Evaluation of the Prediction of COVID-19 recovered and unrecovered cases using symptoms and patient’s meta data based on Support Vector Machine, Neural Network, CHAID and Quest Models
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Abstract : OBJECTIVE: This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predicting recovered cases by using the binary values for recovered cases. MATERIALS AND METHODS: The data were collected from different countries all over the world. The input of the prediction model contains 28 symptoms and four variables of the patient’s information. Symptoms of COVID-19 include a high fever, low fever, sore throat, cough, and so on, where patient metadata includes Province, county, sex, and age. The dataset contains 1254 patients with 664 recovered cases. To develop prediction models, four models are used including neural network, support vector machine, CHAID, and QUEST models. To develop prediction models, the dataset is divided into train and test datasets with splitting ratios equal to 70%, and 30%, respectively. RESULTS: The results showed that the neural network model is the most effective model in developing COVID-19 prediction with the highest performance metrics using train and test datasets. The results found that recovered cases are associated with the place of the patients mainly, province of the patient. Besides the results showed that high fever is not strongly associated with recovered cases, where cough and low fever are strongly associated with recovered cases. In addition, the country, sex, and age of the patients have higher importance than other patient’s symptoms in COVID-19 development. CONCLUSIONS: The results revealed that the prediction models of the recovered COVID-19 cases can be effectively predicted using patient characteristics and symptoms, besides the neural
network model is the most effective model to create a COVID -19 prediction model. Finally, the research provides empirical evidence that recovered cases of COVID-19 are closely related to patients’ provinces.
Keywords : Epidemiology, Symptoms, Infection, COVID-19, Ma-chine learning.
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Impact of COVID-19 pandemic virus on G8 countries’ financial indices based on artificial neural network
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Abstract : Purpose – The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted. Design/methodology/approach – In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE). Findings – The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK. Originality/value – The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.
Keywords : Artificial neural network, Stock market, Lockdown, Group eight
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COVID-19 Symptoms analysis of deceased and recovered cases using Chi-square test
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Abstract : This paper aims to show the relationship between COVID-19 symptoms and patients’ status including recovered and deceased cases. The study uses different CoVID-19 patients’ information from different countries, the dataset contains 13174 patients with 730 as recovered and 34 cases as deceased. The Chi-square
test is adopted with asymptotic significance level to show the strength of each
symptom on recovered and deceased cases independently. The study found that the recovered cases are associated with different symptoms based on the patient history, where the deceased cases showed that high fever is not responsible for increasing the number of deceased cases. In addition, the use of symptoms
will not give evidence of the patients’ status, and therefore gender, age, reason of infection and patients’ province are more dominant in determining the status of patients.
Keywords : Epidemiology, Symptoms, Infection, Chi-square.
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The Impact of External Financing on Firm Value and a Corporate Governance Index: SME evidence
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Abstract : Purpose: This paper investigates the effect of external financing needs on both firm value and corporate governance mechanisms within the UK SME context. This framework is of importance because of the limited external financial resources SMEs might face. Design/methodology/approach: We consider the endogeneity problem between corporate governance mechanisms and firm value, and hence, the three stages least squares (3SLS, hereafter) and the Instrumental Variables (IV, hereafter) based on Two Stages Least Squares(2SLS, hereafter) estimation methods are employed. Findings: We find a positive relationship between external financing needs and firm value. In addition, we detect that size and profitability are positively associated with firm value in our sample. Concerning the corporate governance index, we detect that big SMEs and those with low debt levels have better corporate governance structures. Originality: We employ a corporate governance index for our sample, which is constructed using 10 corporate governance variables. We also examine different factors that affect SMEs’ governance by applying different models, including logistic analysis.
Keywords : SMEs, Corporate governance index, external financing needs
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Modelling and Estimation of Volatility using ARCH/GARCH models in Jordan's Stock Market
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Abstract : Financials have been concerned constantly with factors that have impact on both taking and assessing various financial decisions in firms. Hence modelling volatility in financial markets is one of the factors that have direct role and effect on pricing, risk and portfolio management. Therefore, this study aims to examine the volatility characteristics on Jordan’s capital market that include; clustering volatility, leptokurtosis, and leverage effect. This objective can be accomplished by selecting symmetric and asymmetric models from GARCH family models. This study applies; ARCH, GARCH, and EGARCH to investigate the behavior of stock
return volatility for Amman Stock Exchange (ASE) covering the period from Jan. 1 2005 through Dec.31 2014. The main findings suggest that the symmetric ARCH /GARCH modelscan capture characteristics of ASE, and provide more evidence for both volatility clustering and leptokurtic, whereas EGARCH output reveals no support for the existence of leverage effect in the stock returns at Amman Stock Exchange.
Keywords : emerging markets, firm performance, institutional ownership, Jordan
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Do Ownership Concentration and Leverage Influence Firms’ Value? Evidence from Panel Data in Jordan
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Abstract : Elevating firm performance to optimal levels in order to maximize the firm value has been one of the concerns in corporate finance, due to the challenges accompanied with the failure in corporate control that led to successive financial scandals in the last decade worldwide. Hence, this study extends previous researches that were interested in the investigations of the effect of the key ownership structures in the emerging markets, by depending mainly on panel data analysis applied on a sample that consists of 83 non-financial firms listed at Amman Stock Exchange (ASE) during the period 2005-2013; to form two firm’s value equations by relying on two dependent variables which are; Return on Assets (ROA), and Market to Book value (M/B). Regarding the explanatory variables there are four indicators representing various measures of ownership concentration depending mainly on the percentages of shares held by block holders in firms. In addition, the control variables in the two models consists of indicators for leverage; size, tangibility, business risk, and liquidity. The empirical findings are consistent with many prior studies that were applied on both Jordan and many emerging markets; in that, the concluding remarks support the existence of relationship between corporate concentration, leverage and firm’s value.
Keywords : leverage, ownership concentration, firm value, Jordan, capital structure, emerging markets
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The Effect of Institutional Ownership on Firm Performance: Evidence from Jordanian Listed Firms
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Abstract : Last decade witnessed successive corporate scandals for various firms that points to a failure of corporate control. Expertize and interested parties all over the world proposed to focus on monitoring the management decisions to reduce such failure in firms. Therefore, the structure of ownership became more and more as an important issue to increase both efficiency and effectiveness of management decisions. This study seeks to investigate whether institutional ownership affects the firm’s performance for one of the emerging markets; Jordan. Firm’s performance is measured through applying two accounting measures Return on Assets (ROA) and Return on Equity (ROE), with 6 explanatory variables. Our sample is unique and contains 82 non-financial Jordanian firms listed at Amman Stock Exchange (ASE) for the period of 2005-2013, by applying panel data regression analysis. It depends on building three OLS models: Pooled, Fixed Effects Model and Random Effects Model. In addition, a test for Breusch and Pagan Lagrangian multiplier (LM), and Hausamn test to choose among the three models which model is most suitable for our data. A main finding of the panel data analysis is that; fixed effect regression is the most convenient model. As a result, there is no strong evidence that there is a relationship between both institutional ownership and firm performance for Jordanian listed firms. This conclusion can be due to the fact that institutional ownership has its own pros and cons, therefore, their existence and influence could affect materially the types and risk level of investment decisions taken by the management which in return will affect the firm’s performance as a whole.
Keywords : emerging markets, firm performance, institutional ownership, Jordan
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Developing a multi stage predicting system for corporate credit rating in emerging markets: Jordanian case
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Abstract : Purpose – The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance. Design/methodology/approach –techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007. Findings – BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007. The study adopts two neural network Originality/value – This study ithe first study to investigate credit rating in Jordan using Neural Network technique.
Keywords : Neural network, Jordan, Credit ratings, Default risk
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