Purpose of the Study
This study aims to examine the statistical relationship between stock returns and economic, non-economic, and financial indicators for the top ten companies in the healthcare industry, using multiple factors and GARCH models. GARCH is a useful statistical model that effectively analyzes different types of financial data, such as high-frequency data as bivariate volatility modeling to include day and night volatility (Marius Matei et al., 2019), thus correctly aligns with the needs of this study. The study also seeks to investigate and discuss whether a relationship exists between stock returns and other factors or financial factors, as some researchers have pointed out that there may be an insignificant or significant relationship between other factors and stock returns.
In this respect, the dependent variable is the stock returns, while the independent variables include economic (GDP and CPI), non-economic (COVID-19), and financial indicators (profitability, efficiency, liquidity, leverage, and market capitalization). For its methodology, this study employs quantitative analysis, such as financial ratios and statement analysis, applied econometrics, and quantitative finance, to examine the relationship between variables and to evaluate the relationship between stock returns and economic, non-economic, and financial indicators. Multivariate models are designed to study the relationship between other factors, such as the relationship between economic, non-economic, and financial ratios and stock returns. GARCH models are designed to study time series data with heteroscedasticity.
The regression study uses financial or statistical models to evaluate the relationship between stock returns and economic, non-economic, and financial indicators and evaluate financial statements. Also, financial ratio and statement analysis use five main indicators such as profitability, efficiency, liquidity, leverage, and market value (Beckham, 2021). Profitability ratios are used in a financial matrix to understand how a company’s profits work as revenue. Efficiency ratios evaluate how a company manages its assets and how it converts its assets into cash. Liquidity ratios determine the extent to which a company has immediate access to cash to make payments. Leverage ratios measure the amount of financial leverage or debt a company has at settlement. Market capitalization ratios provide accounting and stock market information. The three statements are the income statement, the balance sheet, and the cash flow statement.
Luna K and Spicer JG (2004) have shown that economic performance is assessed using a nine-unit correlation analysis model that includes quantity, income, price, productivity, markup rate, the quantity of resource, and unit cost of the resource. This nine-unit financial model is used to provide information on the performance of hospitals and the economy in general. The regression analysis may evaluate the top ten companies in the U.S. healthcare industry by revenue in 2020. The reason is that the U.S. healthcare industry is growing, and the U.S. government spends about $10,348 on healthcare from the Centers for Medicare and Medicaid Services. The top ten companies in the U.S. healthcare industry by revenue growth are Aetna ($50 billion), Anthem ($73.9 billion), Johnson & Johnson ($74.3 billion), Walgreens Boots Alliance ($76.4 billion), Cardinal Health ($91.1 billion), Express Scripts Holdings ($100.9 billion), AmerisourceBergen ($119.6 billion), UnitedHealth Group ($130.5 billion), McKesson ($138 billion), and CVS Health ($139.4 billion).
This study analyzes the top ten companies listed above from America through regression analysis using ratios and statement analysis, applied econometrics, and quantitative finance. The dependent variable is stock return, but the independent variable can be other factors, such as COVID-19, or financial factors, such as the five primary ratios and the ratios in the statement. This study aims to gain insight into the U.S. healthcare industry using regression analysis and the relationship between returns and other factors. In addition, the goal is to assess the current state of the U.S. healthcare industry and predict the future of the industry through regression analysis.
Introduction to Theoretical or Conceptual Framework
Stock returns have fluctuated in markets and been affected by the economy and pandemics. Stock returns in investment are the percentage of earnings from the investment to investors as the sell price deducted original price divided by the original price. Su et al. (2021) note that downside risk positively impacted the stock markets, especially stock returns. This means that high-frequency data may have positive impacts on stock returns, such as financial crises. In the oil industry, the direct or indirect resources may be affected by financial crises, indicating that the economic, non-economic, and financial indicators may affect the healthcare industry. The reason is that financial crises or the economy have positive impacts on stock returns, and financial ratios would discuss later.
Economic, non-economic, and financial indicators include the economic, non-economic, and financial ratios, so economic indicators are an example of GDP, CPI and etc., but non-economic indicators are the example of education level, marital status, and time-invariant indicators. Financial ratios include activity ratios, leverage ratios, liability ratios, and profitability ratios. Although financial ratios are calculated from financial statements, they would have a statistical relationship with stock markets, such as stock prices or returns. Chen (2021) states that researchers investigated the relationship between the financial indicator and stock prices fluctuations, and there is a true relationship by using artificial intelligent systems. Some researchers argue that hospitals have financial insufficiency, although profit is not the goal or their missions. However, profit may make hospitals stay financially sustainable. The results show that financial performance is not expected and causes resource waste, meaning hospitals should maintain quality, access, productivity, and efficiency (Matos et al., 2021). This also means that hospitals should maintain and improve performance to avoid lower performance. So, it is necessary to discuss financial performance in hospitals or the healthcare industry.
As for the introduction of the types of stock return fluctuations, what is called volatility in the financial circle represents the degree of variation in the return of an asset within a certain period (conditional), such as conditional variance or conditional standard deviation, as a proxy for volatility. Kunst et al. (1991) state that there are two hypotheses for time series data, such as randomness and stability, but the conclusion is that instability and stock volatility were estimated during the time 1985 to 1990 as a violation of assumptions. Tsay (2000) summarizes the stock return volatility with the following characteristics:
Unobservability
This characteristic pertains to the stock market. For example, the variance of IBM stock cannot be directly known from the return rate of IBM stock on a certain day, because it has only one observation value unless there is intra-day data. Multiple observations can be used to calculate intra-day volatility. The volatility of stocks is derived from intra-day volatility and intra-day changes, and because it cannot be directly observed, it is difficult to evaluate forecast performance using conditional variance models.
Volatility clusters
Mandelbrot (1967) found that changes in stock market returns are correlated before and after, although there is no correlation between price changes and returns over time. Independence, when the price has a large (small) change, is also followed by a large (small) change, which is the well-known “volatility cluster” in finance.
Volatility as continuous
Volatility does not have jumps; it continuously changes over time.
Volatility as stationary
The volatility will change within a certain interval, and its mean, variance, and self-covariance are all constant and limited.
Asymmetry
Cheung & Ng (1992) have found that the volatility of stock returns has an obvious asymmetry; that is, volatility is affected by positive return shocks and negative returns as leverage effects. This feature and impacts play an important role in the future development of volatility estimation models.
Regression analysis is the basic method used by businesses because it is simple and convenient. Although financial ratio analysis requires the calculation of the ratio, it must use traditional theory as the basis of the analysis. However, fundamental analysis is not enough for a dissertation, so this study will use applied econometrics and quantitative finance to study the relationship between stock returns and economic, non-economic, and financial indicators by running multifactor and GARCH models.
Figure 1
Theoretical/Conceptual Framework
Introduction to Research Methodology and Design
This quantitative study examines the relationship between stock returns and economic, non-economic, and financial indicators by regression analysis such as applied econometrics and quantitative finance. This study states two research questions to see whether there is a relationship between stock returns and economic, non-economic, and financial indicators by multiple-factor models and GARCH models. Also, an important question is whether the business problem such as bad turnover ratios is evaluated from regression analysis. The data was collected from financial statements on the public as available resources for the top ten companies by revenues in this industry. Lai Ping-fu and Cho Kwai-yee (2016) state that they examined the relationship between financial ratios and stock returns in the Hong Kong stock market by the regression, but the result shows that there may not be a relationship between all financial ratios and stock returns because the old study and conclusion are violated. This means that this study assumes that there may be a relationship between variables by models.
The quantitative research methodology was the best fit for my research and study because this study uses quantitative analysis such as regression analysis to examine the relationship between stock returns and economic, non-economic, and financial indicators for different companies in this industry. Some researchers used financial statement analysis to investigate problems, so this means that regression analysis is the best fit (Pavone et al., 2021). Moreover, compared to Fema-Franch models, multiple-factor models are better to analyze the relationship or the problem by some researchers’ opinions (Ahmed et al., 2019). This means that five or six factors are better for the research, so this study may include many financial ratios or financial factors to analyze the relationship. Compared to other models, researchers state that GARCH models are better for researchers to estimate returns series within lots of companies, so this means that my methodology is the best fit to analyze returns for the top ten companies (Yelamanchili, 2021).
The regression study is the best fit for my research because some researchers conclude that regression analysis aims to investigate relationships between independent variables and dependent variables. Taloba et al. (2022) state that healthcare costs are related to other factors such as BMI, aging, etc., but the important thing is that the accuracy of the regression study is almost 97.89 percent. This means that compared to other models, such as the random forest algorithm, the regression model is better for the research to investigate relationships through model testing and selection. Moreover, time-series data refers to selecting data as equal to the better time intervals for using time series analysis. Researchers state that time series analysis is practical for the widespread domains, and the stock price fluctuates to reflect the time. Also, the purpose of this research aims to build a statistical model to estimate and predict the stock price (Sheikh Mohammad Idrees et al., 2019), so my research aims to use GARCH models to predict stock returns and other time-series data.
The quantitive research methodology in my study includes multiple factor models and GARCH models, especially multivariate GARCH models. Multiple factor models were developed from CAPM and Fama-French models as multiple factors, so it aims to include many factors such as economic, non-economic, and financial ratios in my study. The goal of this model aims to know the relationship between indicators and stock returns. Moreover, my study would include the GARCH models to know the correlation and heteroskedasticity among models and variables. Although most researchers are familiar with the regression, the standard error and confidence interval are narrow because of the heteroskedasticity of time series data. Due to the heteroskedasticity of time series data, GARCH models can be used to forecast the variance with correct error terms (Scherer Perlin et al., 2021). My study would use specified GARCH models such as multivariate GARCH models to investigate volatility between variables such as each indicator and each stock return (Bauwens et al., 2006). The question is whether the volatility of one indicator causes the change of each stock return at the top ten companies in the healthcare industry.
Research Questions
This paper aims to investigate whether there is a statistical relationship between stock returns and economic, non-economic, and financial indicators by multiple factors and GARCH models. This paper will use regression analysis, applied econometrics, and quantitative finance to investigate problems, relationships, and outcomes, including financial ratio and statement analysis. Applied econometrics is included in this paper, such as GARCH models called time series analysis for financial applications. Moreover, this paper would use Multiple Factor models to examine the relationship between other factors and returns. Although Multiple Factor models can be tracked in 1992 by the scholar, his paper is well-known to demonstrate three factors, including risk premiums, macroeconomic factors, and firms’ factors. Thus, the following questions address the objective of this paper:
RQ1. Is there a statistical relationship between stock returns and GDP, CPI, COVID-19, and ratios of profitability, efficiency, liquidity, leverage, and market capitalization using multifactor models for the top ten companies in the healthcare industry?
RQ2. Is there an asymmetric effect of indicators from RQ1 on stock returns using multivariate GARCH models for the top ten companies in the healthcare industry?
