Real Estate Ownership by Non-Real Estate Firms: The Impact on Firm Returns
|
|
- Emma Tamsyn Crawford
- 5 years ago
- Views:
Transcription
1 Real Estate Ownership by Non-Real Estate Firms: The Impact on Firm Returns Yongheng Deng and Joseph Gyourko 1 Zell/Lurie Real Estate Center at Wharton University of Pennsylvania Prepared for the Corporate Real Estate Alliance Seattle, WA, Meeting January 30, 1999 Preliminary: Please do not quote for attribution without permission. 1 Deng is Post-doctoral Research Fellow at the Zell/Lurie Real Estate Center at Wharton. Gyourko is Professor of Real Estate & Finance and Director of the Zell/Lurie Center at Wharton. Both authors gratefully acknowledge funding from the Corporate Real Estate Alliance and the Research Sponsors Program of the Zell/Lurie Center. This report was prepared for the Corporate Real Estate Alliance. All errors are the responsibility of the authors.
2 OFHEO REPORT TO CONGRESS
3 Executive Summary Recent estimates (guesstimates is perhaps a better word) suggest that the amount of real estate owned by firms and corporations is approaching $2 trillion. A debate is emerging around the issue of whether non-real estate firms should own so much property. One prominent industry researcher, Peter Linneman, argues in a recent paper that it is harmful for non-real estate firms to commit so much of their scarce capital to investments outside their core competencies. Linneman and others also suggest that high cost of capital firms in particular should avoid committing to ownership of relatively low return buildings. Pure finance theory does not support the latter contention in particular. Capital budgeting principles imply that each investment project considered by the firm should be evaluated at its own opportunity cost of capital. Cash flows from risky projects should be discounted more severely than the cash flows from less risky projects regardless of whether the firm itself has a high or low cost of capital. That is, the true cost of capital depends upon the use to which the capital is put. Thus, theory implies that high cost of capital firms should not suffer a penalty when they use their scarce capital to invest in and own relatively low return real estate as long as they receive a return high enough to compensate them for the risks of real estate ownership. Of course, the real world often does not conform perfectly to theory. Linneman s contentions could hold if either of the following factors is relevant: (a) investors believe that firms are more likely to earn higher risk-adjusted returns on investments in their areas of core competence than they are in generic real estate; if so, they could penalize firms that stray too much from investments in core competency projects; or (b) investors i
4 in high cost of capital firms especially do not fully perceive the lower risk associated with real property investment; if so, they effectively discount all projects at the company, not the project, cost of capital contrary to what finance theory suggests should drive corporate value. There is no amount of debate, however clever, that can resolve this issue. The problem is an empirical one, so there is a need to go directly to firm-level data to determine whether too much real estate ownership/investment harms a firm. A capital budgeting-based investigation probably will not be fruitful due to onerous data requirements. Project-by-project data on risk and return would be needed for each investment to see if the firm receives an appropriate risk-adjusted return in each case. Another approach, and the one taken in this paper, is to examine a firm s returns to see if they are correlated with the degree of real estate ownership/investment. More specifically, a capital asset pricing model (CAPM) is estimated for 381 firms across eight industries, many of them relatively capital-intensive. This controls for systematic risk across firms, the factor that asset pricing theory suggests drives return differences across firms. The study then examines the non-systematic, or idiosyncratic, component of return to see if it is associated with the concentration of real estate ownership. More specifically, we test for whether the idiosyncratic component of firm return is less for firms with relatively high levels of real estate ownership. If returns are lower for firms with relatively high concentrations of real estate ownership, then the evidence is consistent with some penalty being imposed by investors. The results show a consistent negative relation between the idiosyncratic component of firm return and the degree of real estate ownership. That is, within an ii
5 industry it is the case that a firm s return is lower if it has a relatively high fraction of its total assets (measured by book value) in real estate (also book value). The impact varies across industries, being greatest in the electronics industry. A pooled regression across industries finds a statistically and economically significant negative impact of relatively high real estate ownership on return. While there are no meaningful differences in returns for firms with very similar real estate concentration measures, for firms with concentration measures ten percentage points above average, implied excess annual returns are about one percentage point lower. This amounts to just under 10 percent of cumulative compound excess return over a ten year holding period. Specifications that allow for threshold effects yield the largest impacts. For example, a firm with a real estate-to-total asset ratio that is above the median for all 381 firms in the sample has about a 4 point lower average annual return than a firm with a real estate-to-total asset ratio that is below the sample median. However, this particular result may be driven by a relatively few outliers, an issue that will be examined more closely in future versions. Unfortunately, we are unable to determine whether the penalty is being imposed for reasons suggested by Linneman or investor ignorance of the true risk profile of real estate ownership. This limitation aside, this is the first study of which we are aware to document a statistically significant negative relation between firm returns and the degree of real estate ownership over a long holding period. Work in progress is attempting to determine whether other correlates might explain the correlation. However, given how noisy return data are and the difficulty of finding any predictable correlation with returns, even such a preliminary result should be taken seriously. iii
6 OFHEO REPORT TO CONGRESS
7 Introduction For many years operating companies around the world generally have owned their real estate assets. In the United States alone it is estimated that corporate users own nearly $2 trillion, or roughly half of all commercial property. Companies own not only their production facilities, but frequently their offices, warehouses, and retail outlets. Although many of these properties are suitable for a broad range of users, these operating companies choose to commit their scarce capital to the ownership and operation of real estate, rather than re-deploying this capital to their core operating businesses. In this study, we test the hypothesis that high levels of corporate real estate concentration in total assets for a non-real-estate company has a negative impact on the company s annual returns. We adopt an empirical approach that is familiar in the finance literature to analyze abnormal returns. Specifically, the analysis is conducted in the following two stages. In the first stage, we estimate the companies excess returns using a Sharpe-Lintner Capital Asset Pricing Model (CAPM). In the second stage, we analyze the estimated idiosyncratic components of returns from the CAPM to see whether they are influenced by the level of corporate real estate concentration. The empirical results suggest a statistically and economically significant relationship between the level of corporate real estate concentration (measured as the fraction of book value of assets held in real property form) and the company s total returns. The strength of this result varies across industries, being strongest among electronics firms. The finding also holds in a pooled regression across industries using a specification that allows for different industry intercepts. The Data The data sets used in this empirical study include the NYSE, AMEX, and NASDAQ monthly stock files maintained by the Center for Research in Security Prices (CRSP), and merged with the Standard & Poor s COMPUSTAT annual industrial files. 1
8 The CRSP Monthly Stock file provides detailed information on individual securities, most importantly including return data. COMPUSTAT is a database of financial, statistical, and marketing information. It provides more than 300 annual Income Statement, Balance Sheet, Statement of Cash Flows, and supplemental data items on more than 7,500 publicly held companies. Based on the information from COMPUSTAT, we compute a variable labeled as RC (Real Estate Concentration) ratio which empirically measures corporate real estate concentration for a non-real estate firm. Specifically, RC ratio is the ratio of the company s Property, Plant, and Equipment (including building at cost plus land and improvements) divided by the company s Total Assets. Both numerator and denominator are book values. Using book numbers reduces endogeneity problems that can bias the estimations reported below. This real estate concentration measure is then merged into the return data from the CRSP monthly stock files. 2 Due to a variety of data limitations in both data bases, we confine our analysis to the period from 1984 to In addition, firms are included only if they have at least 60 months of consecutive monthly returns data, the standard in the finance literature for estimating stable betas. Furthermore, each firm must have balance sheet information about property, plant, and equipment, total assets, as well as the company s year-end equity market capitalization information. After data cleaning and merging, the final sample includes stock returns and balance sheet data from 381 firms in eight major industries. The eight industries covered are: 1. Food and Kindred Products Industry; 2 We also have experimented with three other concentration measures. One is very similar to RC, except that its numerator reflects the value of buildings at cost less accumulated depreciation. Use of this measure yields results very similar to those reported below. The other two measures are ratios of the book value of real estate (gross and net of accumulated depreciation) to total property, plant, and equipment. Use of these two measures yields qualitatively similar results, although missing data is more of problem here, so that the findings often are not statistically significant due to the smaller number of observations. 2
9 2. Printing, Publishing and Allied Industry; 3. Chemicals and Allied Products Industry; 4. Primary Metal Industry; 5. Industrial and Commercial Machinery, Computer Equipment Industry; 6. Electronics, Other Electronics, excluding Computer Industry; 7. Transportation Equipment Industry; 8. Instruments, Photo Goods, Watches Industry. A list of all firms used in the study is available from the authors. The Test Methodology Our test is based on an approach similar to the cross-sectional regression approach suggested by Fama and MacBeth (1973). The basic idea is that for each security, the total return can be broken down into idiosyncratic and systematic components. It is crucial to control for systematic risk (or beta) as theory suggests that is the primary reason why returns vary across firms. After controlling for risk differences across firms, we then examine whether the idiosyncratic component of return (i.e., that part not related to market risk) is related to the company s real estate concentration level. Specifically, in the first stage, we run a series of regressions using the Sharpe- Lintner CAPM model, such thatýþ# ERET = α + β EMKT + ε (1) it i i it it where ERET is the excess return over the risk-free return, which is measured as the difference between the company s monthly holding period return and the 30-day T-bill returns reported by CRSP monthly stock files; EMKT is the excess return on the market portfolio, which is measured as the difference between the monthly return on the CRSP value-weighted market portfolio and the 30-day T-bill return reported by CRSP monthly stock files; α is the idiosyncratic component of the excess return; β is the systematic component of the excess return; ε is an error term following a standard normal distribution; i indexes for firm, and t indexes for time period measured in month. 3
10 In the second stage, we test whether the idiosyncratic component of return is related to the degree of real estate ownership by regressing the estimated α i on the real estate concentration measure, such that ü f IND, RC, SIZE α i i i i = 1 6 (2) where IND is a vector of industry dummy variables, RC is our empirical measure of a company s real estate concentration level, as described in the previous section; and SIZE is the size of a firm, measured by the year-end capitalization reported by CRSP. 3 We suspect that the Alpha-RC relationship, if it exists at all, may vary across industries. Therefore, equation (2) is tested by industry. However, the limited number of firms which satisfy our data screening criteria in each industry may affect the power of our test. Alternatively, we also test the equation (2) using a pooled sample by combining all the industries together and controlling for industry fixed effects in our test. The Empirical Results Summary statistics on the key variables used in the analysis are reported in Table 1. Recall that these data are for the time period and that returns are measured in excess of the CRSP value-weighted market return. There are a number of noteworthy features about these data. First, the mean monthly excess return across all industries is over eight-tenths of one percent (0.0085) or about 10.7 percent per year. There is a fairly wide range across industries, ranging from 0.4 percent per month in electronics to 1.4 percent per month in chemicals. However, the variation in excess returns within industry is even larger. For example, monthly excess returns vary from percent to 229 percent in the Electronics industry, and from 77.1 percent to percent in the Computer Equipment Industry. 4 3 Size is included because recent research has shown that it often is related to differences in firm returns. While there is no theoretical explanation for this correlation, we control for it in case it is related to the degree of real estate correlation. In any event, it could be that firms which reach a certain size tend to accumulate real estate. 4 Obviously, these are not averages but extreme values that occur in at least one monthly observation. 4
11 The average idiosyncratic component of monthly excess return, which soon will be the focus of our interest, is a very small percent. As with industry returns, the variance about this mean is quite large, with the standard deviation being 1.16 percent. We test whether or not this variation in idiosyncratic return is related to the degree of real estate-related property, plant, and equipment that a company holds as a percentage of its total assets. That ratio, RC in our terminology averages just under 18 percent across all industries. The variation in this variable across and within industries is less than for returns. Finally, most of our industries are relatively high beta sectors of the economy. Only the Food industry has an estimated beta less than one and it is close at The Electronics and Transportation Equipment industries carry the greatest systematic risk, with their betas exceeding 1.2. Our estimates are in line with previous research my Gibbons and Fama on systematic risk across industry groupings. Figure 1 plots, for each firm in the Electronics industry, each firm s mean excess return (EMKT) against its real estate concentration measure (RC). The raw data clearly do not indicate any relationship between excess returns and the degree of real estate concentration in assets in this industry or any other. 5 Figure 2 presents a very different picture with its plot of the idiosyncratic component of excess return in the Electronics industry against the measure of real estate concentration. The regression line draws the clear negative relationship. That is, once systematic risk is controlled for, the higher is an electronics firm s real estate concentration, the lower its idiosyncratic return component. Figure 3 then plots the level of the idiosyncratic component of return for firms in the Electronics industry by degree of real estate concentration. There is a fairly large difference depending upon whether the firm has a relatively low or relatively high degree of real estate ownership as a percentage of total assets. For those firms with RC values below the median for the industry, the idiosyncratic component of return is percent per month, versus percent per month for those with RC values above the 5 Plots for other industries are available upon request. None reveal any relationship between excess returns and real estate concentration. 5
12 industry median. The difference is a fairly wide 0.4 percent per month. The divergence is even wider if one divides the firms into those with very high RC values in the top quartile for the industry versus those in the bottom three quartiles. [See the bar graphs on the right side of Figure 3.] The difference in idiosyncratic return components widens to nearly 0.6 percent per month. This is both statistically and economically significant. Figure 4 then shows that this relationship is not peculiar to the Electronics industry. When we pool across all eight industries, a similar pattern results. The difference is widest in the Electronics industry, but it exists in other industries as well. Across all firms, the difference is 0.3 percent per month for those with RC values above the sample median versus those firms with RC values below the sample median. The difference widens to 0.4 percent per month when we compare firms in the top quartile to those in the bottom three quartiles. [Note: The idiosyncratic component of monthly excess returns are computed using the estimates from model 5 and model 6 of Table 3 for the pooled sample, and from models 5 and 6 of Table A-14, and Table A-16, for the Electronics Industry and the Photo Goods Industry, respectively. For the pooled sample, the Alpha for those firms with real estate concentrations equal or below the median (i.e., RC 50%) is computed as the weighted average of the industry fix-effect intercepts (weighted by the number of firms in the industry) adjusted by the log size effect. The Alpha for those firms with real estate concentrations above the median (i.e., RC > 50%) is computed using the previously computed Alpha (for RC 50%) adjusted by the estimated coefficient for the dummy variable indicating RC > 50% (i.e., from Model 5 in Table 3). Similarly, we compute the Alpha for those firms with real estate concentrations equal or below the top quartile (i.e., RC 75%) and the Alpha for those firms with real estate concentrations above the top quartile (i.e., RC > 75%) using the estimates reported in Model 6 from Table 3.] While these differences between high and low RC firms are fairly large, it is worth emphasizing that the implications are different for firms with similar degrees of 6
13 real estate concentration on their books. This can be seen by examining the coefficient from the specification that allows RC to vary continuously (first column, Table 2, pooled regression). The estimate of implies that the idiosyncratic component of excess return is only lower for a firm with a 19 percent versus an 18 percent RC ratio (i.e., for a small change about the mean value of the independent variable). When compounded over a year, this amounts to less than one-tenth of a percent in overall excess return. Thus, while the result is statistically significant, it is not so economically. When we consider the implication for a firm with a 28 versus an 18 percent RC ratio value, the implications are that average annual returns are about 1.1 percentage points lower for the high RC firm. This amounts to nearly 10 percent of excess returns on average. Thus, the data seem to indicate that there are threshold effects around which the impacts on return become much larger. It is clear that for firms with the average amount of real estate investment, small differences about that mean level are not associated with meaningful differences in return. Ten percentage point differences in real estate concentration ratios begin to be associated with meaningful differences in excess returns over time. And, comparing very high versus very low real estate concentration differences are associated with quite large return differences over time. However, these differences may be driven by a few outlier firms. Future versions of the paper will test more rigorously for this. Figures 5 and 6 then present how these threshold differences translate into annual excess returns for the Electronics industry and Pooled industry samples, respectively. Estimated annual excess returns are calculated based on equation (3) AERET (3), = ERET where AERET is the annual excess returns, and ERET is computed based on equation (4): ERET = αü + ü β EMKT (4), 7
14 where α is the estimated idiosyncratic component of excess return from figures 4-6, β is the average of systematic risk estimated from the first stage regression, and the EMKT is the excess market returns. Figure 5 shows that for the Electronics industry, where the relationship is strongest, a firm with a relatively low degree of real estate concentration measured a percentage of total firm assets has a 4.3 percentage point higher annual excess return on average (i.e., ). The penalty associated with real estate ownership is even wider if one divides the sample into those with RC values in the top quartile for the industry. In that case, those firms in the bottom three quartiles have implied annual excess returns that are 7.6 percentage per annum higher on average (i.e., 4.37-(-3.28)). Figure 6 reports the analogous findings for the Pooled sample of firms across all industries. These results indicate that on average a firm with real estate-to-total asset ratio above the median for all 381 firms in the sample has a 4 percentage point lower average annual return than a firm with a real estate-to-total asset ratio that is below the sample median. If a firm has a real estate-to-total asset ratio in the top quartile of all 381 firms in the sample, its average annual return is 5 points lower than the returns earned by the firms with real estate-to-total asset ratio in the bottom three quartiles of the sample. Conclusion There is a growing debate as to the wisdom of non-real estate companies investing large amounts of scarce capital in real estate. Corporate finance theory does not suggest firms should be penalized for investing in real estate per se as long as they obtain the proper return for their investment. However, it could be that investors believe that companies are more likely to reap higher risk-adjusted returns on investments within the core competency of the firm. It also could be that investors do not understand the true risk profile of real estate investments, and, therefore, penalize high cost of capital companies for such investments. 8
15 If so, one should see some evidence that firms with relatively high degrees of real estate investment have lower returns. In this paper, we find such evidence. It is strongest in the Electronics, but holds in a pooled regression of firms across eight industries. Our differences are large when one divides the sample into high and low real estate concentration firms. The return difference is small for small differences in RC. Return differences become meaningful for larger differences in concentration of at least ten percentage points (nearly a standard deviation in the data). The size of the difference across the full range of RC value is so large that one reasonably should suspect that real estate is proxying for some other firm trait. Future versions of the paper will experiment with added correlates to see if the results are robust. However, the difference in the nature of the scatter plots reported in Figures 1 and 2 is strong evidence that there is a real relationship between the idiosyncratic component of firm return and the firm s degree of real estate ownership relative to other firms in its industry. 9
16 Table 1. Summary Statistics of Key Variables by Industry Industry No. of Obs. Means, (Standard Deviations), and [Minimum, Maximum] ERET ALPHA BETA RC RATIO SIZE Food 3,633 Publishing 3,000 Chemicals 7,322 Primary Metal 2, (0.0954) (0.0102) (0.2114) (0.1157) (6.3906) [-0.410, 0.935] [-0.017, 0.029] [0.668, 1.609] [0.028, 0.641] [0.009, 33.00] (0.0916) (0.0046) (0.2592) (0.0542) (1.5261) [-0.473, 0.655] [-0.010, 0.011] [0.629, 1.670] [0.046, 0.262] [0.008, 6.16] (0.1143) (0.0139) (0.3327) (0.1503) (6.5842) [-0.616, 1.605] [-0.018, 0.094] [0.213, 2.215] [0.038, 1.606] [0.008, 30.17] (0.1257) (0.0089) (0.3531) (0.1986) (1.1991) [-0.556, 0.995] [-0.018, 0.021] [-0.130, 1.688] [0.008, 0.921] [0.009, 4.74] Computer Equipment 7, (0.1520) (0.0144) (0.4846) (0.0644) (7.6492) [-0.771, 5.245] [-0.049, 0.050] [-1.617, 2.275] [0.016, 0.346] [0.002, 61.71] Electronics 7, (0.1326) (0.0074) (0.3699) (0.0655) (5.2033) [-0.528, 2.294] [-0.020, 0.013] [0.409, 2.106] [0.022, 0.400] [0.005, 34.62] Transportation Equipment Photo Goods, Watches Pooled Samples 3,955 3,767 39, (0.1100) (0.0088) (0.2896) (0.0585) (4.9032) [-0.523, 1.151] [-0.019, 0.027] [0.513, 1.788] [0.039, 0.278] [0.009, 24.72] (0.1252) (0.0097) (0.3089) (0.0652) (3.0058) [-0.520, 2.130] [-0.024, 0.021] [0.612, 1.995] [0.045, 0.292] [0.007, 14.36] (0.1242) (0.0116) (0.3611) (0.1064) (5.6892) [-0.771, 5.245] [-0.049, 0.094] [-1.617, 2.275] [0.008, 1.606] [0.002, 61.71] NOTES: ERET Monthly Excess Returns over 30-Day T-bill Returns. ALPHA Idiosyncratic Component of the Monthly Excess Returns estimated from the first stage regressions. BETA Systematic Risk of the Monthly Excess Returns estimated from the first stage regressions. RC RATIO Property, Plan and Equipment (building at cost plus land) divided by Total Assets, all at book value. SIZE Year-End Equity Market Capitalization, measured in billion $. 10
17 Table 2. Second Stage Estimation without Size Control for Pooled Samples Variable Model 1 Model 2 Model 3 Food (3.412) (3.530) (3.386) Publishing (0.631) (0.689) (0.475) Chemicals (5.506) (6.099) (6.141) Primary Metal (0.683) (0.683) (1.032) Computer Equipment (0.841) (0.877) (0.585) Electronics (1.936) (2.016) (2.593) Transportation Equipment (0.059) (0.093) (0.234) Instrument, Photo Goods, Watches (0.408) (0.498) (0.256) RC Ratio (1.967) RC > 50% (2.733) RC > 75% (3.207) R NOTES: t-ratios are in parenthesis. Sample size is 381. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC > 50% A dummy variable, indicating that the company s RC Ratio is above the 50 percentile of the industry level. RC > 75% A dummy variable, indicating that the company s RC Ratio is above the 75 percentile of the industry level. 11
18 Table 3. Second Stage Estimation with Size Control for Pooled Samples Variable Model 4 Model 5 Model 6 Food (1.940) (1.914) (2.006) Publishing (0.582) (0.649) (0.740) Chemicals (2.443) (2.451) (2.565) Primary Metal (0.114) (0.056) (0.021) Computer Equipment (0.623) (0.665) (0.757) Electronics (0.506) (0.445) (0.391) Transportation Equipment (0.297) (0.359) (0.422) Instrument, Photo Goods, Watches (0.465) (0.549) (0.635) RC RATIO (1.959) RC > 50% (2.732) RC > 75% (3.239) LOG SIZE (0.305) (0.355) (0.583) R NOTES: t-ratios are in parenthesis. Sample size is 381. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC > 50% A dummy variable, indicating that the company s RC Ratio is above the 50 percentile of the industry level. RC > 75% A dummy variable, indicating that the company s RC Ratio is above the 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 12
19 Appendix Tables: Results by Industry Table A-1. Second Stage Estimation in Food and Kindred Products Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (1.916) (2.295) (2.704) RC Ratio (0.546) RC > 50% (0.401) RC > 75% (0.383) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (1.871) (2.025) (2.044) RC Ratio (0.038) RC > 50% (0.182) RC > 75% (0.065) Log Size (2.591) (2.629) (2.626) R NOTES: t-ratios are in parenthesis. Sample size is 35. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 13
20 Table A-2. Second Stage Estimation in Printing, Publishing & Allied Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (0.310) (0.247) (0.069) RC Ratio (0.381) RC > 50% (0.193) RC > 75% (0.385) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (1.545) (1.383) (1.628) RC Ratio (0.584) RC > 50% (0.132) RC > 75% (0.809) Log Size (1.528) (1.448) (1.633) R NOTES: t-ratios are in parenthesis. Sample size is 29. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 14
21 Table A-3. Second Stage Estimation in Chemicals & Allied Products Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (3.829) (4.547) (4.342) RC Ratio (1.253) RC > 50% (2.102) RC > 75% (1.343) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (3.162) (3.232) (3.133) RC Ratio (1.246) RC > 50% (2.001) RC > 75% (1.375) Log Size (2.338) (2.259) (2.361) R NOTES: t-ratios are in parenthesis. Sample size is 74. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 15
22 Table A-4. Second Stage Estimation in Primary Metal Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (0.984) (1.762) (1.086) RC Ratio (0.586) RC > 50% (0.673) RC > 75% (1.067) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (0.910) (0.692) (0.855) RC Ratio (0.798) RC > 50% (0.546) RC > 75% (1.157) Log Size (0.744) (0.332) (0.694) R NOTES: t-ratios are in parenthesis. Sample size is 29. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 16
23 Table A-5. Second Stage Estimation in Industrial, Commercial Machinery, Computer Equipment Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (0.613) (0.539) (0.370) RC Ratio (0.604) RC > 50% (0.924) RC > 75% (0.976) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (0.393) (0.682) (0.752) RC Ratio (0.637) RC > 50% (0.859) RC > 75% (0.993) Log Size (0.697) (0.579) (0.697) R NOTES: t-ratios are in parenthesis. Sample size is 71. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 17
24 Table A-6. Second Stage Estimation in Electronics, Other Electronics, Excluding Computer Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (0.610) (2.045) (2.981) RC Ratio (2.799) RC > 50% (2.146) RC > 75% (3.407) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (0.850) (1.406) (1.680) RC Ratio (2.803) RC > 50% (2.043) RC > 75% (3.384) Log Size (1.218) (0.953) (1.124) R NOTES: t-ratios are in parenthesis. Sample size is 71. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 18
25 Table A-7. Second Stage Estimation in Transportation Equipment Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (0.216) (0.314) (0.241) RC Ratio (0.113) RC > 50% (0.494) RC > 75% (1.230) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (1.110) (1.156) (0.867) RC Ratio (0.029) RC > 50% (0.469) RC > 75% (0.954) Log Size (1.117) (1.114) (0.839) R NOTES: t-ratios are in parenthesis. Sample size is 37. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 19
26 Table A-8. Second Stage Estimation in Instrument; Photo Goods, Watches Industry Panel (a). Without Size Control Variable Model 1 Model 2 Model 3 Intercept (0.889) (0.692) (0.140) RC Ratio (1.130) RC > 50% (1.330) RC > 75% (0.947) R Panel (b). With Size Control Variable Model 4 Model 5 Model 6 Intercept (0.344) (0.232) (0.206) RC Ratio (1.114) RC > 50% (1.312) RC > 75% (0.951) Log Size (0.032) (0.069) (0.183) R NOTES: t-ratios are in parenthesis. Sample size is 35. RC Ratio Property, Plan and Equipment (building at cost plus land) divided by Total Assets. RC>50% A dummy variable, indicating that the company s RC Ratio is above the industry median level. RC >75% A dummy variable, indicating that the company s RC Ratio is above 75 percentile of the industry level. Log Size Log of Year-End Equity Market Capitalization. 20
Real Estate Ownership by Non-Real Estate Firms: An Estimate of the Impact on Firm Returns
Real Estate Ownership by Non-Real Estate Firms: An Estimate of the Impact on Firm Returns Yongheng Deng and Joseph Gyourko 1 Draft December 16, 1999 1 Deng is Assistant Professor at University of Southern
More informationDecimalization and Illiquidity Premiums: An Extended Analysis
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2015 Decimalization and Illiquidity Premiums: An Extended Analysis Seth E. Williams Utah State University
More informationLiquidity skewness premium
Liquidity skewness premium Giho Jeong, Jangkoo Kang, and Kyung Yoon Kwon * Abstract Risk-averse investors may dislike decrease of liquidity rather than increase of liquidity, and thus there can be asymmetric
More informationWashington University Fall Economics 487
Washington University Fall 2009 Department of Economics James Morley Economics 487 Project Proposal due Tuesday 11/10 Final Project due Wednesday 12/9 (by 5:00pm) (20% penalty per day if the project is
More informationONLINE APPENDIX. Do Individual Currency Traders Make Money?
ONLINE APPENDIX Do Individual Currency Traders Make Money? 5.7 Robustness Checks with Second Data Set The performance results from the main data set, presented in Panel B of Table 2, show that the top
More informationOptimal Debt-to-Equity Ratios and Stock Returns
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2014 Optimal Debt-to-Equity Ratios and Stock Returns Courtney D. Winn Utah State University Follow this
More informationStock price synchronicity and the role of analyst: Do analysts generate firm-specific vs. market-wide information?
Stock price synchronicity and the role of analyst: Do analysts generate firm-specific vs. market-wide information? Yongsik Kim * Abstract This paper provides empirical evidence that analysts generate firm-specific
More informationSFSU FIN822 Project 1
SFSU FIN822 Project 1 This project can be done in a team of up to 3 people. Your project report must be accompanied by printouts of programming outputs. You could use any software to solve the problems.
More informationECCE Research Note 06-01: CORPORATE GOVERNANCE AND THE COST OF EQUITY CAPITAL: EVIDENCE FROM GMI S GOVERNANCE RATING
ECCE Research Note 06-01: CORPORATE GOVERNANCE AND THE COST OF EQUITY CAPITAL: EVIDENCE FROM GMI S GOVERNANCE RATING by Jeroen Derwall and Patrick Verwijmeren Corporate Governance and the Cost of Equity
More informationRevisiting Idiosyncratic Volatility and Stock Returns. Fatma Sonmez 1
Revisiting Idiosyncratic Volatility and Stock Returns Fatma Sonmez 1 Abstract This paper s aim is to revisit the relation between idiosyncratic volatility and future stock returns. There are three key
More informationThe Disappearance of the Small Firm Premium
The Disappearance of the Small Firm Premium by Lanziying Luo Bachelor of Economics, Southwestern University of Finance and Economics,2015 and Chenguang Zhao Bachelor of Science in Finance, Arizona State
More informationPrivate Equity Performance: What Do We Know?
Preliminary Private Equity Performance: What Do We Know? by Robert Harris*, Tim Jenkinson** and Steven N. Kaplan*** This Draft: September 9, 2011 Abstract We present time series evidence on the performance
More informationPersonal Dividend and Capital Gains Taxes: Further Examination of the Signaling Bang for the Buck. May 2004
Personal Dividend and Capital Gains Taxes: Further Examination of the Signaling Bang for the Buck May 2004 Personal Dividend and Capital Gains Taxes: Further Examination of the Signaling Bang for the Buck
More informationCore CFO and Future Performance. Abstract
Core CFO and Future Performance Rodrigo S. Verdi Sloan School of Management Massachusetts Institute of Technology 50 Memorial Drive E52-403A Cambridge, MA 02142 rverdi@mit.edu Abstract This paper investigates
More informationFinal Exam Suggested Solutions
University of Washington Fall 003 Department of Economics Eric Zivot Economics 483 Final Exam Suggested Solutions This is a closed book and closed note exam. However, you are allowed one page of handwritten
More informationNote on Cost of Capital
DUKE UNIVERSITY, FUQUA SCHOOL OF BUSINESS ACCOUNTG 512F: FUNDAMENTALS OF FINANCIAL ANALYSIS Note on Cost of Capital For the course, you should concentrate on the CAPM and the weighted average cost of capital.
More informationTrading Costs of Asset Pricing Anomalies Appendix: Additional Empirical Results
Trading Costs of Asset Pricing Anomalies Appendix: Additional Empirical Results ANDREA FRAZZINI, RONEN ISRAEL, AND TOBIAS J. MOSKOWITZ This Appendix contains additional analysis and results. Table A1 reports
More informationECON FINANCIAL ECONOMICS
ECON 337901 FINANCIAL ECONOMICS Peter Ireland Boston College Fall 2017 These lecture notes by Peter Ireland are licensed under a Creative Commons Attribution-NonCommerical-ShareAlike 4.0 International
More informationECON FINANCIAL ECONOMICS
ECON 337901 FINANCIAL ECONOMICS Peter Ireland Boston College Spring 2018 These lecture notes by Peter Ireland are licensed under a Creative Commons Attribution-NonCommerical-ShareAlike 4.0 International
More informationCan Hedge Funds Time the Market?
International Review of Finance, 2017 Can Hedge Funds Time the Market? MICHAEL W. BRANDT,FEDERICO NUCERA AND GIORGIO VALENTE Duke University, The Fuqua School of Business, Durham, NC LUISS Guido Carli
More informationFama-French in China: Size and Value Factors in Chinese Stock Returns
Fama-French in China: Size and Value Factors in Chinese Stock Returns November 26, 2016 Abstract We investigate the size and value factors in the cross-section of returns for the Chinese stock market.
More informationProcedia - Social and Behavioral Sciences 109 ( 2014 ) Yigit Bora Senyigit *, Yusuf Ag
Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Sciences 109 ( 2014 ) 327 332 2 nd World Conference on Business, Economics and Management WCBEM 2013 Explaining
More informationAsubstantial portion of the academic
The Decline of Informed Trading in the Equity and Options Markets Charles Cao, David Gempesaw, and Timothy Simin Charles Cao is the Smeal Chair Professor of Finance in the Smeal College of Business at
More informationKeywords: Equity firms, capital structure, debt free firms, debt and stocks.
Working Paper 2009-WP-04 May 2009 Performance of Debt Free Firms Tarek Zaher Abstract: This paper compares the performance of portfolios of debt free firms to comparable portfolios of leveraged firms.
More informationRisks and Returns of Relative Total Shareholder Return Plans Andy Restaino Technical Compensation Advisors Inc.
Risks and Returns of Relative Total Shareholder Return Plans Andy Restaino Technical Compensation Advisors Inc. INTRODUCTION When determining or evaluating the efficacy of a company s executive compensation
More informationStyle Timing with Insiders
Volume 66 Number 4 2010 CFA Institute Style Timing with Insiders Heather S. Knewtson, Richard W. Sias, and David A. Whidbee Aggregate demand by insiders predicts time-series variation in the value premium.
More informationDeviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective
Deviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective Zhenxu Tong * University of Exeter Abstract The tradeoff theory of corporate cash holdings predicts that
More informationCorporate Real Estate Holdings and Firm Returns of Shariah-Compliant Firms
Corporate Real Estate Holdings and Firm Returns of Shariah-Compliant Firms Kola Akinsomi* Ong Seow Eng Muhammad Faishal bin Ibrahim School of Construction Economics and Management The University of the
More informationFurther Test on Stock Liquidity Risk With a Relative Measure
International Journal of Education and Research Vol. 1 No. 3 March 2013 Further Test on Stock Liquidity Risk With a Relative Measure David Oima* David Sande** Benjamin Ombok*** Abstract Negative relationship
More informationDo the LCAPM Predictions Hold? Replication and Extension Evidence
Do the LCAPM Predictions Hold? Replication and Extension Evidence Craig W. Holden 1 and Jayoung Nam 2 1 Kelley School of Business, Indiana University, Bloomington, Indiana 47405, cholden@indiana.edu 2
More informationMonetary Economics Risk and Return, Part 2. Gerald P. Dwyer Fall 2015
Monetary Economics Risk and Return, Part 2 Gerald P. Dwyer Fall 2015 Reading Malkiel, Part 2, Part 3 Malkiel, Part 3 Outline Returns and risk Overall market risk reduced over longer periods Individual
More informationHow Markets React to Different Types of Mergers
How Markets React to Different Types of Mergers By Pranit Chowhan Bachelor of Business Administration, University of Mumbai, 2014 And Vishal Bane Bachelor of Commerce, University of Mumbai, 2006 PROJECT
More informationMonthly Holdings Data and the Selection of Superior Mutual Funds + Edwin J. Elton* Martin J. Gruber*
Monthly Holdings Data and the Selection of Superior Mutual Funds + Edwin J. Elton* (eelton@stern.nyu.edu) Martin J. Gruber* (mgruber@stern.nyu.edu) Christopher R. Blake** (cblake@fordham.edu) July 2, 2007
More informationA Comparison of the Results in Barber, Odean, and Zhu (2006) and Hvidkjaer (2006)
A Comparison of the Results in Barber, Odean, and Zhu (2006) and Hvidkjaer (2006) Brad M. Barber University of California, Davis Soeren Hvidkjaer University of Maryland Terrance Odean University of California,
More informationOnline Appendix to. The Value of Crowdsourced Earnings Forecasts
Online Appendix to The Value of Crowdsourced Earnings Forecasts This online appendix tabulates and discusses the results of robustness checks and supplementary analyses mentioned in the paper. A1. Estimating
More informationThe Effect of Kurtosis on the Cross-Section of Stock Returns
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2012 The Effect of Kurtosis on the Cross-Section of Stock Returns Abdullah Al Masud Utah State University
More informationPrinciples of Finance
Principles of Finance Grzegorz Trojanowski Lecture 7: Arbitrage Pricing Theory Principles of Finance - Lecture 7 1 Lecture 7 material Required reading: Elton et al., Chapter 16 Supplementary reading: Luenberger,
More informationVolatility Appendix. B.1 Firm-Specific Uncertainty and Aggregate Volatility
B Volatility Appendix The aggregate volatility risk explanation of the turnover effect relies on three empirical facts. First, the explanation assumes that firm-specific uncertainty comoves with aggregate
More informationDebt/Equity Ratio and Asset Pricing Analysis
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies Summer 8-1-2017 Debt/Equity Ratio and Asset Pricing Analysis Nicholas Lyle Follow this and additional works
More informationStatistical Understanding. of the Fama-French Factor model. Chua Yan Ru
i Statistical Understanding of the Fama-French Factor model Chua Yan Ru NATIONAL UNIVERSITY OF SINGAPORE 2012 ii Statistical Understanding of the Fama-French Factor model Chua Yan Ru (B.Sc National University
More informationThe cross section of expected stock returns
The cross section of expected stock returns Jonathan Lewellen Dartmouth College and NBER This version: March 2013 First draft: October 2010 Tel: 603-646-8650; email: jon.lewellen@dartmouth.edu. I am grateful
More informationDIVIDEND POLICY AND THE LIFE CYCLE HYPOTHESIS: EVIDENCE FROM TAIWAN
The International Journal of Business and Finance Research Volume 5 Number 1 2011 DIVIDEND POLICY AND THE LIFE CYCLE HYPOTHESIS: EVIDENCE FROM TAIWAN Ming-Hui Wang, Taiwan University of Science and Technology
More informationMarket Timing Does Work: Evidence from the NYSE 1
Market Timing Does Work: Evidence from the NYSE 1 Devraj Basu Alexander Stremme Warwick Business School, University of Warwick November 2005 address for correspondence: Alexander Stremme Warwick Business
More informationAlternative Benchmarks for Evaluating Mutual Fund Performance
2010 V38 1: pp. 121 154 DOI: 10.1111/j.1540-6229.2009.00253.x REAL ESTATE ECONOMICS Alternative Benchmarks for Evaluating Mutual Fund Performance Jay C. Hartzell, Tobias Mühlhofer and Sheridan D. Titman
More informationEarnings Announcement Idiosyncratic Volatility and the Crosssection
Earnings Announcement Idiosyncratic Volatility and the Crosssection of Stock Returns Cameron Truong Monash University, Melbourne, Australia February 2015 Abstract We document a significant positive relation
More informationThe Asymmetric Conditional Beta-Return Relations of REITs
The Asymmetric Conditional Beta-Return Relations of REITs John L. Glascock 1 University of Connecticut Ran Lu-Andrews 2 California Lutheran University (This version: August 2016) Abstract The traditional
More informationDoes Transparency Increase Takeover Vulnerability?
Does Transparency Increase Takeover Vulnerability? Finance Working Paper N 570/2018 July 2018 Lifeng Gu University of Hong Kong Dirk Hackbarth Boston University, CEPR and ECGI Lifeng Gu and Dirk Hackbarth
More informationDo Investors Value Dividend Smoothing Stocks Differently? Internet Appendix
Do Investors Value Dividend Smoothing Stocks Differently? Internet Appendix Yelena Larkin, Mark T. Leary, and Roni Michaely April 2016 Table I.A-I In table I.A-I we perform a simple non-parametric analysis
More informationDoes R&D Influence Revisions in Earnings Forecasts as it does with Forecast Errors?: Evidence from the UK. Seraina C.
Does R&D Influence Revisions in Earnings Forecasts as it does with Forecast Errors?: Evidence from the UK Seraina C. Anagnostopoulou Athens University of Economics and Business Department of Accounting
More informationALL THINGS CONSIDERED, TAXES DRIVE THE JANUARY EFFECT. Abstract
The Journal of Financial Research Vol. XXVII, No. 3 Pages 351 372 Fall 2004 ALL THINGS CONSIDERED, TAXES DRIVE THE JANUARY EFFECT Honghui Chen University of Central Florida Vijay Singal Virginia Tech Abstract
More informationThe Importance of Asset Allocation in Australia
The Importance of Asset Allocation in Australia By Michael Furey Background Between fifteen and thirty years ago there were several studies into the importance of asset allocation. Initially, Brinson,
More informationHow do business groups evolve? Evidence from new project announcements.
How do business groups evolve? Evidence from new project announcements. Meghana Ayyagari, Radhakrishnan Gopalan, and Vijay Yerramilli June, 2009 Abstract Using a unique data set of investment projects
More informationAn Analysis of the ESOP Protection Trust
An Analysis of the ESOP Protection Trust Report prepared by: Francesco Bova 1 March 21 st, 2016 Abstract Using data from publicly-traded firms that have an ESOP, I assess the likelihood that: (1) a firm
More informationA Lottery Demand-Based Explanation of the Beta Anomaly. Online Appendix
A Lottery Demand-Based Explanation of the Beta Anomaly Online Appendix Section I provides details of the calculation of the variables used in the paper. Section II examines the robustness of the beta anomaly.
More informationThe Effects of Increasing the Early Retirement Age on Social Security Claims and Job Exits
The Effects of Increasing the Early Retirement Age on Social Security Claims and Job Exits Day Manoli UCLA Andrea Weber University of Mannheim February 29, 2012 Abstract This paper presents empirical evidence
More informationJournal Of Financial And Strategic Decisions Volume 10 Number 2 Summer 1997 AN ANALYSIS OF VALUE LINE S ABILITY TO FORECAST LONG-RUN RETURNS
Journal Of Financial And Strategic Decisions Volume 10 Number 2 Summer 1997 AN ANALYSIS OF VALUE LINE S ABILITY TO FORECAST LONG-RUN RETURNS Gary A. Benesh * and Steven B. Perfect * Abstract Value Line
More informationDo Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?
Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings? Richard G. Sloan, 1996 The Accounting Review Vol. 71, No. 3, 289-315 1 Hongwen CAO September 25, 2018 Content
More informationStock Price Sensitivity
CHAPTER 3 Stock Price Sensitivity 3.1 Introduction Estimating the expected return on investments to be made in the stock market is a challenging job before an ordinary investor. Different market models
More informationTHE BEHAVIOUR OF GOVERNMENT OF CANADA REAL RETURN BOND RETURNS: AN EMPIRICAL STUDY
ASAC 2005 Toronto, Ontario David W. Peters Faculty of Social Sciences University of Western Ontario THE BEHAVIOUR OF GOVERNMENT OF CANADA REAL RETURN BOND RETURNS: AN EMPIRICAL STUDY The Government of
More informationUniversity of California Berkeley
University of California Berkeley A Comment on The Cross-Section of Volatility and Expected Returns : The Statistical Significance of FVIX is Driven by a Single Outlier Robert M. Anderson Stephen W. Bianchi
More informationOPTIMAL RISKY PORTFOLIOS- ASSET ALLOCATIONS. BKM Ch 7
OPTIMAL RISKY PORTFOLIOS- ASSET ALLOCATIONS BKM Ch 7 ASSET ALLOCATION Idea from bank account to diversified portfolio Discussion principles are the same for any number of stocks A. bonds and stocks B.
More informationRisk Taking and Performance of Bond Mutual Funds
Risk Taking and Performance of Bond Mutual Funds Lilian Ng, Crystal X. Wang, and Qinghai Wang This Version: March 2015 Ng is from the Schulich School of Business, York University, Canada; Wang and Wang
More informationEconomics of Behavioral Finance. Lecture 3
Economics of Behavioral Finance Lecture 3 Security Market Line CAPM predicts a linear relationship between a stock s Beta and its excess return. E[r i ] r f = β i E r m r f Practically, testing CAPM empirically
More informationMULTI FACTOR PRICING MODEL: AN ALTERNATIVE APPROACH TO CAPM
MULTI FACTOR PRICING MODEL: AN ALTERNATIVE APPROACH TO CAPM Samit Majumdar Virginia Commonwealth University majumdars@vcu.edu Frank W. Bacon Longwood University baconfw@longwood.edu ABSTRACT: This study
More informationApplied Macro Finance
Master in Money and Finance Goethe University Frankfurt Week 2: Factor models and the cross-section of stock returns Fall 2012/2013 Please note the disclaimer on the last page Announcements Next week (30
More informationAppendix. In this Appendix, we present the construction of variables, data source, and some empirical procedures.
Appendix In this Appendix, we present the construction of variables, data source, and some empirical procedures. A.1. Variable Definition and Data Source Variable B/M CAPX/A Cash/A Cash flow volatility
More informationExplaining After-Tax Mutual Fund Performance
Explaining After-Tax Mutual Fund Performance James D. Peterson, Paul A. Pietranico, Mark W. Riepe, and Fran Xu Published research on the topic of mutual fund performance focuses almost exclusively on pretax
More informationDissecting Anomalies. Eugene F. Fama and Kenneth R. French. Abstract
First draft: February 2006 This draft: June 2006 Please do not quote or circulate Dissecting Anomalies Eugene F. Fama and Kenneth R. French Abstract Previous work finds that net stock issues, accruals,
More informationReturn Reversals, Idiosyncratic Risk and Expected Returns
Return Reversals, Idiosyncratic Risk and Expected Returns Wei Huang, Qianqiu Liu, S.Ghon Rhee and Liang Zhang Shidler College of Business University of Hawaii at Manoa 2404 Maile Way Honolulu, Hawaii,
More informationEmpirical Methods for Corporate Finance. Regression Discontinuity Design
Empirical Methods for Corporate Finance Regression Discontinuity Design Basic Idea of RDD Observations (e.g. firms, individuals, ) are treated based on cutoff rules that are known ex ante For instance,
More informationA Spline Analysis of the Small Firm Effect: Does Size Really Matter?
A Spline Analysis of the Small Firm Effect: Does Size Really Matter? Joel L. Horowitz, Tim Loughran, and N. E. Savin University of Iowa, 108 PBAB, Iowa City, Iowa 52242-1000 July 23, 1996 Abstract: This
More informationThe Effect of Financial Constraints, Investment Policy and Product Market Competition on the Value of Cash Holdings
The Effect of Financial Constraints, Investment Policy and Product Market Competition on the Value of Cash Holdings Abstract This paper empirically investigates the value shareholders place on excess cash
More informationEvaluating the Selection Process for Determining the Going Concern Discount Rate
By: Kendra Kaake, Senior Investment Strategist, ASA, ACIA, FRM MARCH, 2013 Evaluating the Selection Process for Determining the Going Concern Discount Rate The Going Concern Issue The going concern valuation
More informationHow Pension Funds Manage Investment Risks: A Global Survey
Rotman International Journal of Pension Management Volume 3 Issue 2 Fall 2010 How Pension Funds Manage Investment Risks: A Global Survey Sandy Halim, Terrie Miller, and David Dupont Sandy Halim is a Partner
More informationBessembinder / Zhang (2013): Firm characteristics and long-run stock returns after corporate events. Discussion by Henrik Moser April 24, 2015
Bessembinder / Zhang (2013): Firm characteristics and long-run stock returns after corporate events Discussion by Henrik Moser April 24, 2015 Motivation of the paper 3 Authors review the connection of
More informationCan Rare Events Explain the Equity Premium Puzzle?
Can Rare Events Explain the Equity Premium Puzzle? Christian Julliard and Anisha Ghosh Working Paper 2008 P t d b J L i f NYU A t P i i Presented by Jason Levine for NYU Asset Pricing Seminar, Fall 2009
More informationCapital allocation in Indian business groups
Capital allocation in Indian business groups Remco van der Molen Department of Finance University of Groningen The Netherlands This version: June 2004 Abstract The within-group reallocation of capital
More informationRisk-Based Performance Attribution
Risk-Based Performance Attribution Research Paper 004 September 18, 2015 Risk-Based Performance Attribution Traditional performance attribution may work well for long-only strategies, but it can be inaccurate
More informationIn Search of Distress Risk
In Search of Distress Risk John Y. Campbell, Jens Hilscher, and Jan Szilagyi Presentation to Third Credit Risk Conference: Recent Advances in Credit Risk Research New York, 16 May 2006 What is financial
More informationThe Role of Credit Ratings in the. Dynamic Tradeoff Model. Viktoriya Staneva*
The Role of Credit Ratings in the Dynamic Tradeoff Model Viktoriya Staneva* This study examines what costs and benefits of debt are most important to the determination of the optimal capital structure.
More informationInvestment Performance of Common Stock in Relation to their Price-Earnings Ratios: BASU 1977 Extended Analysis
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2015 Investment Performance of Common Stock in Relation to their Price-Earnings Ratios: BASU 1977 Extended
More informationPersistence in Mutual Fund Performance: Analysis of Holdings Returns
Persistence in Mutual Fund Performance: Analysis of Holdings Returns Samuel Kruger * June 2007 Abstract: Do mutual funds that performed well in the past select stocks that perform well in the future? I
More informationFurther Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds. Kevin C.H. Chiang*
Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds Kevin C.H. Chiang* School of Management University of Alaska Fairbanks Fairbanks, AK 99775 Kirill Kozhevnikov
More informationOn Diversification Discount the Effect of Leverage
On Diversification Discount the Effect of Leverage Jin-Chuan Duan * and Yun Li (First draft: April 12, 2006) (This version: May 16, 2006) Abstract This paper identifies a key cause for the documented diversification
More informationIs Information Risk Priced for NASDAQ-listed Stocks?
Is Information Risk Priced for NASDAQ-listed Stocks? Kathleen P. Fuller School of Business Administration University of Mississippi kfuller@bus.olemiss.edu Bonnie F. Van Ness School of Business Administration
More informationDifferential Pricing Effects of Volatility on Individual Equity Options
Differential Pricing Effects of Volatility on Individual Equity Options Mobina Shafaati Abstract This study analyzes the impact of volatility on the prices of individual equity options. Using the daily
More informationMARKET COMPETITION STRUCTURE AND MUTUAL FUND PERFORMANCE
International Journal of Science & Informatics Vol. 2, No. 1, Fall, 2012, pp. 1-7 ISSN 2158-835X (print), 2158-8368 (online), All Rights Reserved MARKET COMPETITION STRUCTURE AND MUTUAL FUND PERFORMANCE
More informationWorking Paper No Stock Market Liberalizations and the Repricing of Systematic Risk. Anusha Chari * Peter Blair Henry ** June 2001
Working Paper No. 101 Stock Market Liberalizations and the Repricing of Systematic Risk by Anusha Chari * Peter Blair Henry ** June 2001 Stanford University John A. and Cynthia Fry Gunn Building 366 Galvez
More informationLong-run Consumption Risks in Assets Returns: Evidence from Economic Divisions
Long-run Consumption Risks in Assets Returns: Evidence from Economic Divisions Abdulrahman Alharbi 1 Abdullah Noman 2 Abstract: Bansal et al (2009) paper focus on measuring risk in consumption especially
More informationAssessing the reliability of regression-based estimates of risk
Assessing the reliability of regression-based estimates of risk 17 June 2013 Stephen Gray and Jason Hall, SFG Consulting Contents 1. PREPARATION OF THIS REPORT... 1 2. EXECUTIVE SUMMARY... 2 3. INTRODUCTION...
More informationSPECULATIVE TRADING IN REITS
SPECULATIVE TRADING IN REITS Benjamin M. Blau a and Ryan J. Whitby b Abstract: The role of speculative trading in markets is often debated. The recent extremes in the real estate economic cycle has created
More informationRegression Discontinuity and. the Price Effects of Stock Market Indexing
Regression Discontinuity and the Price Effects of Stock Market Indexing Internet Appendix Yen-Cheng Chang Harrison Hong Inessa Liskovich In this Appendix we show results which were left out of the paper
More informationCopyright 2011 Pearson Education, Inc. Publishing as Addison-Wesley.
Appendix: Statistics in Action Part I Financial Time Series 1. These data show the effects of stock splits. If you investigate further, you ll find that most of these splits (such as in May 1970) are 3-for-1
More informationAbsolute Alpha by Beta Manipulations
Absolute Alpha by Beta Manipulations Yiqiao Yin Simon Business School October 2014, revised in 2015 Abstract This paper describes a method of achieving an absolute positive alpha by manipulating beta.
More informationVALCON Morningstar v. Duff & Phelps
VALCON 2010 Size Premia: Morningstar v. Duff & Phelps Roger J. Grabowski, ASA Duff & Phelps, LLC Co-author with Shannon Pratt of Cost of Capital: Applications and Examples, 3 rd ed. (Wiley 2008) and 4th
More informationPremium Timing with Valuation Ratios
RESEARCH Premium Timing with Valuation Ratios March 2016 Wei Dai, PhD Research The predictability of expected stock returns is an old topic and an important one. While investors may increase expected returns
More informationAdditional Evidence and Replication Code for Analyzing the Effects of Minimum Wage Increases Enacted During the Great Recession
ESSPRI Working Paper Series Paper #20173 Additional Evidence and Replication Code for Analyzing the Effects of Minimum Wage Increases Enacted During the Great Recession Economic Self-Sufficiency Policy
More informationDay-of-the-Week Trading Patterns of Individual and Institutional Investors
Day-of-the-Week Trading Patterns of Individual and Instutional Investors Hoang H. Nguyen, Universy of Baltimore Joel N. Morse, Universy of Baltimore 1 Keywords: Day-of-the-week effect; Trading volume-instutional
More informationBiases in the IPO Pricing Process
University of Rochester William E. Simon Graduate School of Business Administration The Bradley Policy Research Center Financial Research and Policy Working Paper No. FR 01-02 February, 2001 Biases in
More informationPost-Earnings-Announcement Drift: The Role of Revenue Surprises and Earnings Persistence
Post-Earnings-Announcement Drift: The Role of Revenue Surprises and Earnings Persistence Joshua Livnat Department of Accounting Stern School of Business Administration New York University 311 Tisch Hall
More information