Prediction of cryptocurrency’s price using ensemble machine learning algorithms DOI Creative Commons

N.S.S. Kiranmai Balijepalli,

Viswanathan Thangaraj

European Journal of Management and Business Economics, Год журнала: 2024, Номер unknown

Опубликована: Дек. 31, 2024

Purpose Cryptocurrency markets are gaining popularity, with over 23,000 cryptocurrencies in 2023 and a total market valuation of 870.81 billion USD 2023. With its increasing also susceptible to volatility. Predicting the price least fallacy or more accuracy has become need hour as it significantly influences investment decisions. Design/methodology/approach This study aims create dynamic forecasting model using ensemble method test top 15 cryptocurrencies’ prices. Statistical econometric prediction is examined after hyper tuning parameters. Drawing inferences from statistical model, an machine learning (ML) algorithms developed gradient-boosted regressor (GBR), random forest (RFR), support vector regression (SVR) multi-layer perceptron (MLP). Validation curves utilized optimize parameters boost accuracy. Findings It found that when movement exhibits autocorrelation, autoregressive integrated moving average (ARIMA) performed better. ARIMA, simple linear (SLR), (RF), decision tree (DT), gradient boosting (GB) multi-model (MLR) models well coins, showing trends, seasonality historical patterns prominent. Furthermore, MLR approach produces accurate predictions for coins higher volatility irregular patterns. Research limitations/implications Although dataset includes crisis period data, anomalies outliers yet be explicitly excluded analysis. The employed this still demonstrate high predicting cryptocurrency prices despite these outliers, suggesting robust enough handle unexpected fluctuations extreme events market. However, lack specific analysis on impact performance limitation study, needs fully explore resilience under adverse conditions. Practical implications present contributes body literature methods crypto general, potentially influencing future studies forecasting. motivates researchers empirical testing our framework various asset classes. As result, ability will influence decision-making process traders investors. research benefits investors effectively develop forecast price. findings highlight potential volatile other financial assets. Investors can design strategies allocation decisions by understanding relationship between trends consumer behavior. enhance portfolio mitigate risk incorporating insights into their processes. Policymakers use information effective regulations policies promoting economic stability welfare. emphasizes diversified understand dynamics improving trading strategies. Originality/value research, best knowledge, first above data which have not been adjusted, outperformed statistical, econometric, ML deep (DL) models.

Язык: Английский

Unveiling Cryptocurrency Impact on Financial Markets and Traditional Banking Systems: Lessons for Sustainable Blockchain and Interdisciplinary Collaborations DOI Open Access
Umar Nawaz Kayani,

Fakhrul Hasan

Journal of risk and financial management, Год журнала: 2024, Номер 17(2), С. 58 - 58

Опубликована: Фев. 1, 2024

The advent of cryptocurrencies and blockchain technology has sparked a revolutionary shift in the financial sector. This study sets out on wide-ranging investigation to understand nuanced dynamics, repercussions, potential future paths this shifting environment UK USA. primary goals research are examine how affect markets conventional banking systems; might be used sector; assess policy regulatory considerations; predict plan for future. digs into have revolutionized finance sectors. Analysis adoption rates, market volatility, integration methods sheds light changing position investment portfolios, reconfiguration asset classes, coping mechanisms institutions. When looking at sector as whole, transformational becomes clear. DeFi, smart contracts, tokenization offers new prospects improve transactions, increase transparency, broaden participation market. analyzes from perspective. delicate balancing act between stimulating innovation guaranteeing consumer protection, integrity, stability is highlighted by comparison adopted United Kingdom States, well proposals international organizations. identifies these technologies their implications. Opportunities challenges that will influence emerge, with focus central bank digital currencies (CBDCs), sustainable solutions, interdisciplinary collaborations. As deep dive comes close, power highlighted. It forces altering structures world’s markets, structures, frameworks. findings critical assessment stress need well-considered choices, ethical innovation, cooperation order succeed an ever-changing environment. To further democratize access, reshape economic fabric our planet, resides confluence tradition where exist.

Язык: Английский

Процитировано

24

Impact of Oil Price Shocks on Crypto and Conventional Financial Assets during Financial Crises: Evidence from the Russian Financial Market DOI Creative Commons
Mirzat Ullah, Kazi Sohag,

Farrukh Nawaz

и другие.

International Journal of Energy Economics and Policy, Год журнала: 2024, Номер 14(4), С. 472 - 483

Опубликована: Июль 5, 2024

This study offers a multidimensional solution to mitigate the risk raised due oil price volatility for navigating investments within Russian financial landscape. assesses spillover effects between crypto assets and traditional encompassing equities, bonds, precious metals, foreign currency reserves, crude prices. It adopts significant temporal perspective assess potential ramifications of various crises, including global health crises regional conflicts, on Utilizing daily frequency dataset spanning from January 1, 2018, December 30, 2023, this investigates contagion across normal, bullish, bearish market conditions. introduces shocks first time effectively gauge impact exogenous both conventional asset classes. Additionally, employs Cross Quantilogram (CQ) TVP-VAR estimation techniques examine interconnectedness among underlined assets. Furthermore, utilizes quantile wavelet coherence model unveil patterns, laying groundwork hypotheses related diversification, hedging, safe-haven investment strategies The findings underscore effectiveness in diversifying serving as hedge, particularly evident during leading heightened volatility. Conversely, government-owned bonds exhibit lowest resilience external shocks. Moreover, dynamic provides guidance investors implementing proposed that underscores importance prudent allocation policies management, optimizing portfolio utilization.

Язык: Английский

Процитировано

2

Kingdom of Fintech: crowdfunding shaping the future in the kingdom of Saudi Arabia DOI
Ahmet Faruk Aysan,

Aza Sidi Lemine,

Umar Nawaz Kayani

и другие.

Journal of Science and Technology Policy Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 23, 2024

Purpose This study aims to assess that whether Islamic real estate crowdfunding (RECF) can offer a compelling alternative investment attract substantial funds from traditional securities and other conventional methods or otherwise. Design/methodology/approach The current draws on secondary data was published legitimate website, Twitter official documents. Document analysis is conducted using the statements of privacy policy, Sharia compliance, terms conditions disclosers established facts. Second, achieve in-depth knowledge, qualitative for interviews presentations with Aseel CEO Majed Abalkhail YouTube. Thematic adapted; it among most popular types analyzing data. Findings findings show platform has been successful in providing simple access opportunities by minimizing obstacles, reducing entry exit costs, streamlining process widening investor’s base. Originality/value paper seeks contribute literature crowdfunding, RECF. Its objectives include exploring concept its growth various types. Furthermore, examine expansion system, market position focus Saudi Arabian market. Lastly, investigates first RECF Arabia, Company, which achieved remarkable success seven completed within year establishment.

Язык: Английский

Процитировано

1

Financial citizenship and FinTech transition: evidences in Iran DOI
Sepehr Ghazinoory, Mercedeh Pahlavanian, Meysam Shirkhodaie

и другие.

Journal of Science and Technology Policy Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 12, 2024

Purpose Financial technologies or FinTech have replaced traditional financial services. Large investments been made in FinTechs but there is a gap between service providers and consumers. Due to the high diversity speed of changes, people still do not understand new system resist it. The success transition requires providing an opportunity for citizens' participation which expressed with term, citizenship. This study aims focus on citizenship dimension wants analyze influence citizens technologies. Design/methodology/approach analyzed by using qualitative research method grounded theory. data were collected through open interviews 26 players Iran. Then three-step process open, axial selective coding was performed main categories relationships them identified. Findings Surveys shown that educating informing provides conditions engagement formation Depending level awareness, they can play role as customers, feedback demanders. Of course, disruption technological innovation affects engagement. Finally, conceptual model provided effect citizen has analyzed. Originality/value based belief it citizens’ right matters directly affect their well-being. only customer user goes beyond becomes where would be able like other interested (policymakers providers). integrates literature citizenship; analyzes according position against developments.

Язык: Английский

Процитировано

0

Prediction of cryptocurrency’s price using ensemble machine learning algorithms DOI Creative Commons

N.S.S. Kiranmai Balijepalli,

Viswanathan Thangaraj

European Journal of Management and Business Economics, Год журнала: 2024, Номер unknown

Опубликована: Дек. 31, 2024

Purpose Cryptocurrency markets are gaining popularity, with over 23,000 cryptocurrencies in 2023 and a total market valuation of 870.81 billion USD 2023. With its increasing also susceptible to volatility. Predicting the price least fallacy or more accuracy has become need hour as it significantly influences investment decisions. Design/methodology/approach This study aims create dynamic forecasting model using ensemble method test top 15 cryptocurrencies’ prices. Statistical econometric prediction is examined after hyper tuning parameters. Drawing inferences from statistical model, an machine learning (ML) algorithms developed gradient-boosted regressor (GBR), random forest (RFR), support vector regression (SVR) multi-layer perceptron (MLP). Validation curves utilized optimize parameters boost accuracy. Findings It found that when movement exhibits autocorrelation, autoregressive integrated moving average (ARIMA) performed better. ARIMA, simple linear (SLR), (RF), decision tree (DT), gradient boosting (GB) multi-model (MLR) models well coins, showing trends, seasonality historical patterns prominent. Furthermore, MLR approach produces accurate predictions for coins higher volatility irregular patterns. Research limitations/implications Although dataset includes crisis period data, anomalies outliers yet be explicitly excluded analysis. The employed this still demonstrate high predicting cryptocurrency prices despite these outliers, suggesting robust enough handle unexpected fluctuations extreme events market. However, lack specific analysis on impact performance limitation study, needs fully explore resilience under adverse conditions. Practical implications present contributes body literature methods crypto general, potentially influencing future studies forecasting. motivates researchers empirical testing our framework various asset classes. As result, ability will influence decision-making process traders investors. research benefits investors effectively develop forecast price. findings highlight potential volatile other financial assets. Investors can design strategies allocation decisions by understanding relationship between trends consumer behavior. enhance portfolio mitigate risk incorporating insights into their processes. Policymakers use information effective regulations policies promoting economic stability welfare. emphasizes diversified understand dynamics improving trading strategies. Originality/value research, best knowledge, first above data which have not been adjusted, outperformed statistical, econometric, ML deep (DL) models.

Язык: Английский

Процитировано

0