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Volume III, Issue 1(5), 2024
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This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's important to emphasise the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme money or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.
© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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Over the past few years, the financial sector has witnessed an increase in the adoption of machine learning models within banking and insurance domains. Advanced analytic teams in the financial community are implementing these models regularly. This paper aims to explore the various machine learning approaches utilized in these sectors and offers recommendations for selecting suitable methods for financial applications. Additionally, the paper provides references to R packages that can be used to compute the machine learning methods. Our aim is to bring a valuable contribution to the field of financial research by providing a more comprehensive and advanced method of credit scoring, which in turn improves assessments of customers' debt repayment capabilities and improves risk management tactics.
© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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This paper has several aims. First, it seeks to answer whether a portfolio comprised of top innovators outperforms the S&P 500 index. To achieve this, a strategy was developed to invest long in top innovators based on their ranking, and its performance was compared to that of the broad-based index. Secondly, the paper aims to assess the volatility associated with innovative stocks, given the common belief that higher innovativeness carries higher risk. Additionally, it seeks to analyse the impact of sector factors on the portfolio's performance. Finally, the paper conducts a comparative analysis between the portfolio's performance and that of the ARK Innovation ETF (ARKK), which specifically focuses on investing in companies relevant to the theme of disruptive innovation.
© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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In this paper, we discuss about what and how of data science and data analysis: i.e., the approach and the mechanism that analysts employ while working with data. A Philosophical approach to analysis of data and data science has been undertaken those peeks into the conceptual world of aspects of the epistemology of data science. The paper also highlights the role played by analysts, tools, and specialised techniques that analysts employ in data science to derive insights from data. The discussion demonstrates the complexities associated with data science, and by what mechanism and how organisations and businesses draw insights that constitute the real value of data, and that which lay hidden deep within datasets constituting as a form of resource and asset for the organisations.
© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms.
The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent model’s performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.
© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
