The volatile and unpredictable nature of the cryptocurrency market makes it particularly challenging to make profitable investment decisions. different machine learning-based techniques have been employed for forecasting cryptocurrency value. However, although some works have addressed incorporating the Blockchain transactionsâ data into the analysis, none of them has provided a hybrid solution, including features obtained through complex network modeling. In this paper, we investigated the use of machine learning and complex network techniques to improve the profitability of a cryptocurrency portfolio during a downtrend period. We extracted features through a complex network-building methodology based on the Bitcoin blockchain transactions, merged them with the historical cryptocurrency values, and generated the predictions using different machine-learning models. The results indicated that incorporating complex network features improved the performance in retaining the initial capital at the end of the experiment, leading to an increment of 7.09% and 4.33% for the CNN and LSTM models, respectively. Our findings suggest that the proposed method enhanced the performance of cryptocurrency investment strategies during downtrend periods.
We employ a novel framework to measure the asymmetric nexus between the cryptocurrency market and the carbon futures market based on different market conditions. Specifically, we use a quantile-on-quantile regression (QQR) approach to explore the correlation between cryptocurrencies (Bitcoin, Ethereum, and Ripple) and European Union Allowance (EUA) futures. We find that there is an asymmetric relationship between markets that is affected by different cryptocurrencies and market conditions. Overall, Bitcoin or Ethereum are positively correlated with the carbon market, while the results of Ripple are more complex. Under certain conditions, EUA futures can be a better hedge against cryptocurrency risk.
Federico P. Cortese, Petter N. Kolm, Erik Lindström
Abstract We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.
<abstract> <p>Bitcoin has become quite known after the 2008 economic crisis and the COVID-19 health crisis. For some, these cryptocurrencies constitute rebellion against the existing system as governments encourage uncontrolled expansions in the money supply; for some others, it is a quick source of income. Undeniably, the volume of the crypto money market has grown considerably in recent years, regardless of the reasoning of the people who invest and trade in this field. At this point, one of the most important questions to be investigated is "what variables have caused the tremendous growth in the crypto money quantities in recent years?" This study tests the assumption that changes in cryptocurrencies are affected by changes in national currencies. Thus, the Bitcoin price is the dependent variable, and M1 monetary supply changes in the USA, European Union and Japanese economies are considered independent variables. The variables in this study were tested using the time-varying Granger causality method. The results obtained from this study confirm the philosophy of Bitcoin's emergence and the possibility that it can be a hedge against the inflationary effects of money, especially after the COVID-19 pandemic.</p> </abstract>
After Bitcoin futures were introduced by the Chicago Mercantile Exchange in December 2017, their trading volume has stayed in an uptrend due to speculation, though the scale is still small compared to other traditional futures. As increasing trading indicates more attention and the presence of institutional traders, there exists a need for reliable return and variance forecasts of Bitcoin futures contracts. Therefore, this paper first applies LASSO to pick out best-fitting predictors by shrinking the dimension of a universe of potential determinants sourced from intraday Bitcoin spot trades and daily futures variables. Then, a second round of predictor selection is conducted via Bayesian model averaging so that the modeling uncertainty can be mitigated. We find that factors standing out from this two-step procedure possess a strong predictive power for Bitcoin futures return and volatility in different time horizons. It is further demonstrated that the investment and hedging strategies established based on our forecasts perform well in out-of-sample validations.
Oracles are software components that enable data exchange between siloed blockchains and external environments, enhancing smart contract capabilities and platform interoperability.Oracles play key roles in decentralized finance and blockchain applications in centralized finance.We find that integration into decentralized oracle networks is positively associated with key measures of economic activity such as Total Value Locked, triggered by positive network effects in adoption and usage.Our study reveals symbiotic gains from enhanced interoperability and network effects across protocols on a given chain and among integrated chains.Oracle integration appears to improve risk-sharing and mitigates contagion, increasing resilience during turbulent periods in crypto markets.Overall, oracles emerge as a crucial component to enable informational and economic integration in decentralized finance ecosystems.
Muhammad Rafi, Qublai Khan Ali Mirza, Muhammad Izaan Sohail, Maria Aliasghar · 6 authors
A cryptocurrency is a digitized, encrypted, and decentralized virtual currency, which is impossible to counterfeit or double-spend. It is one of the very popular investment instruments and traded in blockchain based crypto exchanges on ever growing volume. It is quite volatile due to imbalance of supply and demand, government regulations, investor sentiment and above all media hype. Cryptocurrency price forecasting is an active area of research and several approaches have been proposed recently. This study proposed a price forecasting model based on three vital characteristics (i) a feature selection and weighting approach based on Mean Decrease Impurity(MDI) features. (ii) Bi-directional LSTM and (iii) with a trend preserving model bias correction (CUSUM control charts for monitoring the model performance over time) to forecast Bitcoin and Ethereum values for long and short term spans. The data for both currencies were analyzed in three different intervals: (i) April 01, 2013 to April 01, 2016 (ii) April 01, 2013 to April 01, 2017 and (iii) April 01, 2013 to December 31, 2019. Extensive series of experiments were performed and evaluated on Root Mean Square Errors (RMSE). Comparing with the prevalent forecasting models we report a new state of the art in cryptocurrency forecasting.
People trade thousands of non-fungible tokens (NFT) daily. The NFT prices are expressed in cryptocurrency, and it is volatile. As the interest in the NFTs changes, their prices vary with time too. Is there an immanent meter to order NFTs by their value? Within a single collection, a vector of featuresâtraitsâcharacterizes NFT. People construct rarity meters based on the assumed value of the trait vector rarity. But this process lacks formalism. In this paper, we formulate the optimal rarity meter problem and provide a pipeline for optimal rarity meter design. A proposed tournament score function is an essential part of the construction. We demonstrate the approach for the Kanaria NFT collection.
Abstract We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stockâbond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.
Fabian E. Eska, Yanghua Shi, Erik Theissen, Marliese UhrigâHomburg
This paper examines the impact of cryptocurrency design features on their return volatility. We compile a sample of 58 cryptocurrencies, adopt the taxonomy of design features proposed by Eska et al. (2022), and estimate LASSO regressions. We document that older cryptocurrencies tend to be less volatile. Networks with mandatory transaction fees, cryptocurrencies based on (delegated) Proof-of-Stake, and those developed by private for-profit entities tend to be more volatile. Furthermore, we provide evidence that networks passing transaction fees and/or tips on to verifiers are associated with higher volatility levels.
Ethereum has recently surged in popularity, as it can hold various digital tokens and decentralized applications.This paper aims to predict UNI's price in USD through dynamic network analysis and time-series analysis.Previous research in this field rarely considers comprehensive network analysis while predicting token price.This paper puts forward a strengthened Bidirectional LSTM model that includes token economical features and network features.We use Root Mean Squared Error (RMSE) to verify the validity and compare it with other LSTM and GRU models on performance.Lastly, a logarithm difference method for data preprocessing was introduced to resolve the lag problems.
Muhammad Anas, Syed Jawad Hussain Shahzad, Larisa Yarovaya
Abstract As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authorsâ collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrenciesâ investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies.
V. Akila, Nitin M.V.S., I S N V R Prasanth, S. R. M. A. Ayeshmi M. · 5 authors
Cryptocurrencies have gained immense popularity in recent years as an emerging asset class, and their prices are known to be highly volatile. Predicting cryptocurrency prices is a difficult task due to their complex nature and the absence of a central authority. In this paper, our proposal is to employ Long Short-Term Memory (LSTM) networks, a type of deep learning technique to forecast the prices of cryptocurrencies. We use historical price data and technical indicators as inputs to the LSTM model, which learns the underlying patterns and trends in the data. To improve the accuracy of the predictions, we also incorporate a Change Point Detection (CPD) technique using the Pruned Exact Linear Time (PELT) algorithm. This method allows us to detect significant changes in cryptocurrency prices and adjust the LSTM model accordingly, leading to better predictions. We evaluate our approach predominantly on Bitcoin cryptocurrency, but the model can be implemented on other cryptocurrencies provided there are valid historical price data. Our experimental results show that our proposed model outperforms the baseline LSTM algorithm, achieving higher accuracy and better performance in terms of Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Our research findings suggest that combining deep learning techniques such as LSTM with change point detection techniques such as PELT can improve cryptocurrency price prediction accuracy and have practical implications for investors, traders, and financial analysts.