Fatma Ben Hamadou, Taicir Mezghani, Mouna Boujelbène Abbes
Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research. Indeed, this study aims to uncover the role of Google investor sentiment on cryptocurrency returns (including Bitcoin, Litecoin, Ethereum, and Tether), especially during the 2017-18 bubble (January 01, 2017, to December 31, 2018) and the COVID-19 pandemic (January 01, 2020, to March 15, 2022). To achieve this, we use two techniques: quantile causality and wavelet coherence. First, the quantile causality test unveils that investors’ optimistic sentiments have notably higher cryptocurrency returns, whereas pessimistic sentiment has significantly opposite effects. Moreover, the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant. This result supports the role of Tether as a stablecoin in portfolio diversification strategies. In fact, the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.
The cryptocurrency and stock markets are dynamic environments that attract traders, seeking to enhance their investment returns. In cryptocurrency trading, there is a pullback in investors from trading due to recent market crashes, losses, and bankruptcies. For anticipating future market behavior, algorithmic trading has gained popularity due to its ability to provide consistent and accurate price and volatility predictions. Specifically, the bottom turning points of the market are where an investor can use to enter the market. Hence, identifying market turning points, particularly market bottoms, is vital in timing trading strategies for a maximum profit. This study introduces a novel and ground-breaking approach to market forecasting that focuses on identifying market bottoms, particularly in the domain of cryptocurrency trading. The study utilizes a Wasserstein Generative Adversarial Network (WGAN) with Gated Recurrent Unit (GRU) to identify future market trends effectively. A classifier is added into the model as a substantial contribution to forecast future market bottoms by utilizing hidden WGAN features. The research findings indicate that the combination of the price prediction and bottom classification models provides outperforming results in terms of prediction accuracy. In addition, the suitability of the proposed solution for locating stock market bottoms has been evaluated.
André D. Gimenes, Jéfferson Augusto Colombo, Imran Yousaf
Abstract In this study, we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions, selling, or acceptance as a means of payment. Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency. We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows. However, when we perform heterogeneity analyses, we observe significant differences in market reaction between events with high (larger CARs) and low cryptocurrency exposure (lower CARs). Multivariate regressions show that the level of exposure to cryptocurrency ("skin in the game") is a critical factor underlying abnormal returns around the event. Further analyses reveal that economically meaningful acquisitions of BTC or ETH (relative to firm's total assets) drive the observed effect. Our findings have important implications for managers, investors, and analysts as they shed light on the relationship between cryptocurrency adoption and firm value.
Investing in cryptocurrency has become more popular among Americans. Despite this, politicians and social scientists know almost nothing about the politics of cryptocurrency in the American public. By analyzing an original, nationally representative survey of 2500 American respondents, we create the first robust profile of the personalities, demographics, and political attitudes of cryptocurrency owners. We show that Americans who report hardship from inflation are more likely to own cryptocurrency, suggesting that when inflation is high, Americans may be more likely to use cryptocurrency as a medium of exchange and store of value. Americans who favor lower government spending and are more inclined toward conspiratorial thinking are also more likely to own cryptocurrency. Finally, there is a personality to cryptocurrency owners, with those open to new experiences more likely to own it and the conscientious less likely to own it. Our results have implications for how the American public may use cryptocurrency going forward.
We find that planned cryptocurrency forks, like voluntary corporate spin-offs, are wealth-creating. Involuntary forks that are forced due to hacks and other problems with the blockchain are not. We find diminishing returns for second-generation forks, alleviating the concern of forking solely for wealth creation.
I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications.
Sulalitha Bowala, A. Thavaneswaran, Ruppa K. Thulasiram, Thimani Ranathungage · 5 authors
Recently there has been a growing interest in constructing portfolios with stocks and cryptocurrencies. As cryptocurrency prices increase over the years, there is a growing interest in investing in cryptocurrencies, along with diversifying portfolios by adding multiple cryptocurrencies to the existing portfolios. Even though investing in cryptocurrency leads to high returns, it also leads to high risk due to the high un-certainty of cryptocurrency price changes. Thus, more robust risk measures have been introduced to capture market risk and avoid investment loss, along with different types of portfolios to mitigate risks. Many portfolio techniques assume asset returns are normally distributed with constant variance. However, these assumptions are violated in many cases. Unlike the existing work, this study investigates the recently proposed data-driven exponentially weighted moving average (DDEWMA) covariance model to estimate the variance-covariance matrix for high frequency (hourly data) cryptocurrency returns in Markowitz portfolio optimization. The experimental results show that for high-frequency data, the DDEWMA approach outperforms the existing portfolio optimization model that uses the empirical variance-covariance matrix. Improvements have been identified in terms of the Sharpe ratio as well as risks (volatility, mean absolute deviation (MAD), Value-at-Risk (VaR), and Expected shortfall (ES)).
To explore the impact of factors from the traditional financial market, such as economic policy uncertainty, oil prices, the NASDAQ index, and gold prices, to identify factors contributing to Bitcoin volatility. This study uses traditional OLS (ordinary least squares) regression analysis to examine how different external factors affect Bitcoin price volatility from January 2014 to March 2023. By employing a comprehensive approach to recognize the distinctive characteristics of the Bitcoin market, namely, 24-hour trading and the short duration of its existence, we’ve included a wide spectrum of data to ensure a cohesive comparison with other financial datasets. The findings of the statistical analysis indicate that EPU and the NASDAQ index promote positive fluctuations in Bitcoin volatility, whereas gold prices act as a dampener. Conversely, we do not find empirical support for the influence of energy prices, such as oil, on Bitcoin volatility. These findings indicate that we should not undervalue Bitcoin in any financial transaction scenario. It means that all stakeholders should treat the issue of Bitcoin volatility more seriously, even including governments, who should actively regulate the Bitcoin market, and investors, who should recognize the dangers of this volatility, make rational decisions based on individual circumstances, and employ flexible trading strategies.
Non-Fungible Tokens (NFTs) are a developing area in the market of digital assets. NFTs represent digital or real-world items like artwork, gaming collectibles and real estate. We aim to study the daily working of NFT market and its interaction with cryptocurrency (Ether and Bitcoin) and search interest.Our approach involves identification of models encompassing both global and local feature importance. Various regression methods are utilized to determine the feature importance and select the predictive features effectively. Moreover, this study explores the relationship between search interest and weekly NFT sales and vice versa, to comprehend how public interest impacts the NFT market. Lastly, anomalies in daily sales are detected and analysed using STL Decomposition and SHAPely.The study reveals that intrinsic sales attributes and trade profits drive daily NFT sales, with positive sentiment significantly impacting Ethereum volatility and NFT sales. External factors like NFT supply, Ether price, and trade profits also influence anomalies. Positive sentiment significantly shapes crypto and NFT market dynamics.
Purpose The purpose of the research paper is to investigate the relationship between personality traits and investment decisions in the crypto market, including cryptocurrencies and NFTs. The study aims to explore the effect of dark personalities and the big five personalities on investment decisions in the crypto market. Design/methodology/approach The research was conducted through two online questionnaire studies. In Study 1, data were collected from the general public, while in Study 2, data were collected from crypto investors. The researchers analyzed the effect of dark personalities and the big five personalities on investment decisions in the crypto market. Findings The present research found that Machiavellianism, narcissism, psychopath, sadism and extraversion have positive effects on having crypto investments. In addition, focusing on actual crypto investors, the present paper showed that personalities including Machiavellianism, narcissism, psychopath, consciousness and extraversion have statistically significant effect on investment decisions such as making investments in Bitcoin. Originality/value The study is original in exploring the relationship between personality traits and investment decisions in the newly emerging crypto market, including cryptocurrencies and NFTs. The research provides insights into how different personality traits affect investment decisions in the crypto market, which can be valuable for investors in making informed decisions.
Monika Chopra, Chhavi Mehta, Prerna Lal, Aman Srivastava
Purpose The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature. Design/methodology/approach This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously. Findings The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class. Originality/value This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.
Valeriia Baklanova, Aleksei Kurkin, Тамара Теплова
Purpose The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used. Design/methodology/approach The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results. Findings The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices. Practical implications The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market. Originality/value The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.
We obtain daily data of Bitcoin, Ethereum, Travala token, Kemacoin and Guider to investigate the implications of history's most famous five heists on travel and tourism. We find a statistically significant spillover effect in the cryptocurrency and tourism token markets with a limited impact on travel and tourism companies' stock prices. We also find evidence of herding behaviour and observe that overall market quality deteriorated because of the heists. To deal with these negative implications, we propose implementing tools based on artificial intelligence algorithms, emphasising the two leading cryptocurrencies – Bitcoin and Ethereum. Tracking major crypto wallets and ‘whales’ can help regulators identify potential hacks and mitigate systemic risk caused by spillovers in cryptocurrency markets.
This study aims to identify a secure and efficient trading approach for investors in highly volatile cryptocurrency markets. While pairs trading is a promising strategy, the available literature on this topic in cryptocurrency markets is limited and primarily based on traditional methods. This study compares six statistical approaches for selecting trading pairs: Cointegration, Correlation, Distance, Fluctuation Behaviour, Hurst Exponent, and Stochastic Differential Residual. We utilise intraday data from 30 cryptocurrencies on the Binance exchange during a three-month period from 1 January 2022 to 31 March 2022. The trading results are measured by 11 criteria covering return, risk, and trade aspects. The findings show that Distance performs well at all three frequencies of 1 minute, 5 minutes, and 60 minutes, with a total return of 208.12%, 236.31%, and 210.36%, respectively. At the 60-minute frequency, this method exhibits low risk and performs impressively even when all other techniques generate negative returns, as well as displays the lowest number of expired counts and the highest success ratio among the methods. At 1-minute and 5-minute frequencies, Cointegration and Hurst Exponent produce results comparable to those of Distance. The Student t-test conducted confirms these conclusions.
Frequent price manipulation in the Bitcoin market will lead to market risk and seriously disrupt the financial order, but there is less research on its regulation. We address the Bitcoin price manipulation problem by building a regulatory game model. First, we study the price manipulation mechanism of the Bitcoin market based on behavioral finance and clarify the boundary conditions. Second, we introduce regulator constraints and establish a game model between the manipulator and the regulator. Further, through variable deconstruction, parameter verification, and simulation analysis, we explore how to achieve effective regulation of Bitcoin price manipulation. We find that the effective regulation of Bitcoin price manipulation can be achieved in three ways: (1) Adjust the penalty coefficient with a certain lower threshold so that the manipulator's expected return is negative; (2) Set the lowest possible price fluctuation standard while ensuring that it does not interfere with market-based transactions; (3) The simulation of price manipulation regulation is optimized and most efficiently controlled when the probability of investigation is dynamically adjusted by a concave function on the price fluctuation standard.
Modern Portfolio Theory (MPT) has long been a cornerstone in the realm of finance, aiding investors in navigating the complex terrain of risk and reward associated with diverse assets. This theory, formulated by Harry Markowitz in the 1950s, has traditionally guided investment decisions by optimizing the balance between different assets to achieve the desired level of risk and return. However, with the meteoric rise of cryptocurrencies as a new asset class, there is an increasing curiosity surrounding the applicability of MPT to this digital phenomenon. In response to this curiosity, this article undertakes the task of comprehensively assessing the compatibility of MPT with cryptocurrencies. To accomplish this, the research aggregates and analyzes the existing body of knowledge, thereby offering insights into the intersection of modern portfolio theory and the age of cryptocurrencies. A systematic literature review is conducted, encompassing 21 pertinent studies that explore various facets of this confluence. The findings of this article underscore an emerging trend in research, one that showcases the adaptability of MPT to innovative financial instruments like cryptocurrencies. These studies collectively illuminate the ways in which MPT can be employed to optimize portfolios that include digital assets, shedding light on strategies that account for the unique risk-return dynamics inherent in the crypto market. As the cryptocurrency landscape continues to evolve, it is evident that Modern Portfolio Theory is not only relevant but also adaptable, providing valuable tools to guide investors through the exciting yet volatile terrain of digital finance.
With the advent of the Web3.0 era, virtual assets have gained prominence in individuals’ asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the model’s predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.
To explain the volatility of the Bitcoin price, a total of 23 elements from four domains (Bitcoin-related indicators, financial market, exchange rates and commodities, and social sentiment) were collected. With the application of machine learning and game theory, experimental results demonstrate that S&P 500 is the most significant factor on the Bitcoin price and the safe haven effect of Bitcoin for the stock market failed when the Bitcoin price rose and the COVID-19 spread.
The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction via statistical models and Machine Learning demonstrate the importance of variables such as the hash rate, the difficulty of mining or the cost per transaction when it comes to trade Bitcoin assets or predict the direction of price. Variables obtained from the blockchain network will be called here blockchain metrics. The corresponding indicators (inspired by the “Hash Ribbon”) perform well in locating buy signals. From our results, we conclude that such blockchain indicators allow obtaining information with a statistical advantage in the highly volatile cryptocurrency market.
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics.
In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.
Xihan Xiong, Zhipeng Wang, Xi Chen, William J. Knottenbelt · 5 authors
In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. LSDs improve the liquidity of staked assets by facilitating their use in secondary markets, such as for collateralized borrowing on Aave or asset exchanges on Curve. The composability of Lido, Aave, and Curve enables an emerging strategy known as leverage staking, an iterative process that enhances financial returns while introducing potential risks. This paper establishes a formal framework for leverage staking with stETH and identifies 442 such positions on Ethereum over 963 days. These positions represent a total volume of 537,123 ETH (877m USD). Our data reveal that 81.7% of leverage staking positions achieved an Annual Percentage Rate (APR) higher than conventional staking on Lido. Despite the high returns, we also recognize the potential risks. For example, the Terra crash incident demonstrated that token devaluation can impact the market. Therefore, we conduct stress tests under extreme conditions of significant stETH devaluation to evaluate the associated risks. Our simulations reveal that leverage staking amplifies the risk of cascading liquidations by triggering intensified selling pressure through liquidation and deleveraging processes. Furthermore, this dynamic not only accelerates the decline of stETH prices but also propagates a contagion effect, endangering the stability of both leveraged and ordinary positions.