Accounting and the Macroeconomy: The Case of Aggregate Price-Level Effects on Individual Stocks

Abstract

The author used financial statement analysis to examine systematic stock-valuation effects of aggregate price-level changes on individual companies, focusing on the implications for researchers and investment practitioners. Among other insights, he showed that (1) inflation-based investment strategies conditioned on available information resulted in significant risk-adjusted returns and (2) investing using the inflation effect on companies’ net monetary holdings resulted in insignificant abnormal hedge returns whereas investing using the inflation effect on companies’ nonmonetary holdings consistently yielded economically and statistically significant abnormal hedge returns. Taken together, the study sheds new light on the cross-sectional effects of inflation, with substantial implications for valuation.

This study sheds new light on the cross-sectional effects of inflation, which have substantial implications for stock valuation. I used financial statement analysis to examine systematic stock-valuation effects of aggregate price-level changes on individual companies, focusing on the implications for both researchers and investment practitioners. I developed inflation-adjustment procedures that are straightforward for investors to implement in real time for extracting the inflation effect on individual companies. I found that inflation-based investment strategies conditioned on information available to investors as of the initial investment and rebalancing dates result in significant risk-adjusted returns. I also investigated the sources of abnormal returns to inflation-based investment strategies. Specifically, I estimated two separate components of the inflation effect on individual companies, one based on only monetary holdings (using the net position of monetary holdings) and the other based on only nonmonetary holdings. Investigating the stock-valuation implications of extracting the components-based inflation effect revealed striking evidence. In particular, investing based on the inflation effect on companies’ net monetary holdings results in insignificant abnormal hedge returns. In contrast, investing based on the inflation effect on companies’ nonmonetary holdings consistently yields economically and statistically significant abnormal hedge returns. These findings indicate that inflation-based abnormal hedge returns are driven not by the exposure of companies’ net monetary holdings to inflation but, rather, by the exposure of their nonmonetary holdings to inflation. These results are consistent with the fact that companies’ nonmonetary holdings are usually held for several years and thus accumulate inflationary effects over time whereas their monetary holdings are, on average, naturally hedged because the exposure of monetary assets cancels the exposure of monetary liabilities for the average company. In addition, I examined the direction of the stock returns to real-time investment strategies.

I thank Andrew Ang, Scott Joslin, Reut Laufer, and Richard Sloan for helpful comments and suggestions. I am grateful for helpful information from Thomas Stark of the Federal Reserve Bank of Philadelphia and Yvetta Fortova of the Federal Reserve Bank of St. Louis.

Notes

¹ Inflation can affect future CFO because, for example, higher inflation gains accumulated in nonmonetary assets can result in higher future CFO when the assets are used (in the case of property, plant, and equipment) or sold (in the case of inventory). Further, because inflation is correlated with changes in specific prices, predicting higher future CFO from increases in the general price index is consistent with prior evidence that increases in specific prices result in higher CFO (e.g., Aboody, Barth, and Kasznik 1999). Indeed, my untabulated results suggest that monthly inflation rates are highly and significantly correlated with monthly changes in major indices of nonmonetary assets (commodities and housing), with Spearman and Pearson correlations as high as 70% for the past six decades of available data in the Global Insight database.

² Investing in portfolios of stocks with different degrees of exposure to inflation, rather than in the entire stock market, can improve inflation-risk hedging. Note that isolating company-specific inflation effects allows investors to extract information stemming from substantial heterogeneity across companies—heterogeneity that is lost when pooling all companies together in a market index.

³ See, for example, Chordia and Shivakumar (2005); Kothari, Lewellen, and Warner (2006); Ball, Sadka, and Sadka (2009); Hirshleifer, Hou, and Teoh (2009); Shivakumar (2010); Konchitchki (2011); Kothari, Shivakumar, and Urcan (2012); Li, Richardson, and Tuna (2012); Konchitchki and Patatoukas (forthcoming).

⁴ See, for example, Ou and Penman (1989); Lev and Thiagarajan (1993); Abarbanell and Bushee (1998); Nissim and Penman (2001); Konchitchki (2011); Patatoukas (2012); Curtis, Lundholm, and McVay (forthcoming). See also Penman (2013); Subramanyam (2013).

⁵ To conserve space, a supplementary appendix that provides a detailed discussion of the procedure for adjusting all accounting amounts—both monetary and nonmonetary holdings—to obtain inflation effects on individual companies can be downloaded from my website (https://sites.google.com/site/ykonchit; below the title of this study).

⁶ NetMonetaryHolding can be calculated by using two alternative definitions. The first definition is straightforward: NetMonetaryHolding = Current assets (Compustat: CA) – Inventories (Compustat: INVT) – Total liabilities (Compustat: LT + MIB). The second definition is less straightforward but consistent with the algorithm (e.g., it treats as monetary other monetary items that are in stockholders’ equity but are not in retained earnings): NetMonetaryHolding = Total assets (Compustat: AT) – Nonmonetary assets [Net PPE (Compustat: PPENT) + Inventories (Compustat: INVT) + Intangibles (Compustat: INTAN)] – Total liabilities (Compustat: LT + MIB) – Other monetary, where Other monetary is other monetary items in stockholders’ equity but not in retained earnings. Other monetary = Total assets (Compustat: AT) – Total liabilities (Compustat: LT + MIB) – Retained earnings excluding other comprehensive income effect [Retained earnings (Compustat: RE) – Accumulated other comprehensive income (Compustat: ACOMINC)] – [Common stock (Compustat: CSTK) + Preferred stock (Compustat: PSTK) + Capital surplus (Compustat: CAPS)]. To obtain NetMonetaryHolding with either definition, I scaled by the same deflator used to deflate InfEffect (Total assets, Compustat: AT). In the reported analyses, I used the second definition to be consistent with the algorithm, but both definitions result in unchanged inferences.

⁷ Following Hirshleifer et al. (2004), I obtained NOA as NOA = RawNOA/TotalAssets_t–1, where RawNOA = Operating assets – Operating liabilities; Operating assets = Total assets (Compustat: AT) – Cash and short-term investment (Compustat: CHE); and Operating liabilities = Total assets (Compustat: AT) – Debt included in current liabilities (Compustat: DLC) – Long-term debt (Compustat: DLTT) – Minority interests (Compustat: MIB) – Preferred stocks (Compustat: PSTK) – Common equity (Compustat: CEQ). Next, I formed RNOA, an NOA-based factor, by following Fama and French (1993). At the end of each month, I sorted all observations into two NOA groups, with Group 1 (2) including observations with low (high) NOA, and three book-to-market (BTM) groups, with Group 1 (3) including observations with low (high) BTM. I then constructed six portfolios (L/L, L/M, L/H, H/L, H/M, H/H) from the intersections of the two NOA groups and the three BTM groups, with the first letter in each X/X combination referring to the NOA portfolio (low, high) and the second letter referring to the BTM portfolio (low, medium, high). I then calculated monthly value-weighted returns on the six portfolios over the subsequent year, beginning three months after the fiscal year-end. I calculated RNOA for each month as the average of the monthly returns on the three high-NOA portfolios (H/L, H/M, H/H) minus the average of the monthly returns on the three low-NOA portfolios (L/L, L/M, L/H).

⁸ Therefore, what leads to future abnormal returns is whether investors understand the differential effect of inflation on monetary versus nonmonetary assets, rather than their understanding of expected versus unexpected inflation—which is different from investors’ failure in the current period to distinguish between expected and unexpected inflation. Specifically, when I estimate inflation-adjusted amounts, actual inflation is known because it is realized. Whether actual inflation is fully anticipated does not affect my predictions because investors use actual inflation, rather than its expected or unexpected components, to derive InfEffect. Further, although the distinction between expected and unexpected inflation is important when examining how changes in current-period earnings explain contemporaneous stock price changes, under the notion that stock prices respond to unexpected inflation during the year (a setting widely used in the research design of inflationary accounting studies during the 1970s and 1980s), my motivation and design are forward-looking. That is, in my study, the events flow such that current-period (year t) InfEffect is estimated first, and only in the subsequent period (t + 1) does this effect turn into cash flows. The subsequent returns thus do not arise from unexpected inflation that affects year t InfEffect.

⁹ To the extent that inflation effects are perfectly correlated over time, there may be no surprise component when these inflation effects turn into cash flows over time, and thus, there may be no theoretical link between InfEffect and future returns. However, when I calculated serial correlations in InfEffect over years t and t + 1, I found Pearson and Spearman correlations as high as 30%. These correlations indicate that inflation effects exhibit only weak persistence over time because they have low serial correlation. Thus, there is no systematic relation in InfEffect over time. These results are consistent with my expectation that inflation effects are likely to change over time because of the large variation in the composition of companies’ monetary and nonmonetary items over time.

¹⁰ This definition is explained in the supplementary appendix, which can be downloaded from my website (https://sites.google.com/site/ykonchit; below the title of this study).

¹¹ Indeed, I examined the mean and median InfEffect_NetMonetary and found that they are indistinguishably different from zero (absolute values less than 0.001), indicating that monetary assets are approximately equal to monetary liabilities for the average company over the sample period.

¹² Note that the return results are distinct from, and cannot be explained by, inflation illusion. The inflation illusion hypothesis (Modigliani and Cohn 1979) posits that highly levered companies are more undervalued owing to investors’ failure to incorporate gains accruing from purchasing power depreciation of nominal liabilities, or what Ritter and Warr (2002) referred to as the “debt capital gain error” (see also Wilcox 2007). Because the erosion of nominal liabilities leads to higher inflation gains, the direct effect of the inflation illusion hypothesis is higher (lower) future abnormal returns when inflation gains are high (low) because investors who suffer from inflation illusion are positively (negatively) surprised over future periods. Despite the offsetting effect of inflation illusion on my findings from the return analyses, however, my results are incremental to the inflation illusion effect because I found that future abnormal returns are negatively related to inflation gains. The inflation illusion hypothesis also posits that investors irrationally discount inflation-adjusted cash flows by using nominal interest rates. In contrast, here I investigated how inflation directly affects cash flows instead of how cash flows are discounted. In two additional tests, untabulated for brevity, I found the following. First, there is no evidence of a pattern in risk characteristics across portfolios sorted on companies’ inflation effects, and an inflation-based factor is not a priced risk factor (I constructed the factor following the two-step procedure in Fama and MacBeth 1973). These findings suggest that the abnormal returns I documented are not attributable to an omitted inflation-based risk factor. Instead, these findings are consistent with abnormal returns stemming from inflation information that is costly to obtain and process and, hence, are consistent with market efficiency under costly information. Second, examining future abnormal returns to portfolios sorted on the basis of companies’ cash holdings resulted in a significantly positive future abnormal return for a zero-cost portfolio that longs high-cash companies and shorts low-cash companies. Further analysis of future abnormal returns to each of the cash-holding portfolios revealed that the positive future abnormal return stems from only the highest-cash-holding companies.

¹³ For additional examples and reviews of related literature, see Kothari (2001); Bradshaw (2011); Bradshaw, Drake, Myers, and Myers (2012).