This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. Previous investigations have explored the profitability of Bitcoin, yet our research delves into its relationship with energy costs. Regarding the trade-offs, the Pareto analysis reveals that trading profit and energy cost do not strongly inversely correlate. The range of outcomes shows a relatively uniform trading profit (from 1.302,85 to 1.310,22$), but a broader variation in energy costs (from 1.141,66 to 5.657,94$). While the trading profit remains stable, there is a wide array of options for minimizing energy cost, which is influenced by various constraints and market conditions. Solutions tend to cluster more in areas of higher energy costs. However, the variability in energy costs offers Bitcoin miners choices, allowing them to tailor strategies, whether that involves prioritizing energy efficiency, profit maximization or striking a balance.
Abstract This study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities, to improve the liquidity provision of real-world AMMs. The proposed architecture augments Uniswap V3, a cryptocurrency AMM protocol, by using a novel market equilibrium pricing to reduce divergence and slippage losses. Furthermore, the proposed architecture involves a predictive AMM capability, for which a deep hybrid long short-term memory (LSTM) and Q-learning reinforcement learning framework is used. It seeks to improve market efficiency through obtaining more accurate forecasts of liquidity concentration ranges, where liquidity starts moving to expected concentration ranges prior to asset price movement; thus, liquidity utilization is improved. The augmented protocol framework is expected to have practical real-world implications through (1) reducing divergence loss for liquidity providers; (2) reducing slippage for crypto-asset traders; and (3) improving capital efficiency for liquidity provision for the AMM protocol. The proposed architecture is empirically benchmarked against the well-established Uniswap V3 AMM architecture. The preliminary findings indicate that the novel AMM framework offers enhanced capital efficiency, reduced divergence loss, and diminished slippage, which could potentially address several of the challenges inherent to AMMs.
This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges.
In the past years, the widespread diffusion of Artificial Intelligence (AI) in the finance domain transformed different services, with particular attention to the stock market. Although different AI-based approaches have been proposed for stock forecasting, they are focused on news content or sentiment without considering fundamental features and vice versa. In turn, other approaches rely on handmade rules or ones based on technical indicators for providing advice without considering contextual information that can strongly affect the stock market. In this paper, we propose an Advisor Neural Network framework using Long Short-Term Memory (LSTM)-based Informative Stock Analysis for Daily investment Advice. Specifically, the forecasting unit relies on a LSTM-based model, which combines technical indicators, contextual information, and financial data for stock forecasting. Successively, the advice unit provides next-day advice based on predicted information in conjunction with the proposed Heuristic Stocks Selection algorithm. This framework has been evaluated on the Stock and Cryptocurrencies markets, considering a subset of 417 stocks and 67 cryptocurrencies over three years, respectively. We compared the proposed framework with several state-of-the-art approaches, showing how it outperforms the baseline in both markets. Furthermore, we achieved a financial gain greater than 41%, despite the downward trend of the NASDAQ market in the quarter under review, and we obtained a 39.38% return on investment for the Cryptocurrencies market.
Abstract Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, âwagmiâ (Weâre all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiastsâ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (âwagmiâ). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.
Abstract Modeling implied volatility (IV) is important for option pricing, hedging, and risk management. Previous studies of deterministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility. Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy. The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index (RSI) covering multiple time resolutions as a factor, as momentum is often used by investors and speculators in their trading decisions, and in contrast to volatility, RSI can distinguish between bull and bear markets. To the best of our knowledge, prior studies have not included RSI as a predictive factor in modeling IV. Instead of using a simple linear regression as in previous studies, we use a machine learning regression algorithm, namely random forest, to model a nonlinear IV. Previous studies apply DVIF modeling to options on traditional financial assets, such as stock and foreign exchange markets. Here, we study options on the largest cryptocurrency, Bitcoin, which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets. Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation. Our dataset includes short-maturity options with expiry in less than six days, as well as a full range of moneyness, both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building. Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the modelâs accuracy in pricing options. The nonlinear machine learning random forest algorithm also performed better than a simple linear regression. Compared to prevailing option pricing models that employ stochastic variables, our DIVF model does not include stochastic factors but exhibits reasonably good performance. It is also easy to compute due to the availability of real-time RSIs. Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.
This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.
This paper delves into the intricate relationship between liquidity indicators and the price dynamics of Bitcoin, a prominent cryptocurrency.Liquidity, a fundamental aspect of financial markets, profoundly influences market stability and efficiency.Leveraging statistical analysis and AI modeling techniques, my study explores various liquidity metrics-including trading volume, bid-ask spread, volatility, number of transactions, and bid and ask sums as separate indicators-to assess their impact on the price of Bitcoin.The findings offer valuable insights into the factors driving Bitcoin price movements and shed light on the role of liquidity in cryptocurrency markets.Through correlation analysis as well as three different machine learning models -random forests, XGBoost, and linear regression -, my study evaluates the significance of individual liquidity factors and their relationships with Bitcoin prices.The best performing model was the random forest regressor and XGBoost where I identified that the volatility was the feature that was the most informative of the model's performance.My research contributes to advancing our understanding of liquidity and price discovery in cryptocurrency markets and underscores the need for future studies to explore alternative factors and mechanisms shaping cryptocurrency prices.By embracing the findings and continuously refining analytical approaches, researchers can navigate the evolving landscape of cryptocurrency trading, ultimately enhancing market efficiency and informing regulatory decisions.
The research investigates cryptocurrency's function in enhancing Pakistani financial market portfolios while examining the digital asset popularity, surged as an investment choice. The analysis combines cryptocurrencies with conventional financial products to show how they affect both risk performance and risk spread capabilities. The main goal of this research is to understand if adding cryptocurrency investments produces superior returns than standard asset allocation strategies. This study intends to join the current discussion regarding digital asset adoption in emerging economies particularly Pakistan. A time period of six years extending from January 1, 2018 to December 31, 2023 contains daily financial data which includes both traditional assets and cryptocurrencies. The dataset receives preprocessing treatments which include normalization together with outlier removal and missing value imputation. The portfolio optimization process in Jupiter Notebook implements machine learning models under naĂŻve equal weighting and maximum return and maximum Sharpe ratio and minimum variance constraints. Excel was used to run robustness checks for the analysis which demonstrated that cryptocurrency portfolios generate higher risk-adjusted performance than traditional investment collections. This research presents digital assets as a valid investment strategy component by improving portfolio diversity and overall performance while focusing specifically on the Pakistani financial market through combination of machine learning and standard financial modeling. The research enhances available scientific understanding of cryptocurrency integration in emerging market economies while failing to find sufficient existing literature on this subject matter. Succeeding studies should analyze digital asset regulatory measures and economic conditions alongside investor acceptance patterns towards crypto adoption in Pakistan. The research could benefit from additional analysis that incorporates alternative risk management approaches alongside sophisticated portfolio optimization algorithms.
Danial Saef, Odett Nagy, Sergej Sizov, Wolfgang Karl Härdle
Abstract Cryptocurrency markets have recently attracted significant attention due to their potential for high returns; however, their underlying dynamics, especially those concerning price jumps, continue to be explored. Building on previous research, this study examines the presence and clustering of jumps in an extensive tick data set covering six major cryptocurrencies traded against Tether on seven leading exchanges worldwide over nearly 2.5 years. Our analysis reveals that jumps occur on up to 58% of trading days, with negative jumps predominating in both frequency and size. Notably, we observe systematic clustering of jumps over time, especially in Bitcoin and Ethereum, indicating interconnected market dynamics and potential predictive power for market movements. By employing high-frequency econometric tools, we identify temporal patterns in jump occurrence, highlighting heightened activity during specific trading hours and days. We also find evidence of jumps influencing intraday returns, underscoring their significance in short-term price dynamics. Our findings enhance understanding of the cryptocurrency market microstructure and offer insights for risk management and predictive modeling strategies. Nevertheless, further research is needed to develop robust methodologies for detecting and analyzing co-jumps across multiple assets.
Identifying the expected growth in cryptocurrency involves analyzing a combination of market trends, technological advancements, regulatory developments, and economic indicators. Historical performance and adoption rates of major cryptocurrencies provide insight into market trends, while innovations in blockchain technology, such as Ethereum 2.0 and Layer 2 solutions, along with the rise of decentralized finance (DeFi) and non-fungible tokens (NFTs), highlight significant technological advancements. Regulatory developments, including supportive legislation and the involvement of institutional investors through financial products like Bitcoin ETFs, play a crucial role in shaping market confidence and investment. Economic indicators, such as inflation, monetary policies, and global events, also influence interest in cryptocurrencies as alternative assets. Investor sentiment, driven by public perception, media coverage, and social media activity, impacts market dynamics. Additionally, research from financial analysts, market research firms, and academic studies, along with corporate partnerships and the integration of crypto solutions with traditional systems, contribute to growth predictions. Monitoring market capitalization and trading volumes further helps gauge market interest and liquidity. By considering these multifaceted factors, a more comprehensive understanding of the potential growth in the cryptocurrency market can be achieved.
This paper investigates both coin-specific and market-based factors that drive cryptocurrency pump-and-dump schemes. It analyzes a data set comprising 1,457 pump events that occurred from January 3, 2018, to January 2, 2022. Empirical findings, derived from binary cross-sectional regression models, reveal several characteristics that increase the likelihood of cryptocurrencies being pumped. These include lower market capitalization, lower trading volume, greater social media popularity, increased developer activity, and fewer exchanges trading them. Furthermore, the study employs count time-series models to examine market-based factors. The results indicate that periods of higher volatility or uncertainty are associated with an increase in pre-announced pump-and-dump activities. Additionally, the analysis shows that macroeconomic factors and specific time-related effects - such as Sundays, certain months, and the COVID-19 period - are significant in explaining the frequency of pump occurrences. Based on these findings, the article discusses several targeted recommendations.
The COVID-19 pandemic has had a significant impact on various aspects of life, including financial markets. The cryptocurrency market, already renowned for its volatility, experienced a surge in activity and significant changes in investor behavior during this period. This research aims to analyze various economic behavioral anomalies that emerged in cryptocurrency transactions during the COVID-19 pandemic. This research uses a literature study method to identify several dominant behavioral anomalies, such as FOMO (Fear of Missing Out), Herding Behavior, Noise Trading, Overconfidence, and Anchoring Bias. These behavioral anomalies trigger extreme market volatility, asset bubbles, and financial losses for investors. This research highlights the importance of investor education, market regulation, and technology development to minimize the impact of behavioral anomalies and protect investors in the cryptocurrency market.
Nektarios Aslanidis, Aurelio F. Bariviera, Christos S. Savva
This paper adopts a versatile conditional correlation approach to explore daily seasonality in the major cryptocurrencies. Given the lack of clear fundamental value in this market and the active online profile of investors, the study also relates cryptocurrency cross-correlations to online market attention and sentiment. Our results highlight that while investor attention has a positive effect, sentiment has a much stronger negative impact on the correlations. These findings can offer interesting insights for investors and regulators, as the influence of market attention and sentiment on the correlations has important implications for portfolio diversification and market stability.
As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal trading strategies adopted in the cryptocurrency market focus on trading one type of cryptocurrency, whereas most traders in the cryptocurrency market often trade multiple cryptocurrencies. Therefore, the present study proposes a method based on deep Q-learning for identifying the optimal trading strategy for multiple cryptocurrencies. The proposed method uses the same training data to train multiple agents repeatedly so that each agent has accumulated learning experiences to improve its prediction of the future market trend and to determine the optimal action. The empirical results obtained with the proposed method are described in the following text. For Ethereum, VeChain, and Ripple, which were considered to have an uptrend, a horizontal trend, and a downtrend, respectively, the annualized rates of return were 725.48%, â14.95%, and â 3.70%, respectively. Regardless of the cryptocurrency market trend, a higher annualized rate of return was achieved when using the proposed method than when using the buy-and-hold strategy.
Purpose: This study sought to explore cryptocurrency and its role in portfolio diversification. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executiveâs time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cryptocurrency and its role in portfolio diversification. Preliminary empirical review revealed that incorporating cryptocurrencies into investment portfolios offered promising diversification benefits due to their low correlation with traditional assets, despite their high volatility and regulatory uncertainties. It highlighted the significant risk management challenges posed by cryptocurrencies' extreme price fluctuations and the evolving regulatory landscape. The study emphasized the importance of careful, limited allocation to cryptocurrencies, robust risk management practices, and continuous market monitoring. Ultimately, it suggested that cryptocurrencies could enhance portfolio performance when strategically used alongside traditional diversification methods. Unique Contribution to Theory, Practice and Policy: The Modern Portfolio Theory, Efficient Market Hypothesis and Behavioural Finance Theory may be used to anchor future studies on portfolio diversification. The study recommended a cautious yet strategic inclusion of cryptocurrencies in investment portfolios to enhance diversification, emphasizing the importance of ongoing research, robust risk management, and proactive monitoring due to their high volatility and regulatory uncertainties. It called for clear and consistent regulatory frameworks to protect investors while fostering market growth, and highlighted the need for collaboration between academia, industry, and regulatory bodies to improve financial literacy and market stability. These recommendations aimed to contribute to theoretical, practical, and policy aspects of cryptocurrency investments.
G. Zhang, Alexander J. Sannella, Gerard Brennan, Muhammad Talha Afzal
⢠Proposing a dynamic valuation framework for fair value estimates for illiquid cryptocurrency. ⢠Discussing compliance with fair value accounting standards and cryptocurrency reporting requirements. ⢠Factoring in comparable assetsâ market information and news big data analytics that measure market participantsâ attention and sentiment in the valuation framework. ⢠Empirically testing the valuation framework with historical market data. ⢠Developing a machine learning valuation model that achieved 87 % prediction accuracy. To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participantsâ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participantsâ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.
An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
With particular attention to variables like volatility and the performance of the U.S. stock market, this study attempts to conduct a thorough quantitative examination of the relationship between the cryptocurrency market. By using sophisticated mathematical modeling approaches, such as regression analysis and correlation methodologies, it is hoped to identify the key characteristics of these markets as well as the degree to which cryptocurrency volatility and stock market success are causally related. The use of historical data, spanning a specific time (from July 1st, 2019, to July 1st, 2023) around 4 index price-day transaction data will be made, with a focus on high-frequency data for improved accuracy. The results of this study, which examine each option's characteristics or attributes, will add to the larger body of scholarly literature on the integration of cryptocurrencies into conventional financial markets. Moreover, drawing conclusions about some effects or prospective connections between cryptocurrencies and the financial industry based on their similarity to the American stock market. To pave the way for better-informed financial decision-making, this research aims to deepen our understanding of the interactions and spillover effects between cryptocurrency volatility and the American stock market.
This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.
Recent studies document that cryptocurrencies offer an alternative store of value, medium of exchange and can be used to hedge against currency and price fluctuations. However, the frequent collapse of the crypto-market undermines its safe-haven characteristics, as investorsâ fear and anxiety could intensify market volatility and trigger a financial crisis. Motivated by the current global vicissitudes, this study examines the impact of uncertainty and sentiment factors on price behaviour of cryptocurrencies. To estimate our model, we used daily, low, high and closing price data for major crypto projects, from January 2018 to January 2023. We show that economic and political uncertainty factors significantly drive crypto prices. Furthermore, the interaction between sentiment dynamics as expressed by investors on different social platforms has a significant adverse effect on the returns of the cryptocurrency market, and the impact is more pronounced for tokens within the same ecosystem. Using the asymmetric GARCH-MIDAS model and TVP-VAR, we also demonstrate the existence of a significant contagion among tokens within the same ecosystem when bad (or good) news occurs. Considering the massive unprotected losses incurred by crypto investors during crises, our results provide important insights into how portfolio managers can effectively design investment strategies.
The emergence of decentralized finance (DeFi) has transformed traditional financial systems by leveraging blockchain technology to offer decentralized solutions for trading and liquidity provision. Within the CORE Chain ecosystem, the COREDAO VIP token plays a pivotal role in facilitating liquidity pooling and automated trading across various COREDAO-based tokens. This research investigates the impact of COREDAO VIP token within decentralized exchanges (DEX) such as ICECREAMSWAP, LFGSWAP, SHADOWSWAP, and ARCHERSWAP. By analyzing its integration into these platforms, the study explores how COREDAOVIP enhances liquidity management, reduces slippage, and supports automated trading strategies. Key aspects examined include the token's utility, governance implications, and its influence on trading dynamics within the COREDAO ecosystem. Through comprehensive analysis and empirical insights, this research aims to provide a nuanced understanding of COREDAOVIP token's role in advancing decentralized finance practices and its implications for future blockchain-based financial ecosystems.
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the unique opportunities presented by the cryptocurrency market, noted for its on-chain data's transparency and the critical influence of off-chain signals like news, remain largely untapped by LLMs. This work aims to bridge the gap by developing an LLM-based trading agent, CryptoTrade, which uniquely combines the analysis of on-chain and off-chain data. This approach leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market. CryptoTrade incorporates a reflective mechanism specifically engineered to refine its daily trading decisions by analyzing the outcomes of prior trading decisions. This research makes two significant contributions. Firstly, it broadens the applicability of LLMs to the domain of cryptocurrency trading. Secondly, it establishes a benchmark for cryptocurrency trading strategies. Through extensive experiments, CryptoTrade has demonstrated superior performance in maximizing returns compared to traditional trading strategies and time-series baselines across various cryptocurrencies and market conditions. Our code and data are available at \url{https://anonymous.4open.science/r/CryptoTrade-Public-92FC/}.