Cryptocurrency Price Research Articles

Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data

The emergence of cryptocurrency markets has drastically changed how online transactions are conducted and provide a new investment opportunity. This study contributes to the literature on directional cryptocurrency price returns prediction by expanding the set of meaningful features extracted from textual data with sentiment analysis and comparing their usefulness across multiple data sources. In contrast to previous studies, we use fine-grained topic-sentiment features. More specifically, aspect-based sentiment analysis models, JST and TS-LDA, are implemented to incorporate joint topical-sentiment features and the degree of text subjectivity. We collected, and make available, a dataset, which consists of data scraped from Reddit, Bitcointalk and CryptoCompare sources, to demonstrate that proposed features lead to interpretable topics and an improvement in predictive performance.

A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets

The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard & Poor’s 500 Index (S&P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability.

Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

This paper initially presents a brief overview of the cryptocurrency and its history. We discuss the novel nature of literature attempting to create hybrid artificial neural network models to predict prices of cryptocurrency. For the remaining majority of the paper, we present the details of various hybrid artificial neural networks that have successfully been implemented to predict cryptocurrency prices in the form of a survey. Comparison of methods and results follow in the results section.

RMSE calculation of LSTM models for predicting prices of different cryptocurrencies

RMSE calculation of LSTM models for predicting prices of different cryptocurrencies

“Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic

In this paper, we empirically analyse the performance of five gold-backed stablecoins during the COVID-19 pandemic and compare them to gold, Bitcoin and Tether. In the digital assets' ecosystem, gold-backed cryptocurrencies have the potential to address regulatory and policy concerns by decreasing volatility of cryptocurrency prices and facilitating broader cryptocurrency adoption. We find that during the COVID-19 pandemic, gold-backed cryptocurrencies were susceptible to volatility transmitted from gold markets. Our results indicate that for the selected gold-backed cryptocurrencies, their volatility, and as a consequence, risks associated with volatility, remained comparable to the Bitcoin. In addition, gold-backed cryptocurrencies did not show safe-haven potential comparable to their underlying precious metal, gold.

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.

Development and Evaluation of a Novel Investment Decision System in Cryptocurrency Market

ABSTRACT More and more people are entering the cryptocurrency market after Bitcoin (BTC) soared to nearly USD 20,000 in 2017. To promote the development of information technology and cryptocurrency marketing, various computerized systems integrating information technology with investment and financing are innovated continuously. In this study, the daily cryptocurrency prices were input to Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM); and the developing trend plots were drawn to predict and analyze the future cryptocurrency prices through deep learning. Finally, the business practices of cryptocurrency investment were modularized based on High-Level Fuzzy Petri Nets (HLFPNs) to make a better investment decision so that all investors can use this decision system to quickly understand the future cryptocurrency trend. The experimental results have shown that this decision system can provide effective investment information to achieve investors’ personal financial goals with the expectation of improving financial situations.

How does price (in)efficiency influence cryptocurrency portfolios performance? The role of multifractality

This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk–return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.

Policy-Based Blockchain Selection

As the number of blockchain (BC) platforms providing specific features increases, selecting a platform that fits all requirements needed for a specific case becomes a cumbersome task. For example, not only are BCs' technical details relevant, but also their intrinsic characteristics (e.g., cryptocurrency price) must be considered in selecting a BC for a given case. Hence, the management of data stored in multiple BCs and the selection process are not straightforward due to the myriad platforms and both technical and economic details (e.g., BC throughput and the underlying price fluctuation). This article defines a novel refinement flow (based on the policy continuum) of high-level BC selection policies to low-level BC transactions. Experiments with the BC selection framework developed applying policy-based management (PBM) in the BC context do show that such synergy simplifies data management in multiple BCs driven by user requirements (e.g., based on costs or performance policies). The performance analysis of the framework demonstrates the successful employment of PBM for BC selections with minimal overhead.

Common risk factors in the returns on digital assets: evidence from cryptocurrency market

Introduction. Digital financial assets are a relatively new phenomenon. More and more, they include virtual currencies, and in particular cryptocurrencies. Both regulators and financial market players are becoming increasingly interested in such assets. Cryptocurrencies have no intrinsic value, and this encourages scientific studies on the problem of price formation and risk management associated with cryptocurrency operations. Most papers on the problem lack a systematic ap-proach and do not provide solutions to a large number of fundamental issues.
 Purpose. The purpose of our study was to develop a method for the risk analysis of operations with digital financial assets, namely cryptocurrencies.
 Methodology. In our study, we used parametric methods of data analysis and ma-chine learning methods, description, analysis, synthesis, induction, deduction, comparison, and grouping method. The sample was accumulated between April 2013 and April 2021 and included cryptocurrencies with the market capitalization of over 1 million USD.
 Results. The study determined the common risk factors for the cryptocurrency market. The risk factors are presented as linear combinations of returns of subsets of cryptocurrencies with dynamically changing weight coefficients. The risk factors were formed based on the market information, which included the price of the cryptocurrency, the trading volume, and its market capitalization.
 Conclusions. The study demonstrated that the cryptocurrency market is suscepti-ble to market anomalies common to traditional financial asset markets. In addition to the risk factors based on the market capitalization of cryptocurrencies (the size) and their aggregate profitability (the momentum), the article presents statistically relevant risk factors which reflect the growth rate of the market capitalization and the level of illiquidity of cryptocurrencies. In order to explain the market anomalies and the arbitrary strategies based on them, the article presents several factor models of cryptocurrency price formation. These models can be used to develop an in-tegrated approach to the risks associated with operations with digital financial assets.

When tales of money fail: the importance of price, trust, and sociality for cryptocurrency users

ABSTRACT This paper is based on research among blockchain communities in the Netherlands and online terrains. Through an empirical example, it explores tales produced by projects based on the blockchain protocol and on the premise that cryptocurrencies are money and that money has generative potential for social and economic change. By unpacking the pragmatics of a particular project – Bitnation and Pangea – I argue that despite tales of decentralisation through the moneyness of cryptocurrencies, and the distributed and automated character of the blockchain protocol, these currencies, and projects, are deeply entangled with fiat and mainstream economies and markets. This is visible by looking at the ups and downs of cryptocurrency pricing and on the effects this volatility has on (certain) projects. The lack of a sustainable community of trust in cryptocurrencies as money – particularly visible in initiatives following more libertarian and utopian tales, detached from everyday life realities – and the way these retain attention mainly due to speculation, have very real effects for blockchain based projects. No matter how radical their tales for decentralisation and socioeconomic revolution are, utterances for these tales to become real have to be there, and seem absent.

Strategic Diversification for Asynchronous Asset Trading: Insights from Generalized Coherence Analysis of Cryptocurrency Price Movements

Non-linear interactions between cryptocurrency price movements can elicit cross-frequency coupling (CFC) wherein one set of frequencies in the 1st timeseries is coupled to another set of frequencies in the 2nd timeseries. To investigate this, we use a generalized coherence approach to detect and quantify both linear (i.e., iso-frequency coupling, IFC) and non-linear coherence (CFC) and the associated phase relationships between the intra-day price changes of various pairs of cryptocurrencies for the year 2020. Using this information, we further assess the risk reduction associated with diversification of portfolios between each pair of a small market capital and a large market capital cryptocurrency, for both synchronous and asynchronous trading conditions. While mean pairwise IFC values were lower for smaller cryptocurrencies, pairwise CFC values were more heterogeneous and had no correlation with the market capital size. Diversification of portfolios resulted in reduced risk for synchronously-traded pairs of those cryptocurrencies which had low IFC. For asynchronous trading conditions, if the larger market capital cryptocurrency was traded at a higher frequency, diversification almost always reduced risk. Thus, the novel approach used in this study reveals important insights into the complex dynamics that govern the price trends of cryptocurrencies.

The mutual predictability of Bitcoin and web search dynamics

AbstractEconomic theory predicts the price dynamics of an unbacked asset to be inherently unforecastable. The same applies to exchange rates of unbacked currencies. Albeit, empirically investors are found to be driven by online and offline news media. This study analyzes the Bitcoin cryptocurrency price series and web search queries with regard to their mutual predictability and cause‐effect delay structure. Chinese Baidu engine searches and compounded Baidu–Google search statistics predict Bitcoin price dynamics at relatively high frequencies ranging from 2 to 5 months. In the other direction, Granger‐causality runs from the cryptocurrency price to queries statistics across nearly all frequencies. In both directions, the reaction time computed from a phase delay measure for the relevant frequency bands with significant causality ranges from about 1 to 4 months. For either direction, out‐of‐sample forecasts are more accurate than forecasts of a benchmark stochastic process. Bivariate models including the Baidu Search Index slightly outperform competing models that include a Baidu–Google composite index. Predictive power seems less diluted if the September 2017 trade regulations by the Chinese government are controlled for.

How can we predict the impact of the social media messages on the value of cryptocurrency? Insights from big data analytics

Cryptocurrency and blockchain are one of the most beautiful digital transformations occurring around the world. They have changed the orthodox meaning and working of currency as we know it. It is interesting to note how it excites and worries some. The main reason for the popularity of cryptocurrencies is tremendous returns in very little time. Social media platforms like twitter, provide a safe-place where individuals’ can share their thoughts as well as mindsets, which then can be heard and be reciprocated by others. This paper aims to draw a correlation between the hyped tweets and the prices of cryptocurrencies like Bitcoin - The Crypto King and Dogecoin - The Memecoin during those times. We also aim to predict the future price values of Bitcoin using its past values. By using cryptocurrencies’ financial data, twitter data, RAPIDS and cuml, a fine line can be drawn between the amount of impact tweets have on people as well as on the market. The tweets on cryptocurrency were segregated and price forecasting was done using augmented dickey fuller test and ARIMA models, 10 future values of bitcoin were predicted with 96% accuracy and 0.0395 average error.Besides, from the investigations above of the authentic cost of BTC, it is perfectly clear that there have been way more steep falls in the history of Cryptocurrencies even before Elon started tweeting about it. Thus, it can clearly be stated that no one person can control the utter volatile world of cryptocurrencies! And the decentralized system ledger of cryptocurrency remains unharmed.

Using A Feed Forward Neural Network Algorithm to Predict Prices of Multiple Cryptocurrencies

This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.

Down with the #Dogefather: Evidence of a Cryptocurrency Responding in Real Time to a Crypto-Tastemaker

Recent research in cryptocurrencies has considered the effects of the behavior of individuals on the price of cryptocurrencies through actions such as social media usage. However, some celebrities have gone as far as affixing their celebrity to a specific cryptocurrency, becoming a crypto-tastemaker. One such example occurred in April 2021 when Elon Musk claimed via Twitter that “SpaceX is going to put a literal Dogecoin on the literal moon”. He later called himself the “Dogefather” as he announced that he would be hosting Saturday Night Live (SNL) on 8 May 2021. By performing sentiment analysis on relevant tweets during the time he was hosting SNL, evidence is found that negative perceptions of Musk’s performance led to a decline in the price of Dogecoin, which dropped 23.4% during the time Musk was on air. This shows that cryptocurrencies are affected in real time by the behaviors of crypto-tastemakers.

Neurons in the mouse brain correlate with cryptocurrency price: a cautionary tale

In this paper I report the discovery of neurons which showed a neural correlate with ongoing fluctuations of Bitcoin and Ethereum prices at the time of the recording. I used the publicly available dataset of Neuropixel recordings by the Allen Institute to correlate the firing rate of single neurons with cryptocurrency price. Out of ~40.000 recorded single neurons, ~70% showed a significant correlation with Bitcoin or Ethereum prices. Even when using the conservative Bonferroni correction for multiple comparisons, ~35% of neurons showed a significant correlation, which is well above the expected false positive rate of 5%. These results were due to "nonsense correlations": when correlating two signals which both evolve slowly over time, the chances of finding a significant correlation between the two are much higher than when comparing signals which lack this property.

Financial Fear Index in the Digital Financial Assets Market

The relevance of the research topic is due to the increasing role of non-traditional financial instruments that contribute to financial instability. Therefore, various indicators are required to reflect the situation in the digital financial assets market, the volatility quotes, and the level of investor confidence. The aim of the study is to develop and test on empirical data a generalized indicator of financial instability (financial fear index) in the digital financial assets market. The novelty of the research lies in the adaptation of the classic model of building the volatility index to the cryptocurrency market.The authors use statistical methods for collecting and processing data, analyzing time series, weighing, designing economic indicators. The paper summarizes the results of modern research on the correlation between digitalization and financial instability. The authors conclude that at certain short periods of 2020 the ruble-dollar volatility was comparable or even higher than the ruble-bitcoin one. In addition, there is much less fear and uncertainty in the cryptocurrency market today than there was at the end of 2018. The main result of the study is the financial fear index model based on the method of calculating the weighted average option price of the underlying asset and hedging of price risks. The model has been tested using data on the bid and ask prices of cryptocurrencies at a specific point in time. Estimates have been obtained indicating the growing instability in the digital financial asset market. The authors offer recommendations regarding the index threshold values, which indicate the level of investors’ fear.

Global cryptocurrency trend prediction using social media

This paper aims to investigate the global crypto-currency price movement trends with respect to the social media communication data. The idea is to analyze the topical trends in the online communities and social media platforms to understand and extract insights that could be used to predict the price fluctuations in crypto-currencies. The hypothesis rests in finding the empirical evidence to exploit the relationship between price variations and social media activities. Such models and insights will help us better understand the crypto currency ecosystems in context of social media behavior which can be used for real-time trading systems.