In the modern era, researchers are predicting prices of various kinds of cryptocurrency to understand their trend in the sector of finance. In this paper, we focus on price prediction of cryptocurrencies based on a period, i.e., for the year 2013 to 2018. From our research, we have identified the highest prices for bitcoin for historical dates and trained Long Short-Term Memory Networks to learn and predict the highest rate for a future period. Thus, trend analysis of cryptocurrency prices has been done, and neural networks have been leveraged to determine from time series data and predict future values.
In 2017, the Blockchain-based crypto currency market witnessed enormous growth. Bitcoin, the leading crypto currency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this paper, we study Bitcoin and explore features in its network that explain its price hikes. We gather data and analyze user and network activity that highly impact Bitcoin price. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that determine the demand and supply dynamics of a crypto currency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Our regression model predicts Bitcoin price with 99.4% accuracy and 0.0113 root mean squared error (RMSE).
Blockchain is a highly popular paradigm for non-centralized applications, especially in finance and trade. Performance is a major challenge for blockchains, since consensus approaches are known not to scale. In this presentation we address blockchain performance, from the perspective of model-based prediction as well as benchmark-based assessment. We present research results about smart contracts in the Ethereum blockchain and discuss the requirements for generic benchmarks for blockchain performance. Benchmarking is a common approach to compare industry-class systems. As blockchain technologies mature, the role of reliable benchmarks will become increasingly important. However, definitions of benchmarks for blockchains are still in their infancy. We argue that there is a clear need for benchmarks, and that benchmarks should be based on the sound scientific principles of metrology [1]. A variety of important performance issues should be addressed, including the performance of the proof (be it work, stake, or other), transaction processing and block creation. Moreover, in all these situations, establishing energy consumption benchmarks is critical in determining if incentives are in place for miners to operate the blockchain system. A particularly interesting element in some blockchains is the mechanism of smart contracts. For instance, in Ethereum, the fees associated with executing contracts depend on the benchmarked performance of the operation code. In [2] it was demonstrated that uncertainty with respect to the correctness of the anticipated execution time impacts the decisions miners will take. We will discuss improved benchmarking approaches for operational code.
Mareena Fernandes, Saloni Khanna, Leandra Monteiro, Anu Thomas · 5 authors
Advancement in technological developments introduced virtual currency exchange methods viz Bitcoin, Litecoin, Ethereum and so on which are evolving rapidly. Cryptocurrencies were introduced to eliminate financial intermediaries leading to direct peer-to-peer transactions. With the spread of the global Coronavirus pandemic, the relationship between Bitcoin and the equity market has expanded. Cryptocurrencies are highly volatile but can also prove to be good investments. Cryptocurrency, being a novel technique for transaction systems, has led to a lot of confusion among investors and any rumours or news on social media has been claimed to significantly affect the prices of cryptocurrencies. The huge percentage increase/decrease in Bitcoin's price over a short period of time is an intriguing phenomenon that cannot be foreseen. For a long time, bitcoin price prediction has been a hot topic of study.In this paper, we discuss the implementation and results of the Deep Learning Bitcoin Price Prediction Model and prepare a strategy to maximize gains for investors. The paper covers to framework with a set of deep learning models, analysis methods with a fixed set of factors to predict daily Bitcoin prices and design-integration of price prediction of different cryptocurrencies using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) and GRU (Gated recurrent units). The idea of incorporating Public Sentiment in the prediction of the hikes and falls of the Bitcoin market from Social Media platforms like Reddit and Twitter leading to meaningful predicted results. This prediction can bring confidence to the common man to invest with lesser risk and more profit. Also, this can enable the digital new-age currency to become a primary method of transaction.
Stjepan Begušić, Zvonko Kostanjčar, H. Eugene Stanley, Boris Podobnik
Detection of power-law behavior and studies of scaling exponents uncover the characteristics of complexity in many real world phenomena. The complexity of financial markets has always presented challenging issues and provided interesting findings, such as the inverse cubic law in the tails of stock price fluctuation distributions. Motivated by the rise of novel digital assets based on blockchain technology, we study the distributions of cryptocurrency price fluctuations. We consider Bitcoin returns over various time intervals and from multiple digital exchanges, in order to investigate the existence of universal scaling behavior in the tails, and ascertain whether the scaling exponent supports the presence of a finite second moment. We provide empirical evidence on slowly decaying tails in the distributions of returns over multiple time intervals and different exchanges, corresponding to a power-law. We estimate the scaling exponent and find an asymptotic power-law behavior with 2 < α < 2.5 suggesting that Bitcoin returns, in addition to being more volatile, also exhibit heavier tails than stocks, which are known to be around 3. Our results also imply the existence of a finite second moment, thus providing a fundamental basis for the usage of standard financial theories and covariance-based techniques in risk management and portfolio optimization scenarios.
The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.
We examine the impact of positive versus negative macroeconomic news surprises, originating from large developed economies, on the returns and volatility of gold and Bitcoin prices over the period July 19, 2010 – February 7, 2017. We find an asymmetric impact and evidence that gold is different from Bitcoin. Specifically, gold returns and volatility systematically react to macroeconomic news surprises in a manner consistent with its traditional role as a safe-haven, whereas Bitcoin prices and volatility do not mostly react in a similar manner. Our results are useful for investment decision-making.
The trading volume of Bitcoin has increased immensely since its conception. Bitcoin is a cryptocurrency, it is not a legal currency but rather a private monetary system that manages by itself and does not depend on governments or central banks. It is an autonomous currency system that is not liable to any governing body. Some fear that the increase of Bitcoin usage, as it is quite different from traditional currencies and is free from control or regulations by monetary authorities. Although its popularity has grown worldwide, fluctuations of the prices are sometimes erratic. Hence, such large and sudden movements would dampen the sound development of Bitcoin. This paper examines how the volatile price of Bitcoin changes empirically. The empirical results show that there is a difference between short-term volatility and long-term volatility. Traders should see not only the short-term movements in volatile Bitcoin pricing but also long-term developments.
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.
M Vaidehi, Alivia Pandit, Bhaskar Jindal, Minu Kumari · 5 authors
After the boom and bust in cryptocurrencies’ prices in recent years, Bitcoin has been totally regarded as an investment asset. As it is highly volatile in nature, there has been a need for good predictions for carrying base investment decisions. Although current study has used machine learning for more accurate Bitcoin price prediction, some of them did focused on the feasibility of applying different modeling techniques to the samples that has different data structures and dimension features. To predict Bitcoin price on different frequencies after using machine learning techniques, firstly we have to classify the Bitcoin price with daily price and high-frequency price. Here, we attempt to predict Bitcoin price as accurately as possible by taking into consideration various protocols that affect the Bitcoin value. Using the provided data we would predict the sign of daily price change with highest possible accuracy. We have used Random Forest Classifier and compared with benchmark results as daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and machine learning algorithms of 99%. my investigation in Bitcoin price prediction can be considered as a pilot study for the importance of the sample dimension in the machine learning techniques. Keywords Bitcoin, Crypto Currency, Machine Learning, Blockchain, Long Short Term Memory(LSTM), Recurrent Neural Network(RNN), Prediction
We examine the significance of twenty-one potential drivers of bitcoin returns for the period 2010–2017 (2533 daily observations). Within a LASSO framework, we examine the effects of factors such as stock market returns, exchange rates, gold and oil returns, FED’s and ECB’s rates and internet trends on bitcoin returns for alternate time periods. Search intensity and gold returns emerge as the most important variables for bitcoin returns.
Elie Bouri, Mahamitra Das, Rangan Gupta, David Roubaud
This paper contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies, and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from July 19, 2010 to October 31, 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.