Cryptomarkets Research Articles

CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.

Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis

This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and onchain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.

The Impact of COVID-19 on Investors Behavior: Evidence from Risk-aversion, Herding Behavior, and Availability Bias

The unforeseen pandemic has significantly influenced the financial market, and has caused major disruptions in the global economy, leading to a sharp decline in stock prices and increased volatility in financial markets. Assessing the impact of the COVID-19 pandemic on the market is essential for both investors and policymakers. This study recognizes that irrationality exists during the pandemic in investors decision-making and find that the efficient market hypothesis does not always hold perfectly. According to the analysis, emotional and cognitive biases play significant roles in investors decision-making, especially under uncertainty. Using empirical data from the previous study, the existence of risk aversion, herding behavior, and availability bias during the pandemic is testified. The results suggest that the pandemic has led to an increase in risk aversion among investors, as well as a tendency towards herding behavior in the stock market but not the crypto market. Furthermore, evidence of availability bias is found, with investors placing greater weight on recent news and information related to the pandemic, and making decisions based on that information. These findings have important implications for investment strategy and risk management during a crisis.

Multi Indicator based Hierarchical Strategies for Technical Analysis of Crypto market Paradigm

The usage of technical analysis in the crypto market is very popular among algorithmic traders. This involves the application of strategies based on technical indicators, which shoot BUY and SELL signals to help the investors to take trading decisions. However, instead of depending on the popular myths of the market, a proper empirical analysis can be helpful in lucrative endeavors in trading cryptocurrencies. In this work, four technical indicators namely Exponential Moving Averages (EMA), Bollinger Bands (BB), Relative Strength Index (RSI), and Parabolic Stop And Reverse (PSAR) are used individually to devise strategies that are implemented, and their performance is validated using the price data of Bitcoin from yahoo finance for 2018-22, individually for each year and all the five years consolidated to compute the performance metrics including Profit percentage, Net profitability percentage, and Number of total transactions. The results show that the performance of strategies based on trend indicators is better than that of momentum indicators where the EMA strategy provided the best result with a profit percentage of 394.13%. Further, the performance of these strategies is analyzed in three different market scenarios namely Uptrend/Bullish trend, Downtrend/Bearish trend, and Fluctuating/oscillating markets to analyze the applicability of each of these smart strategies in the three scenarios. Based on the insights obtained from the analysis, Hybrid strategies using multiple indicators with a hierarchical approach are developed whose performance is further improved by imposing constraints in a Downtrend market scenario. The novelty of these algorithms is that they identify the scenario in the market using multiple indicators in a hierarchal approach, and utilize appropriate indicators as per the market scenario. Four strategies namely, Multi indicator based Hierarchical Strategy (MIHS) with EMA9, Multi indicator based Hierarchical Strategy (MIHS) with EMA7, Multi-Indicator based Hierarchical Constrained Strategy (MIHCS) with EMA9, and Multi-Indicator based Hierarchical Constrained Strategy (MIHCS) with EMA7 are developed which give profit percentage of 154.45%, 437.48%, 256.31%, and 701.77% respectively when applied on the Bitcoin price data during 2018-22.

Spotting the stock and crypto markets’ rings of fire: measuring change proximities among spillover dependencies within inter and intra-market asset classes

Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. Despite a shaky understanding of the backbone technology, the price mechanism, and the business model, investors’ risk appetites pushed crypto market values to record highs. In addition, pricings are largely based on the perception of the market, making crypto assets naturally embedded with extreme volatility. Perhaps unsurprisingly, the new asset class has become an integral part of the investor’s portfolio, which traditionally consists of stock, commodities, forex, or any type of derivative. Therefore, it is critical to unearth possible connections between crypto currencies and traditional asset classes, scrutinizing correlational upheavals. Numerous research studies have focused on connectedness issues among the stock market, commodities, or other traditional asset classes. Scant attention has been paid, however, to similar issues when cryptos join the mix. We fill this void by studying the connectedness of the two biggest crypto assets to the stock market, both in terms of returns and volatility, through the Diebold Francis spillover model. In addition, through a novel bidirectional algorithm that is gaining currency in statistical inference, we locate times around which the nature of such connectedness alters. Subsequently, using Hausdorff-type metrics on such estimated changes, we cluster spillover patterns to describe changes in the dependencies between which two assets are evidenced to correlate with those between which other two. Creating an induced network from the cluster, we highlight which specific dependencies function as crucial hubs, how the impacts of drastic changes such as COVID-19 ripple through the networks—the Rings of Fire—of spillover dependencies.

An Investigation of Impact of Personality Traits using OCEAN as a Personality Test and Crypto Market Participation in Kolkata

An Investigation of Impact of Personality Traits using OCEAN as a Personality Test and Crypto Market Participation in Kolkata

Blockchain technology as a potential risk source and a risk mitigator: US reflections and outlook

Cryptocurrency, its risks, its volatility, and recent collapses of crypto-related firms have been splashed across the news for years. It is therefore unsurprising that recent developments surrounding disruption in the US banking system have thrust cryptocurrency into the spotlight again, with conventional financial analysts debating the degree to which blockchain-related institutions and products may have contributed to recent bank runs and instability. In the meantime, many US regulators have tended to focus on the potential downsides and risks of blockchain technology, and have recently increased the intensity of their enforcement activities against crypto market participants, often premised on decades-old legislation and case law. In the context of these broad trends, an underexplored idea is that greater integration of blockchain technology into the mainstream financial system could actually reduce bank run risk. If that is right, certain US regulators should modulate their approach and devote further resources to fostering a healthy adoption of blockchain technology within the financial system. Against that backdrop, this paper explores the potential capacity of blockchain technology to curtail widespread risk in the financial system, suggests that blockchain technology indeed has significant power to act as a risk mitigant, offers some practical observations as to how blockchain adoption could improve the risk profile of financial systems, and concludes that the promise of blockchain technology in this regard should spur US regulators to foster and promote the growth of blockchain.

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.

Getting into Good Debt: Race, Debt, and the Pursuit of Freedom

#BankBlack. #BuyBlack. In the wake of deadly police murders of unarmed Black women and men, calls for greater investment in Black business emerged as one panacea for the myriad divisions rending the very flesh of the American promise. In July 2016, hip-hop artist and business owner Michael “Killer Mike” Render issued a call to action days after the police murders of Alton Sterling and Philando Castile. “We owe them our outrage,” he stressed in a call-in to a town hall meeting sponsored by cable television channels MTV and BET and hosted by a popular local Atlanta radio station. “But,” he countered, “we don't have to burn our city down.” Instead of burning, he challenged 1 million people to open a 100-dollar account at a Black bank. Days following Killer Mike's call to “bank Black,” about 8,000 Atlanteans opened accounts in Citizens Trust Bank, a Black-owned bank started in 1921. Riding a catchy hashtag and waves of discontent, the #BankBlack movement quickly spread beyond Atlanta. In less than a year, people around the world moved an estimated $60 million into Black-owned banks.

A K-Means Classification and Entropy Pooling Portfolio Strategy for Small and Large Capitalization Cryptocurrencies.

In this study, we propose three portfolio strategies: allocation based on the normality assumption, the skewed-Student t distribution, and the entropy pooling (EP) method for 14 small- and large-capitalization (cap) cryptocurrencies. We categorize our portfolios into three groups: portfolio 1, consisting of three large-cap cryptocurrencies and four small-cap cryptocurrencies from various K-means classification clusters; and portfolios 2 and 3, consisting of seven small-cap and seven large-cap cryptocurrencies, respectively. Then, we investigate the performance of the proposed strategies on these portfolios by performing a backtest during a crypto market crash. Our backtesting covers April 2022 to October 2022, when many cryptocurrencies experienced significant losses. Our results indicate that the wealth progression under the normality assumption exceeds that of the other two strategies, though they all exhibit losses in terms of final wealth. In addition, we found that portfolio 3 is the best-performing portfolio in terms of wealth progression and performance measures, followed by portfolios 1 and 2, respectively. Hence, our results suggest that investors will benefit from investing in a portfolio consisting of large-cap cryptocurrencies. In other words, it may be safer to invest in large-cap cryptocurrencies than in small-cap cryptocurrencies. Moreover, our results indicate that adding large- and small-cap cryptocurrencies to a portfolio could improve the diversification benefit and risk-adjusted returns. Therefore, while cryptocurrencies may offer potentially high returns and diversification benefits in a portfolio, investors should be aware of the risks and carefully consider their investment objectives and risk tolerance before investing in them.

Signaling in the Market for Security Tokens

Security token offerings (STOs) are a new means for ventures to raise funding, where digital tokens are issued as regulated investment products on the blockchain. We study market outcomes in the primary and secondary markets for security tokens and examine the associated determinants in the context of signaling theory. We analyze success determinants of 138 STOs and find that a pre-sale and the announcement of token transferability are positively related to the funding success and serve as positive quality signals for investors to overcome information asymmetries. We examine 108 security tokens traded on centralized and decentralized exchanges related to the rapidly evolving area of decentralized finance. There is hardly any underpricing in the market, and it is positively associated with the crypto market sentiment as an external signal. When traded on the secondary market, security tokens generate both extremely positive and negative returns for various short-term time horizons. We disentangle the liquidity situation in the market between centralized and decentralized exchanges and find that decentralized marketplaces are less liquid and offer lower barriers to entry, indicating slow market completion.

Cryptoverse - A Multi-Asset Platform with Crypto Swapping Technique using Blockchain

Cryptocurrencies have gained significant attention and adoption in recent years, leading to an ever- growing number of digital assets. As the cryptocurrency market expands, the need for a comprehensive and user- friendly platform that supports multiple assets and seamless swapping capabilities has become evident. This paper presents a comprehensive study and implementation of a cutting-edge crypto App that serves as a Multi-Asset platform, empowered by blockchain technology. The application is designed to offer real-time crypto market data analysis, up-to-date news, secure crypto wallet services, and advanced crypto swap functionalities. The primary goal is to provide users with a seamless and user- friendly experience for managing their cryptocurrency assets efficiently. The paper also presents a detailed analysis of Cryptoverse, outlining its system architecture, features and functionalities, security and privacy measures, user experience, regulatory compliance, and scalability. Additionally, the paper explores the impact of Cryptoverse on financial inclusion and its potential to shape the future of digital asset management. Key Words: Cryptocurrency, Multi-Asset, Crypto market analysis, Wallet, Crypto swap, security and privacy.

Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation

Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation

Cryptocurrency Trading and Downside Risk

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.

How connected is the crypto market risk to investor sentiment?

We examine the connection of investor sentiment and crypto market risk within the decomposed and partial connectedness framework. By identifying interconnectedness and bi-directional causal relationship between online investor sentiment, bitcoin investor sentiment and 12 dominant cryptos, we find strong correlations between investor sentiment and volatility spillovers in the crypto market, emphasizing the importance of considering sentiment contagion when analyzing crypto market movements. This study offers valuable insights for market participants and regulators, while also contributing to the existing literature on sentiment analysis and market dynamics in the crypto domain.

Making Informed Decisions in the Volatile Crypto Market: An Analysis of Portfolio Risk and Return

Despite widespread skepticism linked to cryptocurrencies, they are constantly gaining the interest of scholars, investors, media, and regulators. Recognizing the importance that portfolio risk maintains for crypto participants, this study attempts to shed light on this issue. We investigate the risk-return tradeoffs of the most tradable cryptocurrencies based on portfolio diversification techniques. Three different crypto portfolios containing a diverse number of cryptocurrencies were created to analyze the diversification risk from a historical perspective. Data concerning daily prices and their trade volume was collected from the Coin Market Cap database and covered the period from 1 January 2016 to 31 December 2022. The results regarding the risk-reward tradeoff stand in line with the portfolio theory, where higher expected returns offset higher risk. On average, the portfolio composed of 10 cryptocurrencies offers better optimization than the one with five, as it generates the same returns with lower risk. The year 2018 reflects the maximum diversification benefits in the three portfolios, corresponding to the period when cryptocurrencies gained massive popularity. From the managerial perspective, results inform crypto and institutional investors of the possible diversification benefits of the 15 most traded cryptocurrencies.

Gauging investors' investment decisions in the crypto market through the PRISM of behavioral biases: a fuzzy AHP approach

PurposeResearch in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.Design/methodology/approachA fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.FindingsThe findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.Research limitations/implicationsThis survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.Practical implicationsThe study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.Originality/valueTo the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.

Towards Safe Cyber Practices: Developing a Proactive Cyber-Threat Intelligence System for Dark Web Forum Content by Identifying Cybercrimes

The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used “secretive spaces” to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements.

Illicit COVID-19 products online: A mixed-method approach for identifying and preventing online health risks.

The COVID-19 pandemic triggered a demand for vaccines, cures, and the need of related documentation for travel, work and other purposes. Our project aimed to identify the illicit availability of such products across the Dark Web Markets (DWMs). A retrospective search for COVID-19 related products was carried out across 118 DWMs since the start of the pandemic (March 2020-October 2021). Data on vendors as well as advertised goods such as asking price, marketplace, listed date were collected and further validated through additional searches on the open web to verify the information relating to specific marketplaces. Both quantitative and qualitative methods were used for data analysis. Forty-two listings of unlicenced COVID-19 cures and vaccination certificates were identified across 8 marketplaces sold by 25 vendors with significant variation in prices. The listings were found to be geographically specific and followed the progression of the pandemic in terms of availability. Correlations between vendor portfolios of COVID-19 products and variety of goods of other illicit nature such as illegal weaponry, medication/drugs of abuse also emerged from our analysis. This study is one of the first attempts to identify the availability of unlicenced COVID-19 products on DWMs. The easy accessibility to vaccines, fake test certificates and hypothetical/illegal cures poses serious health risks to (potential) buyers due to the uncontrolled nature of such products. It also exposes buyers to an unwanted contact with vendors selling a variety of other dangerous illicit goods. Further monitoring and regulatory responses should be implemented to protect the health and safety of citizens especially at times of global crisis.

Exploring the Potential of Decentralized Currency for E-Commerce: Case Study of an E-Commerce Website Using Decentralized Currency

The purpose of this project is to develop Crypto Market, an innovative online shopping site thataccepts Ethereum payments using Web3 technologies. The approach included using ReactJS for front-enddevelopment, integrating WalletConnect and MetaMask for secure purchases, and using the PostgreSQLdatabase with the Django Rest framework for efficient data management. The goal was to create an easyto-learn interface that would appeal to a wide range of users. The main results of the project were thesuccessful integration of WalletConnect and MetaMask, which allow users to securely transfer Ethereumfrom their personal wallets to the site’s pool wallet with minimal transaction fees. The integration ofCoinGecko has increased transparency for customers by showing them the real-time dollar equivalent ofthe Ethereum price on product pages. Additionally, the existence of a membership system enabled userregistration and facilitated store opening requests, providing access to a dedicated panel for productmanagement for approved vendors. Important results from this research show that Web3 technologies offera secure shopping experience using Ethereum. WalletConnect and MetaMask integration provided areliable environment for transactions, minimizing the risk of failed or incorrect payments. The successfuladoption of cryptocurrency as a payment method in the context of online shopping highlights the potentialof decentralized finance in traditional e-commerce applications. In summary, the Crypto Market projectdemonstrates the advantages and feasibility of incorporating cryptocurrency payments into online shoppingsites. With this research, it has been revealed that cryptocurrencies should be more involved in commercialtransactions and that they are a system that can be easily integrated.