Ningzhong Li, Scott Richardson, and Irem Tuna
Macro to Micro: Country Exposures, Firm Fundamentals and Stock Returns
Journal of Accounting and Economics | Volume 58, Issue 1 (Aug 2014), 1-20

In this paper we examine whether information about a company's geographic (macroeconomic) exposure is useful for forecasting firm fundamentals and stock returns. While the link between firm operating and investing decisions and broader macroeconomic features seems relevant for forecasting, surprisingly little archival, empirical research has examined these relations. Indeed, with an increasingly inter-connected system of economic and financial markets across developed and developing countries, understanding the macroeconomic landscape is important for security valuation.

We outline a systematic approach to incorporate macroeconomic information into firm level forecasting from the perspective of an equity investor. Using a global sample of 198,315 firm-years over the 1998-2010 time period, we find that combining firm level exposures to countries (via geographic segment data) with forecasts of country level performance, generates superior forecasts for firm fundamentals. This result is particularly evident for purely domestic firms. We further find that this forecasting benefit is associated with future excess stock returns. These relations are stronger after periods of higher dispersion in expected country level performance.

Combining country exposures to form a firm level forecast

We consider how each company is exposed to its home country and other countries. We identify country exposures via our own manual coding of the geographic segment sales disclosures in Compustat and FactSet Fundamentals and the geographic sale data collated by FactSet Geographic Revenue Exposures. For each firm-year observation we disaggregate total sales into country level sales based on the geographic segment data in the most recent annual report. We retain companies with a purely domestic footprint (i.e., those companies with zero foreign sales) as this allows us to more cleanly assess the importance of macroeconomic information. For example, if firm A has 50 percent of its sales in Germany and 50 percent of its sales in Greece and Firm B has 100 percent of its sales in Greece, and Greece is expected to outperform Germany, then holding all else equal, the `best' portfolio exposure to express that view would be via Firm B, the purely domestic firm.

After gathering the sales data for firm i, for each country c, at each point in time t, Salesi,t,c, we standardize these sales measures so that they sum to one. We then use our forecasts of expected country level performance for each county c at each point in time t, E[Performance]c,t to construct a firm level measure of exposure to expected macroeconomic performance. We use forecasts of real GDP growth from Consensus Economics as our primary measure of expected country level performance. To generate a company specific fundamental forecast, we take the sum-product of Salesi,t,c and E[Performance]c,t which we label MACRO_{i,t}(i.e, MACRO_{i,t}= sum_c^1 Sales_{i,t,c}  cdot E[Performance]_{c,t} This measure captures both cross sectional and time series variation in firm level sensitivities to macroeconomic (country level) performance drivers.

In our full sample, we group together the `domestic' and `non-domestic' firms. However, we also separately examine the importance of macroeconomic information for these two groups. A potential benefit of examining `domestic' and `non-domestic' firms separately is to highlight two related effects. First, investors and analysts may be ignoring macroeconomic information in general. If this is true, then we should see strong predictive ability for the domestic only sample. Second, investors and analysts may be ignoring information in the differential geographic reach of companies. If this is true, then we should also see predictive ability for the `non-domestic' sample.

Predictive power of MACRO for future firm profitability

For a sample of 198,315 US and non-US firm-year observations over the 1998-2010 period, we find that combining country exposures with country level forecasts (MACROi,t) improves forecasts of firm fundamentals. The predictive power of MACROi,t is evident in annual regressions, which suggest that a one percentage point increase in expectations of real GDP growth translates to an additional 27 basis points of return on net operating assets (RNOA) over the next year. The predictive power of MACROi,t is robust to including a wide set of explanatory variables, including sell-side analyst forecasts.

Our sample includes 135,974 `domestic' firm-year observations with exposure only to their home country, and 62,341 `non-domestic' firm-year observations that have exposures to multiple countries. We separately estimate the usefulness of country exposures and country level forecasts to improve forecasts of firm fundamentals for `domestic' and `non-domestic' firms. For domestic firms we find that MACROi,t is strongly associated with future RNOA: a one percentage point increase in expectations of real GDP growth translates to an additional 31 basis points of RNOA. For non-domestic firms we find that MACROi,t is weakly associated with future RNOA: a one percentage point increase in expectations of real GDP growth translates to an additional 20 basis points of RNOA.

There are two distinct effects driving the positive relation between MACROi,t and future firm performance. First, we find that forecasts of real GDP growth are useful in forecasting future firm performance for domestic firms. This result does not require the use of potentially noisy geographic segment data. To the best of our knowledge this is a new result in the literature. Second, we find that forecasts of real GDP growth are also useful (albeit less strongly) in forecasting future firm performance for non-domestic firms. This result does require the use of potentially noisy geographic segment data. However, additional tests suggest that despite the potential measurement error in the geographic segment disclosure data, they are still useful to equity investors interested in forecasting future firm performance. Specifically, we compare our measure of geographic exposure to `naive' alternatives which ignore the information about the countries from which sales are sourced. We show that these naive measures have no ability to forecast future firm fundamentals. Thus, despite the measurement error in our geographic exposures based on subjective, and potentially inconsistent, geographic categories by management, they are superior to ignoring the level of disaggregation provided in geographic segment sales disclosures.

Predictive power of MACRO for future stock return

We also show evidence that stock returns appear to incorporate the information in geographic exposures with a lag. We sort firms into quintiles each month based on MACROi,t and compute value weighted raw returns, as well as value weighted characteristics adjusted returns, to a dollar neutral hedge portfolio. As shown in Table 1, in the full sample, the average monthly raw (characteristics adjusted) return to this hedge portfolio is 1.28% (1.15%), which translates to an annualized Sharpe ratio of 0.85 (0.93).

1: Monthly Returns to Hedge Portfolios Formed on MACRO
Raw Hedge Return Characteristics-Adjusted
All High Disp Low Disp All High Disp Low Disp
Avg Monthly Return 1.28% 2.08% 0.47% 1.15% 1.61% 0.69%
T-stat 3.00 4.18 1.45 3.26 4.17 2.23
Ann. SR 0.85 1.18 0.41 0.93 1.19 0.63
N 148 73 73 148 73 73
Monthly returns and Sharpe ratios for various hedge portfolios.

When is the predictive power stronger?

We also show that the fundamental and return predictability of information contained in current country exposures and country level forecasts is greater after periods of increased dispersion in real GDP growth forecasts across countries. This suggests that when the information content of MACRO is high ex ante, there is a stronger predictive content. As indicated in Table 1, the return to the hedge portfolio formed on MACRO is much larger in the subsample of months with higher dispersion in real GDP growth forecasts across countries than that in lower dispersion months. We also show that the return predictability of MACROi,t is greater when there is greater ex post information content. For this test we split our sample based on the ex post forecast accuracy improvement of including our MACROi,t measure into forecasts of RNOA. Mechanically this partition identifies sub-samples where MACROi,t is (is not) useful for forecasting RNOA. Interestingly, the return predictability is only evident in the sub-sample where there is an ex post improvement in forecasting RNOA. This suggests that the return predictability we document is attributable to improved forecasts of firm fundamentals that the market does not price correctly.

A key contribution of this paper is to introduce a simple framework to identify and exploit linkages between firm performance and its potential macroeconomic drivers. Our paper is different from prior studies in that we (i) examine a different basis for identification of linkages (geographic location as opposed to industry membership or supply chain links), (ii) use explicit forecasts based on the links we identify (i.e., we focus on forecasts of real GDP growth), and (iii) demonstrate predictive ability for firm fundamentals and stock returns and link these results together.