Muhammad Zakhwan, Mohamed Rafik, Noraisyah Mohamed Shah, Anis Salwa Binti Mohd Khairuddin
Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognised as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency in-vestments, many Alternative Coins (Altcoins) resurfaced as to mimic the fiat currency. Altcoins prediction, as the name suggests the alternative coins from the traditional cryptocurrency which is Bitcoin (BTC). There are several methods to forecast cryptocurrency prices namely Technical Analysis and Fundamental Analysis which has been widely used in forecasting fiat and stock prices. With the emergence of Artificial Intelligence (AI), Machine Learning and Deep Learning algorithms provide a different perspective on how investors can estimate the trend or the movement of prices. In this thesis, as cryptocurrency price are time-dependent, Recur-rent Neural Network (RNN) is presented due to RNN’s nature that is well suited for Time Series Analysis (TSA). The topology of proposed RNN model consists of 3 stages which are model groundwork, model development and testing and optimisation. The RNN architecture are extended to two different models specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU). There are 4 hyperparameters that will affect the accuracy of the deep learning model in predicting cryptocurrency price. Hyperparameters tuning set the basis of optimising the model to improve the accuracy of cryptocurrency prediction. Hyperparameters listed in this project are limited to number of epochs, adaptive optimisation algorithm, dropout rate, and batch size. Next, the models are tested with data of different coins listed in the cryptocurrency market with different input features to find out the effect on the accuracy and robustness of the model in predicting the cryptocurrency price. This research demonstrates that GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Executional Time, scoring 2.2201, 0.8076 and 200s using intra-day trading strategy Open, High, Low, Close Price (OHLC) as input features.
In recent years, cryptocurrencies have received much attention due to their recent price surge and crash. In fact, their prices have been volatile, making them very difficult to predict. Accordingly, various machine learning methods have been used by researchers to investigate factors that affect cryptocurrencies prices and the patterns behind their fluctuations. From various machine learning and deep learning methods, this study aims to find an efficient and accurate model for predicting Bitcoin, Ethereum, and Binance Coin prices. Our experiments show that the Ridge regression model outperforms more complicated prediction models, such as RNNs and LSTM, in predicting the exact closing price. On the other hand, LSTM can anticipate the direction of the cryptocurrency price better than others.
Abstract: Predicting future events is difficult, particularly with regards to cryptocurrency, where the media, influential people and governments have a sharp and vital impact on worth. Cryptocurrency market analysis is a method through which the realworld data of the cryptocurrency market is used to predict where it will go next. If foretold accurately, it helps investors to invest when the value is low (purchasing in bulk when the price is dipping) and sell once it's high so as to gain a profit. This research provides two machine learning algorithms which are Long Short-Term Memory (LSTM) and Linear Regression for predicting the values of six different types of crypto currencies such as Bitcoin (BTC), Dash coin (DASH), Lite coin (LTC), Dogecoin (DOGE), Ethereum (ETH), and Monero (XMR). The accuracy of the models is analyzed using mean squared error.
Formerly, predictions of time series were usually based on numbers. In some cases, however, one has to make predictions based on linguistic information. In this paper, we experiment with time series forecasting based on textual information. We combine sentiment analysis with time series forecasting to predict fluctuations in variables that change over time. Due to the time-sensitivity of the target variable, models from the Time Series Prediction Database (tspDB) are used to infer the value of Bitcoin. We implemented an innovative architecture that accepts text as input and produces numerical predictions for certain values. The market value of bitcoin was used to verify the applicability of the architecture. The program inputs Twitter posts and outputs the market value of bitcoin for the next few days. The program first calculates a sentiment score (which reflects social media confidence in the price of bitcoin) and then makes a multivariate time series prediction of the price of bitcoin. The results can provide some insight into how the price of bitcoin fluctuates. The purpose of using Bitcoin as our primary benchmark is that we want to demonstrate a sound application of the combination of NLP and tspDB, and while Bitcoin price is not an objective variable, the method we apply can also be used in other situations that contain linguistic information as input.
Asha Rani Borah, Annamalai Senthil Kumar, S V Kasish, M S Jashwanth · 5 authors
The financial ecology has changed thanks to blockchain technology. The first known instance of blockchain may be found in a 2008 whitepaper written by a person using the alias Satoshi Nakamoto. Non-Fungible Tokens, or NFTs, are a blockchain product that has attracted a lot of public interest. A NFT is a digital property that is implemented via blockchain. It is used to prove ownership and authenticity as it cannot be duplicated, replaced, or divided. An NFT's ownership is documented in the blockchain and is transferrable by the owner, enabling the sale and trading of NFTs. Through this paper we try to establish a relationship between NFT value and various factors such as social media, OpenSea data and so on. We aim to achieve this by using Support Vector Machine, Recurrent Neural Networks, Regression for accurate results. Thus, predicting the value of the NFT precisely.
With a market cap of $1.9 trillion and more than 10 thousands active trading cryptocurrencies, the global crypto market is claimed to be an attractive and vibrant market that strongly attracts many participants. Studying cryptocurrency price tendency is one of the most challenging and interesting research fields. Despite the increasing number of studies tackling this field, it is essential to understand the factors influencing the price and analyze the most efficient model for working with crypto data. This study applies three regression models: Multiple Linear Regression, Ridge Regression, and Lasso Regression for cryptocurrency price prediction. By exploiting features that are directly tied to the closing price, the study evaluates the performance of three models on four distinct cryptocurrencies. The final results reveal the outstanding performance of Lasso Regression that could be applied in the crypto context for future studies.
Recently, cryptocurrency investment is one of the most interesting investment channels offered on the market. Investors are always trying to find an effective prediction model to increase profit as well as reduce loss in investment. In this paper, a combination of technical indicators and deep learning is applied to predict cryptocurrency price trends in the short term. The work also used the Multi-scale Residual Convolutional (MRC) module for feature extraction and a Long Short-Term Memory (LSTM) to predict price trends. Experimental results using Bitcoin and Ethereum time-series data in time frame as 1-hour and 30-minute show that our proposed method has better accuracy in a comparison to some other methods. Moreover, an automation trading bot using the proposed model was tested on the Binance Sandbox environments and got good results.
Bitcoin and other cryptocurrencies are emerging markets that are growing more and more important in the financial world. Since the definition of money has changed and its price has fluctuated, cryptocurrencies like Bitcoin and others have grown in popularity. In this study, we suggest the use of machine learning technologies and readily accessible social media data for forecasting the price movement of the Bitcoin market. We used sentiment analysis and machine learning techniques to extract tweets from Twitter postings to examine the relationship between bitcoin price changes and tweet sentiment. We used a variety of machine learning methods to create a prediction model and insightful analysis of future market values. We use five distinct machine learning models, including Support Vector Regression (SVR), Prophet, An Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and XGBoost. Every model was tested on the last 30% of the data after training on the first 70%. The models' Root Mean Square Errors (RMSE) are compared. The expansion of the collection of significant characteristics retrieved from textual data using sentiment analysis employing long short-term memory is another way that this work adds to the body of knowledge on directed bitcoin price returns forecast (LSTM). The findings demonstrate that cryptocurrency markets may be predicted using machine learning and sentiment analysis, however other coins may be predicted using only Twitter data. The most impressive outcome from the LSTM model.
In the financial market, Bitcoin analytics has gained lots of attention due to its high-risk high-reward nature. It is interesting to find better techniques to analyze and predict the Bitcoin price change. In this paper, we propose Bitcoin Evolution Analytics, which aims to predict the Bitcoin price change after one hour as Bearish or Bullish. For the prediction, the approach combines the Sentiment analysis and the Technical indicators. For Sentiment analysis of tweets related to Bitcoin, the approach uses three Natural Language Processing (NLP) libraries, namely VADER, FinBERT, and TextBlob, which generated eight different sentiment scores. For Technical indicators, the approach used three features of Bitcoin: User Sentiment Score, Aroon Indicators, and Accumulation/Distribution Line Indicators. We represented all these features of Bitcoin Data over time, which created a novel Bitcoin State Series. To predict the price change of the next hour as Bearish or Bullish, we built the state series for each hour of continuous 13 months (March 2021 - March 2022). To find the most reliable set of features, we have trained 27 ML models. For each feature set, we compared the average and maximum of the accuracies and f-measures. The results of our experiment show that considering the followers of the user as the "weight" of the sentiment gives a more accurate prediction. We found that a combination of Sentiment Analysis and Technical Indicators performs better than using only Sentiment Analysis.
With the rapid growth of technology, cryptocurrency like Bitcoin is attracting more and more attention. Its high volatility in prices creates many difficulties for predicting and there has been much work on this. This paper aims to provide a comparison of various machine learning models like linear regression, SVM, random forest, and neural networks for predicting the directions for Bitcoin close prices. The dataset used is from Jan 2012 to March 2021 and all four prices are used for predictions: Close, Open, High, and Low. Two different methods are used to fit the different types of machine learning algorithms: for regressors, close price predictions are first done and then construct in the predicted direction; for classifiers, direction predictions are done directly. Accuracy is used to do the comparison, which is the percentage of correct direction predictions made via the algorithm. It is shown that LSTM, a neural network algorithm generates the highest accuracy of about 58% and the random forest classifier has the lowest accuracy of about 55.47%.
Recent years have witnessed the rapid development of bitcoin as the first digital currency. Considering the advantages of bitcoin for both individuals and society, the price prediction of bitcoin is a hot topic. However, there remain two main challenges to be addressed in this task. Firstly, because bitcoin is vulnerable to the attitudes of the investors, incorporating the sentiment semantics from the social media into the prediction is challenging. Secondly, it is intractable to predict extreme volatility of the bitcoin price. To tackle the above challenges, this paper proposes to incorporate sentiment and temporal information simultaneously. For the first challenge, this paper employs external unsupervised corpus to conduct the domain-specific post-pretraining on the off-the-shelf language model. And the sentiment analysis on the tweets is done to obtain the scores. For the second one, Long Short Term Memory (LSTM) network is leveraged to joint model the temporal price data and the sentiment scores, thus deriving the final predictions. Experiments on the real data show that compared with the single-layer LSTM model, the model in this paper works better, which provides help for investors to specify trading strategies and also provides implications for government agencies that are developing digital currencies.
The use of cryptocurrencies is trending worldwide and represents cutting-edge development in the financial technology sector. Bitcoin is the most widely used cryptocurrency and holds the top spot in terms of market capitalization at present. This study contributes to the continuing discussion regarding whether Bitcoin is useful for financial investments. This paper aims to evaluate the viability of bitcoin as an alternative instrument for portfolio diversification and hedging. In this paper, the volatility of bitcoin is compared with stock market indices (SENSEX, Nifty-50), major currencies (USD (${\$}$), Euro (€), Pound Sterling (£), and Japanese Yen (¥) and commodity (Gold). DCC-GARCH model has been applied by using R language to evaluate the potential of bitcoin as an alternative hedging and diversification tool for the short and long term. The result of this study represents that Bitcoin can be used as an alternative tool for hedging and diversification.
Bitcoin is the most famous digital currency in the world and has become an investment asset. Prediction is one of the important matters in the investment market. In the economic field, there are different studies on the reasons for the price change of Bitcoin and how to predict the price trend of Bitcoin or how Bitcoin studies the market. Therefore, for Bitcoin, predicting the trend of Bitcoin price can effectively help Bitcoin investors. Data from www. Coingecko, the price of bitcoin is sorted according to the time sequence. Using the time series model, the change of bitcoin price in a specific period which is from 28 April 2013 to 22 August 2022 is calculated to predict the future trend of bitcoin price. Data preprocessing includes attributes removal, stationary test, and differencing. In predicting the price of Bitcoin, the ARIMA method that can produce high accuracy in short-term prediction is adopted. Use prediction test AIC and Check the residuals to select the best prediction model among the candidate models. The results of model testing show that AIC of ARIMA (5,1,2) is the smallest among all candidate models, and the results of residual check also show that ARIMA (5,1,2) model is the best model for predicting four periods.
In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used for analysing and forecasting the adjusted closing price of bitcoin. The whole dataset used is daily bitcoin closing price dating from Jan 2017 to Sep 2022. However, for testing the performance of the ARIMA model, the dataset is divided into two parts: the ARIMA model is built on training set and later using the test set to check the accuracy of the prediction. Two models, namely ARIMA (5, 2, 1) and ARIMA (0, 2, 2) are selected and comparations between them are made. ARIMA (5, 2, 1) is chosen with stepwise selection and approximated information criteria while ARIMA (0, 2, 2) is without stepwise selection and the information criteria is not approximated. Both pass the residual test and ARIMA (0, 2, 2) is slightly better according to AIC, AICc and BIC. Later, the predicting accuracy of the two models for different forecasting periods (5-day, 10-day, and long-term forecasting) are compared. It is not surprising that ARIMA performs better while making short term prediction. The 5-day and 10-day forecast works well while the long-term forecast is of limited practical value.
The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.
With the rise of Blockchain technology, the cryptocurrency market has been gaining significant interest. In particular, the number of cryptocurrency traders and the market capitalization have grown tremendously. However, predicting cryptocurrency price is very challenging and difficult due to the high price volatility. In this paper, we propose a classification machine learning approach in order to predict the direction of the market (i.e., if the market is going up or down). We identify key features such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to feed the machine learning model. We illustrate our approach through the analysis of Bitcoin close price. We evaluate the proposed approach via different simulations. Particularly, we provide a backtesting strategy. The evaluation results show that the proposed machine learning approach provides buy and sell signals with more than 86% accuracy.
This paper uses ARIMA-GARCH model to predict the prices of gold and bitcoin from 9/10/2016 to 9/11/2021, and fully analyzes the transaction date and constructs a price prediction model based on ARIMA-GARCH model, which can accurately predict the short-term price fluctuations in the future. When the transaction commission increases, the investor’s return decreases, but the gap between the investor’s return and the basic investment return gradually shortens. According to the official data in this thesis, the simulated transaction finally changed 1000 dollars into more than 9700 dollars, which performs well in the actual market.
Currently, the most popular cryptocurrency is bitcoin. Predicting the future value of bitcoin can help investors to make more educated decisions and to provide authorities with a point of reference for evaluating cryptocurrency. The novelty of the proposed prediction models lies in the use of artificial intelligence to identify movement cryptocurrency prices, particularly bitcoin prices. A forecasting model that can accurately and reliably predict the market’s volatility and price variations is necessary for portfolio management and optimization in this continually expanding financial market. In this paper, we investigate a time series analysis that makes use of deep learning to investigate volatility and provide an explanation for this behavior. Our findings have managerial ramifications, such as the potential for developing a product for investors. This can help to expand upon our model by adjusting various hyperparameters to produce a more accurate model for predicting the price of cryptocurrencies. Another possible managerial implication of our findings is the potential for developing a product for investors, as it can predict the price of cryptocurrencies more accurately. The proposed models were evaluated by collecting historical bitcoin prices from 1 January 2021 to 16 June 2022. The results analysis of the GRU and MLP models revealed that the MLP model achieved highly efficient regression, at R = 99.15% during the training phase and R = 98.90% during the testing phase. These findings have the potential to significantly influence the appropriateness of asset pricing, considering the uncertainties caused by digital currencies. In addition, these findings provide instruments that contribute to establishing stability in cryptocurrency markets. By assisting asset assessments of cryptocurrencies, such as bitcoin, our models deliver high and steady success outcomes over a future prediction horizon. In general, the models described in this article offer approximately accurate estimations of the real value of the bitcoin market. Because the models enable users to assess the timing of bitcoin sales and purchases more accurately, they have the potential to influence the economy significantly when put to use by investors and traders.
Bitcoin is a very attractive financial asset for traders and speculators worldwide. This paper first describes how traditional analysis methods can be applied to build statistical models (ARIMA and GARCH) to forecast future returns of bitcoin trading activity. Then a recurrent neural network (RNN) is constructed and trained for bitcoin time-series data. The RNN is a deep learning model for time series, which demonstrates ability to acquire more pattern information from the data.
Wajid Shakeel Ahmed, Ahsan Mehmood, Talha Sheikh, Allah Bachaya
This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to analyse bivariate connectedness. Our findings unveil the absence of mean reversion in majority cases which indicates high persistence in series. Furthermore, bivariate analysis reveals disconnection between cryptocurrencies prices and stock indices. Surprisingly, a different picture emerges on using conditional volatility instead of prices. Like, conditional volatility-based estimation uncovers evidence of mean reversion in univariate analysis as expected. There is some evidence of cointegration on volatility grounds between cryptocurrencies and emerging stock market indices. Our findings implies that investment decision regarding digital currencies should be taken cautiously. As cryptocurrencies are extremely volatile with high degree of persistence which can make them counterproductive.
J.C. Chan, Michael Nayat Young, Yogi Tri Prasetyo, Reny Nadlifatin
This paper presents a comparative analysis of Mean-Variance Theory (MVT) and Safety - First model with SP/A criterion utilizing Non-Fungible Tokens and Collectibles in the crypto market. The criterion used to determine the investment pool is the mean volume price of the Top 100 NFTs and collectibles. Historical data are gathered and computed for return estimation with equal probability. Also, different portfolio weight threshold parameters were used for the mean-variance (risk-return threshold) and safety first (relative value for fear and hope). Using backtesting, safety-first portfolios show higher cumulative returns from the US dollar benchmark. Overall, this study offers portfolio optimization and an alternative portfolio selection model as references for generic investment procedures for digital asset investors, and for educational purposes.