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Strategic Revenue Management: Revenue Stability and Maximizing Shareholder Value.


In the competition for investor capital, organizations strive to provide increasingly positive rates of return to the investor. For publically traded corporations, the returns are generally measured in terms of shareholder value, i.e., the total return on investment, also commonly referred to as the 'holding period return', which is computed from the change in the share price plus dividends. The stock price has generally been viewed as a reflection of the investor's expectations of the corporation's future earnings and earnings growth, a subject where extant studies have devoted considerable investigations (e.g., into the relationships of historical and projected earnings on the changes in stock price and maximizing shareholder value) (Boudoukh, Richardson, & Whitelaw, 2008; Fama & French, 2017). Yet, a corporation's earnings are a computational result of two related variables, revenue and cost (i.e., revenue minus cost equals earnings). Wasilewski (2013) has argued that, for the existence of corporations, since a cost structure likely exists whether or not revenue exists, then the existence of revenue drives the existence of earnings. Thus, while cost structures are important to the derivation of the resultant earnings, the generation and growth of revenue are crucial (perhaps necessary) to the generation and growth of the earnings outcome. Expectations of future earnings are, thus, effectively 'derived' from the expectations of future revenues. However, an initial review of the literature, as noted above, found that prior studies are heavily dominated by earnings and earnings growth linked to shareholder value, and while there is a robust literature around the subject of revenue and revenue growth, the latter tends to encompass such topics as has revenue enhancement, customer service, product expansion, and the like (see Olson, van Bever, & Verry, 2008). However, there seems to be little discussion as to how to incorporate revenue and revenue growth directly into the maximized shareholder value model, the latter of which is the subject of this paper. In addition, as discussed below, this paper adds another normally ignored revenue variable, revenue momentum.

An important consideration to the holding period return is the time horizon of the return, short-term versus long-term (where short-term and long-term are usually considered, respectively, quarterly (or annual) and at least 5-year periods). It is generally assumed that investors seek high levels of return over the long term. Consistently, Antia, Pantzalis, and Park (2010) have found that corporations where CEOs had shorter time decision horizons were associated with lower firm valuations (i.e., stock market value), which suggests that corporate strategic decisions should focus on long-term results. However, investors also look to maximize shareholder value in the short-term and, thus, corporate strategic decision makers are also pressured to provide for short-term (e.g., quarterly) results. The foregoing indicates that corporate strategic decision makers need to attain a balance between short-term and long-term financial performance (Welch & Welch, 2007). An initial review of the literature found extensive attention to the long-term (see Boudoukh et al., 2008; Fama & French, 2017), however, relatively little attention to the short-term generation, growth, and stability of revenue, as related to maximizing shareholder value, the latter of which will be a subject of this paper.

Thus, this paper offers insight into the question: how should revenue be strategically managed in the short-term to maximize shareholder wealth?

This paper begins with a brief discussion of the nature and role of corporate revenue, revenue growth, and revenue momentum, with aim to maximize shareholder value. Then the study data and analysis methodology are discussed, followed by the analysis results. The results are discussed with ideas for future study and implications for corporate strategic decision making.


As discussed above, revenue and revenue growth needs to be strategically managed to maximize short-term shareholder wealth (through holding period returns). But knowledge of the revenue and revenue growth while perhaps necessary information to the investor, is likely to be insufficient for making future investment decisions (e.g., expectations of future share prices). This is because such information is already known to the investor and is likely, therefore, to have been incorporated into the current share price. However, a change in the revenue growth rate is an indicator of the future growth rate of the corporation's revenue and, thus, should be an indicator of the direction of future holding period returns. Therefore, to maximize future shareholder value, one needs to look at the magnitude and direction of change in the revenue growth. This change in revenue growth is commonly termed the revenue momentum, where a positive and a negative number represents, respectively, an acceleration and deceleration of the revenue growth and a zero indicates that revenue growth is unchanging. For the investor, a strong positive or negative revenue momentum suggests that 'something strategic' is occurring within the corporation that may be altering the fundamental basis for the revenue stream. Mathematically, if we begin with a simple plot of revenue over time, the first derivative of the resultant curve (for a specific time period) is the revenue growth, and the second derivative (for specific time frames) would be the revenue momentum (i.e., the rate of change, hence, an indicator of the 'stability of the revenue and its growth').

The working hypothesis for this paper is that high short-term revenue momentum (STRM) will out- perform both the low STRM and the S&P 1500 on a coefficient of variation basis. The underlying basis for the above is that the market is not efficient. If this be the case, corporate strategic decision making would dictate that managers concentrate on short-term revenue efforts that would enhance the maximization of wealth. Further, it would also imply that a series of such accomplishments would, over the long-run, result in long-term wealth maximization.


The working hypothesis was tested by sectoring the S&P 1500 into revenue momentum deciles, with the top decile sector expected to outperform both the overall S&P 1500 (Equally-Weighted), as well as the bottom decile sector, on a risk-adjusted basis before transaction costs, based on the portfolio's Coefficient of Variation (CV). (Thus, this paper is conducting a test of the semi-strong form of the Efficient Market Hypothesis.)

Short Term Revenue Momentum (STRM) measures the acceleration or deceleration in revenue growth for the 12-month revenue series ending in each of the past five quarters. The 12-month data is used to avoid seasonality issues. The relationship uses the second derivative, which can result in large values either positive or negative. Since the magnitude of these values are questionable, all values are then percentiled. A value of 100 indicates the highest revenue momentum within the S&P 1500.

The deciles were rebalanced on a quarterly basis. The research period is thirteen years, from December 31, 2002 through December 31, 2015. The CV analysis was applied against intermediate or yearly period data. While the statistics for a total run period seldom vary for the intermediate period, the intermediate yearly periods are far more important to investment managers due to the drawdown character of investment management.

Dividing the S&P 1500 into ten portfolios based on revenue momentum alone was done to assure efficient diversification. While industry and/or sector groups should be accounted for in normal portfolio construction, the ten 150 security portfolios obtained from deciles of the S&P 1500 mitigates this problem.

This paper, as noted, will explore the total portfolio's return on a risk-adjusted basis through the above noted hypothesis. Only one data source, Ford Equity Research of San Diego, was used in this study. Ford Equity Research is a data vendor with proprietary models for investment managers globally and is affiliated with Mergent through stock ownership. Mergent is a subsidiary of the London Stock Exchange.

A review of the data and methods used by Ford Equity Research is constructed such that the three most common biases in investment data, no look-ahead bias; no restatement bias; and any survivorship bias, were eliminated.

Ford Equity Research likewise, provided all variables utilized in this study. Total return includes both price changes and dividends. Dividends are included in the appropriate period based on their ex-dividend date. All returns were computed on a geometric basis, as were the standard deviations in conformity with accepted professional investment standards.

All returns were calculated on a monthly basis including the index. Note that re-balancing occurs only on a quarterly basis for the entire study period. All returns were computed equally-weighted. All stocks were selected from the noted benchmark S&P 1500 Index.

All deciles previously noted were constructed utilizing the highest positive STRM to the lowest negative STRM based on a percentile basis. The bottom decile is, therefore, also constructed as the "short" portfolio, since the research hypothesis states that it will underperform the top decile portfolio as well as the S&P 1500 (Equally-Weighted).

The selection of the sample size is a concern for all researchers. The selection of ten portfolios of 150 stocks each reduces the impact of industry concentration, especially in short time frame studies. Ideally, the number of stocks from any specific industry should be in line with the benchmark index. Even more ideally, the selected portfolio should be of the same industry weightings as the benchmark index. Unfortunately, such back-testing requires significant manual analysis and introduces questions of inappropriate manipulation of results.


The results of the investigation are presented in Table 1. The results confirmed that the high short-term revenue momentum (STRM) outperformed the low short term revenue momentum (STRM) with Coefficient of Variation (CV) of 1.66 versus 3.90. However, the hypothesized relationship of the high short-term revenue momentum (STRM) did not outperform the S&P 1500 index. The Coefficient of Variations was 1.66 versus 1.58 for the index.

The data suggest that those management teams that focus on short-term revenue advancements as a way to achieve wealth maximization will be disappointed. Investors apparently do not want high short term revenue changes as, apparently, they feel this will not continue. However, it does appear that short-term revenue downturn does have an effect. Investors have a tendency to move away from the stock.

What was not anticipated at the offset of the investigation nor hypothesized were the coefficient of variations of the mid-ranged deciles. Sectors 2 through 8 outperformed both the top decile as well as the S&P 1500 universe.

As noted above, the short-term revenue momentum (STRM) is derived from the second derivative of the past five 12 month's quarterly revenue figures. An interesting characteristic, as shown in the results in Table 1, is that a company with a high but consistent growth will have a low STRM number. In fact, such type companies will tend to fall into the middle of the deciles and not the top or bottom deciles.

The results of this study, thus, support the following key points regarding short-term strategic revenue management: (i) corporate strategic management is not rewarded (in terms of holding period returns) for large revenue growth in the presence of large positive changes in the revenue growth rate; (ii) as it seems that investors seeking short term returns prefer consistency in short-term revenue growth if corporate strategic management fails to maintain consistent revenue growth, there is likely to have an adverse impact on the firm's stock price.

There are a number of limitations to this study. For example, the study did not investigate the differences that industry or firm size may have on the study results. These variables could be considered in future studies to more fine-tune the investment portfolio to maximize shareholder wealth. In addition, only the short-term (quarterly and annualized) periods were analyzed. The long-term holding period return (for example, 5-year increments) should be investigated to compare the short- versus long-term impacts of revenue momentum on firm performance and the maximization of shareholder wealth which in turn could improve the balance of short- and long-term portfolios for maximizing shareholder wealth

The keys to the contents in Table 1 are as follows:

1. Sector 1 through Sector 10 are the revenue momentum deciles, with Sector 1 being the highest decile and Sector 10 being the lowest decile.

2. Comp Univ is the 'comparable universe', i.e., the entire S&P 1500.

3. Perf is the 'holding period return' in percent, which are the numbers in the upper part of the table.

4. The Intermediate Period refers to each of the time frames (13 years) in the upper part of the table

5. EXAMPLE (upper part of Table 1)--for the intermediate period 12/04-12/05, the holding period return was 3.9% for Sector 4.

6. EXAMPLE (lower part of Table 1)--the total cumulative return for the entire 13-year period 12/02-12/15 was 409.6% for Sector 5, with an Annualized return of 13.3%, and an Annualized return standard deviation of 1.47.

7. CV is the coefficient of variation which is the standard deviation divided by the mean; for example, for Sector 10, the Annualized STD = 22.6, the Annualized performance =5.8, and thus CV = 22.8/5.6 = 3.9. The CV is a measure of relative performance, i.e., a measure of per unit risk versus return, with a smaller number indicating less variability (a performance more consistent with the mean) and a 'better result'. (A search of the literature did not locate tests of statistical significance for the CV, so the usual 'relative comparison' approach was employed.)


Antia, M, Pantzalis, C, & Park, J. C. (2010). CEO decision horizon and firm performance: An empirical investigation. Journal of Corporate Finance, 16, 288-301.

Boudoukh, J., Richardson, M, & Whitelaw, R. (2008). The myth of long horizon predictability. Review of Financial Studies, 21, 1577-1605.

Fama, E. F., & French K. R. (2017). Long-horizon returns. Chicago Booth Paper No. 17-17, Fama-Miller Center for Research in Finance. University of Chicago.

Olson, M. S., van Bever, D., & Verry, S. (2008, March). When growth stalls. Harvard Business Review, 51-61.

Wasilewski, N. (2013). Cost structures in organizations: A strategic paradox. Management, 3(1), 39-44. doi:10.5923/

Welch J., & Welch S. (2007). That's management. Business Week, February 19, 94.

Darrol Stanley, Pepperdine University

Nikolai Wasilewski, Pepperdine University

Short Term Revenue Trend (STRT) S&P 1500 Deciled
Deciles rebalanced quarterly
Intermediate Period

                Sector 1  Sector 2  Sector 3  Sector 4  Sector 5
                Perf      Perf       Perf      Perf      Perf

12/02-12/03      39.4      42.3        46      41.7      51
12/03-12/04      16.8      12.9      20.8      22.5      19
12/04-12/05       7.7       9.2       9.3       3.9       7.6
12/05-12/06      13.6      14.4      15.8      13.9      14.6
12/06-12/07       4         8         2.1       0.1      -5
12/07-12/08     -39.6     -34.3     -39.2     -32.3     -34.9
12/08-12/09      28.5      42.9      36.7      48.7      52.8
12/09-12/10      33        31.6      29.3      32        28.6
12/10-12/11      -1.1       0.4       5.8      -3.9       3.4
12/11-12/12      21        20.4      17.7      16.9      18.8
12/12-12/13      35.5      41.3      42.9      35.7      38.4
12/13-12/14       6.6       7.9      10.7      12.2      12.5
12/14-12/15       4.2      -1.4       0.3       2.8      -0.9
Total Cumulative for Intermediate Periods
                Sector 1  Sector 2  Sector 3  Sector 4  Sector 5
                Perf       Perf      Perf      Perf      Perf
12/02-12/15     284.5     385.5     383.9     380.9     409.6
Annualized       10.9      12.9      12.9      12.8      13.3
Annualized STD   18.1      18.4      17.8      18        18.2
CV                1.66      1.43      1.36      1.41      1.47

                Sector 6  Sector 7  Sector 8  Sector 9  Sector 10
                Perf       Perf     Perf      Perf      Perf

12/02-12/03      43.9      43.7      39.8      43.1      57.4
12/03-12/04      19        17.5      21        20.6      19.1
12/04-12/05       1.8       9.6       9.6       7.7       9.8
12/05-12/06      10.5      20.9      20.4      21.7      19.9
12/06-12/07      -1.8      -0.2      -4.2      -6.9     -12.3
12/07-12/08     -33.2     -30.9     -35.8     -33.9     -47.4
12/08-12/09      45.6      50.2      56.8      64.5      49.6
12/09-12/10      31.1      24        27        19.9      14.4
12/10-12/11       1.6       2.3      -4.4      -7.3     -10.6
12/11-12/12      19.2      15.9      21.5      16.3      12.7
12/12-12/13      40.7      43.9      39.7      34.3      39.2
12/13-12/14      12.1      12.7       7.4       4.7       0.8
12/14-12/15      -0.9       0.2        -6     -14.6     -24
Total Cumulative for Intermediate Periods
                Sector 6  Sector 7  Sector 8  Sector 9  Sector 10
                 Perf      Perf      Perf      Perf      Perf
12/02-12/15     357       454.5     347.9     255.1     109.2
Annualized       12.4      14.1      12.2      10.2       5.8
Annualized STD   17.8      18.7      19.5      19.7      22.6
CV                1.43      1.33      1.6       1.93      3.9

                Comp Univ

12/02-12/03      44.5
12/03-12/04      18.9
12/04-12/05       7.6
12/05-12/06      16.6
12/06-12/07      -1.7
12/07-12/08     -36.6
12/08-12/09      46.9
12/09-12/10      27
12/10-12/11      -1.6
12/11-12/12        18
12/12-12/13      39.2
12/13-12/14       8.9
12/14-12/15      -4.4
Total Cumulative for Intermediate Periods
                Comp Univ
12/02-12/15     321.5
Annualized       11.7
Annualized STD   18.5
CV                1.58
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Author:Stanley, Darrol; Wasilewski, Nikolai
Publication:Competition Forum
Date:Jan 1, 2017
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