governance, and regulatory-aligned operation across jurisdictions. This research presents an end-to-end blueprint for future-proof tokenization ecosystems that harness AI and decentralized cloud networks to deliver trust, efficiency, and inclusivity in digital markets.