Dhanya Pramod, S. Vijayakumar Bharathi, Kanchan Patil
This study explores the influence of the investorsâ personality dimensions on the Non-Fungible Tokens (NFT) technology readiness index (TRI) and financial instrument-specific factors of technology acceptance. The survey comprised of 197 respondents. The responses-data were tested on the conceptual model using SEM (structural equation modeling). TRI 2.0 explored customersâ predispositions towards NFT investments. Mental enablers and inhibitors explained NFT perceived value and NFT perceived trust. The research found that the enabler of TRI (innovativeness) and inhibitors of TRI (insecurity and discomfort) along with perceived trust significantly affect the NFT investment intention It was also found that optimism and perceived value for NFT did not significantly impact NFT investment intentions. These research results shall facilitate government, NFT marketplaces, brands, and private organizations to prioritize TRI enablers and decrease inhibitors to ensure successful NFT investments.
<strong>ABSTRACT: </strong>Cryptocurrency, an innovative asset class that is widely adopted by investors around the world. Indonesia is no exception to this, increasing the investor adoption up to 12 million investors in 2022. This number is very significant compared to Indonesia stock market investors that is only around 7 million investors. Various literatures have covered cryptocurrency in terms of pricing strategy and technicalities, so this paper extends the understanding of cryptocurrency dynamics from a behavioral finance perspective that is still less developed in Indonesia. This paper aims to explore the relationship between financial literacy, behavioral bias as well as its implication on the investment decision making process and investment performance from the perspective of investors based on Indonesiaâs cryptocurrency investors at online communities. This paper used Structural Equation Modeling (SEM) to predict the relation between variables. Our results show that financial literacy has an impact on each behavioral bias. While the behavioral biases that investigate in this study have different result in term of impact on decision-making process and the investment performance. Overconfidence, herding and anchoring are the biases that significantly influence invertorâs decision making in scope of cryptocurrency market in Indonesia. This study outcome may help investors understand and increase the awareness of investorâs investment behavior and decision-making process, and parallel to that the regulators and other stakeholders may use the insight to improve investorâs protection.
Mohamed Elhoseny, Abdelaziz Darwiesh, A. H. El-Baz, Joel J. P. C. Rodrigues
With the help of social media indicators, this study offers a brand-new intelligent risk management model to enhance the security of cryptocurrency. Based on surveying the previous studies, we found most of them focused on employing many techniques to enhance virtual currencies' security. However, there is no study concentrated on mining threats depending on investors' perceptions. These perceptions can give us a clear overview about the critical risks and threats. This model employs natural language processing techniques to perform risk analysis for the interactions of users on social media platforms. Additionally, a case study on investors of virtual currencies in the USA is presented where the findings of the obtained results refer to almost a quarter of the sample includes risk indications that can be classified as not only technological risks but also financial, operational, and geopolitical risks. Furthermore, performance metrics are calculated to show the new model's capabilities such that the mean accuracy for risk analysis, risk identification, and risk assessment is 77%.
Aims: To determine the investment feasibility of evaluating cryptocurrency opportunities as an investment product under the possibility of crypto price valuation selection. The study analyzes three indicators: asset price returns in unrelated time, selection of cryptocurrency investment price weights, and crypto price forward contract opportunities on ARCH-GARCH probability forecasts in the selection of price valuations by individual cryptocurrency prices. Study Design: Quantitative research. Place and Duration of Study: The period from 10 September 2021 to 4 September 2022 using sample data downloaded from the Yahoo Finance website database with metric data retrieval bound in amount, data quantity, or distance relative to writing opportunities to examine the distribution of the amount of research data. Methodology: This study employed Bitcoin (BTC), Ethereum (ETH), and Tether (USDT) cryptocurrencies as the research objects with used panel and multiple regression analysis methodologies and using forecasting the appropriate ARCH and GARCH methods Results: The results show that the prediction of future crypto price selection in BTC and ETH tokens has a probability of 78.6% and 59.6%, respectively. The study highlights the prediction of future BTC and ETH price selection with 79.21% and 78.64% forecast results as found in the ARCH-GARCH(1, 0, 1) technique. Meanwhile, USDT token has no possibility to be forecasted in the future, leaving a 7.3% possibility of crypto price selection under probability by investors in the form of high (or different) price fluctuation inequalities. Conclusion: Conclusions could state that the partial (combined) selection of crypto coin price assessments and individual crypto assets can reduce the expected return from the selection of the asset price so that this form of investment in crypto assets can reduce the level of observation of return on wealth from crypto assets for investors especially in expecting the chance on that investment.
21st century is a digital world, where new and improved tech is introduced on a daily basis, in short, it is an ever evolving world, Cryptocurrency latest inclusion of this trend, which was launched in 2009. In this paper you read about the emergence of Cryptocurrency, how itâs created and some of the major cons of it. Cryptocurrencies usually run on private blockchains- which are digitally distributed decentralized ledger that exist across a network and in this blockchains, every transaction is recorded and it also controls and oversees the transactions and maintains an updated data of existing users and their Crypto related digital portfolios, when ever a new unit of Crypto is created, blockchains store their unique code to avoid duplicates. Many experts believe Cryptocurrency will be a flagship currency of the digital world and because it doesnât use any third parties in transaction its considered to be a game changer and if the predictions of Analyst do come true then many investors, brads, businesses, may also join the trend due to the growing popularity and benefits.
This research examines the intersection of disruptive technologies specifically blockchain, AI, and Big Data with corporate governance, synthesizing insights from a diverse range of scholarly works. This research employed a comprehensive literature review methodology. We sourced and analyzed scholarly articles and papers from prominent databases, focusing on key themes such as the impact of these technologies on corporate governance, potential benefits, and associated challenges. We investigate the implications of these technologies on corporate governance, scrutinizing potential benefits and associated challenges. Our findings reveal that blockchain, with its potential to enhance transparency and reduce information asymmetry, promises to transform accounting and auditing practices. Nevertheless, the successful integration of blockchain into mainstream financial systems necessitates addressing technical and regulatory challenges. AI's potential to augment decision-making capabilities offers opportunities for improved auditing efficiency, yet raises ethical concerns and implementation complexities. Lastly, Big Data presents opportunities for driving sustainable innovation and informed decision-making, while posing significant challenges concerning data privacy and security. This study's findings carry implications for practitioners, policy-makers, and academics alike, providing critical insights into the dynamic interplay between disruptive technologies and corporate governance. Future research may extend this work through empirical studies and comparative analyses across industries and regions.
Detection of vulnerabilities in smart contracts is of great importance for protection of digital assets. Today many researches reveal the features of the contract code using deep learning, but often use a single form of code representation. This does not allow extracting the semantics and structure of the code fully to detect various vulnerabilities. The article proposes vulnerability detection model based on fusion of syntactic and semantic features. Using TextCNN tool and graph neural networks, it is possible to extract syntactic and se-mantic representation from the abstract syntactic tree and the graph of the contract control flow. Combining the features the model increases the detection accuracy and recovery rate for five types of vulnerabilities reaching the average accuracy of 95% and the recovery rate of 91% that ensures effective detection of smart contract vulnerabilities.
Aim/Purpose: The study we present aims to explore several factors pertaining to Consumer Acceptance of business technology as it relates to cryptocurrencies. Identifying and developing the relevant measures is of importance to business technology managers and soft-ware development managers today. We ask the important question of âwhat measures best represent the established constructs of the technology acceptance model?â To address this issue, it is important to identify the key measurements that help us to under-stand the proposed constructs as they relate to cryptocurrencies as well as confirm their validity in isolation and in combination with each other. In this study, the factors we explore are perceived reputation, risk, and usefulness and transaction intentions. Methodology: A survey was used whereby the methodology adapted previous measurements from related works and new measurements pertaining to usefulness and risk were developed to adhere to cryptocurrencyâs consumer acceptance framework. 202 students completed the questionnaire, and an exploratory factor analysis was used to analyze the constructs and their measurements. Contribution: To address this issue, it is important to identify the key measurements that help us to under-stand the proposed constructs as they relate to cryptocurrencies as well as confirm their validity in isolation and in combination with each other. In this study, the factors we explore are perceived reputation, risk, and usefulness and transaction intentions. Findings: Through the results, we were able to identify and validate the relevant measurements as well as the proposed constructs.
Cryptocurrencies have emerged as a popular investment option in recent years. This paper aims to identify and analyse the factors that determine the pricing of cryptocurrencies. The existing problem is the lack of a comprehensive framework for understanding cryptocurrency pricing. The study is necessary to help investors make informed decisions about investing in cryptocurrencies. To examine determinants of cryptocurrency prices, the study used five cryptocurrencies and employs the GMM techniques. The study used multiple variables (coin prices; coins issued per day; difficulty and market capitalisation) to test how they can determine cryptocurrency prices. Findings showed that coins that uses higher hash rate, which has higher difficulty, higher market capitalisation and has lower number of coins that are mined on daily basis, is likely to have its pricing improved over short to medium terms. Overall, this research work provides valuable insights into the factors that determine the pricing of cryptocurrencies.
Mohammed Abdullah Alhumayzi, Luciano Batista, Vladlena Benson
Blockchain technology is a distributed digital ledger that boosts decentralised applications. This technology has many potential applications in the Higher Education Institutions (HEIs) industry. Yet, blockchain technology adoption is still low in HEIs. Within the adoption process, neglecting employeesâ acceptance of blockchain technology might cause a failure in adopting blockchain. To address the blockchain acceptance problem, this study aims to determine the factors that impact employeesâ acceptance of blockchain technology within HEIs. To accomplish this aim, this paper proposes a framework that extends the unified theory of acceptance and use of technology (UTAUT) with blockchain characteristics to determine the factors that affect blockchain acceptance among HEIsâ employees. Specifically, the proposed model includes UTAUT constructs: effort expectance, performed expectancy, social influence, facilitating conditions, behavioural intention and technology use, and blockchain characteristics, including security and trust. Also, this study investigates HEIs employeesâ awareness as a moderator of UTAUT factors. This paper contributes to academia as it proposes a new theoretical framework that contains factors that might facilitate or hinder the implementation of blockchain technology applications among employees. The present paper also contributes to practitioners in HEIs as it informs decision-makers about potential factors concerning employeesâ acceptance of the blockchain technology.
With its decentralized structure and unchangeable record-keeping system, blockchain technology has gained widespread acceptance in a number of industries, including supply chain management, healthcare, and finance. However, there are issues with scalability, security, and efficiency with its conventional implementation. One way to automate transactions and processes on the blockchain is through smart contracts, which are self-executing agreements with the terms of the contract directly written into lines of code. When combined with smart contracts, artificial intelligence (AI) can unleash a new range of capabilities, such as predictive analytics, adaptive contract execution, and autonomous decision-making. The potential, design, implementation, and effects on blockchain networks of AI-driven smart contracts are the main topics of this paper. It explores the advantages, difficulties, and uses of this integration in addition to the direction that AI-enhanced smart contract systems will take in the future.
In the dynamic world of financial markets, the prediction of stock performance and bitcoin trading is undergoing a significant transformation due to the integration of advanced technologies and novel methodologies.The incorporation of Transformer models alongside Time Embeddings significantly improves the precision of stock market predictions by effectively capturing intricate temporal relationships and mitigating the presence of overly simplistic assumptions.The integration of real-time social media data with sentiment analysis based on BERT provides significant value in understanding investor sentiment.Additionally, the application of language model pre-training, as exemplified by BERT, brings about a transformative impact on text classification for predicting stock prices.Within the domain of cryptocurrency, sophisticated algorithms such as Transformers, Long Short-Term Memory (LSTM), Deep Convolutional LSTM (DC-LSTM), and Neural Networks (NN) have demonstrated enhanced capabilities in predicting price movements.These algorithms are further bolstered by the implementation of a comprehensive trading strategy.Automated systems for bitcoin trading introduce elements of personalization and adaptability to the trading process, thereby facilitating broader access to a diverse group of traders.The progress highlights the significant importance of the integration of technology and methodologies in the field of financial analysis.This integration enables investors and traders to possess the necessary resources for making well-informed choices within the ever-changing landscape of financial markets.
Jan 1, 2023·Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China
The market for cryptocurrency has thrived for more than 10 years and has experienced a drastic change. The success of cryptocurrencies was concerned and analyzed worldwide. This research discusses the way to build machine learning and statistical models to predict the future price of the cryptocurre
As an investor, volatility plays an important role in decision making. It is defined as the rate at which a securityâs price increases or decreases, i.e., shows pricing behavior during a definite span of time. A high volatility will lead to high risk. Thus, it becomes critical to determine the volatility and the risk-return trade-off among investments. This paper tries to document the volatility and risk-return trade-off of four prominent crypto-currencies (Bitcoin, Ethereum, Binance and Ripple), based on market-capitalization. For analysis, closing prices of cryptocurrencies has been accumulated through secondary method for 365 days, starting from 1st March 2022 and ending on 28th February 2023. Standard Deviation and Kurtosis, used together for volatility and risk assessment, documented that Bitcoin has the highest volatility and risk associated with expected returns. Regression, for assessing the impact of volatility in BTC price on others, derived that ETH has a strong, but not very strong, bivariate relationship with BTC, among all the pairs. Durbin Watson (DW) test concluded that there was no auto-correlation in the prices of crypto-currencies, i.e., previous dayâs price does not play significant role in todayâs price. For risk-return trade-off, Coefficient of Variation (CoV) has been applied. It determined that Ethereum has the highest ratio indicating its non-suitability to a conservative investor because of having the lowest returns as compared to risks involved; while Binance has the lowest Coefficient of Variation (CoV) depicting lower risk and maximum return among all.
The speed of adoption of a new technology is one the most challenging questions we face when looking into the rewards of innovation. The article explores the barriers in a buyer's adoption of bitcoin based on blockchain technology and discusses factors that drive and inhibit their adoption. Our result concentrates only on the barriers to adoption which includes complexity of the system, absence of regulatory authority, trust deficit and speculation in value. In this article, perceived risk structure was proposed based on perceived risk theory, prospect theory, and perceived value theory. Exploratory factor analysis and six regression machine learning techniques were compared and applied on the captured data to examine the degree and direction of relationship among the latent variables used to test the research model. It was found that perceived financial performance risk, perceived trust risk and perceived government risk have major impacts on the adoption intention of the investor in India and the result clearly indicates that Kernel support vector regression is the best ML technique to predict bitcoin adoption intentions.
Andrea VADKERTIOVĂ, Jaroslava GburovĂĄ, Daniela MatuĆĄĂkovĂĄ, Lenka MIKLE
Technological progress is the driving force behind significant changes in the world economy. Even money, monetary aggregates, payment systems and the exchange of goods and services did not avoid these changes. The rapid expansion of internet commerce and mobile technologies, advances in encryption and network computing, and the emergence of new business and communication platforms have also fuelled the emergence and development of digital currencies and cryptocurrencies. Cryptocurrencies, such as Bitcoin, Ethereum and others, have seen an increase in popularity in recent years and have become an important part of the worldâs economic system. This âcryptocurrencyâ trend is not avoided in Slovakia either, and it is becoming part of the financial and investment portfolio of many consumers. From a civic point of view, cryptocurrencies have become interesting not only for investment enthusiasts and technology gurus, but also for ordinary consumers. The aim of the paper is to examine and analyse significant differences in the perception of the level of awareness of cryptocurrencies depending on the age and gender of the surveyed respondents. To achieve this, we conducted a thorough analysis and used a combination of quantitative and qualitative methods. The paper provides a detailed look at the relationships between age, gender and perceptions of cryptocurrencies and assesses how these factors influence individualsâ attitudes towards this new development in finance. The paper seeks to provide a deeper insight into these relationships and assess how these factors influence individualsâ attitudes towards this new form of finance. The results of the presented paper can contribute to a better understanding of the relationship between age, gender and the perception of cryptocurrencies, thereby providing useful insights for further research and practical applications in the field of finance and digital activities.