Value Of Bitcoin Research Articles

Hybrid Optimal Time Series Modeling for Cryptocurrency Price Prediction: Feature Selection, Structure and Hyperparameter Optimization

The prime aim of the research is to forecast the future value of bitcoin that is commonly known as pioneer of the Cryptocurrency market by constructing hybrid structure over the time series. In this perspective, two separate hybrid structures were created by using Artificial Neural Network (ANN) together with Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO). By using the hybrid structures created, both the network model and the hyper parameters in the network structure, together with the time intervals of the daily closing prices and how many data should be taken retrospectively, were optimized. Employing the created GA-ANN (DCP1) and PSO-ANN (DCP2) hybrid structures and the 721-day Bitcoin series, the goal of accurately predicting the values that Bitcoin will receive has been achieved. According to the comparative results obtained in line with the stated objectives and targets, it has been determined that the structure obtained with the DCP1 hybrid model has a success rate of 99% and 97.54% in training and validation, respectively. It should also, be underlined that the DCP1 model showed 47% better results than the DCP2 hybrid model. With the proposed hybrid structure, the network parameters and network model that should be used in the ANN network structure are optimized in order to obtain more efficient results in cryptocurrency price forecasting, while optimizing which input data should be used in terms of frequency and closing price to be chosen.

When Bitcoin is high: cryptocurrency value, illicit markets and US marijuana bills

PurposeThe purpose of this paper is to estimate the implications of illicit market use for the value of Bitcoin in an event studies framework.Design/methodology/approachThis study uses a data set of 58 state-level marijuana decriminalisation and legalisation bills and referenda in the USA in 2010–2022.FindingsDecriminalisation is associated with a strong and consistent positive Bitcoin price response around the event, recreational legalisation induces a more ambiguous reaction and medical legalisation is found to have a negative albeit small impact on Bitcoin value. This suggests decriminalisation enhances shadow economy use value of Bitcoin, whereas recreational and medical legalisation are not consistently reducing illicit drug cryptomarket activity. The effects are robust to various estimation windows, in subsamples, and also when outliers, heavy tails, conditional heteroskedasticity and state size are accounted for.Originality/valueNew to the literature, the choice of US marijuana bills, specifically as sample events, is based on both theoretical and empirical grounds.

Code changes could drastically reduce bitcoin’s enormous electricity requirements

As the value of bitcoin soars to record levels, the environmental impacts of cryptocurrency mining are attracting scrutiny from governments.

Survey on Bitcoin Price Prediction using Machine Learning

Abstract: With the help of specific factors, this effort seeks to improve the present analysis of bitcoin and forecast its price. After conducting a thorough investigation, it was determined which factors all contribute to daily fluctuations in the value of bitcoin. All of the data in this work is made up of various aspects from daily records from the previous few years. The first step in this endeavour is gathering all the data necessary to forecast the price of bitcoin. All of the data was compiled during the previous few years, and it was incorporated into this work. The Recurrent neural network (RNN) algorithm is employed in this work because it provides significantly improved accuracy than earlier techniques. In order for investors to invest in bitcoin easily and for beginners to this market or business, this study forecasts signs of change in the price of the cryptocurrency.

Systematic Review on Bitcoin Research

Bitcoin is one type of payment instrument that was created and developed not under any authority. The use of Bitcoin is growing from year to year with a high exchange value. Even in 2018, the value of Bitcoin reached USD 19,783.06 per 1 (one) Bitcoin, which is fluctuating because the number of Bitcoin is limited, which only reaches a maximum of 21 million (twenty-one million). Against the popularity of Bitcoin, Bank Indonesia issued a statement that Bitcoin is not a legal means of payment and the risk of use will be borne by the user. This study aims to determine the development map and trend of Bitcoin published by well-known journals in the fields of economics and finance. The data analyzed were more than 1166 Google Scholar indexed research publications. The export data was then processed and analyzed using the R Biblioshiny application program to find out the bibliometric map of Bitcoin development. The results showed that in the motor themes or driving themes quadrant, the emerging themes are Blockchain, Money, Digital, Cryptocurrencies, Financial, and Risk. Finally, the basic and transversal themes quadrant is represented by the themes Bitcoin, Volatility, and Markets.

Forecast of Bitcoin Prices Based on ARIMA Model

Bitcoin, a pioneering cryptocurrency, has captivated the world with its volatility and price swings. Its price forecasts hold vital importance for investors, policymakers, and technologists. This article delves into the intricate domain of researching and predicting Bitcoin prices, grounded in diverse data exploration and stability assessment. The application of sophisticated predictive models further underscores the analysis, encompassing mathematics, statistics, and AI. Beyond financial gains, these forecasts impact regulatory decisions and technological advancements. This article converges multiple disciplines, bridging finance, technology, and data science to unveil Bitcoin's enigmatic behavior. This paper finds that the ARIMA Model can help predict the price of bitcoin. Its not just about predicting prices; it's about deciphering the potential of blockchain and reshaping our understanding of modern finance in an era of profound technological transformation. So investors should consider bitcoin as a long-term investment. The value of Bitcoin has historically appreciated over time, but short-term price fluctuations are common. Investors should avoid making impulsive decisions based on daily price movements. The second is to use reputable cryptocurrency exchanges and hardware wallets to securely store investors' bitcoins.

A Review Paper on Bitcoin Price Prediction using Machine Learning Techniques

Bitcoin uses a peer-to-peer technology to operate with no central authority or banks. Bitcoin is open-source; its design is public, nobody owns or controls Bitcoin and everyone can take part. It is a crypto currency, so-called because it uses cryptography to control the creation and transfer of money. Users send payments by broadcasting digitally signed messages to the network. Participants known as miners verify and timestamp transactions into a shared public database called the block chain, for which they are rewarded with transaction fees and newly minted bit coins. The Bit coins value varies just like any other stock . There are many algorithms used on stock market data for price forecast. However, the parameters affecting Bit coin are different. Therefore it is necessary to foretelling the value of Bitcoin so that correct investment decisions can be made. The price of Bitcoin does not depend on the business events or intervening government authorities, unlike the stock market. Thus, to forecast the value it is proposed to leverage machine learning technology to predict the price of Bitcoin.

Analysis of Whether Bitcoin Can Help Individual Investors Hedge Their Risks as a Safe-haven Asset

Nowadays, investment in Bitcoin has become the focus of many investors. Many researchers have conducted a lot of research and discussion on the value of Bitcoin. To this day, there is still a lot of debate about the investment value of Bitcoin. This paper mainly reviews the origin of Bitcoin, its development degree in todays economic life, and some of its advantages and disadvantages based on relevant literature research. The author compares it to Safe-Haven Asset from an investors point of view. Bitcoin investments are still, in many ways, less safe-haven and valuable than traditional gold, for example. Finally, it is concluded that investors should not use Bitcoin as a safe-haven asset to make a lot of inappropriate investments.

Bitcoin Price Prediction

Abstract: In this paper, we have proposed to predict the value of Bitcoin accurately considering the various parameters that affect the value of Bitcoin. Collecting information from various references and using papers in real time, I discovered the advantages and disadvantages of predicting the price of Bitcoin. Each paper has its own set of ways to predict the price of Bitcoin. Many papers have an accurate value but some do not, but time complexity is high in those predictions, so to reduce the complexity of the time here in this paper we use an algorithm linked to artificial intelligence called LASSO (at least one opt-out operator. Some papers have used different algorithms such as SVM (support vector machine), GLM, CNN (Convolutional Neural Networks), and RNN (Recurrent neural networks) which do not have good time management, but LASSO acquisition of results on a larger website is faster and faster so for this purpose, we find comparisons between other algorithms and the LASSO algorithm, this test paper helps future researchers to make an impact on their papers. The process takes place in the first paper moment of research, we aim to understand and discover everyday trends in the Bitcoin market while gaining insight into the relevant features surrounding Bitcoin price. Our data set contains various features related to the value of Bitcoin and the payment network throughout the years, which are recorded daily. We previously processed data, using other data mining techniques to reduce data noise. Then for the second minute of our study, we use available information, and we will predict the daily price change signal with the highest accuracy.

Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm

Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.

Investment in the Covid-19 Period

From 2019 to 2022, the covid-19 pandemic has lasted for three years. Its impact to society activity and economy is prominent. As a result, investors must change their investment strategy to suit the new environment. In this research, I used Markowitz model and Index Model to analyze the past data I collected and handled from past years in some specific conditions and the current information, like the impact of covid-19 to supply chain and Ukraine war, to correct the simulation coming from these two models. By the Markowitz model and Index model, people can find the weights of stocks in portfolio that can help they get maximum expected return over risk rate and minimum risk. Of course, people can some other suggestions from these weights, like which sector’s stocks is more stable or has higher interest rate. Additionally, I concluded some investment suggestions about the investment direction and strategies from some articles. They include the relationship between government debt and financial debt, impact of Covid-19 to different sectors, spread of collapse, natural resource volatility, investment diversification, value of bitcoin for investment, and others.

Score-driven cryptocurrency and equity portfolios

ABSTRACT This paper discusses whether the Bitcoin exchange-traded fund (ETF), which tracks the value of Bitcoin, improves equity portfolios, by using a robust portfolio performance analysis. The equity portfolio is represented by an ETF that tracks the Standard & Poor’s 500. We use data from a turbulent investment period within the coronavirus pandemic, to study the diversification benefits of Bitcoin. We compare the performances of diverse portfolios composed of both ETFs, which include 40 classical dynamic volatility model-based portfolios and 900 score-driven portfolios. For the score-driven portfolios, the dynamic association is modelled by score-driven Clayton, rotated Clayton, Gumbel, rotated Gumbel and Student’s t copulas. We compare portfolio strategies using the model confidence set test. We find that score-driven portfolios outperform classical volatility model-based portfolios and the equity portfolio. Our results may provide suggestions for cryptocurrency investors on portfolio optimization and may also have policy implications for regulators and policymakers.

Bitcoin Forecasting Performance Measurement: A Comparative Study of Econometric, Machine Learning and Artificial Intelligence-Based Models

Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoins’ explosive behavior during 2013–2022 including the most volatile COVID-19 pandemic and Russia–Ukraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoins’ opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next day’s opening price.

WIND AND SOLAR POWER PLANTS AND CRYPTOCURRENCIES AS OBJECTS FOR INVESTMENT DURING THE RUSSIAN-UKRAINIAN WAR

Purpose. The aim of the article is theoretical substantiation of investment prospects in the development of wind and solar power plants and cryptocurrency as an effective investment object in the conditions of a full-scale Russian-Ukrainian war. Methodology of research. The methodological basis of economic research is the dialectical method of scientific knowledge. General scientific and special methods were used in the process of economic research, the most important of which are: abstract and logical methods – for formulating general conclusions of economic research; systematic approach – in determining the causes and factors of the impact of investing in wind and solar power plants and cryptocurrencies on the national economy. Also, the authors used the following special methods of economic research during the writing of the article: direct observation was carried out – collection of information about the value of the cryptocurrency Bitcoin on the website https://www.coingecko.com/en/coins/bitcoin in the period from 01.01.2023 to 01.11.2023; statistical methods of information processing were applied to determine the value of the Bitcoin cryptocurrency from January 1, 2023 to November 1, 2023. Findings. It was determined that after the start of the large-scale invasion of the Russian army into Ukraine on February 24, 2022, the structure of attractiveness of investment objects in Ukraine changed dramatically – when making investments in shares of national enterprises, Ukrainian domestic government loan bonds, and foreign currencies, economic losses occur, but investing in cryptocurrencies and green energy facilities has become much more attractive. Investment investments of economic entities during 2022‒2023 in electric and wind power plants and other green energy facilities, which are due to both the significant economic effect of the introduction of "green" aggregates and the provision of energy independence for economic entities, have been analysed. It is substantiated that mining and trading of cryptocurrencies in Ukraine causes significant positive effects on both the macro and micro levels. The increase in the value of cryptocurrencies in 2023 has brought a significant economic effect for cryptocurrency miners and direct investors in cryptocurrency coins. It has been proven that the spheres of application of cryptocurrencies and green energy are intertwined and cause significant mutual economic impact. Therefore, it makes economic sense for an investor to engage in two businesses in parallel and simultaneously to ensure the maximum positive synergistic effect. It has been found that the impact of green energy and cryptocurrencies on the economy will undergo significant changes with the expansion of the spheres of use of cryptocurrencies and the expansion of the geography of the use of wind, power plants and other objects of green energy and the change in their legal regulation in the conditions of military operations, which will require reflection in further scientific economic research. Originality. The possibilities of parallel investment by economic entities in cryptocurrencies and wind and solar power plants with the possibility of achieving a positive synergistic economic effect during the full-scale Russian-Ukrainian war in 2022‒2023 are considered. Practical value. According to the authors, investing in wind and solar power plants of cryptocurrency is simultaneously advisable in order to achieve a positive synergistic economic effect during the full-scale Russian-Ukrainian war and the post-war period. Key words: investment, wind farms, solar farms, green energy, cryptocurrency market, cryptocurrency, crypto coin, Bitcoin.

Bitcoin Price Forecasting and Trading: Data Analytics Approaches

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.

Deep Learning and Machine Learning Based Sentiment Analysis on BitCoin (BTC) Price Prediction

Emotions form an essential and fundamental aspect of our lives. What we do and say reflects some of our feelings in some way, though not directly. We must examine these feelings using emotional data, also known as affect data, to comprehend a person's basic behavior. Text, voice, facial expressions, and other data types can be included. Since social networking websites have become so popular, many individuals have started reading the material on these numerous sites.Twitter is one of these social networking sites. People's feelings and thoughts about a subject reveal positive, negative, and neutral emotional values. Doing sentiment analysis on Twitter is a very important and challenging task. In this study, we aim to investigate the sentiments of Bitcoin and provide an overview of its effect on the value of Bitcoin by utilizing the power of deep learning architectures and machine learning methods. The study collected tweets in English shared on Twitter between December 12, 2021, and March 13, 2022. First, people's feelings about Bitcoin were assessed using TextBlob, a natural language processing (NLP) tool. Then, it was done using basic machine learning algorithms for sentiment classification and CNN, LSTM, and BiLSTM deep learning architectures that we modeled. However, deep learning models were tested separately with the TF-IDF and Glove word embedding approaches. Experimental results prove the success of deep learning architectures using the Glove word embedding approach.

Bitcoin price prediction using optimized multiplicative long short term memory with attention mechanism using modified cuckoo search optimization

SummaryFor the past few years, Bitcoin plays a vital role in both the economical and financial industries. In order to gain a huge return on investment, the investors are eager to forecast the future value of Bitcoin. However, Bitcoin price variation is quite nonlinear and chaotic in nature, so it creates more difficulty in forecasting future value. Researchers found that the multiplicative long short term memory (LSTM) model will be more efficient for predicting those complex variations. So, target mission is about to develop an optimized multiplicative LSTM with an Attention mechanism using Technical Indicators derived from historical data. A modified cuckoo search optimization model is proposed to tune the hyperparameter of the Deep Learning model. This novel optimization algorithm eliminates the local optimum and slower convergence problem of the cuckoo search optimization algorithm. Deibold Mariano test is performed to statistically evaluate the proposed model and it is inferred that the recommended methodology is statistically fit. Regression metrics such as root mean square error, mean square error and mean absolute error has been used for comparative evaluation with related benchmark techniques such as genetic algorithm optimized LSTM (GA–LSTM), particle swarm optimized LSTM (PSO–LSTM) and cuckoo search optimized LSTM (CSO–LSTM). The empirical result shows that the recommended methodology outperforms the taken benchmark models and provides better accuracy.

Research on the application of time series ARIMA model in trade strategy

Market trading has always been a popular choice for bold investors, but market trading doesn't rely on pure luck. More need for daily experience and data. In this article, we will model the random forest algorithm for a specific situation and find the best strategy. Then it is proved that the model can provide the best trading strategy, which can be understood as proving the fit of the model established above. Using the real value of bit coin and gold prices in 5 years, establish a time series ARIMA model, analyze the smoothness and pure randomness of the series.Then get the ARIMA model fitting of bit coin and gold, indicating that the prediction model is very suitable. For the dynamic programming model, this paper adopts a genetic algorithm for the judgment of the optimal buy-sell node, which accelerates the convergence speed. To understand how the trading price affects the strategy and the results, sensitivity analysis is also done in this paper. Considering giving a certain perturbation to the results and giving a perturbation margin of 5% to the buying and selling fees respectively, the algorithm is iterated to calculate the sensitivity of the investment strategy to the trading cost and analyze how the change in the trading cost would affect the buying and selling strategy.

A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price

Several machine learning techniques and hybrid architectures for predicting bitcoin price movement have been presented in the past. Our paper proposes a hybrid model encompassing classification and regression models for predicting bitcoin prices. Our analysis found that the automated feature interactions learner (deep cross networks) error performance using a plethora of technical indicators, including crypto-specific technical indicator difficulty ribbon compression and control variables such as Metcalfe’s value of bitcoin, number of unique active addresses, bitcoin network hash rate, and S&P 500 log returns, in a hybrid architecture is better than the single-stage architecture. The hybrid model predicted a 100% directional hit rate and maintained steady volatility in returns for the out-of-sample period. Our paper concludes that in terms of risk (Sharpe ratio 1.03) and profitability (260% and 82%), the hybrid model’s bitcoin futures strategy performed better than the deep cross network regression and buy-and-hold benchmark strategies.

Cryptocurrencies in the COVID-19 era

The purpose of the article is to describe the state of the financial market during and after the COVID-19 pandemic. The financial environment is facing new questions about the future, one of which concerns the role played by cryptocurrencies in the coming years. In recent years the cryptocurrency market has been experiencing spectacular growth, with more than 5,500 cryptocurrencies now in existence worldwide. They can either be used as safe haven currencies that provide protection against market volatility or as a payment system with the prospect of consolidation. Whatever the case may be, the pandemic circumstances favour the development of these digital assets. The crisis generated by the pandemic has also affected the value of bitcoin, which has been quite volatile. While it seems to be recovering, the near future is uncertain. The uncertainty brought about by the coronavirus health crisis has caused a general collapse in the stock markets. In this context, the authors review the behaviour of bitcoin, with its dips and recoveries, which can occur in a matter of a few hours. The first months of national lockdowns were mainly marked by social issues. When social distancing has become the norm, cryptocurrencies can play a bigger role than they did previously, driving the evolution of money away from cash in the direction of cashless society.