Ferdiansyah Ferdiansyah, Siti Hajar Othman, Raja Zahilah Raja Md Radzi, Deris Stiawan · 6 authors
Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this research, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.
Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.
The uncertainties in future Bitcoin price make it difficult to accurately\npredict the price of Bitcoin. Accurately predicting the price for Bitcoin is\ntherefore important for decision-making process of investors and market players\nin the cryptocurrency market. Using historical data from 01/01/2012 to\n16/08/2019, machine learning techniques (Generalized linear model via penalized\nmaximum likelihood, random forest, support vector regression with linear\nkernel, and stacking ensemble) were used to forecast the price of Bitcoin. The\nprediction models employed key and high dimensional technical indicators as the\npredictors. The performance of these techniques were evaluated using mean\nabsolute percentage error (MAPE), root mean square error (RMSE), mean absolute\nerror (MAE), and coefficient of determination (R-squared). The performance\nmetrics revealed that the stacking ensemble model with two base learner (random\nforest and generalized linear model via penalized maximum likelihood) and\nsupport vector regression with linear kernel as meta-learner was the optimal\nmodel for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values\nfor the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and\n0.9967 respectively. These values show a high degree of reliability in\npredicting the price of Bitcoin using the stacking ensemble model. Accurately\npredicting the future price of Bitcoin will yield significant returns for\ninvestors and market players in the cryptocurrency market.\n
А. Е. Мисник, S. K. Krutalevich, Siarhei А. Prakapenka, Peter Borovykh · 5 authors
Neural network is the universal approximator, but its precision highly depends on sufficient set of inputs. Cryptocurrencies have great volatility, due to absence of fundamentals to back up their price. In this paper we analyze approaches to obtain additional parameters for neural networks and explore their impact on its prediction accuracy. This study indicates significant improvement of neural network predictions due to inclusion of wider selection of relevant data points.
The paper describes the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends and Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.
Iman Abu Hashish, Fabio Forni, Gianluca Andreotti, Tullio Facchinetti · 5 authors
With the recent advances in the Blockchain technology, and due to its decentralized nature, it has been a much considered approach for solving issues in the Internet of Things (IoT) sector, in particular, for IoT payment platforms. As Machine-to-Machine (M2M) payments are fundamental in the IoT economy, the development of Blockchain-based payment platforms, using cryptocurrency, is continuously increasing as it enables a pure M2M, secure and private financial transactions. Unlike traditional assets, cryptocurrencies have a higher index of volatility, which makes it essential to understand the movement of their prices, as a first step to optimize Blockchain-based M2M payment transactions. In this paper, we propose a novel hybrid model that deals with this challenge from a descriptive, as well as predictive points of view. We use Hidden Markov Models to describe cryptocurrencies historical movements to predict future movements with Long Short Term Memory networks. To evaluate the proposed hybrid model, we have chosen 2-minute frequency Bitcoin data from Coinbase exchange market. Our proposed model proved its effectiveness compared to traditional time-series forecasting models, ARIMA, as well as a conventional LSTM.
I Made Wirawan, Triyanna Widiyaningtyas, Muchammad Maulana Hasan
Bitcoin is currently the most widely used digital currency. The popularity of bitcoin continues to increase and become an asset of investment. To handle with the erratic bitcoin price changes, a prediction method is needed to help its users in predicting the price in the future. By utilizing a method that is able to recognize the pattern of change in the data time series in a certain period of time can be known bitcoin price for several days ahead with a high degree of accuracy. This research uses experimental methods. Data obtained from www.coingecko.com on May 1, 2013 to June 7, 2019. Preprocessing stage includes attribute removal, stationary test, and differencing. Determination of the model candidate using correlogram method. The predictions are done using the Autoregressive Integrated Moving Average (ARIMA) method, which is capable of generating high accuracy in short-term predictions. Evaluating the prediction results using Mean Absolute Percentage Error (MAPE). The results showed that ARIMA (4,1,4) models resulted in predictions with the smallest MAPE, 0.87 for the next one-day prediction and 5.98 for the next seven days. Thus the ARIMA (4,1,4) model is feasible to be used as a predictive method of Bitcoin for one to seven days ahead.
After Bitcoin Introduced around the world, many Cryptocurrencies was created that followed the standard of bitcoin. The use of Bitcoin or other Cryptocurrency as a currency is also an interesting study from an Islamic economic perspective. They tried to use gold with value back by gold , which gold itself is famous for its exchange rate stability. From abu bakar There is a need for monitoring organization of the cryptocurrency, to controlling from Riba (Interest), Maysir (gambling) and ghahar (Uncertainty). To solve this problem there is a need a tool that can predict with certainty based on valid historical data, to produce accurate prediction results and produce Economic value estimations that are close to Gold real value. With the results we can monitoring day by day, see next day value and continuously based on Cryptocurrency with value back by gold, and see what other impact influences the value by looking the factor negative or positive with sentiment analysis. In the last section we discuss and provide method that we analyse from previous work to produce method to estimate value cryptocurrency value back by gold.
A. Shamila Ebenezer, S. Jebapriya, Jefferson Raja Bose
The main objective of this paper is to analyze the characteristics and features that affects the fluctuations of cryptocurrency prices and to develop aninteractive cryptocurrencychatbot for providing the predictive analysis of cryptocurrency prices. The chatbot is developed using IBM Watson assistant service. The predictive analytics is performed by analyzing the datasets of various cryptocurrencies and applying appropriate time series models. Time Series Forecasting is used for predicting the future values of the prices. Predictive models like ARIMA model is used for calculating the mean squared error of the fitted model. Facebook’s package prophet () which implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly and weekly seasonality are further used to predict cryptocurrency prices.
Aug 1, 2019·CEUR Workshop Proceedings, Vol-2422: Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-EEMLPEED 2019)
Vasily Derbentsev, Наталія Даценко, Olga Stepanenko, Vitalii Bezkorovainyi
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).
Investment in cryptocurrency has been in trend from last many years. Bitcoin is one of the most popular and valuable cryptocurrency. Many studies have been done on bitcoin price prediction using various parameters which includes bitcoin factors, social media etc. In this paper, a comparative study of the various parameters affecting bitcoin price prediction is done based on Root Mean Square Error (RMSE) using various deep learning models like Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We have studied the effect of Gold price on the price of bitcoin.
Despite the current growing interest in Bitcoins-and cryptocurrencies in general-financial instruments, as well as studies related to them, are quite underdeveloped. Therefore, this article aims to provide a suitable pricing model for options written on this peculiar underlying. This is done through an artificial neural network approach, where classical pricing models-namely the trinomial tree, Monte Carlo simulation, and explicit finite difference method-are used as input layers. Results show that options written on Bitcoin turn out to be systematically overpriced when considering classical methods, whereas a noticeable improvement in price predictions is achieved by means of the proposed neural network model.
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
Lun Li, Arab Ali, Jiqiang Liu, Jingxian Liu · 5 authors
Although Bitcoin and other cryptocurrencies continue to attract attention, the inaccurate pricing of Bitcoin options has resulted in an inefficient crypto derivative market. In this paper, we adopt a multiple input LSTM-based prediction model in conjunction with the Black-Scholes (BS) model to address this challenge in Bitcoin option pricing. We discuss the relationship between on/off-chain transactions and predict the implied price volatility of the next 30 days, which is the main factor in the BS model. First, we analyze the Blockchain statistics and social network trends of Bitcoin as inputs to our model, including the liveness of Blockchain wallet, the scale of active Blockchain nodes and the impact factor of Google, Reddit, Twitter to name a few. Next, we implement the LSTM-based prediction model and evaluate it in various historical window sizes and network parameters. Finally, we compare the performance with the baseline model without Blockchain statistics inputs. The experimental results show that the proposed multi-input LSTM-based prediction model provides the risk-informed pricing of the Bitcoin call options and our Blockchain statistics reduce the root-mean-square error (RMSE) by up to 46.2%.
Public information arrivals and their immediate incorporation in asset price is a key component of semi-strong form of the Efficient Market Hypothesis. In this study, we explore the impact of public information arrivals on cryptocurrency market via Twitter posts. The empirical analysis was conducted through various methods including Kapetanios unit root test, Maki cointegration analysis and Markov regime switching regression analysis. Results indicate that while in bull market positive public information arrivals have a positive influence on Ripple’s value; in bear market, however, even if the company releases good news, it does not divert out the Ripple from downward trend.
Η ιδέα όπου υλοποιείται σε αυτή την εργασία, είναι η δημιουργία ενός μοντέλου Μηχανικής Μάθησης, το οποίο αξιοποιεί παρελθοντικές τιμές του Bitcoin, δεδομένα τάσεων της Google και χαρακτηριστικά, τα οποία δημιουργήθηκαν με εξόρυξη γνώσης απο tweets σχετικά με το Bitcoin. Σκοπός αυτής της μελέτης είναι η πρόβλεψη των μελλοντικών τιμών του Bitcoin. Για το σκοπό αυτό, συγκρίνεται ένα Βαθύ Νευρωνικό Δίκτυο και συγκεκριμένα ένα δίκτυο με στρώματα LSTM, ένα μοντέλο Παλινδρόμησης Ενίσχυσης Κλίσης και μοντέλο XGBoost. Σύμφωνα με τα αποτελέσματα, το Βαθύ Νευρωνικό Δίκτυο είχε καλύτερη απόδοση, με ρίζα μέσου τετραγωνικού σφάλματος 0.999 % στα δεδομένα ελέγχου.
Zhengyang Wang, Li Xingzhou, Ruan Jinjin, Kou Jiaqing
Nowadays, encrypted digital currency offers a new way of secure trading and exchanging and has become increasingly important in our financial system. However, the temporal dynamics of cryptocurrencies is highly complex, and predictions are still challenging. In this study, we establish two prevailing machine learning models, fully-connected Artificial Neural Network (ANN) and the Long-Short-Term-Memory (LSTM), to predictively model the price of several popular cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Stellar Lumens (XLM), Litecoin (LTC), and Monero (XMR). We evaluate model performance and conduct sensitivity analysis to further understand our model behaviors. We find that although LSTM seems more appropriate for time sequence prediction task, ANN, in general, outrivals LSTM in our experiments. Using price information from other different cryptocurrencies for joint training and prediction could largely facilitate the prediction of BTC. Finally, the model predictive error is highly sensitive to the time scale of interest.
Ziaul Haque Munim, Mohammad Hassan Shakil, Ilan Alon
This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.
Franco Valencia, Alfonso Gómez-Espinosa, Benjamín Valdés-Aguirre
Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.