Aleksandar Petrović, Luka Jovanović, Miodrag Živković, Nebojša Bačanin · 6 authors
The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM).The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies.The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem.The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA).The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.
Thomas Conlon, Shaen Corbet, Yang Hou, Yang Hu · 5 authors
Despite frequent Blockchain splits stemming from Bitcoin, few studies have examined the determinants of Bitcoin fork returns. In this paper, we investigate the relationships between the returns of Bitcoin forks and a range of common risk factors, including Bitcoin, currency, network and equity-based factors. From a statistical perspective, we find consistent and significant associations between fork returns, their Bitcoin counterparts, and equity markets. Other common factors, such as the equity small-minus-big factor and changes in the Japanese Yen, are found to have occasional links with fork returns. From an economic perspective, Bitcoin returns are the predominant driver of fork returns, accounting for essentially all of the explained variation. These findings are confirmed using orthogonalised common factors and with an alternative methodology, quantile regression. This research broadens our understanding of Bitcoin forks, indicating that a change in blockchain protocol is insufficient to sever links with the Bitcoin parent.
Abstract We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.
Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait.” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.
Hasib Shamshad, Fasee Ullah, Asad Ullah, Victor R. Kebande · 6 authors
The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.
Hae Sun Jung, Seon Hong Lee, Haein Lee, Jang Hyun Kim
Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.
Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social media. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocurrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.
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%.
This research assesses the prediction of Bitcoin prices using the autoregressive integrated moving average (ARIMA) and long-short-term memory (LSTM) models. We forecast the price of Bitcoin for the following day using the static forecast method, with and without re-estimating the forecast model at each step. We take two different training and test samples into consideration for the cross-validation of forecast findings. In the first training sample, ARIMA outperforms LSTM, but in the second training sample, LSTM exceeds ARIMA. Additionally, in the two test-sample forecast periods, LSTM with model re-estimation at each step surpasses ARIMA. Comparing LSTM to ARIMA, the forecasts were much closer to the actual historical prices. As opposed to ARIMA, which could only track the trend of Bitcoin prices, the LSTM model was able to predict both the direction and the value during the specified time period. This research exhibits LSTM's persistent capacity for fluctuating Bitcoin price prediction despite the sophistication of ARIMA.
Grzegorz Dudek, Piotr Fiszeder, Paweł Kobus, Witold Orzeszko
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threats and developing risk management strategies. The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several models and forecasting methods are compared in terms of their forecasting accuracy, i.e., HAR (heterogeneous autoregressive), ARFIMA (autoregressive fractionally integrated moving average), GARCH (generalized autoregressive conditional heteroscedasticity), LASSO (least absolute shrinkage and selection operator), RR (ridge regression), SVR (support vector regression), MLP (multilayer perceptron), FNM (fuzzy neighbourhood model), RF (random forest), and LSTM (long short-term memory). The realized variance calculated from intraday returns is used as the input variable for the models. In order to assess the predictive power of the models considered, the model confidence set (MCS) procedure is applied. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF. The research provides a useful reference point for the development of more sophisticated models.
Anand Shankar Raja M., Benita Priyadarshini D., Janani Govindaraj, Saket Agarwal
Cryptocurrency is a commonly used term in the current world, and the COVID-19 pandemic has indirectly increased the awareness and the investor base for cryptocurrencies. Various research has been conducted to understand the complex working structure of these investment options and to analyse the volatile nature of cryptocurrencies. There are multiple factors and triggers that impact the price movements in the crypto market. Classifying these factors would help streamline the process of analysing these factors for further studies. These factors cause both positive and negative impacts on the price fluctuations. Classifying the major factors under the period of impact will help understand each factor's role in the market. This classification would help in the diagnostic and prescriptive analysis of cryptocurrencies. In this research, well-cited and published research papers, journals, and articles have been studied to classify some of the major factors affecting cryptocurrencies carefully. A model has been created to easily comprehend the classification of factors based on time of impact. This model simplifies the understanding of the factors and would help conduct further analysis on these factors.
In recent decades, data analytics has become increasingly involved in people's daily lives. Machine learning, an important part of data analysis, has also been used in the financial sector. Contemporarily, the high volatility feature of cryptocurrencies has attracted lots of investors, which also brings lots of difficulty to predict and analyze. In fact, the price of cryptocurrencies can also be forecasted based on machine learning. This paper uses historical data of Bitcoin, Ethereum and Dogecoin as inputs to predict the future value based on the LSTM. LSTM model can learn the long-term dependencies in data. According to the analysis, mean absolute error calculate the average size of the error in a set of predictions, regardless of its direction. The results produced can roughly predict the future trends of these three cryptocurrencies. This paper combines the fields of machine learning and finance to predict the future value of cryptocurrencies. These results shed light on guiding further exploration of predicting cryptocurrency assets valuation based on LSTM model.
The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.
Since it was first founded by Satoshi Nakamoto in 2008, cryptocurrencies have attracted the attention of investors significantly. Until now, many investors have invested their money in cryptocurrencies. This study aims to prove and analyze the effect of volatility, liquidity and world oil price indices on cryptocurrency returns. It also examines whether the occurrence of market efficiency is weak or not. This study aims to prove and analyze weak efficiency market and the impact of liquidity, volatility and the world oil price index on the return of the cryptocurrency market. This study uses quantitative methods and secondary data, so that the number of samples taken are 3 types of cryptocurrencies (Bitcoin, Ethereum and Binance Coin) which are listed on coinmarketcap.com and investing.com, as well as the oil price index taken from investing.com The analytical methods used in this study are series correlation test and runs test as well as multiple linear regression analysis. The results of this study indicate that volatility and liquidity affect the efficiency of the cryptocurrency market while the oil price index has no effect. The results of autocorrelation test on return itself prove that these coins have weak form efficiency. Keywords: Market Efficiency, Cryptocurrency, Liquidity, Oil Price Index, Volatility
Cryptocurrency is a kind of virtual currency that came into existence with the recent advancement of technology in finance. It is used to complete transactions in a secure way by using the techniques of cryptography. This virtual currency is created with the help of block chain technology. In many countries, the transactions using cryptocurrency are not legalised by the banks. Some of the most popular cryptocurrencies are Bitcoin, Dogecoin, Litecoin, etc. The value of each cryptocurrency keeps varying from time to time. In this research paper, we build a data analytics model of the various cryptocurrency and also a machine learning model using the LSTM (Long Short-Term Memory) algorithm to forecast the value of a certain cryptocurrency on a particular day. This paper uses a web application called Yahoo Finance(yfinance) which has all the details of the live stock market and this acts as a source of dataset for this paper. Also, various python packages such as numpy, pandas, tensorflow, seaborn, matplotlib, etc. are used for building a model. The LSTM algorithm makes use of RNN (Recurrent Neural Network) which is powerful to model data sequencer because it has an internal memory state to store the past seen data. The proposed LSTM algorithm has a 98% accuracy which outperforms the accuracy of other existing models.
Abstract: Cryptocurrency, also known as crypto, is any digital or virtual currency that uses cryptography to safeguard transactions and circulates without the authority of a central bank. Bitcoin, the first and most widely used decentralized cryptocurrency, was introduced in 2009. After a few years of unrivalled dominance, it lost its monopoly in 2011, when the first competitive alternative currencies arose. As of November 2022, there are almost 21,000 cryptocurrencies in circulation. Because there is no government credit backup, cryptocurrency prices are typically volatile. The cost of one Bitcoin rose from zero at its debut in 2009 to $13 in 2013 and then to $68789 in 2021, with numerous shifts and fluctuations along the way. The accurate forecasting of the Bitcoin price is critical for investors to make decisions and for governments to create regulatory laws. This paper examines the ability of the models - Prophet, Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost) to predict the price of Bitcoin reliably. Using the performance metrics like RMSE, each model was thoroughly trained and tested to discover which one operates more efficiently. After examining the price of Bitcoin from 2012 to 2021, we concluded that the Long Short-Term Memory (LSTM) model proves to be the most efficient when dealing with variable and difficult-topredict data such as Bitcoin values since it portrays promising results in comparison
Bitcoin as a decentralized cryptocurrency is the most popular cryptocurrency in the world today. Such that, a lot of people know it as a way to invest their money, which shows that is vital to predict the price trend of this cryptocurrency. In this article, we are trying to predict the price of Bitcoin using a machine learning approach. To do so, we apply collected data from trading sources to train an artificial deep neural nenvork model, and then use the model to predict Bitcoin fluctuation. Our deep model results show that it achieves the best performance among other methods.