The objective of this paper is to examine whether the unanticipated change of specific macroeconomic variables influences the US stock market represented by the S&P 500 using monthly data from 1986 to 2007. Thereby, the performance of the arbitrage pricing theory of
Ross (cp. Ross, S., 1976) shall be studied. To explain the behavior of the US stock market return the paper contains the five predefined variables consumer price index (CPI), industrial production index (IPT), money stock M1 (M1), total consumer credit outstanding (TCC) and the term structure of interest rates (Term) which are approximately similar to those variables used by Ross (cp. Chen N. F. et al., 1986, pp. 383-403). Applying the OLS method, it was found that CPI, IPT and Term are negatively related to the US stock return. It was also detected that M1 affects the stock market lagging 8 months and 12 months. However, the test statistics showed
that TCC has rather no impact on the US stock market return. To ensure that the ultimate results are not spurious, care will be taken in regards to autocorrelation, multicollinearity, serial correlation as well as heteroskedasticity.
Table of Contents
1 Introduction
2 Applied Macroeconomic Time Series Data Source
2.1 Standard & Poor’s 500 Index
2.2 Stock Prices and Industrial Production Index
2.3 Stock Return and M1 Money Stock
2.4 Stock Return and Total Consumer Credit Outstanding
2.5 Stock Return and Term Structure of Interest Rates
2.6 Stock Return and Consumer Price Index
3 The Expected Outcome of Regressors
4 Analysis of Test Results
4.1 Stationarity and Weak Dependence
4.2 Testing for Time Trend and I(1) process
4.3 Lagged Variables
4.4 Multicollinearity
4.5 Model Re-Specification
4.6 Regression Results
4.6.1 CPI
4.6.2 IPT
4.6.3 Money Stock M1
4.6.4 TCC
4.6.5 Term
5 F-Test
6 Serial Correlation
7 Testing for Heteroskedasticity
8 Model Re-Specification
8.1 Using Robust-Standard Errors
8.2 Omitting all Insignificant Variables
9 Conclusion
Objectives and Research Themes
The objective of this paper is to empirically examine the influence of unanticipated changes in specific macroeconomic variables on the US stock market, specifically the S&P 500, utilizing monthly data from 1986 to 2007. The study seeks to test the applicability of the Arbitrage Pricing Theory (APT) within this context by evaluating whether factors such as the Consumer Price Index (CPI), Industrial Production Index (IPT), M1 Money Stock, Total Consumer Credit, and the term structure of interest rates explain stock market returns.
- Application of Arbitrage Pricing Theory (APT) to US stock market data.
- Analysis of the relationship between macroeconomic volatility and market returns.
- Methodological evaluation including stationarity testing, multicollinearity assessment, and heteroskedasticity handling.
- Testing for lagged impacts of monetary policy on market performance.
Excerpt from the Book
4.4 Multicollinearity
When running an OLS regression, the implicit assumption is that the independent variables are not correlated with each other, so that they are said to be orthogonal. In practice the correlation will be non-zero so that a correlation between the explanatory variables occurs almost constantly. But a small degree of correlation does not cause much of imprecise results. However, it becomes problematic when there is significant correlation between regressors, which is then called multicollinearity. One problem that comes along is a high R2 and a high standard error of the individual regressors. Hence, the OLS regression appears to explain a lot of the variation of the dependent variable although the regressors are insignificant overall. Another problem that arises is a high sensitivity to small changes in the OLS regression. That is, adding or subtracting independent variables causes essential changes of the coefficient values or changes in the statistical significance. In the end the confidence intervals of the parameters become fairly wide and significance tests may give misleading results. To measure whether multicollinearity affects the regression model, the simple tool of a correlation matrix between the regressors can reveal if multicollinearity exists in the OLS model (cp. Brooks, C., 2008, pp. 170-172).
Summary of Chapters
1 Introduction: This chapter outlines the motivation for the study, establishing the relationship between macroeconomic factors and financial markets through the lens of Arbitrage Pricing Theory.
2 Applied Macroeconomic Time Series Data Source: This section details the data set, defining the variables and their transformations, and justifying their inclusion based on existing economic theories.
3 The Expected Outcome of Regressors: The author provides theoretical predictions regarding the anticipated signs of the coefficients for each macroeconomic variable.
4 Analysis of Test Results: This chapter covers the rigorous econometric testing, including stationarity, time trends, lagged variables, multicollinearity, and initial regression outcomes.
5 F-Test: This section conducts joint significance tests to determine if the regressors as a group impact the S&P 500.
6 Serial Correlation: The author performs tests to detect AR(1) serial correlation in the error terms to ensure the validity of the OLS model.
7 Testing for Heteroskedasticity: This chapter identifies and tests for heteroskedasticity, a necessary step for ensuring consistent estimators.
8 Model Re-Specification: The final model is refined using robust standard errors and the exclusion of statistically insignificant variables.
9 Conclusion: The study concludes by summarizing the findings on how macroeconomic variables drive US stock market returns and suggests potential areas for future research.
Keywords
Econometrics, S&P 500, Arbitrage Pricing Theory, Macroeconomic Variables, OLS Regression, Stationarity, Multicollinearity, Heteroskedasticity, Stock Market Returns, Monetary Policy, CPI, Industrial Production, M1 Money Stock, Interest Rates, Time Series Analysis
Frequently Asked Questions
What is the core purpose of this study?
The study aims to determine if unanticipated changes in selected macroeconomic variables have a statistically significant effect on the US stock market, as measured by the S&P 500 index.
Which specific variables are being analyzed?
The analysis focuses on five variables: the Consumer Price Index (CPI), Industrial Production Index (IPT), M1 Money Stock, Total Consumer Credit, and the term structure of interest rates.
What is the primary research question?
The primary research question is: Do macroeconomic variables have an effect on the US stock market, and can this be explained by the Arbitrage Pricing Theory?
What econometric method is employed?
The research primarily utilizes Ordinary Least Squares (OLS) regression, while employing various diagnostic tests for stationarity, autocorrelation, multicollinearity, and heteroskedasticity.
What does the main body of the paper cover?
The main body covers the theoretical justification of variables, data preparation, diagnostic econometric testing, regression modeling, and the subsequent re-specification of the model based on diagnostic results.
Which keywords best characterize this research?
The research is characterized by terms such as Econometrics, Arbitrage Pricing Theory, S&P 500, and various macroeconomic indicators like CPI and M1 Money Stock.
How does the author handle the problem of heteroskedasticity?
After detecting heteroskedasticity via F-tests and Lagrange Multiplier tests, the author re-estimates the model using robust standard errors to ensure the validity of the test statistics.
What conclusion does the author reach regarding the significance of the models?
The author concludes that while certain variables are significant, the overall explanatory power (R²) of the model is relatively low, suggesting that other unincluded factors also drive stock market fluctuations.
- Quote paper
- Dennis Sauert (Author), 2010, Do Macroeconomic Variables have an Effect on the US Stock Market?, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/158131
