The impact of macroeconomic announcements on stock prices: in search of state dependence.

• Author: Poitras, Marc

1. Introduction

   Federal bureaus make regularly scheduled public announcements of macroeconomic variables such as employment, the price level, and the money stock. The impact of these announcements on financial markets has received considerable attention in both the popular press and the academic literature. For instance, as noted by Krueger (1996), virtually every time since 1983 that the Bureau of Labor Statistics has released employment data, on the following day, the New York Times has cited the release as influencing the bond or stock market. The emphasis on announcement effects in the popular press, however, relies merely on anecdotal evidence. In contrast, numerous academic studies have more formally analyzed the effects of announcements by employing econometric methods. In particular, Schwert (1981) and Urich and Wachtel (1984) analyze the effects of inflation announcements, while Pearce and Roley (1985) and Jain (1988) examine the effects of several different announcement variables. Such studies typically find statistical evidence that financial markets do respond systematically to macroeconomic announcements.

   Recently, a line of research has emerged that seeks to establish a relationship between the impact of scheduled announcements on financial markets and the state of the business cycle. Specifically, Boyd, Jagannathan, and Hu (2001) conclude that an announcement of an unexpected increase in the unemployment rate tends to decrease stock prices during economic contractions but to increase stock prices during economic expansions. Similarly, Adams, McQueen, and Wood (1999) maintain that the impact of an inflation announcement on prices of large cap stocks depends on whether the economy's state of economic activity happens to be high or low. McQueen and Roley (1993) find similar state dependence in the effect of several different announcement variables on the Standard and Poor's (S&P) 500. Inevitably, the idea of state dependence of announcement effects has also found its way into the popular press (Mehring 2001).

   This study employs the set of variables used by McQueen and Roley to reexamine the evidence of state dependence of announcement effects. The analysis is extended in two directions. First, the data are updated by 10 years, which nearly doubles the number of available observations. The larger sample is desirable because most of the inferences in McQueen and Roley (1993) are based on relatively modest numbers of observations. Second, to establish the robustness of results, the study employs several alternative approaches to defining the state of the economy. Defining the state of the economy proves to be a rather subjective endeavor. The study finds, however, that, regardless of definition, the estimated effect of announcements on the S&P 500 does not significantly differ across economic states. This result is reinforced by the application of a general test for coefficient stability due to Nyblom (1989) and Hansen (1992). The findings cast doubt on the robustness of the results of previous studies and imply that state dependence of announcement effects cannot be considered an empirical stylized fact.

   Like previous studies, we do find that announcements have a correlation with prices in financial markets that is statistically significant. But the relationship found, while statistically nonzero, is not a close one. The announcement variables we examine explain together less than 2% of the variation of daily changes in the S&P 500, and no individual variable (other than the discount rate) explains more than about 3% of the variation occurring on its own announcement days. Notwithstanding the considerable attention given to announcements, their weak explanatory power calls into question their economic significance.

   The article proceeds as follows. Section 2 discusses the data and the empirical model. Section 3 explores the issue of how to define economic states. Section 4 presents the results of stability tests that use slope-shift dummy variables to define specific breakpoints. In contrast, the tests in section 5 are based on a definition of the economic state that varies continuously. Section 6 yields additional results by applying Nyblom-Hansen stability tests, which do not require specification of the economic state. Finally, section 7 provides concluding remarks.

2. Data and Empirical Model

   The existing empirical literature motivates the state-contingency hypothesis by making an argument based on a positive Phillips-curve relationship between economic activity and inflation. The argument states that, when economic activity is high, an announcement that employment and production are rising implies that the economy might be overheating. This leads the public to expect higher future inflation and interest rates, which causes stock prices to fall. On the other hand, if the economy is experiencing a downturn, an announcement of rising employment and production is good news for stocks because it implies economic recovery and higher future earnings. Hence, the impact of a macroeconomic announcement on the stock market is state contingent. More formally, Veronesi (1999) develops an intertemporal, rational expectations model in which one of two possible economic states, high or low, prevails but is unobserved. Investors form a posterior probability, [pi](t), that, at time t, the high state prevails. When investors perceive a high state, [pi](t) [congruent to] 1, the arrival of bad macroeconomic news not only decreases expected future dividends but also lowers [pi](t) closer to 0.5, which implies greater uncertainty regarding the economic state. This additional source of risk means that risk-averse investors require deeper discounts in asset prices, so that asset prices fall by more than just the present value of expected dividends. In the low state, [pi](t) [congruent to] 0, uncertainty increases ([pi](t) moves closer to 0.5) upon the arrival of good news. In this case, asset prices increase by less than the increase in the present value of expected dividends. Thus, assets prices are less elastic with respect to news in the low state than in the high state.

   To test the hypothesis of state contingency, I examine the effect of several different announcement variables on the S&P 500 index. Specifically, I follow McQueen and Roley (1993) by considering the following eight announcement variables: the consumer price index, the producer price index, the unemployment rate, total nonfarm employment, the index of industrial production, the U.S. trade balance on goods and services, the MI money stock, and the Federal Reserve's discount rate. Although government bureaus announce many other variables, the aforementioned variables are among the most closely watched and analyzed, and they encompass a wide spectrum of macroeconomic and policy issues. (1)

   The econometric model takes the form

   (1) S[P.sub.t] = [alpha] + [[alpha].sub.L][Low.sub.t] + [[alpha].sub.H][High.sub.t] + [a.sup.a.sub.t][beta] + [a.sup.u.sub.t][gamma] + [Low.sub.t][a.sup.a.sub.t][[beta].sub.L] + [Low.sub.t][a.sup.u.sub.t][[gamma].sub.L] + [High.sub.t][a.sup.u.sub.t][[beta].sub.H] + [High.sub.t][a.sup.u.sub.t][[gamma].sub.H] + [D.sub.t][delta] + [epsilon].sub.1,t].

   The dependent variable, S[P.sub.t], is defined as the percentage change in the daily closing value of the S&P 500 index. To indicate whether the state of the economy is high or low, we define dummy variables [Low.sub.t] and [High.sub.t]. When these dummy variables equal zero, the state of economic activity is defined as "medium." The row vectors [a.sup.a.sub.t] and [a.sup.u.sub.t] contain the anticipated and unanticipated components of the macroeconomic announcements for day t. In order to allow the estimated effects of announcements to vary with economic states, Equation 1 includes terms that interact the announcement variables with dummy variables [High.sub.t] and [Low.sub.t]. We denote the coefficients of the announcement variables with parameter vectors [beta] and [gamma] and denote the coefficients of the interaction variables [[beta].sub.L], [[beta].sub.H], and [[gamma].sub.L], [[gamma].sub.H]. Finally, [D.sub.t] represents a vector of dummy variables indicating the day of the week, [delta] is a vector of coefficients, and [[epsilon].sub.1,t] is a random disturbance with mean zero.

   Let [a.sub.i,t] denote day t's actual announcement of variable i. The announcement takes the form of a percentage change occurring since the variable's previous announcement date. We define the anticipated component of the percentage change, [a.sup.a.sub.i,t], to be the mathematical expectation of day t's actual announced percentage change, conditional on information available on day t - 1,

   (2a) [a.sup.a.sub.i,t] = E[[a.sub.i,t] | [I.sub.t-1].

   The difference between the actual and anticipated announcements defines the unanticipated announcement,

   (2b) [a.sup.u.sub.i,t] = [a.sub.i,t] - [a.sup.a.sub.i,t].

   The need to distinguish between anticipated and unanticipated components of announcements arises from the theory of efficient markets. In an efficient market, prices reflect the information set, [I.sub.t-1]. Accordingly, only information that is new on day t can move stock prices on day t. Thus, the anticipated component on day t can have no effect because it reflects only information available on day t-1, which is not news on day t. The definition of market efficiency implies that only the unanticipated component can affect stock prices.

   An issue generally overlooked by the literature on announcements, however, is whether announcements could ever be unanticipated in an efficient market. According to so-called strong versions of market efficiency, market prices reflect all available information, public or private (see Ross 1987). No additional information is revealed by government announcements because they typically reflect only existing information gathered by government survey. For instance...