Dividends, Momentum and Macroeconomic Variables as Determinants of the U.S. Equity Premium Across Economic Regimes

The equity premium of the S&P 500 index is explained in this paper by several variables that can be grouped into fundamental, behavioral, and macroeconomic factors. We hypothesize that the statistical significance of these variables changes across economic regimes. The three regimes we consider are the low‐volatility, medium‐volatility, and high‐volatility regimes in contrast to previous studies that do not differentiate across economic regimes. By using the three‐state Markov switching regime econometric methodology, we confirm that the statistical significance of the independent variables representing fundamentals, macroeconomic conditions, and a behavioral variable changes across economic regimes. Our findings offer an improved understanding of what moves the equity premium across economic regimes than what we can learn from single‐equation estimation. Our results also confirm the significance of momentum as a behavioral variable across all economic regimes

Asset Price Momentum and Monetary Policy: Time-varying Parameter Estimation of Taylor Rules

In this article, we consider two new independent variables as inputs to the Taylor Rule. These are the equity and housing momentum variables and are introduced to investigate the potential usefulness of these two variables in guiding the Fed to lean against potential bubbles. Such effectiveness cannot adequately be evaluated if the Taylor Rule estimation follows the standard regression methodology that has been criticized in the literature to be econometrically incorrect. Using a time-varying parameter estimation methodology, we find that equity momentum as an input in the Taylor Rule does not contribute to changes in Fed Funds. However, the housing momentum plays an important role econometrically and can be a useful tool in setting Fed Funds rates

Oil Prices and the Impact of the Financial Crisis of 2007-2009

Oil prices increased dramatically during 2004–2006. Industry experts initially attributed these price increases to fundamental factors such as the rise in global demand, but also because of disruptions in the supply of oil. The price increases however were so substantial that additional factors are needed to explain such dramatic changes. We propose that the decline in the value of the U.S. dollar measured both by the appreciation of the Euro and of gold prices, played an important role as oil suppliers demanded compensation for the declining value of the dollar. Using a Markov switching regime methodology we find evidence that this hypothesis is true prior to the financial crisis, but its validity does not hold after the crisis when oil prices crashed and the dollar rallied

The Impact of Large-scale Asset Purchases on the S&P 500 index, Long-term Interest Rates and Unemployment

After the bankruptcy of Lehman Brothers in September 2008 and the financial panic that ensued, the Federal Reserve moved rapidly to reduce the federal funds rate to .25%. It was quickly judged that additional measures were needed to stabilize the US economy. Beginning in December 2008, the Federal Reserve Bank initiated three rounds of unconventional monetary policies known as quantitative easing (QE). These policies were intended to reduce long-term interest rates when the short-term federal funds rates had reached the zero lower bound and could not become negative. It was argued that the lowering of longer-term interest rates would help the stock market and thus the wealth of consumers. This article carefully investigates three hypotheses: QE impacting long-term interest rates, QE impacting the stock market and QE impacting unemployment using a Markov regime switching methodology. We conclude that QE has contributed significantly to increases in the stock market but less significantly to long-term interest rate and unemployment

Are There Rational Bubbles in the US Stock Market? Overview and a New Test

A speculative bubble is usually defined as the difference between the market value of a security and its fundamental value. Although there are several important theoretical issues surrounding the topic of asset bubbles, the existence of bubbles is inherently an empirical issue that has not been settled yet. This paper reviews several important tests and offers one more methodology that improves upon the existing ones. The new test is applied to the annual US stock market data spanning over a century and at the monthly frequency covering the post-war period. Although we find evidence of stock price bubble in both cases, the post-war period exhibit only positive component whereas the annual data exhibit some episode of negative bubble

Speculative Non-Fundamental Components in Mature Stock Markets: Do They Exist and Are They Related?

Economists have long conjectured that movements in stock prices may involve speculative components, called bubbles. A bubble is defined as the difference between the market value of a security and its fundamental value. The topic of asset bubbles remains controversial because the existence of a bubble is inherently an empirical issue and no satisfactory test has yet been devised to estimate the magnitude of a bubble. This paper proposes a new methodology for testing for the existence of rational bubbles. Unlike previous authors, we treat both the dividend that drives the fundamental part and the nonfundamental process as part of the state vector. This new methodology is applied to the four mature markets of the US, Japan, England, and Germany to test whether a speculative component was present during the period of January 1951 to December 1998 in these markets. The paper also examines whether there are linkages between these national speculative components. We find evidence that the nonfundamental component in the US market causes the other three markets but we find no evidence for reverse causality

Quantitative Easing and the U.S. Stock Market: A Decision Tree Analysis

The Financial Crisis of 2007-09 caused the U.S. economy to experience a relatively long recession from December 2007 to June 2009. Both the U.S. government and the Federal Reserve undertook expansive fiscal and monetary policies to minimize both the severity and length of the recession. Most notably, the Federal Reserve initiated three rounds of unconventional monetary policies known as Quantitative Easing. These policies were intended to reduce long-term interest rates when the short term federal funds rates had reached the zero lower bound and could not become negative. It was argued that the lowering of longer-term interest rates would help the stock market and thus the wealth of consumers. This paper investigates this hypothesis and concludes that quantitative easing has contributed to the observed increases in the stock market’s significant recovery since its crash due to the financial crisis

What Has Driven the U.S. Monthly Oil Production Since 2009? Empirical Results from Two Modeling Approaches

From the early 1970s to the Global Financial Crisis of 2007–09, U.S. crude oil production followed a declining trend. After the Global Financial Crisis, U.S. crude oil production increased rapidly. This paper addresses the important question “what economic factors have driven U.S. crude oil production since the Global Financial Crisis?”. We propose that factors such as: the price of oil, the one period lagged price of oil, the price of copper, the crude oil price volatility, the Trade Weighted U.S. Dollar Index, and the high yield index spread, are important explanatory variables. Using two modeling approaches, namely, multiple regression, and the random tree methodology, we conclude that the one month lagged price of oil is the most significant explanatory variable, among all considered, for the upward trend of U.S. oil production from 2009 to early 2020

Australia's national health programs: An ontological mapping

Australia has a large number of health program initiatives whose comprehensive assessment will help refine and redefine priorities by highlighting areas of emphasis, under-emphasis, and non-emphasis. The objectives of our research are to: (a) systematically map all the programs onto an ontological framework, and (b) systemically analyse their relative emphases at different levels of granularity. We mapped all the health program initiatives onto an ontology with five dimensions, namely: (a) Policy-scope, (b) Policy-focus, (c) Outcomes, (d) Type of care, and (e) Population served. Each dimension is expanded into a taxonomy of its constituent elements. Each combination of elements from the five dimensions is a possible policy initiative component. There are 30,030 possible components encapsulated in the ontology. It includes, for example: (a) National financial policies on accessibility of preventive care for family, and (b) Local-urban regulatory policies on cost of palliative care for individual-aged. Four of the authors mapped all of Australia's health programs and initiatives on to the ontology. Visualizations of the data are used to highlight the relative emphases in the program initiatives. The dominant emphasis of the program initiatives is: [National] [educational, personnel-physician, information] policies on [accessibility, quality] of [preventive, wellness] care for the [community]. However, although (a) information is emphasized technology is not and (b) accessibility and quality are emphasized cost, satisfaction, and quality are not. The ontology and the results of the mapping can help systematically reassess and redirect the relative emphases of the programs and initiatives from a systemic perspective

Radio Frequency Nonionizing Radiation in a Community Exposed to Radio and Television Broadcasting

Exposure to radio frequency (RF) nonionizing radiation from telecommunications is pervasive in modern society. Elevated disease risks have been observed in some populations exposed to radio and television transmissions, although findings are inconsistent. This study quantified RF exposures among 280 residents living near the broadcasting transmitters for Denver, Colorado. RF power densities outside and inside each residence were obtained, and a global positioning system (GPS) identified geographic coordinates and elevations. A viewshed model within a geographic information system (GIS) characterized the average distance and percentage of transmitters visible from each residence. Data were collected at the beginning and end of a 2.5-day period, and some measurements were repeated 8–29 months later. RF levels logged at 1-min intervals for 2.5 days varied considerably among some homes and were quite similar among others. The greatest differences appeared among homes within 1 km of the transmitters. Overall, there were no differences in mean residential RF levels compared over 2.5 days. However, after a 1- to 2-year follow-up, only 25% of exterior and 38% of interior RF measurements were unchanged. Increasing proximity, elevation, and line-of-sight visibility were each associated with elevated RF exposures. At average distances from > 1–3 km, exterior RF measurements were 13–30 times greater among homes that had > 50% of the transmitters visible compared with homes with ≤ 50% visibility at those distances. This study demonstrated that both spatial and temporal factors contribute to residential RF exposure and that GPS/GIS technologies can improve RF exposure assessment and reduce exposure misclassification