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2,329 results · page 55 of 98
Simon Trimborn, Ying Chen, Ray‐Bing Chen
No abstract is available for this record.
Samet Günay, Vladimir Dženopoljac, Nick Bontis
This paper investigates the interaction of public information arrivals and volatility in the cryptocurrency market from the perspective of intellectual capital, specifically, relational capital. The empirical analysis was conducted using Kapetanios' unit root test, various scaling (Hurst exponent) tests, a fractionally integrated generalised autoregressive conditionally heteroskedastic model, and Markov regime-switching regression for different series, including the logarithmic returns and abnormal returns, of price and volume series. Following modelling volatility and the derivation of conditional variance, Twitter posts were employed as an independent variable over each series. The results indicate that, while public information arrivals have a positive impact on the volatility of Ripple returns, they cannot divert away the variability of the volume.
Brady Lund
This chapter describes the author's personal experience as a member of a crypto-trading “pump-and-dump” group – groups organized on Reddit and Discord channels that use social media to spread positive misinformation about a cryptocurrency in order to temporarily inflate its value and collect huge profits. It discusses the nature of cryptocurrency marketplaces, social networking related to crypto, the pump-and-dump phenomenon, its social and economic impacts, and ethical concerns. Following the rise in the value of Bitcoin and the WallStreetBets/GameStop saga in December 2020 and January 2021, these pump-and-dump groups used the frenzy surrounding “get rich quick” investing to generate inordinate profits off of these ambitious individuals' losses. Rallying around a shared philosophy and profit motive, these groups utilized social media disinformation campaigns to fool new crypto investors in squandering their funds, often while failing to acknowledge the legal and ethical conundrum of stealing from the poor and ambitious.
Ran Duchin, David H. Solomon, Jun Tu, Xi Wang
No abstract is available for this record.
Najaf Iqbal, Sitara Karim, Brian M. Lucey, Muhammad Abubakr Naeem
No abstract is available for this record.
Eduardo Lopes
Cryptocurrency has become a popular asset in global financial markets, meaning that not only individual investors but also asset management companies around the world are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with and attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). The results showed that the TCN outperformed other architectures considered for a short-term forecast period in terms of processing time and it is amongst the most accurate models using an ARIMA model as a baseline.
Crypto Trader, Expert in Data Analysis, Anna Ignatenko, Larysa Dokiienko
No abstract is available for this record.
心雨 陈
No abstract is available for this record.
Forbes Kaseke
The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.
Kittiwin Kumlungmak
Reinforcement learning has emerged as a promising approach for enhancing profitability in cryptocurrency trading. However, the inherent volatility of the market, especially during bearish periods, poses significant challenges in this domain. Existing literature addresses this issue through the adoption of single-agent techniques such as deep Q-network (DQN), advantage actor-critic (A2C), and proximal policy optimization (PPO), or their ensembles. Despite these efforts, the mechanisms employed to mitigate losses during bearish market conditions within the cryptocurrency context lack robustness. Consequently, the performance of reinforcement learning methods for cryptocurrency trading remains constrained within the current literature. To overcome this limitation, we present a novel cryptocurrency trading method, leveraging multi-agent proximal policy optimization (MAPPO). Our approach incorporates a collaborative multi-agent scheme and a local-global reward function to optimize both individual and collective agent performance. Employing a multi-objective optimization technique and a multi-scale continuous loss (MSCL) reward, we train the agents using a progressive penalty mechanism to prevent consecutive losses of portfolio value. In evaluating our method, we compare it against multiple baselines, revealing superior cumulative returns compared to baseline methods. Notably, the strength of our method is further exemplified through the results obtained from the bearish test set, where only our approach demonstrates the ability to yield a profit. Specifically, our method achieves an impressive cumulative return of 2.36%, while the baseline methods result in negative cumulative returns. In comparison to FinRL-Ensemble, a reinforcement learning-based method, our approach exhibits a remarkable 46.05% greater cumulative return in the bullish test set.
Kristof Lommers
No abstract is available for this record.
Nicole Horta, Rui Dias, Catarina Revez, Paulo Alexandre · 5 authors
The purpose of this study is to examine the synchronism between the US capital markets (DJ, S&P 500), the United Kingdom (FTSE 100), Canada (S&P/TSX), Germany (DAX 30), France (CAC 40), Japan (Nikkei 225), Italy (Italy Ds Market and major cryptocurrencies such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and the Crypto 10 index, from February 2018 to November 2021. Based on the findings, we found that BTC and ETH cryptocurrencies drastically reduced their level of integration with their peers over the 2020 worldwide pandemic era, whereas LTC maintained. We also discovered that the Dow Jones, S&P 500, and DAX 30 stock indexes lowered their level of integration when compared to the pre-covid subperiod. For the UK capital market (FTSE 100), Canada (S&P/TSX), Japan (Nikkei 225), France (CAC 40), and Italy (Italy Ds Market) the level of integration increased significantly. These findings support, in part, our research question, that during periods of stress and uncertainty in the global economy capital markets tend towards integration, thus calling into question the hypothesis of efficient portfolio diversification.
Kevin Robertson, Jiani Zhang
No abstract is available for this record.
Wen Hui Long, Maohua Wang, Man Guo
In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance.
Ignatios Draklellis, Yeonjoon Lee
No abstract is available for this record.
Guglielmo Maria Caporale, José Javier de Dios Mazariegos, Luis A. Gil‐Alana
Abstract This paper applies fractional integration and cointegration methods to examine respectively the univariate properties of the four main cryptocurrencies in terms of market capitalization (BTC, ETH, USDT, BNB) and of four US stock market indices (S&P500, NASDAQ, Dow Jones and MSCI for emerging markets) as well as the possible existence of long-run linkages between them. Daily data from 9 November 2017 to 28 June 2022 are used for the analysis. The results provide evidence of market efficiency in the case of the cryptocurrencies but not of the stock market indices considered. The results also indicate that in most cases there are no long-run equilibrium relationships linking the assets in question, which implies that cryptocurrencies can be a useful tool for investors to diversify and hedge when required in the case of the US markets.
Huali Zhao, Martin Crane, Marija Bezbradica
Cryptocurrencies have won a lot of attention as an investment tool in recent years. Specific research has been done on cryptocurrencies’ price prediction while the prices surge up. Classic models and recurrent neural networks are applied for the time series forecast. However, there remains limited research on how the Transformer works on forecasting cryptocurrencies price data. This paper investigated the forecasting capability of the Transformer model on Bitcoin (BTC) price data and Ethereum (ETH) price data which are time series with high fluctuation. Long short term memory model (LSTM) is employed for performance comparison. The result shows that LSTM performs better than Transformer both on BTC and ETH price prediction. Furthermore, in this paper, we also investigated if sentiment analysis can help improve the model’s performance in forecasting future prices. Twitter data and Valence Aware Dictionary and sEntiment Reasoner (VADER) is used for getting sentiment scores. The result shows that the sentiment analysis improves the Transformer model’s performance on BTC price but not ETH price. For the LSTM model, the sentiment analysis does not help with prediction results. Finally, this paper also shows that transfer learning can help on improving the Transformer’s prediction ability on ETH price data.
Donglian Ma, Jun Tu, Zhaobo Zhu
The failure probability and economic losses are astonishingly high in the cryptocurrency market. We perform a comparative analysis of a dynamic logit model and machine learning methods for the predictors for cryptocurrency failure and the pricing of crypto failure risk. We document different significant market- and characteristic-based predictors for coin and token failures. Moreover, we document a significantly positive relation between failure risk and returns, which cannot be explained by the common pricing factors and arbitrage costs in the cryptocurrency market. The high failure risk premium suggests that investors require extra returns for bearing high failure risk of crypto assets.
Shaen Corbet, Yang Hou, Yang Hu, Les Oxley
Abstract Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets.
Ramit Sawhney, Shivam Agarwal, Vivek Mittal, Paolo Rosso · 6 authors
The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a fresh challenge in the financial realm. Investigating such "bubbles" - periods of sudden anomalous behavior of markets are critical in better understanding investor behavior and market dynamics. However, high volatility coupled with massive volumes of chaotic social media texts, especially for underexplored assets like cryptocoins pose a challenge to existing methods. Taking the first step towards NLP for cryptocoins, we present and publicly release CryptoBubbles, a novel multi-span identification task for bubble detection, and a dataset of more than 400 cryptocoins from 9 exchanges over five years spanning over two million tweets. Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media. We further test the effectiveness of our models under zero-shot settings on a test set of Reddit posts pertaining to 29 "meme stocks'', which see an increase in trade volume due to social media hype. Through quantitative, qualitative, and zero-shot analyses on Reddit and Twitter spanning cryptocoins and meme-stocks, we show the practical applicability of CryptoBubbles and hyperbolic models.
Thomas Conlon, Shaen Corbet, Yang Hou, Yang Hu · 5 authors
No abstract is available for this record.
Pietro Saggese, Alessandro Belmonte, Nicola Dimitri, Angelo Facchini · 5 authors
We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, in order to detect the triangular arbitrage conducted on the platform. To this end, we exploit user identifiers per trade to identify and describe the individual trading patterns of 440 arbitrageurs. Moreover, we introduce proxies for expertise and document that the expert users' distribution of profits first-order stochastically dominates that of non-expert users. Most importantly, by including user fixed effects, we show that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. A small number of expert arbitrageurs are able to conduct the vast majority of the arbitrage actions and systematically yield higher profits: our results provide empirical evidence that arbitrageurs are few and sophisticated users, characterized by the ability to incorporate information and to quickly react to exogenous shocks within short time scale intervals.
Gang Chu, Michael Dowling, Dehua Shen, Yongjie Zhang
No abstract is available for this record.
Jie Wu, Xingchen Guo, Mingqi Fang, JunHao Zhang
The price of cryptocurrency is easily affected by various economic, political and other factors, with huge fluctuation, which makes it difficult to predict, compared with stocks and other financial products. Therefore, the prediction of its short-term return in this paper can provide some valuable suggestions for investors. This paper uses XGBoost algorithm to predict 14 kinds of cryptocurrency markets, experiments based on the data applied by KAGGLE competition platform, and expands the data features combined with feature engineering. Experimental data express that our advanced model has significantly improved forecast performance compared with other traditional machine learning algorithms. Specifically, the prediction performance of XGBoost algorithm is 12.5%, 16.6% and 43.3% higher than that of Gradient Boosting model, SVM algorithm and Linear Regression algorithm respectively. In addition, we also rank the importance of all the features of the simulation, and give some constructive suggestions to guide the future work.