Cryptocurrency is one of the famous financial state in all over the world which cause several type of risks that effect on the intrinsic assessment of risk auditors. From the beginning the growth of cryptocurrency gives the financial business with the wide risk in term of presentation of money laundering. In the institution of financial supports such as anti-money laundering, banks and secrecy of banks proceed as a specialist of risk, manager of bank and officer of compliance which has a provocation for the related transaction through cryptocurrency and the users who hide the illegal funds.In this study, the Hierarchical Risk Parity and unsupervised machine learning applied on the cryptocurrency framework. The process of professional accounting in term of inherent risk connected with cryptocurrency regarding the occurrence likelihood and statement of financial impact. Determining cryptocurrency risks comprehended to have a high rate of occurrence likelihood and the access of private key which is unauthorized. The professional cryptocurrency experience in transaction cause the lower risk comparing the less experienced one. The Hierarchical Risk Parity gives the better output in term of returning the adjusted risk tail to get the better risk management result.The result section shows the proposed model is robust to various intervals which are re-balanced and the co-variance window estimation.
Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework,DL-Gues, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction ofDashcarried out using price history and tweets ofDash,Litecoin, andBitcoinfor various loss functions for validation. Further, to check the usability ofDL-GuesSon other cryptocurrencies, we have also inferred results for price prediction ofBitcoin-Cashwith the price history and tweets ofBitcoin-Cash,Litecoin, andBitcoin.
Bitcoin has grown in popularity and has now attracted the attention of individual and institutional investors. Accurate Bitcoin price direction forecasts are important for determining the trend in Bitcoin prices and asset allocation. This paper addresses several unanswered questions. How important are business cycle variables like interest rates, inflation, and market volatility for forecasting Bitcoin prices? Does the importance of these variables change across time? Are the most important macroeconomic variables for forecasting Bitcoin prices the same as those for gold prices? To answer these questions, we utilize tree-based machine learning classifiers, along with traditional logit econometric models. The analysis reveals several important findings. First, random forests predict Bitcoin and gold price directions with a higher degree of accuracy than logit models. Prediction accuracy for bagging and random forests is between 75% and 80% for a five-day prediction. For 10-day to 20-day forecasts bagging and random forests record accuracies greater than 85%. Second, technical indicators are the most important features for predicting Bitcoin and gold price direction, suggesting some degree of market inefficiency. Third, oil price volatility is important for predicting Bitcoin and gold prices indicating that Bitcoin is a substitute for gold in diversifying this type of volatility. By comparison, gold prices are more influenced by inflation than Bitcoin prices, indicating that gold can be used as a hedge or diversification asset against inflation.
Gyeongho Kim, Dong-Hyun Shin, Jae Gyeong Choi, Sunghoon Lim
Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency’s volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework’s effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.
Naila Aslam, Furqan Rustam, Ernesto Lee, Patrick Bernard Washington · 5 authors
The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples’ perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models.
Sanal Kripto Para kavramı Bitcoin ile birlikte 2009 yılında dikkat çekmeye başlamış ve özellikle 2013 yılındaki fiyat artışı ile popülaritesi artmıştır. Sanal Kripto Para birimlerinin ilki ve öncüsü olan Bitcoin ile 2. En büyük piyasa değerine sahip olan Ethereum tasarımsal yapıları ve amaçları bakımından birbirlerinden oldukça farklıdır. Sanal bir para birimi olması için tasarlanan Bitcoin ile üzerinde akıllı kontratlar çalışmasına olanak vermek için tasarlanan Ethereum birçok alanda olduğu gibi iktisat alanında da oldukça dikkat çekmiş ve literatürde birçok çalışmaya konu olmuştur. Bu çalışmanın amacı Türkiye GSYH’sı, M2 Tanımı ile para arzı ve tüketici güven endeksinin Bitcoin ve Ethereum fiyatına olan etkilerini karşılaştırmalı olarak tahmin etmektir. Bu amaçla, Bitcoin ve Etherum’un Türk Lirası cinsinden fiyatlarını bağımlı değişken alan iki farklı model kurulmuştur. Kurulan modeller, Ocak 2016 ile Aralık 2020 dönemini kapsayan aylık veriler kullanılarak, zaman serisi analizi kapsamında, Johansen Eşbütünleşme Testi ve Tam Uyarlanmış En Küçük Kareler Yöntemi (FMOLS) kullanılarak uzun dönemde sınanmıştır. Yapılan analizin sonucunda hem Bitcoin hem de Ethereum’un Türk lirası cinsinden fiyatları, GSYH ve tüketici güven endeksi ile pozitif ilişkili, M2 para arzı ile negatif ilişkili bulunmuştur. Çalışmanın bulgularından bir diğeri ise toplam piyasa değeri Bitcoin’e göre daha düşük olan Ethereum’un tüm değişkenlerden daha çok etkilendiğidir. Elde edilen sonuçlar bu çalışmanın türetildiği Yüksek Lisans Tezindeki bulgular ile örtüşmekte ve birbirini desteklemektedir.
This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.
Bu çalışma 2011 yılından itibaren temel olarak “belirsizlik” ve “ekonomi” anahtar kelimelerini içeren tweetlerin baz alınarak oluşturulduğu Twitter Bazlı Belirsizlik Endeksinin, son yılların gözde yatırım araçlarından olan kripto paraların volatilitesine etkisini incelemeyi amaçlamaktadır. Piyasa değeri en yüksek, Binance, Bitcoin, Cardano, Ethereum, Ripple ve Tether kripto paralar 18/01/2018- 11/07/2021 dönemi için günlük verilerle ARCH-GARCH ailesi modelleri ile incelenmiştir. Çalışmada öncelikle ortalama denklemi oluşturulan modellerin ARCH-GARCH modellerine uygunluğu sınanmış ve incelenen dönemde Bitcoin ve Ethereum için ARCH etkisinin olmadığı ancak Binance, Cardano, Ripple ve Tether için volatilite modellerinin kullanımının uygun olduğu bulgusu elde edilmiştir. Binance için GARCH (1,1), Cardano için GARCH-M (1,1), Ripple için ARCH (2) modeli volatiliteyi en iyi yakalayan model olarak seçilmiştir. Twitter Bazlı Belirsizlik Endeksinin bu modellerin hepsinde istatistiki olarak anlamlı ve pozitif bir etkiye sahip olduğu tespit edilmiştir. Bu sonuçlara göre bir sosyal medya platformu olan Twitter’da yer alan belirsizlik ve ekonomi içerikli tweetlerin kripto varlıkların volatilitesini etkilediğini söylemek mümkündür.
Sanjib Kumar Nayak, Sarat Chandra Nayak, Subhranginee Das
Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.
Sarafatema Peerzade, Dnyaneshwari Wayal, Gauri Kale
The proposed project work is totally supported and easy yet effective strategy named as Martingale. An automatic system which only requires only some pre-coded instructions to execute trades on variety of market variables starting from asset price to trading volume. The strategy along with each cryptocurrency, the benchmark against which the algorithm is tested is that the market’s performance. Returns are compared with the buying and so multiplying the trade volume at each loss and different scenarios are analysed to work out the chance related to the buying compared with an algorithmic strategy. Results are going to be in love with the market’s actual trends and also with some alternate possible trends to check all market scenarios. An internet interface will accompany the presentation allowing the users to check the strategies by entering their parameters and instantly seeing the results
We execute a comparative analysis of machine learning models for the time-series forecasting of the sign of next-day cryptocurrency returns. We begin by compiling a proprietary dataset that encompasses a wide array of potential cryptocurrency valuation factors (price trends, liquidity, volatility, network, production, investor attention), subsequently identifying and evaluating the most significant factors. We apply eight machine learning models to the dataset, utilizing them as classifiers to predict the sign of next day price returns for the three largest cryptocurrencies by market capitalization: bitcoin, ethereum, and ripple. We show that the most significant valuation factors for cryptocurrency returns are price trend variables, seven and thirty-day reversal, to be specific. We conclude that support vector machines result in the most accurate classifications for all three cryptocurrencies. Additionally, we find that boosted models like AdaBoost and XGBoost have the poorest classification accuracy. At length, we construct a probability-based trading strategy that secures either a daily long or short position on one of the three examined cryptocurrencies. Ultimately, the strategy yields a Sharpe of 2.8 and a cumulative log return of 3.72. On average, the strategy’s log returns outperformed standalone investments in all three cryptocurrencies by a factor of 5.64, and Sharpe ratios more than threefold.
Due to the rise in popularity of Bitcoin as both a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools to gain an advantage over the market. Although traditionally trading was done by professionals, nowadays a majority of market participants are market-data processing bots due to their inherent advantages in processing large amounts of data, lack of emotions of fear or greed, and predicting market prices through artificial intelligence. A large number of approaches have been brought forward to tackle this task, many of which rely on specially engineered deep learning methods with a focus on specific market conditions. The general limitation of these approaches, however, is the reliance on customized gradient-based methods which limit the scope of possible solutions and don't necessarily generalize well when solving similar problems. This paper proposes a method which uses neuroevolutionary techniques capable of automatically customizing offspring neural networks, generating entire populations of solutions and more thoroughly exploring and parallelizing potential solutions. Our approach uses evolutionary algorithms to evolve increasingly improved populations of neural networks which, based on sentimental and technical analysis data, efficiently predict future market price movements. The effectiveness of this approach is validated by testing the system on both live and historical trading scenarios, and its robustness is tested on other cryptocurrency and stock markets. Experimental results during a 30-day live-trading period show that this method outperformed the buy and hold strategy by over 260%, even while factoring in standard trading fees.
There is a growing interest in the activities of the crypto market by various stakeholders. These stakeholders generally include investors, entrepreneurs, governments, fund managers, climate activists, institutional managers, employees with surplus funds, and crypto miners. This study aims to investigate the accuracy of the GARCH models for measuring and estimating Value-at-risk (VaR) using the Cryptocurrency index for future investment and managerial decision making. Because of this, the present study uses the top 30 Cryptocurrencies index in terms of Market capitalization excluding stable coins to determine the best GARCH models. Many entrepreneurs, institutional managers, fund managers, and other stakeholders have recently included cryptocurrency in their investment portfolio because of the increase in transactions and high returns growth in the global financial market with its associated high returns and volatility. Information communication technology has paved the way for such activities in the global markets. The daily data frequency was applied because of the availability of the data. The empirical analysis has been carried out for the period from January 2017 to December 2020 for a total of 1461observation. The returns volatility is estimated using SGARCH and EGARCH models. The findings evidenced that, using both normal distribution and Student t distribution, EGARCH provides a better measure and estimate than SGARCH concerning high persistence and volatility. Against this background, the present study also examined Backtesting to estimate Value at Risk. Interestingly, the findings of the available study would provide industry players, practitioners, entrepreneurs, and investors the maximum edge on how to use or measure such variables against others to make investment decisions. Also, the findings would subsequently contribute more insight into academia on the study area.
Two distinct and non-redundant understandings of volatility, as deviation from consistency, exist for a time-series: (1) exhibiting high standard deviation and, closer to the dictionary definition of the term, (2) appearing highly irregular and unpredictable. We find that Bitcoin is a prime example of an asset for which the two concepts of volatility diverge. We show that, historically, Bitcoin combines high Standard Deviation and low Approximate Entropy, relative to Gold and S&P 500. Moreover, subsample analysis for different time-scales (daily, weekly, monthly) shows that lower sampling frequencies drastically reduce the Kurtosis of the distribution of log-returns of Bitcoin. The opposite effect is observed for Gold and S&P 500. These properties suggest that, contrary to the volatility of the two traditional assets, Bitcoin’s high volatility is essentially an intra-day phenomenon that is strongly attenuated for a weekly or monthly time-preference.
Purpose The purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model. Design/methodology/approach The joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market. Findings Empirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model. Social implications The proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks. Originality/value A novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.
Sampat Kumar U, S P Aanandhi, S P Akhilaa, Vijayakumar Vardarajan · 5 authors
Cryptocurrency is a tangible or digital currency protected with the help of Cryptography, making it almost impossible to counterfeit or double. Many cryptocurrency networks are categorized primarily based on blockchain technology. The present socio-economic situation also creates an environment for people to hold less cash and remain marginalized by the market trends. The objective of the project is to build a profitable Machine Learning prediction model. We begin by collecting the data from Yahoo Finance website using inbuilt python libraries. Our objective was to perform price prediction of various Cryptocurrencies using Machine Learning, and we have implemented the Autoregressive Integrated Moving Average (ARIMA) Model. We have performed feature engineering on various set of lagged values, on previous day, one for 7 days and another looking back for 30 days. We have forecasted the outcome of the model and plotted the outcome in a responsive chart using Plot graph.
Cryptocurrencies can be traded 24 hours a day over the globe where crypto market never stop working. This market is known for being highly volatile and prices fluctuate rigorously in a minute. Even seasoned traders are unable to react quickly enough to this volatility. This is why automated trading bots get into the picture. On the other hand, crypto trading bots need more improvement to mimic expert traders' activities. In this paper, we propose a bot which is able to buy and sell using a dynamic price-action technique. Experimental results from back-testing show that the proposed bot effectively utilized the classical price-action technique for trading in cryptocurrency markets.
Purpose Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis. Design/methodology/approach Concretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information. Findings Relative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction. Originality/value To the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis.
In this study, Bitcoin data is examined by Stochastic Differential Equation Modeling(SDEM). At first, the parameters of SDE established for the given Bitcoin data areestimated by using maximum likelihood estimation method. Then, we have obtainedreasonable Stochastic Differential Equation (SDE) based on the Bitcoin data. Finally, byapplying Euler-Maruyama Approximation Method trajectories of SDE according to thefixed time are achieved. The performances of trajectories are established by Chi-Squarecriteria. The results are acquired by using statistical software R-Studio.<br>
As Machine Learning (ML) models are becoming increasingly complex, one of the central challenges is their deployment at scale, such that companies and organizations can create value through Artificial Intelligence (AI). An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data. However, the problem of valuation of models, as well the questions of incentives for collaborative training and trading of data/models, have received limited treatment in the literature. In this paper, a new ecosystem of ML model trading over a trusted Blockchain-based network is proposed. The buyer can acquire the model of interest from the ML market, and interested sellers spend local computations on their data to enhance that model's quality. In doing so, the proportional relation between the local data and the quality of trained models is considered, and the valuations of seller's data in training the models are estimated through the distributed Data Shapley Value (DSV). At the same time, the trustworthiness of the entire trading process is provided by the distributed Ledger Technology (DLT). Extensive experimental evaluation of the proposed approach shows a competitive run-time performance, with a 15\% drop in the cost of execution, and fairness in terms of incentives for the participants.
Chinchu Thomas, Zillah Watson, M Kim, Anushuya Baidya · 8 authors
Unlike typical banking transactions, blockchain-assisted cryptocurrencies are touted as the currency of the future, allowing peer-to-peer transactions without the need for an intermediary [1]. According to investors, the crypto share market has grown significantly in terms of market capacity, increasing by 300 percent in a year to approximately 1.6 trillion dollars [2]. Crypto investments, on the other hand, are thought to be dangerous given the crypto market's extremely volatile, latent, and non-stationary nature [3]. Stakeholders and investors may be able to easily incorporate crypto into their investment strategy if they can accurately predict the temporal change of the market price over time. In order to anticipate future prices, machine learning (ML) and big data analytics are extremely effective in deciphering stochastic and nonlinear patterns within market data [4].