This study employs event study methodology to investigate the impact of various types of events on cryptocurrency market returns and volatility. The research focuses on six major cryptocurrenciesâETH, BTC, BNB, XRP, DOGE, and TRXâover the period from December 31, 2017, to October 30, 2023. Six types of events are analyzed: cybersecurity events, block reward adjustment events, political conflict events, public health emergency events, cryptocurrency recognition and support events, and social media sentiment events. The findings reveal that cybersecurity and block reward adjustment events have minimal and short-lived impacts on market returns. Political conflict events cause significant short-term return volatility depending on market expectations. Public health emergency events, such as the COVID-19 pandemic, have significant and lasting negative impacts on market returns. Cryptocurrency recognition and support events have significant and sustained positive impacts on market returns. Social media sentiment events have significant but short-lived impacts on market returns. The robustness of the results was validated through the analysis of abnormal returns during the event period. This study provides valuable insights for investors and policymakers in managing market volatility.
Purpose This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management. Design/methodology/approach We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmazâs connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management. Findings Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors. Practical implications There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies. Originality/value The studyâs major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.
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 investigates whether investorâs sentiment and attention information collected via YouTube can improve bitcoin return forecasts. Accordingly, we collected daily data over the period 2017â2023, covering calm and turbulent periods marked by different types and episodes of emotions. Unlike previous studies, we used YouTube videos to propose two sentiment proxies: investor attention to YouTube (daily number of YouTube video views) and investor sentiment on YouTube (number of positive and negative videos on YouTube). Interestingly, we break down both attention and sentiment per subject. Econometrically, we assess lead-lag effects between sentiment/attention and bitcoin return using causality tests and Vector Auto-regressive (VAR) model. We also evaluate the forecasting power of YouTube attention/sentiment data using a deep learning LSTM model. Our study shows two main results. First, we find lead-lag effects between bitcoin returns and per subject investorâs attention and sentiment proxies. Second, we show that our deep learning LSTM model relying on the information provided by attention and sentiment supplants benchmark Buy and Hold Strategy to forecast future bitcoin returns.
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.
Purpose The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period. Design/methodology/approach The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto tradersâ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrenciesâ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005). Findings Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies. Practical implications The study provides crucial guidelines for investorsâ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the marketâs information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior. Originality/value This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.
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.
We analyse the impact of cybercrime, particularly cryptocurrency heists and scams, on dynamic conditional correlations and abnormal returns in cryptocurrency. Our high-frequency, hourly data set covers three years, from January 2020 to December 2022, and our results show that certain hacking events have a negligible negative impact on investors, especially when focusing on less popular tokens or coins. This new perspective has significant implications for the investment community. Existing literature generally assumes that the impact of cybercrime is situational and argues for increased awareness, proactive cybersecurity measures and multi-stakeholder collaboration in traditional financial markets such as equities and currencies. However, the cryptocurrency market â a relatively young and still developing area â has a unique dynamic. Less popular tokens or coins often have lower market integration and liquidity, indicating a lower impact of cybercrime. If such a token or coin already has a limited reputation or investor base, the overall negative sentiment may be further mitigated, lessening the expected negative impact.
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.
This study recovers the Bitcoin option-implied risk aversion by jointly estimating a cross-sectional dataset of option prices and time-series data of realized returns on underlying asset prices. The empirical analysis of Bitcoin options on Deribit shows that the risk aversion function exhibits a peak shape, with the level of implied risk aversion of Bitcoin options ranging from â0.2 to 0.05, which is significantly lower than that of the traditional options market; furthermore, maturity affects the level of option-implied risk aversion, with shorter maturity implying higher risk-aversion levels. Moreover, our research indicates that after halving of Bitcoin, investorsâ risk aversion function becomes higher and steeper than before.
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.