ABSTRACT
Many people are trading in the forex market during the COVID-19 pandemic with the hope of earning money, but they are experiencing shortages due to the lack of information and technology-based tools for existing daily data. Sometimes traders only use moving averages in trading data, even though this information needs to be processed again to get the right inflection point. The objective of this research is to find inflection points based on Forex trading database. Another algorithm can also be used to determine the inflection point between two points on a moving average. This can be supported by the Bisection method used because it can guarantee that convergence will occur. The results show that the points resulting from the bisection calculation on the moving average provide a fairly accurate decision support for the location where the inflection point is located. From 10,000 data there is a standard deviation of 0.71 points which is very small compared to an average of 20 pips (points used as the difference in price values in forex). The use of the bisection method provides an accuracy of the results in seeing the inflection point of 87%. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
ABSTRACT
This paper analyzes the correlation between bitcoin, oil price fluctuations and the DOW Jones Industrial Index in the time-frequency framework. Coherent wavelet method applied to recent daily data in the United States (1863 in total). Our research has several implications and supports for policy makers and asset managers. We find that oil prices lead the U.S. market at both low and high frequencies throughout the observation period. This result suggests that sanctions against Russia by a number of countries, including the U.S., are influencing oil prices, while oil remains a major source of systemic risk to the U.S. economy and economic uncertainty between the international level is exacerbated by tensions between Russia and Ukraine. © COPYRIGHT SPIE.
ABSTRACT
The COVID-19 pandemic has brought dramatic changes to our daily lives and social activities. Anxiety over one and one's family becoming infected, stress caused by limitations imposed on personal behavior, changes in lifestyle, etc. have greatly affected everyone's mental and physical condition. This article introduces health science that aims for a society of lifelong health by visualizing one's daily data covering basic lifestyle habits (eating, exercising, and sleeping) and self-regulating one's biological rhythms. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.
ABSTRACT
Cryptocurrencies have been widely used as financial instruments over the past decade. Given the development of the cryptocurrency market and the increasing awareness of greener and more energy-efficient tokens, their connection to the green economy has become a popular topic for understanding economic and policy issues. However, the literature still lacks clear evidence on how cryptocurrencies interact with green economy indicators. Therefore, this study examines the correlations and spillover relationships between green economy indices, five black cryptocurrencies, and five clean cryptocurrencies for the U.S., Euro, and Asian markets. To this end, it applies the novel quantile spillover index approach of Ando et al. (2018) to daily data from November 9, 2017, to April 4, 2022. The empirical results show that the overall linkage is stronger for green economy indices and clean cryptocurrencies than for dirty cryptocurrencies. Moreover, green economy indices show net receiving behavior, while cryptocurrencies' results differ across variables, quantiles, and time. In addition, a notable point for clean cryptocurrencies is 2020, which was the start of the COVID-19 pandemic. The overall spillover effect is very high for all quantiles for the three markets, especially for Asia. This outcome signifies the safe harbor property for diversification purposes of the green economy. The results presented in this study are important for investors, regulators and, policymakers, cryptocurrency founders as they seek to be financially integrated and develop a more sustainable business. © 2023
ABSTRACT
The variations in the price of crude oil are very erratic, nonlinear, and dynamic with a high degree of uncertainty making it much more difficult to predict accurately. As a result, the opacity and intricacy in determining the crude oil price have been a significant topic of interest for researchers. This paper develops an efficient Genetic Algorithm(GA) based fine-tuned Support Vector Regression(SVR) model for predicting crude oil prices. The strategy utilizes key economic factors that ascertain the price per barrel, which serves as the input. The NASDAQ dataset used in this work encompasses ten years of daily data. The GA technique fine-tunes the parameters of the SVR model to boost the model's ability to foresee crude oil price fluctuations. The proposed model's performance is evaluated by employing various major criteria that compare our model to its counterparts, such as SVR and Long Short-Term Memory (LSTM) approaches. In light of these criteria, the findings of root mean square error (RMSE) and mean absolute percentage error (MAPE) indicate that this model surpasses others in predicting crude oil prices more accurately. Finally, this study also analyzes the impact of persistent uncertainness concerning the COVID-19 outbreak on crude oil price trends. © 2022 IEEE.
ABSTRACT
Pair trading strategy is a well-known profitable strategy in stock, forex, and commodity markets. As most of the world stock markets declined during COVID-19 period, therefore this study is going to observe whether this strategy is still profitable after COVID-19 pandemic. One of the powerful algorithms of DBSCAN under the umbrella of unsupervised machine learning is applied and three clusters were formed by using market and accounting data. The formation of these three clusters was based on book value per share, earning per share, classification of sector, market capitalisation and with other factors formed from PCA on the returns of daily data of six months of the 80 sample firms for year 2019-2020. An average of -0.32% average excess monthly return with Sharpe ratio of -0.0012 and Treynor ratio of -0.0231 is to be observed in COVID-19 pandemic period. However, the result of risk-adjusted performance under Jensen's alpha is observed to be insignificant. The policy implication of this study, for different portfolios and fund managers is suggested to use machine learning approach to get positive and higher returns for their clients. © 2022 World Scientific Publishing Company.
ABSTRACT
Asset pricing has been regarded as a popular subject in modern financial research. Covid-19 has imposed a remarkably severe impact on the global economy. Taking the book industry of 49 industries in the French database for analysis, the paper classifies the daily data into pre-epidemic (June 2019-February 2020) and post-epidemic (March 2020-November 2020), and calculates multiple factor coefficients via multiple linear regression. According to the research findings, the book market became sensitive to the impact of the epidemic, while value stocks and firms with stable revenues are favored. There has been no impact caused by the epidemic on the small-scale effect of the industry, with the investment profile factor consistently failing. It is suggested that investors can consider the companies with small market capitalization, high book-to-market ratios, and stable earnings during the outbreak of epidemic. © 2021 IEEE.
ABSTRACT
This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future. © 2021 IEEE.
ABSTRACT
The paper aim is to investigate whether males or females are more likely to get infected by the COVID-19 disease. Due to the fact that the COVID-19 disease is a new disease about which a lot of things are not well known yet, in the analysis daily data from the one-year period from March 1, 2020 to February 28, 2021 are used. The comparison of total confirmed COVID-19 cases according to gender is conducted for Croatia and Slovenia. In addition, the comparison is conducted by taking into account age groups as well. © 2021 Samo Drobne – Lidija Zadnik Stirn – Mirjana Kljajić Borštnar – Janez Povh – Janez Žerovnik
ABSTRACT
The capital asset pricing model (CAPM) and the Fama-French model are of great significance to the study of all aspects of the capital market, which lays the foundation of modern finance. This paper analyzes both data before Covid-19 and after Covid-19, which are daily data with the same time length in the chemistry industry. It finds that Covid-19 makes a significant impact on the chemistry industry due to its negative influence on economics. The data were adopted from Kenneth R. French's database and fitted with the Fama-French five-factor model. After data processing, the T-value used for significant testing and five corresponding coefficients is obtained using multiple linear regression. The results indicated that Covid-19 causes an anomaly because the intercept is significant after the epidemic, a decrease of market sensitivity from changes of market factor, investors' more attention to the companies that have a good ability to gain profitability by analyzing RMW, and better returns for the companies with high book-to-market ratios due to HML. Besides, SMB and CMA factors are redundant. In conclusion, the influence made by Covid-19 on the chemistry industry is significant, and the investors are recommended to pay attention to companies with robust profitability and high book-to-market. © 2021 ACM.