Abstract

Aims: This study has looked at how changes in monetary policy impact the profitability of US companies in an attempt to provide insight into how these changes impact corporate financial outcomes.

Method: The study has investigated the relationship between changes in monetary policy and corporate profitability using quantitative approaches. The kind and intensity of the relationships between the variables were ascertained by statistical analysis of the data. The main conclusions of the study were highlighted in a sequence of organised tables and figures.

Findings: The results of this study have highlighted the significance of monetary policy decisions on firm performance, providing policymakers and business leaders with insightful information. With implications for future policy and company strategy creation, this work has added to the body of literature by presenting empirical data on the dynamic interplay between monetary policy and corporate profitability.

Keywords: Monetary policy, Public traded companies, Return on Assets, Return on Equity, Profitability.

Introduction

Through direct access to security and the impact on market participants’ expectations of short-term pricing, the central bank sets short-term prices and influences long-term money supply Monetary policy is obviously affected largely on interest rates (Hoffman, Borio, and Gambacorta, 2017). Nonetheless, both conventional and unconventional monetary policies have been vital in resolving the macroeconomic slowdown and bolstering financial intermediaries (Altavilla, Boucinha and Peydró, 2018). Monetary policy controlled by central banks affects interest rates, inflation, and economic stability everywhere. All of these have a significant impact on the economic climate. To maintain monetary policy, central banks often adjust the quantity of money available by buying or selling assets on the open market. Free market activity affects short-term interest rates, and vice versa affect long-term profitability and financial performance. When central banks cut interest rates, the monetary policy slows sharply. As interest rates rise, monetary policy becomes more restrictive (IMF, 2023a).

The Federal Reserve makes the most influential policy decisions in the US business environment. The Federal Reserve (Fed) defines monetary policy as its actions to influence the creation and availability of credit and money. Since market players’ expectations are a major factor in setting prices and driving economic growth, the Fed’s instructions, policies, comments, and actions that shape future views can also be considered to be part of monetary policy (Labonte, 2015).  Amidst the financial crisis, several foreign officials charged that the Federal Reserve was involved in “currency wars” and unintentionally causing “financial spillovers.” They contended that these accusations were particularly significant because of the U.S. dollar’s hegemonic position in global trade and banking (Bernanke, 2017). Officials are seeking further data due in part to an unanticipated spike in US inflation over the previous quarter. The timing and speed of U.S. support central banks for the US, the lender has been hot headlines since the Federal Reserve said it was ending the upward phase of its historic war on inflation (Financial times, 2024).

There are two reasons for conducting this investigation. Firstly, although monetary policy and macroeconomic stability have been the subject of much research, studies explicitly examining the effect of monetary policy on firm-level profitability are rather rare. Policymakers and corporate executives who must negotiate the intricacies of changing economic situations must comprehend this relationship. Second, there is an urgent need to reevaluate the consequences of recent changes to monetary policy frameworks, such as inflation targeting and interest rate adjustments, for the financial results of corporations.

By emphasizing the relationship between structural change and measures of economic performance, this study seeks to examine how monetary policy change affects the U.S. economy. Guided by corporate profits and make intelligent decisions, politicians and business executives must be well aware of the impact of the economy uncertainty.

Literature Review

A topic of great interest in economic and financial study is the connection between monetary policy and corporate profitability. The accommodating monetary policy cycle that many countries went through in the wake of the financial crisis, according to Altavilla, Boucinha, and Peydró (2018), sparked a heated discussion about the potential negative effects of a (very) low interest rate environment on the banking sector, particularly when it lasts for a long time. Interest rates and inflation are heavily influenced by monetary policy, which also has an effect on corporate financial performance and the macroeconomic climate. Financial vulnerabilities amplify the effects of adverse shocks to the economy, according to Ajello et al. (2022). Small changes in fundamentals or beliefs thus initiate a self-reinforcing feedback loop that depresses asset prices, restricts access to credit, and reduces inflation and economic activity Economics policy can affect vulnerability enhancement, although the direction of the effects with some transfer mechanisms is theoretically uncertain and may vary depending on economic conditions

This review of the literature reviews the empirical evidence and theoretical foundations on the impact of monetary policy changes, focusing on the US on the environment.

heoretical Foundations

Interest rates, the money supply, and inflation control are the main tools used by monetary policy to influence company profitability, both directly and indirectly. Bernanke (2019) said that monetary policymakers face obstacles to central bank independence (CBI) in addition to issues brought on by low nominal interest rates. The political fallout from the financial crisis has increased the task facing CBI.

The following theoretical frameworks serve as a roadmap for comprehending these relationships:

Interest Rate Channel

Although monetary policy seems to have lost its usefulness as a means of directing the economy, monetary policy seems to be the cornerstone of modern monetary policy. However, there has been discussion in economics regarding the means by which this is accomplished (Ndubuisi, 2015). The profitability approach is a well-established approach where budgets affect the profitability of businesses. Remittances are the means by which monetary policy affects the real sector, such as production (Morales and Reding, 2021). According to Keynesian economic theory, lower interest rates due to monetary policy expansion also mean cheaper borrowing costs. Keynes maintained that free markets lack equilibrium mechanisms that lead to perfect employment. Keynesian economists defend government market action citing the goals of price stability and full employment (IMF, 2014).

Inflation and Profitability

Another important factor in monetary policy is inflation, which affects the real value of income and expenditure. Inflation expectations influence monetary policy decisions. Furthermore, with actual inflation rates well below targets in many countries around the world, there has been considerable focus on inflation expectations and their continued stability (IMF, 2023b). High levels of inflation can put pressure on businesses’ profit margins by driving up the cost of labour and raw materials, which they may not be able to pass along to customers in the form of higher pricing. According to McKinsey & Company (2024), inflation is a gradual loss of purchasing power leading to an exponential increase in the prices of goods and services.

Empirical Evidence

Various findings from research on the relationship between financial structure and firm profitability reveal the complexity and nuance of this relationship. The sections that follow cover major empirical findings in the literature.

Impact of Interest Rates on Firm Profitability

Several researches have examined how interest rate changes affect corporate profitability. For instance, Odusanya, Yinsa, and Ilo (2018) looked at the variables influencing the operational profitability of 114 businesses that were listed between 1998 and 2012 on the Nigerian Stock Exchange (NSE). Panel data was analysed using the generalised method of moments (GMM) in this study. Research has shown that short-term liquidity, inflation, interest rates and currency risk all have a negative impact on operating profitability. According to the data, increasing interest rates make borrowing more expensive and therefore less profitable. The study emphasises that in order to maximise business profitability, macroeconomic policies that lower costs and inflation in Nigeria are necessary.

The effect of interest rate risk on the firm value and profitability of banks in the ASEAN-5 (Indonesia, Malaysia, Philippines, Singapore, and Thailand) is reported by Ebenezer et al. (2019). Panel data from 63 commercial banks between 2009 and 2017 was used in the study. According to interest rates available and interest rates on assets, the study discovered that interest rate risk significantly lowers operating value and return on assets (ROA). However, debt issuance and equity had a positive impact on ROA and firm value. The findings highlight the importance of effective risk management measures to mitigate the negative impact of interest rate fluctuations on profitability.

POPESCU, ALECU and GRIGORAS (2009) conducted a simulation study at Macora, an aid agency. The objective of the study was to examine the relationship between interest rates and financial success and to identify the debt policy that would maximize company profitability. According to the theory, one should borrow money only when the economic benefits exceed the interest rate. For example, when interest rates were 18% and financing rates were 12%, the debt reduced profits. However, with the interest rate at 9%, borrowing became more economically viable, generating larger amounts of money. This study demonstrates the importance of interest rate factors in inflation decisions and profitability.

Inflation and Firm Performance

Many studies have looked at the relationship between operating profits and inflation, but the findings vary depending on economic conditions and inflation Simon, Sawandi, and Ali Abdul-Hamid (2019) looked at how prices affected working capital consumption (WCM) and employment growth in Nigeria during the 2007–2008 financial crisis. A study by Kaaba and Minia (2024) used data from 31,027 firms in developing countries 47 was used to examine the effect of targeted inflation on productivity. According to research, inflation targeting improves productivity by reducing macroeconomic volatility and encourages productivity growth and sales expansion. Study by Alcock and Steiner (2017). Examine the relationship between unexpected inflation, firm risk-adjusted performance, and capital structure. The study highlights the importance of managing inflation expectations to minimize the negative impact of inflation on corporate financial performance.

Gaps in Literature and Relevance to Current Study

Despite providing insightful information on the relationship between monetary policy and corporate profitability, there are still many gaps in the literature. Most studies focus on specific sectors or sectors, and they are not taken focus on the U.S. the situation is greatly increased. Furthermore, more research is needed to determine how non-conservative monetary policies such as quantitative easing affect operating profitability in the long run. To close these gaps, this study looks at how recent reforms in the U.S. financial systems in have affected corporate profit. The findings are relevant to executives and policymakers.

Hypotheses development

H1: Changes in monetary policy, specifically alterations in interest rates and inflation rates, have a significant impact on firm profitability in the United States.

H2: Variations in inflation rates significantly influence firm profitability, with higher inflation rates leading to increased costs and reduced profitability.

H3: The impact of monetary policy on firm profitability varies by industry sector, with interest rate changes having a more pronounced effect on capital-intensive industries compared to service-oriented industries.

H4: Firm size moderates the relationship between monetary policy and profitability, with larger firms being better able to absorb the impacts of monetary policy changes compared to smaller firms.

Methodology

Methods and measures for assessing the potential impact of monetary policy changes in the United States are discussed. The profitability of the company is discussed in the strategic section of this study. By providing detailed information on the variables, economic models, data sources and software used in the analysis, this section aims to improve the rigor and reliability of the analysis.

Research Design

This study examines the relationship between financial variables and operating income through quantitative analysis and econometric methods. Quantitative analysis refers to the rate of change and uses questions such as “how much”, “how long”, and “to what extent”. The purpose of quantitative research is to measure data and present findings from research samples from multiple angles (Ghanad, 2023). The main objective is to examine causal relationships and measure the impact of monetary policy factors on financial performance metrics across sectors.

Data Collection

To ensure a comprehensive analysis of how monetary policy changes affect the company’s profitability in the US. Appropriate data for the dependent, independent, and control variables are collected from reliable sources as part of the data collection for this project.

Dependent Variables

Firm Profitability Indicators

The key indicators of the company’s profitability in this study are return on equity (ROE) and return on assets (ROA). These measurements will come from Reuters, which provides comprehensive financial data on publicly listed companies.

Independent Variables

Monetary Policy Indicators

Interest rate data will be collected from the World Development Index Database. World development indicators also provide inflation, as measured by the Consumer Price Index (CPI).

Control Variables

Firm Size

The corporate size by total assets will be obtained from Reuters.

Industry Sector

The study will include industry sector classifications to account for differences in how various sectors respond to monetary policy changes.

GDP Growth

The GDP growth rate, which provides a larger economic perspective, will come from the World Development Indicators.

Econometric Model

The study uses panel data econometric methods to estimate time-series and cross-sectional data variability. A fundamental area of study in contemporary multivariate statistics and econometrics is panel data analysis (Biørn, 2016). The main models used are as follows:

Random Effects Model (RE)

Predictors whose numbers observed in the analysis are sampled from a large number are included in the random effects model. Any variable that is intended to be generalised to every possible level over the whole population should be categorised as having a random effect (Gurka, Kelley and Edwards, 2012). The commands xtreg roa interestrate inflationrate gdp firmsize, re and xtreg roe interestrate inflationrate gdp firmsize, re were used to estimate it first.

Fixed Effects Model (FE)

The fixed effects model conditionalizes on intercept differences between units, which is essentially how it varies from the random effects model (Kelejian and Piras, 2017). This was estimated using xtreg roa interestrate inflationrate gdp firmsize, fe and xtreg roe interestrate inflationrate gdp firmsize, fe.

Hausman Test

Based on the correlation between individual effects and the independent variables, the Hausman Test is a statistical test used in econometrics to decide whether to employ the Fixed Effects (FE) or Random Effects (RE) estimator in a regression model (Baltagi, 2014).

Generalised Least Squares (GLS) Regression

In order to get the best set of model parameters, the generalised least squares (GLS) method makes use of both historical data and current information (Menke, 2015). The study also used GLS regression (xtgls roa interestrate inflationrate gdp firmsize, panels(correlated) corr(independent) and xtgls roe interestrate inflationrate gdp firmsize, panels(heteroskedastic) corr(independent)) to correct for these problems because the panel data contained autocorrelation and heteroskedasticity.

Tests for Panel Data Validity

Modified Wald Test for Heteroskedasticity

When groupwise heteroskedasticity occurs in the residuals of a fixed effect regression model, xttest3 computes a modified Wald statistic. It should be used (with the default panels option) after xtreg, fe, or xtgls (Baum, 2000).

Wooldridge Test for Autocorrelation

The test looks for first-order autocorrelation in a panel data model’s errors. The dependent variable’s first-differenced regression on its lag values yielded residuals, which form the basis of the test (Drukker, 2003).

Estimation Process

Initial Correlation Analysis

The phrase “correlation” is most frequently used in reference to a linear relationship—Pearson product-moment correlation—between two continuous variables (Schober, Boer and Schwarte, 2018). Here, A pairwise correlation was conducted using pwcorr roe roa interestrate inflationrate gdp firmsize, star(5) to understand the relationships between variables.

Model Estimations

As dependent variables, ROA and ROE were calculated using the Random Effects, Fixed Effects, and GLS models. The estimations shed light on how each independent and control variable affects the profitability of the company.

Model Comparison and Selection

Each dependent variable was assigned to the appropriate model based on the Hausman test and model diagnostics. For ROA, the Fixed Effects model was used, and for ROE, the Random Effects model.

Results

Descriptive Statistics

Descriptive statistics, which include metrics like mean, standard deviation, minimum and maximum values for the variables under investigation, give a summary of the data used in the analysis. Descriptive statistics are an essential technique for data analysis and summarisation in many academic fields (Alabi and Bukola, 2023). The important descriptive data for GDP growth, firm size, interest rates, inflation rates, return on equity (ROE), and return on assets (ROA) for all 77 observations are compiled in the table below.