Deep learning methods have achieved significant success in various applications, including trend signal prediction in financial markets. However, most existing approaches only utilize price action data. In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the trend signal of Ethereum cryptocurrency. We conduct experiments to investigate the relationship between price action, candlestick patterns, and Ethereum-Bitcoin correlation, aiming to achieve highly accurate trend signal predictions. We evaluate and compare two different training strategies for Convolutional Neural Networks (CNNs), one based on transfer learning and the other on training from scratch. Our proposed 1-Dimensional CNN (1DCNN) model can also identify inflection points in price trends during specific periods through the analysis of statistical indicators. We demonstrate that our model produces more reliable predictions when utilizing multiple data representations. Our experiments show that by combining different types of data, it is possible to accurately identify both inflection points and trend signals with an accuracy of 98%.
In recent years, with the rapid development of blockchain technology, the emergence of Non-Fungible Tokens (NFTs) has become a disruptive and innovative application that has attracted widespread attention and triggered frenzy. This study examines the momentous but may be easily neglected price factor in the NFT market. Using hand-collected daily data on the number of followers of 150 NFTs on Discord from April 18 to 15 October 2022, empirical results find that the fan economy on social media platforms has a positive impact on NFT pricing. Furthermore, this impact has a certain time-lagged effect. To ensure the robustness of the research, this paper also collects Twitter followers as an alternative indicator to measure the fan economy, and all the empirical results of the Twitter platform are significant. The findings of this paper are of great significance for studying the factors affecting the price of NFTs and provide certain assistance for the decision-making of NFT issuers and investors.
Ahmet Faruk Aysan, Massimiliano Caporin, OÄuzhan Ăepni
This paper analyzes the relationship between price jumps and news sentiment in cryptocurrencies. We detect jumps at the intraday level and correlate their occurrence with sentiment-related events through logistic regressions. We show that the release of information increases the probability of price jumps. By examining the content of news stories, we find that sentiment dimensions limited to emotions or related to market fundamentals have more potential to result in price jumps than others, suggesting that âwords are not all created equalâ. Jump sensitivity to news sentiment varies across different coin characteristics.
The long-lasting intermediated structure of international bond markets has come under scrutiny in recent times because of the risks it exposes final investors to, mostly in relation to the difficulties these investors face in enforcing their rights. Distributed ledger technologies (DLTs) have emerged as a strong contender in efforts to improve the position of final investors by shifting the market to a direct holding structure. In this context, it is necessary to ask if organising international bond markets under a DLT-based direct holding structure will effectively address the risks surrounding intermediated securities. Furthermore, it is important to assess the impact such a change is likely to have on other players in the market (including intermediaries and issuers), as well as on the financial system as a whole. With these questions in mind, this article begins with an explanation of the primary legal implication of holding intermediated securities, i.e., that final investors do not hold legal title over the bonds they have invested in because they are not engaged in a direct relationship with the issuer. The paper then proceeds to dissect the risks such arrangements expose investors to and contrast those risks with the benefits that intermediation afford to investors, issuers and the financial system in general. It is then argued that DLTs are not only inadequate to the task of addressing those risks, but would also eliminate most of the advantages of intermediation. The paper goes on to examine how investors and issuers are not incentivised to promote the development of a DLT-based bond market organised under a direct holding structure. It concludes with the suggestion that a better way to improve the position of final investors in bond markets is to explore how DLTs may enhance the benefits already created by intermediation, rather than relying on these technologies to eliminate intermediation altogether. In particular, it is submitted that DLTs may introduce efficiencies in the management of the bonds, the performance of obligations by issuers, the settlement process, the performance of securities financing transactions, and the provision of services by intermediaries.
Henrik Axelsen, Ulrik Terp Rasmussen, Johannes Rude Jensen, Omri Ross · 5 authors
The promising markets for voluntary carbon credits are faced with crippling challenges to the certification of carbon sequestration and the lack of scalable market infrastructure in which companies and institutions can invest in carbon offsetting. This amounts to a funding problem for green transition projects, such as in the agricultural sector, since farmers need access to the liquidity needed to fund the transition to sustainable practices. We explore the feasibility of mitigating infrastructural challenges based on a DLT Trading and Settlement System for green bonds. The artefact employs a multi-sharded architecture in which the nodes retain carefully orchestrated responsibilities in the functioning of the network. We evaluate the artefact in a supranational context with an EU-based regulator as part of a regulatory sandbox program targeting the new EU DLT Pilot regime. By conducting design-driven research with stakeholders from industrial and governmental bodies, we contribute to the IS literature on the practical implications of DLT.
This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.
With the advent of 2022, the impact of the COVID-19 pandemic has weakened, the US labor market has recovered, and inflation has been severe, creating the conditions for the Fed to tighten its policies. At the same time, cryptocurrencies as a hot topic in recent years; ETH is one of the most popular cryptocurrencies in the market; this article aims to assess the impact of the Fed's raised interest rates on the yield and volatility of cryptocurrency Ethereum (ETH) based on data on the ETH price and the US dollar/CNY exchange rate since 2022. And further, simulate the impact on the overall cryptocurrency market. This paper constructs VAR and ARMA-GARCH models to analyze ETH returns and volatility variations. The results of these models suggest that the exchange rate rise triggered by the Fed's rate hike has had a negative impact on ETH yields and increased the volatility of its returns. Further, this article recommends that investors should adjust their portfolios according to their risk appetite in an uncertain market environment.
This paper investigates the performance of cryptocurrencies and market indices. Using the dynamic conditional correlation (DCC) model, the result shows that cryptocurrencies and market indices, contrary to much of the literature, tend to move in the same direction, resulting in little or no benefits in portfolio management. Dividing into the sub-sample period, cryptocurrencies have moved even more strongly with market indices during the recent period after the COVID-19 pandemic, indicating the possibility of no hedging benefit. This paper shows that inclusion of cryptocurrency in a portfolio increases the return as well as volatility, as the risk-adjusted return does not show any sign of improvement. A portfolio comprising the FTSE 100 Index seems to receive the greatest benefit of including cryptocurrencies as the risk-adjusted performance improves.
Since the formulation of the Efficient Market Hypothesis, countless studies have been developed that try to either prove or refute it. Event studies, analysing the impact of different events on asset prices, are one of the most important research fields but there is a lack of evidence on cryptocurrencies. For that reason, we analyse the existence of over- and under- reaction effects on Bitcoin after hourly price shocks defined by filter sizes. We also do this using three alternative approaches. Our results show clear evidence of overreaction after negative shocks. We also observe that these overreactions tend to be greater as more hours pass after the event, with those that occur between 6 and 24 hours after the event being especially important. These results have important economic implications because they show that investors would be able to develop a profitable trading strategy simply by focusing on investing after negative shocks.
Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge, and elaborated a systematic literature review along with a bibliometric analysis. Our results indicate a fast-growing literature evidencing cryptocurrenciesâ ability to hedge against stocks, fiat currencies, geopolitical risks, and Economic Policy Uncertainty (EPU) risk; also, that cryptocurrencies present diversification and safe-haven properties; that stablecoins reveal unstable peg with the US dollar; that uncertainty is a determinant for cryptocurrency returns. Additionally, we show that investors should consider Gold, along with the European carbon market, CBOE Bitcoin futures, and crude oil to hedge against unexpected movements in the cryptocurrency market.
Reza Nourmohammadi, Mahdi Arabian, Masoumeh Ghorbanpour, Mohammad M. Nazemi · 5 authors
As a cutting-edge technology, non-fungible tokens (NFT) have attracted a great deal of attention since 2021. Considering the numerous applications of these non-interchangeable digital assets in various industries and their tradability, NFTs have become an important element of many investorsâ portfolios. Therefore, in order to evaluate NFTs and determine their main value, different tools must be used. The purpose of this study is to understand the dominant factors that influence the valuation of NFT assets. The purpose of this paper is to present a novel methodology for constructing a utility valuation model for NFTs as a whole. We will be able to analyze and diagnose the dynamics and performance of NFT markets using this model. We developed three models for scoring NFTs in this study, which can be used to speed up the evaluation process in three different dimensions.
The aim of the International Conference "Economic Scientific Research - Theoretical, Empirical and Practical Approaches"- ESPERA, initiated in 2013 by the "Costin C. KiriÈescu" National Institute for Economic Research (NIER), is to present and evaluate the economic scientific research portfolio, to argue and substantiate the Romanian development strategies - including European and global best practices. The 2021 edition of the Conference will be held on 9th -10th December, under the title: "The crisis after the crisis. When and how the New Normal will be". The event scientific program addresses a wide diversity of themes, bringing together researchers from all NIER institutes and centres, members of the Romanian Academy, Romanian academic researchers and also guests from other countries. Researchers are encouraged to present articles on economic scientific research that they have focused, as much as possible, on paradigm shifts for the world after the COVID-19 crisis, since some deep and long-lasting changes are expected building up to a "New Normal". Singular relevant aspects could be related to complete digitalization and digital sovereignty, people and workforce management, virtual training and reskilling, digital currency, de-carbonization in all production processes, supply chains traceability, cybersecurity, automation, artificial intelligence and machine learning,
This paper empirically assesses the ability of three putative stablecoins (two dollar-backed, Tether and USD Coin; and one gold-backed, Digix Gold) to mitigate the risk of facing severe losses (downside risk) of a traditional cryptocurrency portfolio. There are institutional features that induce cryptoinvestors to use stablecoins as diversifiers instead of withdrawing dollars or adding assets traditionally considered as safe havens, such as gold, crude oil, etc. Stablecoins, however, are not as stable as their name and collateralized peg suggest. A monthly rebalance experiment is conducted over an out-of-sample period considering higher order conditional moments when dynamically measuring the tail risk of cryptocurrency portfolios. The empirical evidence shows that the low conditional correlations of dollar-backed stablecoins with cryptocurrency portfolios make them particularly suitable as a hedge for crypto investors. It also shows that all stablecoins considered have high diversification capacities by systematically reducing portfolio tail risk.
Covid-19 has had a significant impact on financial markets around the world, and various countries have adopted their methods to combat the impact of Covid-19 on equity markets.And because of Covid-19, the market share of the cryptocurrency market is increasing rapidly.This article focuses on how exchange rate changes caused by the Fed's interest rate hike affected the cryptocurrency market after the epidemic.The article uses ARMA-GARCH and VAR models to analyze the future change of the cryptocurrency market after 2022 and how an increase in interest rate affects cryptocurrency market volatility.Furthermore, the model predictions have not been found that the interest rate increase in early 2022 has produced volatility in the cryptocurrency market.Compared to traditional equity markets or real estate markets.Cryptocurrencies are not responsive to government intervention.
This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perronâs structural break test, InclĂĄn and Tiaoâs iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Studentât distribution fits the cryptocurrency marketâs price dynamics well.
Mohak Goyal, Geoffrey Ramseyer, Ashish Goel, David MaziĂšres
Constant Function Market Makers (CFMMs) are a tool for creating exchange markets, have been deployed effectively in prediction markets, and are now especially prominent in the Decentralized Finance ecosystem. We show that for any set of beliefs about future asset prices, an optimal CFMM trading function exists that maximizes the fraction of trades that a CFMM can settle. We formulate a convex program to compute this optimal trading function. This program, therefore, gives a tractable framework for market-makers to compile their belief function on the future prices of the underlying assets into the trading function of a maximally capital-efficient CFMM. Our convex optimization framework further extends to capture the tradeoffs between fee revenue, arbitrage loss, and opportunity costs of liquidity providers. Analyzing the program shows how the consideration of profit and loss leads to a qualitatively different optimal trading function. Our model additionally explains the diversity of CFMM designs that appear in practice. We show that careful analysis of our convex program enables inference of a market-maker's beliefs about future asset prices, and show that these beliefs mirror the folklore intuition for several widely used CFMMs. Developing the program requires a new notion of the liquidity of a CFMM, and the core technical challenge is in the analysis of the KKT conditions of an optimization over an infinite-dimensional Banach space.
Everyone is eager for high yield and low risk. In this research, we use Markowitz's investment theory and Monte Carlo simulation to find the optimal investment portfolio and then study the impact of adding Bitcoin to the traditional investment portfolio on the cumulative rate of return. Our results show that the return performance of the investment portfolio with Bitcoin is better than that of the traditional investment portfolio. Moreover, despite the impact of COVID-19 on the global economy and the Federal Reserve's quantitative easing policy, it is beneficial for investors to include Bitcoin in their portfolio allocation.
Blanka ĆÄt, Konrad SobaĆski, Wojciech Ćwider, Katarzyna WĆosik
Abstract This article sheds new light on the informational efficiency of the cryptocurrency market by analyzing investment strategies based on structural factors related to on-chain data. The study aims to verify whether investors in the cryptocurrency market can outperform passive investment strategies by applying active strategies based on selected fundamental factors. The research uses daily data from 2015 to 2022 for the two major cryptocurrencies: Bitcoin (BTC) and Ethereum (ETH). The study applies statistical tests for differences. The findings indicate informational inefficiency of the BTC and ETH markets. They seem consistent over time and are confirmed during the COVID-19 pandemic. The research shows that the net unrealized profit/loss and percent of addresses in profit indicators are useful in designing active investment strategies in the cryptocurrency market. The factor-based strategies perform consistently better in terms of mean/median returns and Sharpe ratio than the passive âbuy-and-holdâ strategy. Moreover, the rate of success is close to 100%.
This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.