In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying the latest methodology of quantile cross-spectral dependence. Daily data comprising a total of 1947 observations and covering the period of 1 October 2013 to 31 January 2019 are used in this study. The findings indicate that a positive return interdependence between BTC and EPU is high in the short term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on the time-varying and time–frequency structure of interdependence is also extracted by applying wavelet coherence analysis. The estimated results of wavelet coherence suggest that the correlation between BTC and EPU is positive during a short-term investment horizon. Finally, the frequency domain Breitung and Candelon causality test is applied, and results show the evidence of insignificant causality between Bitcoin and EPU. Overall, the findings highlight the diversification benefits of Bitcoin during the period of uncertainty.
Since following Sraffa (and Keynes), most everything may have its "own rate of interest", economists should place "interest" within the more general category of rent, since most anything, not just money, may be rented; that should then be analyzed separately from sale price. Financial asset sale prices, given their importance as a link to the future (along with money), should be analyzed in detail within the category of sale prices, or as a 3rd special, distinct price category. Keynes' "general theory" may be interpreted as a generalization of his own, distinctive earlier "special theory"; not, as he thought, as a generalization of previous "classical "theory". That was a completely different theory dealing with an imaginary barter exchange economy. Rothbard's pseudo-history of money should be read as imitating Rousseau's "Second Discourse"; for Rothbard, mankind's secular fall is not caused by the introduction of private property, but by the introduction of government.
In this paper, we explored the price-volume cross-correlation asymmetric multifractal properties of some cryptocurrencies using multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). Bitcoin, Ethereum, Tether, Binance Coin, Cardano, and Dogecoin are examples of cryptocurrencies. The empirical results reveal that the price-volume cross-correlations of all six cryptocurrencies display anti-persistent, multifractal, and asymmetric features, although the degree of these characteristics differs depending on the cryptocurrencies and market trends. Specifically, the anti-persistence multifractal of the price-volume cross-correlation is significantly stronger in the upward market than in the downward market for Bitcoin and Ethereum; for the remaining four cryptocurrencies, the opposite result is obtained. Furthermore, Dogecoin price-volume cross-correlation exhibits the most marked anti-persistence and the most significant asymmetric multifractality among the six cryptocurrencies analyzed.
Cardano is an open-source and decentralized public blockchain platform, with consensus achieved using proof of stake. It can facilitate peer-to-peer transactions with its internal cryptocurrency, ADA, with no third-party involvement. In recent years, machine learning has been proliferating and has made many theoretical breakthroughs that find its application in many fields. The study of the machine learning approach in price prediction in Bitcoin and Ethereum has gained much attention, while relatively little research focuses on ADA forecasting. The experiment objective is to investigate the prediction of ADA's short period future prices dealing with real-world data. A comparative study of the results produced by different machine learning models, data visualizations, and statistical approaches. The experiment indicates that Gradient Boosting is the best-suited algorithm that can be selected to predict future ADA prices for short-term trading strategies.
Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.
Michael Demmler, Amilcar Orlian Fernández Domínguez
This article explores the concepts of cryptocurrencies and speculative bubbles, as Bitcoin’s price behaviour shares characteristics with speculative bubbles that have occurred in recent years. Using a quantitative research design, the study examines daily market prices for the period between 2013 and 2019. Statistical moments, return stationarity, TARCH-type model estimations and Supremum Augmented Dickey-Fuller and Generalised Supremum Augmented Dickey-Fuller tests are analysed. We find evidence for multiple speculative bubble tendencies in Bitcoin prices caused by speculation, which reached their maximum at the end of 2017. Our results are in line with recent studies, which characterise Bitcoin as both highly speculative and vulnerable to financial bubbles.
The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.
Sulalitha Bowala, Japjeet Singh, A. Thavaneswaran, Ruppa K. Thulasiram · 5 authors
Data-driven volatility models and neuro-volatility models have the potential to revolutionize the area of Computational Finance. Volatility measures the variation of a time series data, and thus it is also a driving factor for the risk forecasting of returns from investment in cryptocurrencies. A cryptocurrency is a decentralized medium of exchange that relies on cryptographic primitives to facilitate the trustless transfer of value between different parties. Instead of being physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions.Many commonly used risk forecasting models do not take into account the uncertainty associated with the volatility of an underlying asset to obtain the risk forecasts. Some tools from the fuzzy set theory can be incorporated into the forecasting models to account for this uncertainty. Interest in the use of hybrid models for fuzzy volatility forecasts is growing. However, a major drawback is that the fuzzy coefficient hybrid models used in fuzzy volatility forecasts are not data-driven. This paper uses fuzzy set theory with data-driven volatility and data-driven neuro-volatility forecasts to study the fuzzy risk forecasts. The study focuses on long-term volatility forecasts with daily price data while briefly exploring forecasting models with high-frequency (hourly) data as an avenue for future research. Simple yet effective models incorporating fuzziness to obtain fuzzy risk volatility forecasts and fuzzy VaR forecasts are presented. The key underlying idea, unlike the existing risk forecasting, is the use of a hybrid nonlinear adaptive fuzzy model for volatility.
Abstract The Bitcoin futures market has grown rapidly since its 2017 introduction. Along with enabling institutional traders to access a regulated cryptocurrency product, futures provide a means to improve market efficiency by shorting Bitcoin. We examine trading behavior in Bitcoin futures utilizing the Commodity Futures Trading Commission Commitment of Traders report. Leveraged money traders tend to hold the largest positions, be net short, and their trading behavior plays a key role in the Bitcoin futures market. Our empirical results show that leveraged money traders display market timing ability, largely by adjusting their short positions. It seems that other trader types follow this “smart money” in adjusting their own positions in subsequent periods. We also demonstrate that it is possible to construct profitable trading strategies based on observed variations in leveraged money positions.
Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.
Olaf Kampers, Abdulhakim A. Qahtan, Swati Mathur, Yannis Velegrakis
As a financial asset, cryptocurrencies innovated the financial industry in different ways. However, the lack of regulations and transparency in cryptocurrency markets is hindering the industry from reaching its full potential. There is a need for extensive technical analysis of the cryptocurrency market data to detect possible market manipulation attempts. Anomaly detection techniques can reveal information about abnormal activities in the market and provide insights on manipulation attempts. In this study, a robust unsupervised anomaly detection tool (ADT) is developed for this purpose. Experiments show that ADT outperforms a set of methods in detecting the anomalies in features extracted from the cryptocurrency exchanges data and on a set of benchmark data sets.
Stock market price and cryptocurrency price prediction is a very challenging task. We are proposing dynamic algorithms which make use of LSTM and another time Series algorithm, i.e., prophet and we have various trained models on these two algorithms. We will make use of this dynamic algorithm which will self-evaluate different datasets and different pretrained models and will provide us with the best possible output for different test cases. For the longer duration, we are just focusing on up and down, but for the small duration, we are focusing on price-related accuracy. The main and challenging work is to deal with the dynamic dataset, so we require some dynamic algorithm for this.
Overview The aim of the International Conferences "Economic Scientific Research-Theoretical, Empirical and Practical Approaches"- (ESPERA), initiated in 2013 by the "Costin C. Kirițescu" National Institute for Economic Research (NIER) within the Romanian Academy is to present and evaluate the economic scientific research portfolio, to argue and substantiate the Romanian development strategies - including European and global best practices, to provide an opportunity for researches, practitioners, and academics interested in economic scientific research, both theoretical, practical and empirical discuss and exchange insightful research ideas. The 7th edition of the International Conferences “Economic Scientific Research-Theoretical, Empirical and Practical Approaches”- (ESPERA), under the title ”30 Years of Inspiring Academic Economic Research – From the Transition to a Market Economy to the Interlinked Crises of 21st Century” was organized virtually during 26th -27th November 2020, In Bucharest, Romania. The event, dedicated to the 30th anniversary of NIER and its economic research network of its return under the auspices of the Romanian Academy, will include a scientific program of wide diversity initiatives, bringing together researchers from all NIER institutes and centers, members of the Romanian Academy, Romanian academic researchers and also guests from other countries. The
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&D dataset on GitHub: https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.
Ali Raheman, Anton Kolonin, Igors Fridkins, Ikram Ansari · 5 authors
In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference. We study how the different sentiment metrics are correlated with the price movements of Bitcoin. For this purpose, we explore different methods to calculate the sentiment metrics from a text finding most of them not very accurate for this prediction task. We find that one of the models outperforms more than 20 other public ones and makes it possible to fine-tune it efficiently given its interpretable nature. Thus we confirm that interpretable artificial intelligence and natural language processing methods might be more valuable practically than non-explainable and non-interpretable ones. In the end, we analyse potential causal connections between the different sentiment metrics and the price movements.
Leonardo Kanashiro Felizardo, Francisco Caio Lima Paiva, Catharine de Vita Graves, Élia Yathie Matsumoto · 7 authors
The interdisciplinary relationship between machine learning and financial markets has long been a theme of great interest among both research communities. Recently, reinforcement learning and deep learning methods gained prominence in the active asset trading task, aiming to achieve outstanding performances compared with classical benchmarks, such as the Buy and Hold strategy. This paper explores both the supervised learning and reinforcement learning approaches applied to active asset trading, drawing attention to the benefits of both approaches. This work extends the comparison between the supervised approach and reinforcement learning by using state-of-the-art strategies with both techniques. We propose adopting the ResNet architecture, one of the best deep learning approaches for time series classification, into the ResNet-LSTM actor (RSLSTM-A). We compare RSLSTM-A against classical and recent reinforcement learning techniques, such as recurrent reinforcement learning, deep Q-network, and advantage actor–critic. We simulated a currency exchange market environment with the price time series of the Bitcoin, Litecoin, Ethereum, Monero, Nxt, and Dash cryptocurrencies to run our tests. We show that our approach achieves better overall performance, confirming that supervised learning can outperform reinforcement learning for trading. We also present a graphic representation of the features extracted from the ResNet neural network to identify which type of characteristics each residual block generates.
This study examines the volatility changes of 20 cryptocurrencies from January 2018 to May 2021 using sparse VHAR-MGARCH model. Our proposed model incorporates the high-dimensionality and time-varying conditional heterogeneity of cryptocurrency markets. We examined the time-varying spillover index, dynamic correlation structure, and connectivity between cryptocurrencies. Our empirical analysis clearly shows that there was a volatility shift on 13 March 2020, due to a market crash caused by COVID-19. This naturally divides the data into three periods: pre-crisis, during the crisis, and post-crisis regimes. The pre-crisis regime exhibited long-term cyclic fluctuations in the spillover index. However, after the market crash, the spillover index remained at a very high level with almost no interconnections between cryptocurrencies. The post-crisis regime showed quite a few irregular and sharp spikes in the spillover index, together with record-breaking prices and volumes.
Samuel Kwaku Agyei, Anokye M. Adam, Ahmed Bossman, Oliver Asiamah · 7 authors
We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniques—bi-wavelet, partial wavelet, bivariate contemporary correlations (BCC), wavelet multiple correlations (WMC) and wavelet multiple cross-correlations (WMCC) are applied. Findings from the study provide that the interdependencies between the cryptocurrencies and VCRIX are high and mostly positive across investment horizons. Furthermore, the comovements between the cryptocurrencies designate long memory dynamics. The high comovements between cryptocurrencies are highly influenced by idiosyncratic shocks they possess rather than the VCRIX. In addition, the BCC and the WMC indicate that there is a high integration among all the cryptocurrencies. Categorically, the VCRIX could not lead or lag the interdependencies among the cryptocurrencies in the WMCC analysis. Findings from the study, therefore, divulge that investing in a single or few cryptocurrencies is highly risky due to the adverse impact of the VCRIX on individual cryptocurrencies. In general, investors should effectively hedge against volatilities in the cryptocurrency markets due to the significant predictive ability of VCRIX as an effective proxy.
The large-scale application of blockchain technology is an expected to be an inevitable trend. This study revolves around published papers and articles related to blockchain technology, relevance analysis and sorting through the retrieved documents with six core layers of blockchain: Application Layer, Contract Layer, Actuator Layer, Consensus Layer, Network Layer and Data Layer. Based on the analysis results, this study found that China's research is more towards the preference and application of landing and industry and smart cities with blockchain as the underlying technology. International research is more focused on the research of finance as the underlying technology of blockchain and tries to combine crypto assets with real industries, such as crypted assets and payment systems for traditional industries. This paper studies the impact of monetary entropy on cryptocurrencies in smart cities and uses the monetary entropy formula to measure the crypto-economic entropy. We use Kolmogorov entropy to describe the degree of chaos in the cryptocurrency market in a smart city. The study illustrates the current status of blockchain technology and applications from the perspective of cryptocurrency in a smart city. We find that smart cities and cryptocurrencies have a mutually reinforcing effect.