The valuation of high-tech "New Economy" companies.

EXECUTIVE SUMMARY

This study explores the valuation of high-tech "New Economy" stocks by comparing the value relevance of three key performance measures. earnings before discontinued operations and extraordinary items, sales revenues, and cash flows from operations, for high tech companies

over the period 1990 through 1999. The results are consistent with the hypothesis that sales revenues outperform earnings before discontinued operations and extraordinary items and cash flows from operations for high tech companies in the 90's. The evidence obtained is especially meaningful for loss firms: while negative earnings demonstrate an anomalous negatively significant relation with stock prices, sales revenues, on the other hand, convincingly document a positive and significant one.

INTRODUCTION

The bubble burst of high-tech "New Economy" companies in April 2000 has left many unanswered questions, one of which, for instance, is the valuation of these companies and the value relevance of key performance measures such as earnings and cash flows. "New economy" requires new approaches for valuation. Old-fashioned price-to-earnings ratios, market-to-book ratios, dividend yields, and discounting net cash flows were not applicable because these companies did not have earnings (nor pay dividends) and were not expected to have earnings (nor pay dividends) in the years to come. In fact, the existence of earnings was seen as a negative, demonstrating a lack of entrepreneurial aggressiveness and commitment. Very often, valuations were based on such imaginative concepts as customer growth, web site visits, peer group comparison, "engaged shoppers," and "leading mind share," terms hardly measurable and recognizable for accountants and accounting researchers (Jahnke, 2000, 2001)

How to evaluate high-tech "New Economy" companies, however, remains an important and relatively new area of accounting research. Accountants and accounting researchers are interested in the effectiveness of the accounting system in capturing firm value. Accountants and accounting researchers want to know which line items are, if earnings and cash flows are probably not, value relevant for "New Economy" firms (Keating, 2000).

One promising candidate is sales. Sales are much harder to "manage." The explosion in high tech stocks forced investors to look for ways to value companies with lots of potential, but no earnings. The rising popularity of the PSR (price/sales ratio) reflects investor belief that it is more important for high tech companies to grow revenue than profit, and that revenue is proxy for marketplace acceptance and market valuation (O'Shaughnessy, 1998). This study hypothesizes that sales is more value relevant than earnings before discontinued operations and extraordinary items and cash flows from operations. We regress high-tech stock prices against earnings before discontinued operations and extraordinary items, cash flows from operations and sales respectively each year from 1990 to 1999. Evidence from this study suggests that sales outperform earnings and operating cash flows in the valuation of high-tech "New Economy" companies, especially for loss firms.

This study adds to the current literature by comparing the value relevance of key performance measures for the high-tech sector. Section 2 is a review of the literature of the valuation of high-tech new economy companies. In section 3 we give a brief description about the working data sample drawn from 2001 Compustat Research Insight and provide descriptive statistics of the sample. Section 4 discusses the evidence obtained from our empirical test model. Section 5 summarizes and concludes.

THE VALUATION OF HIGH-TECH "NEW ECONOMY" COMPANIES

The valuation of high-tech "New Economy" companies is a very challenging one. High-tech "New Economy" companies do not usually report profits or positive operating cash flows. They do generate sales revenues and have big investments in intangible assets and large R&D expenditures. Because of this, many high-tech valuation researches study the value relevance of R&D, intangible assets, and sales. SFAS # 2 requires R&D be fully expensed because no significant correlation is found between R&D expenditures and subsequent benefits such as increase in sales, profit or market share. Lev and Sougiannis (1996) investigate the FASB concerns with the reliability, objectivity, and value-relevance of R&D capitalization. They estimate the R&D capital of a large sample of public companies and find that R&D adjustments are value-relevant to investors. They also demonstrate a significant inter-temporal association between firms' R&D capital and subsequent stock returns, suggesting either a systematic mispricing of the shares of R&D-intensive companies, or a compensation for an extra-market risk factor associated with R&D.

Aboody & Lev (1998) examine the value relevance of information on the capitalization of software development costs, in accordance with SFAS 86. Software capitalization pertains to the development component of R&D. Contemporaneous as well as intertemporal analyses for a sample of 163 firms during the period 1987-95 indicate that capitalization-related variables are significantly associated with capital market variables and future earnings. It is concluded that software capitalization summarizes information relevant to investors.

Today's production requires not only capital and labor, but also skills, organizational structures and processes, software, brand name, culture, and other factors collectively referred to as "intangible assets." They are often large in magnitude and have important productivity benefits. Investors also attempt to incorporate intangible assets into their valuation of firms, and this is one reason that the market value of a firm may differ markedly from the value of its tangible assets alone. Barth et al, (2001) examines the relation between analysts' incentives to cover firms and the extent of their intangible assets. Because intangible assets typically are unrecognized and estimates of their fair values are not disclosed, absent analyst coverage firms with more intangible assets likely have less informative prices. They find that analyst coverage is significantly greater for firms with larger research and development and advertising expenses relative to their industry. For firms in industries with larger research and development expense, analyst coverage is increasing in firm size, growth, trading volume, equity issuance, and perceived mispricing. The evidence indicates that intangible assets, most of which are not recognized in firms' financial statements, are value relevant and thus offer greater incentives for analyst coverage.

Brynjolfsson et al, (2002) explores value relevance of intangible organizational assets using data on 1,216 large firms over eleven years (1987-97). Evidence obtained from the study support their hypotheses that investment in high tech intangible assets increases a firm market valuation. Firms that are intensive IT users are also more likely to adopt work practices that involve a specific cluster of organizational characteristics, including greater use of teams, broader distribution of certain decision rights, and increased worker training. This cluster of organizational characteristics increases a firm's market valuation beyond what can be accounted for by tangible assets.

Barron at al, (2002) studies the association between firms' intangible assets and properties of the information contained in analysts' earnings forecasts. They find that the consensus in analysts' forecasts is negatively associated with a firm's level of intangible assets and that the degree to which the mean forecast aggregates private information and is more accurate than an individual analyst's forecast increases with a firm's intangible assets. Additional analysis reveals that lower levels of analyst consensus are associated with high-technology manufacturing companies, and that this association is explained by the relatively high R&D expenditures made by these firms.

Bartov et al, (2002) explores the differences between valuations of Internet and non-Internet firms, especially at the prospectus and final IPO stage. They conclude that the valuation of non-Internet firms generally follows the conventional wisdom: positive earnings and cash flows are priced, while negative earnings and negative cash flows are not. The valuation of Internet firms, however, departs from convention, with earnings not being priced, and negative cash flows being priced perhaps because they are viewed as investments. These differences are significant at the end of the first trading day. The difference in valuation, for non-Internet firms, is largely ascribed to the relative offering size. For Internet firms, however, the differences are attributed mostly to cash flows, sales growth, and R&D.

Davis (2002) investigates the market's response to revenue and revenue announcements and whether the value relevance of revenue differs when Internet firms report grossed-up or barter revenue. His results indicate that revenue announcements are highly associated with 3-day market returns and provide information incremental to that contained in earnings announcements. The use of grossed-up and barter revenue is common for certain sectors of Internet firms, but not pervasive across sectors. Evidence suggests that the value relevance of revenue for firms reporting grossed-up and barter revenue declined subsequent to the "crash" in April 2000. He further explores the effect of active individual investors on the pricing of revenue for firms reporting grossed-up and barter revenue and finds higher pricing of revenue for firms reporting grossed-up or barter revenue with relatively greater individual investor following.

SAMPLE SELECTION AND SUMMARY STATISTICS

Sample for the high tech "new economy" companies in this study includes firms in the drug, computer, networking and telecommunication industries. Table 1 is a description of the industries in the sample in the three-digit SIC code. These industries are also believed to be among the most active in business combinations and have relatively bigger expense on amortization of intangibles.

Data used in this study are from Standard & Poor's 2001 Compustat Research Insight. All variables in this study are measured on a per share basis. Firm-year observations are eliminated of which (1) December is not the fiscal year-end, (2) stock price three months after the fiscal year end is missing or negative, (3) sales are negative, and (4) earnings per share data is missing. The data set consists of 8,992 usable firm-year observations from 1990 to 1999, as this is the period that high tech companies are at their peak growth and before the crash of 2000.

Table 2 reports the descriptive statistics for the selected sample. [P.sub.t] (PRICE) is cure-dividend price of the firm's stock price three months after the end of the fiscal year t plus its dividend per share for year t. EP[S.sub.t] is the reported net income per share after taxes but before extraordinary items for year t. OC[F.sub.t] is the operating cash flows, and [Sale.sub.t] is the firm's total sales revenues, at year t divided by the total number of common shares outstanding. After data treatment, we have a total of 8,892 usable firm-year observations. The ten-year mean for stock price per share is $16.049, for the reported net income per share after taxes but before extraordinary items is a loss of $0.843, for cash flows from operations per share is a negative $0.064, and for sales per share is $7.612. The data itself is revealing. During the ten-year period, stock price of the high-tech "New Economy" firms can be as high as $850 and as low as 3 cents. The mean of earnings per share is negative every single year and cash flows from operations are also negative five out of the ten years, with a negative mean for all ten-year observations.

TEST RESULT AND DISCUSSIONS

Table 3 reports the results of regressing stock price against the traditional earnings per share each year during the ten years from 1990 to 1999. The estimated coefficient on the traditional earnings per share after taxes but before extraordinary items is positively significant in only three of the ten-year period, but negatively significant in six of the same ten years. It is very much the same case with cash flows. The estimated coefficient on the operating cash flows per share is both positive and significant in only four, while negative and significant in other five, of the ten-year time. For sales, the test results are totally different. The estimated coefficient on sales per share is both positive and highly significant in each and every one of the ten-year time period. Evidence strongly suggests that sales is more value relevant than earnings and cash flows for high-tech "New Economy" companies in the decade of the 1990's.

This study then divides firm-year observations into profit firms for those reporting positive earnings per share and loss firms for those reporting losses. Table 4 reports the test results for the profit firms of the 90's. The estimated coefficient of regressing stock prices on the traditional earnings per share is both positive and significant in every year during the ten-year period. The results are very much the same when regressing stock prices against cash flows or sales: for either operating cash flows per share or sales per share, the estimated coefficient is both positive and significant in nine of the ten years. The mean adjusted [R.sup.2] of regressing price against EPS is 46.2 percent, much higher than the 29.4 percent against cash flows and the 29.6 percent against sales, suggesting that the traditional earnings per share after taxes but before extraordinary items is more value relevant than operating cash flows and sales for those high-tech "New Economy" companies that report positive earnings in the 90's. The average coefficient estimate indicates that every $;1 of earnings per share corresponds to $11.21 of market price. The average adjusted [R.sup.2] of 46.2 percent indicates that the traditional earnings per share explains 46.2 percent of the variation in equity market values, which is very much in line with reported results of market-based accounting research in this area.

The results, for the loss firms, of regressing stock price against the traditional earnings per share, the operating cash flows per share, and the operating cash flows per share from 1990 to 1999 are reported in Table 5. The estimated coefficient on the traditional earnings per share after taxes but before extraordinary items is negative and significant in nine of the ten-year period. The estimated coefficient on the operating cash flows per share is negative and significant in seven of the ten-year period. The negatively significant relationship between stock prices and traditional earnings per share is anomalous, because it suggests that the bigger the loss, the higher the stock price. The negatively significant relationship between stock prices and operating cash flows per share is not easily explainable, even though it is suggested that negative operating cash flows imply aggressive growth and is therefore valued by the stock market. Only the relationship between stock prices and traditional earnings per share is normal. The estimated coefficient on sales per share is both positive and significant in seven of the ten years. Evidence suggests that for high-tech loss firms in the 90's, only sales is value relevant.

SUMMARY AND CONCLUDING REMARKS

This study regresses stock prices against traditional earnings per share after taxes but before extraordinary items, cash flows from operations, and sales revenues for high tech companies over the period 1990 through 1999 using the data of 2001 Compustat Research Insight Database. The results indicate that sales revenues outperform traditional earnings per share and cash flows from operations for high-tech companies in the 90's. The evidence obtained is especially meaningful and strong for loss firms: while negative earnings and negative cash flows demonstrate an anomalous negatively significant relation with stock prices, sales revenues, on the other hand, convincingly document a positive and significant one. Evidence from this study suggests that sales are more value relevant and outperform earnings and operating cash flows in the valuation of high-tech "New Economy" companies, especially for loss firms.

REFERENCES

Aboody, D. and B. Lev. (1998). The value relevance of intangibles: The case of software capitalization. Journal of Accounting Research 36:161-191.

Barton, O.E., D. Byard, C. Kile, & E.J. Riedl. (2002). High-technology intangibles and analysts' forecasts. Journal of Accounting Research 40 (2): 289-312.

Barth, M.E., R. Kasznik, & M.F. McNichols. (2001). Analyst coverage and intangible assets. Journal of Accounting Research 39 (1):1-34.

Bartov, E., P. Mohanram, & C. Seethamraju. (2002). Valuation of Internet stocks-an IPO perspective. Journal of Accounting Research 40 (2): 321-346.

Brynjolfsson, E., L.M Hitt, & S. Yang. (2002). Intangible assets: Computers and organizational capital. Brookings Papers on Economic Activity 1:137-198.

Davis, A.K. (2002). The value of relevance of revenue for Internet firms: Does reporting grossed-up or barter revenue make a difference? Journal of Accounting Research 40 (2): 445-477.

Francis, J., and K. Schipper. (1999). Have financial statements lost their relevance? Journal of Accounting Research 37:319-352.

Jahnke, W. (2000). Valuing new economy stocks. Journal of Financial Planning 13 (6): 46-48.

Jahnke, W. (2001). Tech wreck. Journal of Financial Planning 14 (6): 46-48.

Keating, E.K. (2000). Discussion of the eyeballs have it: Searching for the value in Internet stocks. Journal of Accounting Research 38: 163-169.

Lev, B. and T. Sougiannis. (1996). The capitalization, amortization, and value-relevance of R&D. Journal of Accounting & Economics 21(1): 107-138.

O'Shaughnessy, J.P. (1998). What works on wall street. McGraw-Hill.

Lianzan Xu (xul@wpunj.edu) is a professor of Accounting at William Paterson University of New Jersey

Francis Cai (caiF@wpunj.edu) is a professor of Finance at William Paterson University of New Jersey

TABLE 1
Industries in the High Tech "New Economy" Sample*

283    Drugs
357    Computer and Office Equipment
360    Electrical Machinery and Equipment, Excluding Computers
361    Electrical Transmissions and Distribution and Equipment
362    Electrical Industrial Apparatus
363    Household Appliances
364    Electrical Lighting and Wiring Equipment
365    Household Audio, Video Equipment, Audio Receiving
366    Communication Equipment
367    Electronic Components, Semiconductors
368    Computer Hardware (Including Mini, Micro, Mainframes, Terminals,
       Discs, Tape Drives, Scanners, Graphics Systems, Peripherals, and
       Equipment)
481    Telephone Communications
737    Computer Programming, Software, Data Processing
873    Research, Development, Testing Services

* The three digit SIC codes and names of the industries are reported.
Industries are selected based on, among other reasons, whether firms
in the industry are likely to have significant intangible assets,
reported or unreported. (Jennifer Francis and Katherine Schipper, 1999)

TABLE 2
Descriptive Statistics for Price, EPS, Sales, and Operating Cash Flows

    Year        Obs.               [P.sub.t] (PRICE)

                         Mean     StdDev    Minimum    Maximum

     90           444   11.751     21.332      0.031   328.828
     91           496   14.301     20.115      0.062   205.000
     92           573   14.837     35.507      0.156   650.001
     93           684   17.359     52.954      0.125   850.000
     94           764   13.891     23.897      0.016   469.000
     95           980   16.139     20.455      0.010   301.000
     96          1202   12.476     14.495      0.001   306.000
     97          1265   15.888     15.858      0.015   126.000
     98          1167   16.594     21.282      0.030   168.375
     99          1417   27.256     29.184      0.160   242.875
 Total/Mean      8992   16.049     25.508      0.061   364.708

    Year                            [OCFs.sub.t]

                         Mean     StdDev     Minimum   Maximum

     90           444    0.559      2.482    -21.214    14.551
     91           496    0.386      3.998    -59.330    50.226
     92           573   -0.015      6.248   -126.758    12.130
     93           684   -1.597     31.989   -802.398     9.757
     94           764   -0.256     10.254   -255.409    14.473
     95           980   -0.060      6.588   -139.861    79.770
     96          1202    0.147      3.634    -59.488    81.390
     97          1265    0.145      2.438    -41.646    24.094
     98          1167    0.082      2.291    -36.476    11.468
     99          1417   -0.034      3.475   -112.142    10.757
 Total/Mean      8992   -0.064      7.340   -165.472    30.862

    Year                     [EPS.sub.t]

                Mean    StdDev    Minimum    Maximum

     90        -0.322    2.716    -26.800      6.250
     91        -0.299    2.481    -28.200     11.000
     92        -0.333    3.120    -55.400      9.990
     93        -2.235   30.606   -768.908      3.190
     94        -1.655   18.069   -364.318     10.840
     95        -0.596    5.574   -127.451     33.240
     96        -0.450    4.458    -67.927     93.820
     97        -0.617    3.128    -63.700      6.050
     98        -0.693    3.185    -60.620      4.890
     99        -1.232    8.326   -186.690     14.680
 Total/Mean    -0.843    8.166   -175.001     19.395

    Year                    [Sales.sub.t]

                Mean    StdDev    Minimum    Maximum

     90        10.127   17.597          0    169.714
     91         8.056   11.139          0     97.214
     92         8.605   13.517          0    151.840
     93         9.242   37.617          0    894.556
     94         7.716   12.307          0    158.418
     95         7.562   15.710          0    346.050
     96         6.771   14.892          0    380.820
     97         6.244    9.865          0    114.493
     98         6.488   10.126          0    135.670
     99         5.310    9.030          0    141.157
 Total/Mean     7.612   15.180          0    258.993

TABLE 3
Summary Statistics from Regression of Price on EPS, Sales, & Cash Flows
All Firms 1990-1999

                [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                        + [[epsilon].sub.t]

Year      N       [beta]         t         Adj[R.sup.2]

  90      444     -1.678     -4.60 **         0.044
  91      496     -1.493     -4.16 **         0.032
  92      573     -3.025     -6.59 **         0.069
  93      684     -1.12     -22.17 **         0.418
  94      764     -0.442     -9.79 **         0.111
  95      980     -1.157    -10.39 **         0.099
  96     1202      1.016     11.40 **         0.097
  97     1265      0.478      3.37 **         0.008
  98     1167      0.332      1.70 *          0.002
  99     1417      0.15       1.61            0.001

                [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                        + [[epsilon].sub.t]

Year              [beta]         t         Adj[R.sup.2]

  90               2.485      6.35 **         0.082
  91              -0.139     -0.61           -0.001
  92              -1.929     -8.62 **         0.114
  93              -1.073    -22.22 **         0.419
  94              -0.626     -7.69 **         0.071
  95              -0.426     -4.34 **         0.018
  96               1.662     15.87 **         0.173
  97               1.78      10.11 **         0.074
  98               1.938      7.28 **         0.043
  99               0.808      3.64 **         0.009

                [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                        + [[epsilon].sub.t]

Year              [beta]         t         Adj[R.sup.2]

  90               0.534     10.32 **         0.192
  91               0.39       4.92 **         0.045
  92               0.556      5.18 **         0.043
  93               0.892     21.4 **          0.401
  94               0.602      8.99 **         0.095
  95               0.66      18.40 **         0.256
  96               0.659     31.83 **         0.457
  97               0.767     19.31 **         0.227
  98               0.662     11.33 **         0.098
  99               0.361      4.23 **         0.012

Hereafter, * indicates significant at the 10% level,
while ** 1% level.)

TABLE 4
Summary Statistics from Regression of Price on EPS, Sales, & Cash Flows
Profit Firms 1990-1999

               [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                         + [[epsilon].sub.t]

Year      N      [beta]         t         Adj[R.sup.2]

  90     252     14.601     26.99 **         0.743
  91     283     11.314     21.42 **         0.619
  92     329     12.558      9.99 **         0.232
  93     369     14.255     19.96 **         0.519
  94     422     11.174     23.21 **         0.561
  95     503      9.232     27.90 **         0.608
  96     570      3.423     30.18 **         0.615
  97     530     13.949     18.58 **         0.394
  98     468     15.185     13.47 **         0.279
  99     495      6.398      5.09 **         0.048
Mean             11.209     19.68**          0.462

               [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                         + [[epsilon].sub.t]

Year             [beta]         t         Adj[R.sup.2]

  90              4.176     12.42 **         0.379
  91              2.177      4.46 **         0.063
  92             -4.588     -9.38 **         0.210
  93              3.623     11.28 **         0.255
  94              4.292     12.79 **         0.279
  95              3.725     24.53 **         0.545
  96              3.842     34.57 **         0.677
  97              3.636     11.69 **         0.204
  98              7.606     12.73 **         0.257
  99              5.405      6.27 **         0.072
Mean              3.390     12.14 **         0.294

               [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                         + [[epsilon].sub.t]

Year             [beta]         t         Adj[R.sup.2]

  90              0.543     12.87 **         0.396
  91              0.740     11.99 **         0.336
  92              1.235     12.18 **         0.310
  93              0.543     13.09 **         0.317
  94              0.477     10.24 **         0.198
  95              0.709     22.40 **         0.499
  96              0.668     29.05 **         0.597
  97              0.661     12.12 **         0.216
  98              0.528      6.73 **         0.087
  99              0.117      1.51            0.003
Mean              0.622     13.21 **         0.296

TABLE 5
Summary Statistics from Regression of Price on EPS, Sales, & Cash Flows
Loss Firms 1990-1999

               [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                         + [[epsilon].sub.t]

Year      N      [beta]         t         Adj[R.sup.2]

  90     192     -3.711      -7.60 **        0.229
  91     213     -3.636      -7.25 **        0.196
  92     244     -4.885      -8.02 **        0.207
  93     315     -1.123     -15.35 **        0.428
  94     342     -0.485      -7.94 **        0.154
  95     477     -1.912     -20.59 **        0.470
  96     632     -1.002     -12.86 **        0.207
  97     735     -0.828      -7.01 **        0.062
  98     699     -0.957      -5.23 **        0.036
  99     922     -0.020      -0.23          -0.001

               [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                         + [[epsilon].sub.t]

Year             [beta]         t         Adj[R.sup.2]

  90              0.951       1.23           0.004
  91             -0.426      -1.36           0.004
  92             -1.702      -5.55 **        0.109
  93             -1.078     -15.47 **        0.431
  94             -0.736      -6.64 **        0.112
  95             -1.488     -16.09 **        0.351
  96             -1.306     -11.99 **        0.184
  97             -0.690      -3.61 **        0.016
  98             -0.780      -2.69 **        0.009
  99             -0.400      -0.18          -0.001

               [P.sub.t] = [alpha] + [beta][EPS.sub.t]
                         + [[epsilon].sub.t]

Year             [beta]         t         Adj[R.sup.2]

  90              0.516      4.88 **         0.107
  91             -0.107     -0.63           -0.003
  92              0.015      0.08           -0.004
  93              0.914     14.77 **         0.409
  94              0.711      5.17 **         0.070
  95              0.377      3.62 **         0.025
  96              0.233      3.57 **         0.018
  97              0.487      7.34 **         0.067
  98              0.466      3.95 **         0.021
  99             -0.028     -0.16           -0.001