Value-relevance of Capital Expenditures and Business Cycle

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1 Value-relevance of Capital Expenditures and Business Cycle Charles Chen, Sungsoo Kim and Byunghwan Lee Working Paper Series WCRFS: 09-11

2 Value-relevance of Capital Expenditures and Business Cycle Charles Chen Department of Accountancy City University of Hong Kong/CEIBS 83 Tat Chee Ave Kowloon, Hong Kong Sungsoo Kim* School of Business Rutgers University Camden, NJ and Byunghwan Lee School of Business Administrtation California State Polytechnic University Pomona 3801 West temple Avenue, Pomona, CA January, 2009 *Corresponding author Preliminary draft. All errors are our own responsibility. Charles Chen, Sungsoo Kim and Byunghwan Lee acknowledge financial support from City University of Hong Kong, Rutgers School of Business-Camden and School of Business Administration, California State Polytechnic University Pomona, respectively.

3 Abstract We examine the relation between coporate capital investments and business cycles. Specifically, we test whether the stock market exhibits different reactions to corporate capital expenditures under different business conditions. It is natural to expect that managers adjust the extent and timing of their long-term capital expenditures to adjust their production capacity to meet the demand for their products in the market place in tandem with different business conditions. Using 33,146 firm-year observations over 20 year period we provide empirical evidence, consistent with our prediction, that US industial firms capital expenditures during an expansionary (contractionary or slump) business cycle are more (less) value-relevant, measured by buy-and-hold stock return, to the capital market participants. 2

4 1. Introduction Committing significant sum of firm s economic resources to a long-term capital investment is a fundamental, yet vitally critical managerial decision which entails both risk and rewards in the years following for the stakeholders including the firm, managers, investors, creditors, employees, among others. The magnitude as well as timing of capital investment is, therefore, critical for a firm to survive and thrive in a competitive business environment. One would expect that a manager would adjust the timing and the magnitude of capital expenditures as they gather more updated information in projecting the future demand for their products. One of the essential pieces of information managers need to consider is possible impacts of business conditions on their investments, as changes in investment environments are expected to be incorporated into their decision-making proecesses. In this paper we try to gain better understanding of a timing side of corporate capital investment. More specifically, we examine how stock market reacts to firm s capital investment under different business cycles. The answer to this valuation question is vital to those who seek value-relevant information in the capital market. The magnitude issue of the capital investment, however, has attracted relatively more attention from the literature than the timing issue (McConnel and Muscarella 1986, Lev and Thiagaranjan 1993, Kerstein and Kim, 1995). Borrowing extensively from economics and finance literature on the relationship between uncertainty and corporate investments we study differences in value-relevance of corporate capital expenditures during different business cycles. Although this relationship has been rigorously examined in the economics and finance literature (please see Pindyck 88, 89) the question of how the capital market values capital investments during different business cycles remains unanswered both theoretically and empirically, to the best of our knowledge. We predict and report empirical 3

5 findings as hypothesized that capital expenditures during an expansionary (slump or contractionary) business cycle will be more (less) value-relevant. Our test results from 32,146 firm-year observations from Computstat and CRSP ( ) lend overall support to this prediction. We contribute to the literature by empirically identifying business cycle/conditions as a valuation determinant of corporate capital investments. In particular, we investigate whether there are differences in value relevance of corporate capital expenditures between different business conditions by pairing two different types together in our OLS regressions. Exploring this question is of particular importance to capital market participants as they can garner invaluable information from corporate investing activities to make their informed investment decisions. The remainder of the paper is organized as follows. Section 2 provides literature review and hypothesis development. In section 3, we describe our sample, methodology, and empirical models. Section 4 presents our results including those of sensitivity tests. Finally, we offer our conclusions in Section Literature review and hypothesis development The relationship between investment and uncertainty has been extensively explored in the theoretical framework in eonomics and finance literature. Although there is little consensus as to how the changed business condition affects corporate investment spending, recent theoretical works speculate that increased uncertainty/changes in business conditions slow down irreversible investment spending, ceteris paribus (Caballaro 1991). It is assumed in the literature that cost of downsizing capital investments exceeds upward adjustment costs, suggesting undoing corporate investment entails unjustifiable costs. Going a step further, Ghosal and Loungani (2000) report not only negative relationship between uncertainty and investment, but also the negative impact is 4

6 substantially greater in industries dominated by small firms. It is reasonable to assume, therefore, that undoing capital investment is a costly option, especially for small firms whose capital investment could have more significant impact on their investing and financing activities than larger firms due to its economy of scale. As the downsizing cost exceeds the upward adjustment the corporate investment becomes de facto sunk costs in many cases. In particular, many capital investment projects are, by nature, not possible in installments, i.e., construction of new plant or purchase of large equipment. There have been few studies that examine the value-relevance of corporate investment in conjunction with the business conditions despite the significant body of investing and agency literature in economics and finance. In this paper we focus on differences in stock market reactions to capital investments under different business conditions. In order to make asset replacement/investment decisions a manager needs to asses both current and future states of the economy. Under the rational expectation framework (Sargent 2001), a manager possesses a precise knowledge about the laws of motion for the economy. In the context of investment decision assessing the relationship between current and future states correctly requires the knowledge of the underlying laws of motion for the economy. In the absence of foresight knowledge a manager natualy uses a heuristic to do so (Lee et. al, 2008). Historically, US economy shows much longer expansionary business cycles than contractionary cycle in the last forty years (NBER.org). Accordingly, it is a natural expectation that a contractionary business cycle should be eventually bound to be recovered. This suggests that contractionary cycles are more uncertain, unstable and/or temporary than expansionary business cycles. From this observation, one would expect to experience more uncertainty in predicting capital expenditures in a contractionary cycle than in an expansionary business cycle. 5

7 Agency aspect of corporate investments has been getting most attention from economics and finance researchers in the past. Agency costs arise, as the story goes, when there are conflicts of interest between corporate insiders, such as managers and shareholders with controlling interests, on the one hand, and outside investors, such as those who hold non-controlling shares, on the other hand (Jansen and Meckling, 1976). Mangers who control corporate assets have a wide range of discretion as to how they utilize their assets, which, at times, could be detrimental to the interests of stakeholders outside of the firm. Insiders, for instance, can use corporate assets to pursue corporate investment strategies which are primarily designed to benefit their personal interests at the expense of shareholders. Agency problems manifest themselves primarily through non-value maximizing investment choice (La Porta et. al., 2000). Using plant level data, Cooper et. al. (1999) provide theoretical property as well as empirical findings that the investment spikes are more likely to be procyclical. Along the same line of research Ees et. al. (1997) examine Dutch manufacturing data and conclude the impact of liquidity constraint increases if the economy goes into a recession, to which firms respond to the changes in business condition with reduced investments. If the general sentiment in the capital market is such that a downturn (upturn) is expected, then acting upon such belief might results in liquidity constraints and, in turn, reduced (increased) investments. Turning on the body of literature on the valuation effects of capital investment, the majority of studies use the event study methodology. McConnell and Muscarella (1985), for example, report that increases (decreases) in capital spending is a good (bad) news to stock market, resulting in a positive (negative) excess returns. While the evidence reported in the crosssectional valuation studies of capital spending is mixed (Chung, Wright, and Charoenwong 1998, Chen and Ho 1997, Vogt 1997, and Blose and Shieh 1997), it is worthy to mention the work of 6

8 Lev and Thiagaranjan (1993), who demonstrate the cross-sectional value relevance of capital spending in association study context; the capital investment above industry (SIC 2-digit) average results in positive excess returns. Going a step further in spirit and methodology Kerstein and Kim (1995) estimate, employing all US manufacturing firm sample, the capital spending response coefficient and report incremental information contents contained in capital spending above and beyond that contained in earnings. In addition to controlling for the size-related pre-disclosure information environment they use mediating variables by interacting earnings and capital spending with growth and risk. What s conspicuously missing from the above literature is the effect of business condition on the cross-sectional valuation of capital spending. Our primary motivation is, therefore, to gain a better/additional understanding of the cross-sectional difference in market response to one of the most basic managerial decision, the amount the firm decides to commit to capital spending in different business condition. Our hypothesis is, therefore, a natural consequence of blending the valuation of capital spending together with business conditions. Formally stated; H1: Stock market exhibits less (more) value-relevance to corporate capital investment during contraction or slump (expansion) period. 3. Sample, methodology and empirical models The source of our data is the 2007 Compustat Annual Industrial and Research File, CRSP database. We exclude utility firms (SIC Code 4000s) and financing firms (SIC 6000s). We exclude firms in these industries to maintain homogeneity in our sample as these types of firms are expected to exhibit differences in investing, capital structure and other characteristics from the rest of the sample industries. We impose our sample firms to have December fiscal year-end to be consistent with the previous valuation literature (Kerstein and Kim, 1995) and to match our dataset 7

9 with NBER use of calendar year for business cycle. This procedure, we believe, decreases the probability of confounding inference from the empirical findings by mixing different calendar year firms. NBER's Business Cycle Dating Committee (here in after, the Committee ) announces business cycles. For example, on November 26, 2001 (July 16, 2003), the committee determined that the peak of (a trough in) economic activity occurred in March 2001 (November 2001). The committee defines expansion period between trough to peak period. In 1990s and early 2000s, an expansion period lasted exactly 10 years in 1990 s (March 1991 to March 2001). The committee defines contraction period between peak to trough period. In early 2000s, an expansion period lasted 9 months (March 2001 to November 2001). Last four decades, expansion periods lasted much longer than contraction periods. An expansion period lasted several years while a contraction period lasted less than a year on average. (See NBER s announcement of US Business Cycle Expansions and Contractions from Our study uses annual data which may not fit for the (NBER s definition of) contraction period which is less than one year. Furthermore, the committee makes a post facto of peak and trough. Especially, a peak of November 2001 was announced on July 16, 2003, which implies that economy in general was suffering from March 2001 (peak) until (at least) July 16, 2003, the date of announcement of trough in November In short, we expect that economic contraction period should be much longer (at least based on the people s perception of economic status) than the contraction period defined by the Committee. In our study, therefore, we made the following adjustments to NBER classification. SLUMP is a diminished period in which one can combine contraction period and early stage of recovering period but still below economic activity level compared to prior peak level since trough. 8

10 SLUMP includes years of 1982, 1983, 1990, 1991, 1992, 2001, 2002, and The Committee s announcement on November 26, 2001 of business-cycle peak of March 2001 includes FQA s section in which SLUMP is defined as diminished period. Our classification of SLUMP period follows the Committee s definition of SLUMP. GROW (Growth period) includes years of steady growth period after the economic activity level is above compared to pre-peak level since trough. GROW includes years of 1984, 1985, 1986, 1987, 1993, 1994, 1995, 1996, 1997, 1998, 2004, and MATU (Matured Period) contains stable stage with diminishing growth which includes 1988, 1989, 1999, and Maturity period is arbitrarily assigned as two consecutive years just before the peak year. We compare SLUMP period with GROW and MATU period. We adjust contraction period as the years of the peak-to-through period (1982, 1990, and 2001) in order to capture the economic downturn from a peak. Our contraction period, which is a subset of SLUMP period, is similar to the Committee s contraction period because the latter lasted less than a year in our sample period. We modify expansion period as the years prior to the peak after the SLUMP period in order to reflect the good economic status in people s perception. Thus, our definition of expansion includes GROW and MATU period. When we compare contraction period with expansion, we exclude early stage of recovering period from SLUMP, i.e., year(s) after trough year, which by NBER s definition, belongs to expansion period. In this comparison, we exclude samples of 1983, 1991, 1992, 2002, and To the best of our knowledge our adjustment best serves to analyse market response on capital investment across the business cycles. We require a sample firm to have market value of equity of last-year-end (Compustat #24, #25), capital investment of last- and current-year-end (#128), net income before extraordinary item of last- and current-year-end (#18), book value of equity of the last year-end (#60), and market return from CRSP for each firm. We include firms with stock that are traded in NYSE, 9

11 AMEX, and NASDAQ only. We exclude firms of ADR stocks. The final sample in our analyses includes a total of 33,146 firm-year observations from 1982 to We also winsorize the top and bottom one percent of continuous variables in our models to remove the effect of outliers from the analyses 1. We follow NBER classification of business cycles as described above. In addition to examining inherent differences we use following pooled cross-sectional OLS regression to further investigate the market response (RET) 2 on the change of capital investment (CAPCH), earnings change (NICH) and other variables. Model 1: =a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2: RET =a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e; for b 5, b 6, and b 7 : BC =1 if SLUMP; else 0. Model 2-1: RET =a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e; for b 5, b 6, and b 7 : BC =1 if Contraction period; else 0. Model 3: RET =a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e; For b 5, b 6, and b 7 : BC =1 if GROW; else 0. Model 4: RET =a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e; For b 5, b 6, and b 7 : BC =1 if MATU; else 0. Model 5: RET =a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC1 + b 6 (BC1 * CAPCH) + b 7 (BC1 * NICH) + b 8 BC2 + b 9 (BC2 * CAPCH) + b 10 (BC2 *NICH) + Σ INDUSTRY + e; For b 5, b 6, and b 7 : BC1 =1 if SLUMP; else 0 and for b 8, b 9, and b 10 : BC2 =1 if GROW; else 0. 1 The results are qualitatively equivalent when we do not remove the top and bottom 1 % of the variables in our model. 2 RET is stock return measured by 12 month buy-and-hold abnormal market return of fiscal year after adjusted by equally weighted market index. We use monthly market return data from CRSP database to calculate buy-and-hold abnormal return. 10

12 We expect capital expenditure in SLUMP (coefficient b 6 ) to be less value-relevant (negative) in Models 2. We also anticipate that capital expenditure in GROW, MATU (coefficient b 6, b 10 ) in model 5 to be more value-relevant (positive) compared to SLUMP, respectively. We also expect capital expenditure in Contraction (coefficient b 6 ) to be less value-relevant (negative) in Models Results Panel A of Table 1 reports the yearly distribution of firm-year observations from 1982 to Numbers of firm-year observations are generally on the increase over the years as the membership of Compustat firms increases over time. Panel B of the same table shows the industry membership. More than 60% of the sample observations come from SIC 2000 s and 3000 s combined (manufacturing industries) 3, while both 0000 s and 9000 s produce less than 1 percent of the sample observations. Table 2 reports descriptive statistics on the variables used in the regression models 1 to 5. The figures here are generally consistent with those reported in the prior literature. Mean (median) value of of RET are slightly above (below) the zero. Both mean and median value of CHCAP and NICH are slightly above zero. Both MVE and BTM report comparable statistics with prior literature. Table 3 report univariate mean difference t-test results for the key variables used for a pair of business cycles. In Panel A, we first compare SLUMP with GROW, and find significant differences between these two business cycles for all the variables used. SLUMP cycle reports lower market returns, lower capital expenditures and firm size than GROW cycle as expected. This makes sense as GROW cycle is expected to have higher capital expenditures with less 3 Firms in this range are expected to exhibit more homogenous pattern of capital investmnent as they have more capital intensive oparations than others. 11

13 uncertainty, and therefore more positive reactions (Lev and Thiagaranzan 93, Kerstein and Kim 95), resulting in an increase in firm size. Furthermore, SLUMP cycle reports higher BTM than that of GROW cycle, which is expected because SLUMP cycle will show smaller firm size measured by market value of equity and therefore higher BTM compared to those of GROW cycle where market value of equity is a denominator in BTM. It is interesting to find that SLUMP cycle reports higher earnings change than that of GROW cycle in mean value, though it is marginal (p-value > 0.01). We interpret this result in the way that firms show dramatic decrease in earnings in the earlier stage of SLUMP cycle, especially with the effect of big bath of earnings in bad period, and that firms show opposite happenings in the later stage of SLUMP cycle, that is, earnings dramatically increase after the earlier stage of SLUMP cycle (and after big bath). Therefore, the average earnings increase in SLUMP cycle is higher than that in GROW period in which earnings might not dramatically increase. In contrast with this SLUMP versus GROW mean comparison of earnings change in Panel A, contractionary period reports lower earnings change than that of expansionary period both in mean and median value in Panel B as expected. We conjecture that firms in contraction period will show big bath thus (big) earnings decrease in general while firms in expansion period will not. Comparison between SLUMP and MATU in Panel A yields similar patterns to comparison between SLUMP and GROW except for that of earnings change in mean value; and firm size measured by MVE. We find that earnings change in mean value is not significantly different between SLUMP and GROW cycle (p-value > 0.1). We conjecture that the big bath in earlier SLUMP stage and the big jump in earnings in later SLUMP period as explained earlier will result in the finding above. The difference of firm size (MVE) is not significant with p-value of 0.1 for both mean and median. In contrast with this SLUMP versus MATU comparison of firm size (MVE) in Panel A, contractionary period reports lower MVE than that of 12

14 expansionary period in Panel B as expected. Firms in contraction period show big MVE decrease while firms in expansion not. After the dramatic decrease in MVE in contraction period, i.e., in the earlier stage of SLUMP cycle, firm size will increase in the later stage of SLUMP cycle. Therefore, in overall, firm size in both SLUMP and MATU period may not be significantly different as in the Panel A. Finally, GROW cycle reports less returns, less BTM, but invest more long-term capital expenditures than MATU cycle. Firm size (MVE) in GROW cycle is marginally bigger than that in MATU cycle with p-value of 0.1 in median median value while such difference is not significant in mean value. These differences are further examined later with pooled cross-sectional regressions in a multivariate context. We conduct a sensitivity test for an alternative classification of business cycles in Panel B of Table 3. We compare expansion (GROW + MATU) 4 period with contraction period (1982, 1990, 1991) which includes only early stage of the economic down-turn period from SLUMP, thus we remove the transition period, i.e., the later stage of SLUMP cycle. Contraction period reports less returns, less (change in) capital expenditures, less earnings change, less firm size, but higher BTM than expansion period. The results are qualitatively the same as the prevous comparisons between SLUMP and GROW (MATU), except for the fact mentioned in the above. Firms commit less capital investment during the contraction period than expansion period. Table 4 reports Pearson and Spearman correlation coefficients among variables used in our regressions. Although most coefficients are statistically significant none seems to be economically significant except, possibly, for return-earnings relation and the reverse (negative) relations of BTM-MVE. 4 We define contraction years for , and expansion years for both Growth years (1985, 1986, 1987, 1993, 1994, 1995, 1996, 1997, 1998, 2004, 2005) and Maturity years (1988, 1989, 1999, 2000). Years of 1983, 1991, 1992, 2002, 2003 are deleted. 13

15 Model 1 of Table 5 shows our regression of buy-and-hold returns, our dependent variable of market return, on the variables that we analyzed in Tables 3 and 4. Model 1 of Table 5 shows that market responds poswitively to CAPCH as well as NICH. We include the business cycle dummy in order to control for the overall market return difference between the different business cycles, and other controlling variables as our predictor varibles in model 2 of table 5. The coefficient of the interaction of SLUMP dummy and CAPCH (b 6 ) becomes negative and significant in these two model 2, suggesting there is lower market reaction to capital investments during slump cycle. Specifically, in model 2 of Table 5, b 6 (b 7 ) the interaction of SLUMP cycle dummy and CAPCH (NICH) shows significantly negative coefficients which is the incremental effect of CAPCH (NICH) over market returns in SLUMP cycle, which implies market responds less seriously to CAPCH (NICH) in SLUMP cycle than in other cycles. Still market positively responds to CAPCH (NICH) in SLUMP cycle as in model 2 of Tabgle 5 (coefficient of CAPCH (NICH) in SLUMP cycle is b 1 +b 6 (b 2 +b 7 ), i.e., = ( = 0.529)). We report an opposite result for MATU period in model 4; market more seriously responds to CAPCH in MATU cycle than that in other cycles because the interaction of MATU cycle dummy and CAPCH shows significantly positive coefficients (b 6 ), which is the incremental effect of CAPCH over market returns in MATU cycle from those effects in all the other periods. Market responds more seriously to NICH in MATU cycle than that in other cycles as shown by the coefficient of the interaction of MATU cycle dummy and NICH which is significant (b 7, p-vlaue < 0.01). This is qualitatively same capital expenditure spending during GROW cycle as reported in model 3 when firms spend more capital investments as they try to meet increasing demand for their products during the growth stage. b 7 (b 6 ), the incremental effect of NICH (CAPCH) on 14

16 market returns in GROW cycle is (not) significant, while the direction is positive as expected. Our findings suggest that there is (not significantly) higher market reactions to earnings change (capital investments) during growth cycle than those in other cycles. When we put both GROW and MATU in model 5. b 6 (b 7 ), incremental effect of CAPCH (NICH) on market returns in GROW cycle compared to the effect of CAPCH (NICH) on market returns in SLUMP cycle is significant and positive, which means that market more significantly responds to CAPCH (NICH) in GROW cycle than in SLUMP cycle. This is also the case during MATU cycle as reported by the coefficirnt of b 9 (b 10 ) in model 5. In sum, market more seriously responds to CAPCH (NICH) in GROW and MATU cycle than in SLUMP. We also run the same models without adjacent years between two cycles of contraction and expansion. The transition period may obscure our test results by double-counting adjacent years. We exclude the later years of SLUMP cycle to dichotomize the sample years into two groups; contraction (1982, 1990, 2001) and expansion which includes both grow (1984, 1985, 1986, 1987, 1993, 1994, 1995, 1996, 1997, 1998, 2004, and 2005) and maturity (1988, 1989, 1999, and 2000). Model 2-1 of Table 6 shows the same test results regarding capital expenditure as in model 2 of Table 5. That is, market responds less seriously to CAPCH (NICH) in contraction cycle than that in expansion cycle, nevertheless market positively responds to CAPCH (NICH) in contraction cycle. Our test results from model 2, 5 (2-1) of Table 5 (6) support out hypothesis in that stock market exhibits less value-relevance to corporate capital investment during contractionary (slump) period than expansionary (growth or maturity) period. We perform several sensitivity tests. Following Hyan (1995) we run regression models in tables 5 and 6 for profit and loss firms, separately. The value relevance of capital investment, 15

17 given differences in risk, growth and the configuration of cash flows between two groups, is expected to exhibit contrasting pattern. We repeat the same models in table 5 here in table 7 for profit firms only to see if having profit only has a different impact on the reaction. The overall results from profit firms are generally stronger than the results from all samples, but produce qualitatively the same results. When we regress returns on the same variables, without adjacent years in table 7-1, capital investment during contraction period exhibits significantly lower (and negative) market reactions for profit firms. Tables 8 (all firms) and 8-1 (without adjacent years) show the regression results for loss firms which produce a similar results to those reported in Table 5 through 7-1 regarding market response on cspital investment but not on earnings change. The size of the coefficients the interaction of the industry dummy and CAPCH is slightly higher (in absolute value) than those of profit firms, although t-value and therefore p-value of these coefficients of the interaction are slightly lower (in absolute value) than those of the profits firms. Nevertheless, the coefficients of the interaction effect of business cycle of SLUMP, Contraction (GROW, MATU) with CAPCH are significantly negative (positive) in model 2, 2-1 (5) of table 7, 8, or 7-1, 8-1, which implies that investors, whether the current state is profit or loss, tend to focus on the future growth. 5. Summary and conclusion We investigate whether there are inherent differences in corporate investment patterns, and whether the stock market exhibits different reactions to the value-relevance of the capital expenditures in different business conditions. It is natural to expect that managers adjust their long-term capital expenditures to meet the demand for their products in the market place, in 16

18 tandem with business cycles. Using 33,146 firm-year observations over 20 year period we provide empirical evidence, consistent with our prediction, that US firms capital expenditures during an expansionary (contractionary or slump) business cycle are more (less) value-relevant, measured by buy-and-hold abnormal stock returns, to the participants in capital market. 17

19 Reference Bernanke Ben. S., Irreversibility, Uncertainty, and Cyclical Investment. Quarterly Journal of Economics. 98. No. 1. pp Blose and Shieh Tobin s Q-ratio and market reaction to capital investment announcements. The Financial Review 32. Issue 3. Pp Caballero, Ricardo J, On the Sign of the Investment-Uncertainty Relationship," American Economic Review, American Economic Association, vol. 81(1), pages , March. Campello, Murillo, Capital structure and product markets interactions: evidence from business cycle. Journal of Financial Economics 68. pp Chen, Sheng-Syan & Kim Wai Ho, 1997."Market Response to Product-Strategy and Capital- Expenditure Announcements in Singapore: Investment Opportunities and Free Cash Flow," Financial Management, Financial Management Association, vol. 26(3), Fall. Chung, Kee H. & Wright, Peter & Charoenwong, Charlie, Investment opportunities and market reaction to capital expenditure decisions," Journal of Banking & Finance, Elsevier, vol. 22(1), pages 41-60, January Cooper, Russel, John Haltiwanger, and Laura Power, Machine replacement and the business cycle: lumps and bumps. The American Economic Review, Vol. 89. No. 4, pp Ees, Hans Van Gerard H. Kuper and Elmer Sterken, 1997, Investment, finance and the business cycle: evidence from the Dutch manufacturing sector. Cambridge Journal of Economics 1997, 21, Ferderer, J. Peter, 1993, The impact of uncertainty on aggregate investment spending: an empirical analysis, Journal of Money, Credit and Banking, Vol. 25, No. 1 pp Hribar, P., and Nichols, C., The use of unsigned earnings quality measures in tests of earnings management. Working paper. Ghosal Vivek, and P. Loungani, The differential impact of uncertainty on investment in small and large business. The Review of Economics and Finance, 82(2): Jensen, M. C., and Meckling, W.H., The theory of the firm: managerial behavior, agency costs, and capital structure, Journal of Financial Economics 3, Kerstein J. and S. Kim, The incremental information contents of capital expenditures. The Accounting Review, 75. pp Rafael La Porta, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert Vishny Journal of Financial Economics, Volume 58, Issues 1-2, 2000, Pages

20 Lee, Byunghwan, J. O Brien and K. Sivaramakrishan, An analysis of financial analysts optimism in long-term growth forecasts. The Journal of Behavioral Finance 9 (3): Lev. B. and Thiagranjan, Fundamental information analysis. Journal of Accounting and Research 31 (Autumn): McConnell, John J. and Muscarella, C. J., Corporate capital expenditure decisions and the market value of the firm," Journal of Financial Economics, Elsevier, vol. 14(3), pages , September NBER (National Bureau of Economic Research) Business Cycle Dating Committee. Announcement of business-cycle peak of March NBER, November 26, 2001., Announcement of business cycle trhough/end of last recession. NBER, July 17, 2003., US business cycle expansions and contractions, 2003 Pindyck, R.S. (1988), Irreversible Investment, Capacity Choice, and the Value of the Firm, American Economic Review, 78, 5, pp Pindyck, R.S. (1991), Irreversibility, Uncertainty, and Investment, Journal of Economic Literature, Vol. XXIX, pp Sargent, T. Bounded rationality in macroeconomics, Oxford Univeristy Press. Vogt, Stephen C.,1997, Cash Flow and Capital Spending: Evidence from Capital Expenditure Announcements," Financial Management, Financial Management Association, vol. 26(2), Summer. 19

21 Table 1 Sample Distribution Panel A: Yearly Distribution of firm-year observations Year #s Percent Cum. Pct , , , , , , , , , , , , , , , , Total 31, Panel B: Industry Distribution Industry #s Percent Cum. Pct , , , , , , Total 33,

22 Table 2 Descriptive statistics for key variables Variable Mean Median Std. Dev. Min Max RET CAPCH NICH MVE BTM Variable Definitions: RET: Stock return measured by 12 month buy-and-hold abnormal market return of fiscal year after adjusted by equally weighted market index. We use monthly market return data from CRSP database to calculate buy-and-hold abnormal return.. CAPCH: Capital investment (data128) change scaled by market value of equity of the last year-end NICH: Net Income before extraordinary item (data18) change scaled by market value of equity of the last year-end MVE: Log-transformed market value of equity of the last year-end (log (data24 * data25)) BTM: Book-to-Market (value of equity) of the last year-end (data60 / (data24 * data25) ) The source of our data is the 2007 Compustat Annual Industrial and Research File, CRSP database. We exclude utility firms (SIC Code 4000s) and financing firms (SIC 6000s). We exclude sample firms in these industries to maintain homogeneity in our sample as these types of firms are expected to exhibit differences in investing, capital structure and other characteristics from the rest of the sample industries. We impose our sample firms to have December fiscal year-end to be consistent with the previous literature. We require market value of equity of last-year-end (Compustat #24, #25), capital investment of last- and current-year-end (#128), capital investment of last- and current-year-end (#128), Net Income before extraordinary item of last- and current-year-end (#18), Book value of equity of the last year-end (Compustat Data60), and market value of equity of last-year-end (#24, #25), market return from CRSP for each firm. We include firms with stock that are traded in NYSE, AMEX, and NASDAQ only. We exclude firms of ADR stocks. The final sample in our analyses includes a total of 33,146 firmyear observations from 1982 to We also winsorize the top and bottom one percent of continuous variables in our models. 21

23 Table 3 Comparison ofkey variables between business cycle Panel A: Comparison among SLUMP, GROW, and MATU. BC a n RET CAPCH NICH MVE BTM SLUMP 10,621 Mean Median GROW 16,959 Mean Median MATU 5,566 Mean Median Difference SLUMP-GROW b Mean t-statistic c * Median z-statistic d SLUMP-MATU Mean t-statistic ** -1.00** Median z-statistic ^^ GROW-MATU Mean t-statistic * 1.62** Median z-statistic ^^ 2.20^ Panel B: Comparison between Contraction and Expansion. BC a n RET CAPCH NICH MVE BTM Contraction 3,647 Mean Median Expansion 22,525 Mean Median Difference Contraction - Expansion b Mean t-statistic c Median z-statistic d

24 Table 3 notes: See Table 2 for variable definition and sample selection. a Business Cycle: SLUMP(Diminished period): Years belong to diminished period which combines contraction period and early stage of recovering period but still below economic activity level compared to pre-peak level since trough, including 1982, 1983, 1990, 1991, 1992, 2001, 2002, and 2003 (See the Committee s FQA s section announced on November 26, 2001). GROW (Growth period): Years belong to steady growth period after the economic activity level is above compared to pre-peak level since trough, including 1984, 1985, 1986, 1987, 1993, 1994, 1995, 1996, 1997, 1998, 2004, and Grow period is right after the end of slump (year) just prior to the beginning of the Matured Period as defined below. MATU (Matured Period): Years belong to steady growth period including 1988, 1989, 1999, and Maturity period is arbitrarily assigned as two consecutive years just before the peak year. Contraction: Years belong to diminishing period which includes the peak-to-trough period ( ). Expansion: Years of trough-to-peak period, but excluding years of trough. Expansion period includes both Growth period (1984, 1985, 1986, 1987, 1993, 1994, 1995, 1996, 1997, 1998, 2004, 2005) and Maturity period (1988, 1989, 1999, 2000). In the comparison between expansion and contraction period, we exclude years of, 1983, 1991, 1992, 2002, 2003, which are adjunct years between two different cycles, with the intent that our test results clearly show the business cycle effect in determining market response on capital investment. b SLUMP GROW (Contraction Expansion): Mean and Median Difference between SLUMP (Contraction) period and GROW (Expansion) period. c t-statistic: T-test of mean difference. d z-statistic t-statistic: Wilcoxon Two-Sample Test of median. The italicized numbers represent median values. * (**) Not significantly different between the means of the two different groups at the one (ten) percent level with two-tailed test. All else are significantly different with p-value of ^ (^^) Not significantly different between the medians of the two different groups using nonparametric test at the one (ten) percent level with two-tailed test. All else are significantly different with p-value of Number of Observations: 33,

25 Table 4 Correlation Coefficients (Pearson above the diagonal \ Spearman below) Variables a Variables a RET CAPCH NICH MVE BTM RET CAPCH ** NICH MVE * BTM * Table 4 notes: See Table 2 for variable definition and sample selection. * (**): is not significantly different with p-value of 0.01 (0.1). All else are significantly different with p- value of Number of Observations: 33,

26 Table 5 Pooled Cross-sectional Regression Results Model 1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if SLUMP; else 0. Model 3: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if GROW; else 0. Model 4: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if MATU; else 0. Model 5: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC2 + b 6 (BC2 * CAPCH) +b 7 (BC2 * NICH) + b 8 BC3 + b 9 (BC3 * CAPCH) + b 10 (BC3 * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC2 =1 if GROW; else 0 and for b 8, b 9, and b 10 : BC3 =1 if MATU; else 0. a 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 b 8 b 9 b 10 Adj R 2 Model 1 Coeff t-value 0.95** Model 2 Coeff t-value 1.13** Model 3 Coeff t-value 0.77** * 1.06** 9.44 Model 4 Coeff t-value 0.50** Model 5 Coeff t-value -0.21** Notes: See Table 2 for variable definition, sample selection, and see Table 3 for business cycle classification. * (**) Not significantly different from zero at the one (ten) percent level (two-tailed test). Number of Observations: 33,146. F-test between b 6 and b 9 in model 5 that is the difference of market response on CAPCH between GROW and MATU period is 6.68, significant (Pr > F is ). F-test between b 7 and b 10 in model 5 that is the difference of market response on NICH between GROW and MATU period is 4.64, marginally significant (Pr > F is ).

27 Table 6 Regression of Years without adjacent cycles Model 1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2-1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if Contraction; else 0. a 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 b 8 b 9 b 10 Adj R 2 Model 1 Coeff t-value 0.42** Model 2 Coeff t-value 0.35** ** ** Notes: See Table 2 for variable definition, sample selection, and see Table 3 for business cycle classification. * (**) Not significantly different from zero at the one (ten) percent level (two-tailed test). Number of Observations: 26,172. Contraction year (1982, 1990, 2001) and Expansion (= Growth: 1984, 1985, 1986, 1987, 1993, 1994, 1995, 1996, 1997, 1998, 2004, 2005, plus Maturity: 1988, 1989, 1999, 2000) Years of 1983, 1991, 1992, 2002, 2003 are deleted. 26

28 Table 7 Regression for Profit firms only Model 1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC* NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if SLUMP; else 0. Model 3: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC* NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if GROW; else 0. Model 4: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC* NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if MATU; else 0. Model 5: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC2 + b 6 (BC2 * CAPCH) +b 7 (BC2 * NICH) + b 8 BC3 + b 9 (BC3 * CAPCH) + b 10 (BC3 * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC2 =1 if GROW; else 0 and for b 8, b 9, and b 10 : BC3 =1 if MATU; else 0. a 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 b 8 b 9 b 10 Adj R 2 Model 1 Coeff t-value 1.86* Model 2 Coeff t-value 2.00* Model 3 Coeff t-value 1.49** ** 2.90 Model 4 Coeff t-value 1.79* ** Model 5 Coeff t-value 0.96** * Notes: See Table 2 for variable definition, sample selection, and see Table 3 for business cycle classification. * (**) Not significantly different from zero at the one (ten) percent level (two-tailed test). Number of Observations: 24,059. F-test between b 6 and b 9 in model 5 that is the difference of market response on CAPCH between GROW and MATU period is 8.57, significant (Pr > F is ). F-test between b 7 and b 10 in model 5 that is the difference of market response on NICH between GROW and MATU period is 1.12, not significant (Pr > F is ). 27

29 Table 7-1. Only Profit firms: Regression of Years without adjacent cycles Model 1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2-1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if Contraction; else 0. a 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 Adj R 2 Model 1 Coeff t-value 1.11** Model 2 Coeff t-value 1.14** ** * Notes: See Table 2 for variable definition, sample selection, and see Table 3 for business cycle classification. * (**) Not significantly different from zero at the one (ten) percent level (two-tailed test). Number of Observations: 19,

30 Table 8 Regression Results for Loss firm only Model 1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if SLUMP; else 0. Model 3: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if GROW; else 0. Model 4: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if MATU; else 0. Model 5: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC2 + b 6 (BC2 * CAPCH) +b 7 (BC2 * NICH) + b 8 BC3 + b 9 (BC3 * CAPCH) + b 10 (BC3 * NICH) + ΣINDUSTRY + e For b 5, b 6, and b 7 : BC2 =1 if GROW; else 0 and for b 8, b 9, and b 10 : BC3 =1 if MATU; else 0 a 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 b 8 b 9 b 10 Adj R 2 Model 1 Coeff t-value -0.75** Model 2 Coeff t-value -0.66** ** Model 3 Coeff t-value -0.61** ** Model 4 Coeff t-value -0.92** * Model 5 Coeff t-value -1.01** * 2.30* -0.26** * Notes: See Table 2 for variable definition, sample selection, and see Table 3 for business cycle classification. * (**) Not significantly different from zero at the one (ten) percent level (two-tailed test). Number of Observations: 9,083. F-test between b 6 and b 9 in model 5 that is the difference of market response on CAPCH between GROW and MATU period is 4.53, marginally significant (Pr > F is ). F-test between b 7 and b 10 in model 5 that is the difference of market response on NICH between GROW and MATU period is 5.08, marginally significant (Pr > F is ). 29

31 Table 8-1. Only Loss firms: Regression of Years without adjacent cycles. Model 1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + Σ INDUSTRY + e Model 2-1: BHAR=a 0 + b 1 CAPCH + b 2 NICH + b 3 MVE + b 4 BTM + b 5 BC + b 6 (BC * CAPCH) + b 7 (BC * NICH) + Σ INDUSTRY + e For b 5, b 6, and b 7 : BC =1 if Contraction; else 0. a 0 b 1 b 2 b 3 b 4 b 5 b 6 b 7 Adj R 2 Model 1 Coeff t-value -0.54** Model 2 Coeff t-value -0.59** * ** Notes: See Table 2 for variable definition, sample selection, and see Table 3 for business cycle classification. *(**) Not significantly different from zero at the one (ten) percent level (two-tailed test). Number of Observations: 6,

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