Aqua America beta estimation, fundamental/technical analyses

Aqua America Beta Estimation Fundamental Analysis Technical Analysis March 25, 2009

Ryan D. Lazzeri

Applied Investment Management

Since Aqua America (WTR) is a member of the S&P 400 Midcap Index and not the S&P 500 Index, I strongly

considered that each might be an appropriate independent variable against which I could regress Aqua’s returns and

determine beta. Firstly, however, the results of testing: Weekly and monthly return series regressions over one-, three-,

and five-years, and against both indices, produced low R2 and t-statistics, and negative raw betas.

1 When compared to

daily regressions, which were quite robust in terms of relevance, these data were easily discarded. Daily regressions over

each testing period produced significant results and sensible betas. Further, one-year betas produced the highest R2

statistics (and therefore the most relevant dats), so I used that series to determine beta.2

The question of “which beta” to use, then, becomes philosophical. Though the S&P 500 is traditionally used for

American companies, one may argue that WTR is better represented by a basket of more similarly sized firms. After all,

smaller companies historically have enjoyed a different risk and payoff profile than their large counterparts. Yet, WTR is

a leader in a heavily regulated, capital intensive industry, so it may compare better versus other leading firms. Ultimately,

I decided to split the difference: 50% S&P 500, 50% S&P 400 Midcap. As I noted, the data over one year proffered good

results – beta of 0.60 and R2 of 41% against the Midcap index, and 0.66 and 44%, respectively, versus the S&P 500

3 – but

my inquiry did not end there. Published betas were significantly lower than my estimate – Yahoo! Financial opined a

measly 0.07, and Thomsen-Reuters 0.29 – so I took heart in knowing that inclusion of the S&P 400 Midcap beta would

reduce my average beta. Also, with an understanding of how weekly and monthly data might vary so drastically, I felt

more confident that my beta – ultimately “smoothed” towards βM for a final estimate of 0.75 – best represents a growing,

likely-to-be-regressing (more on this later) firm whose outlook is mixed.4

True to the reputation of regulated utilities, Aqua America historically has displayed low variation of sales and

earnings.5 More importantly, it also scores low relative to other water suppliers. Sales variability is best represented by

the constant growth model (VM-est. = 0.049) while the linear model fits EBITDA variability best (VM-est. = 0.025).

Since 1999, sales have exhibited 48.8% of the variability of industry sales, and EBITDA only 9%. Perhaps not

unsurprisingly, WTR’s capital structure has not changed substantially over the test period; meanwhile, other water utilities

American States Water (AWR) and California Water Service (CWT) experienced earnings hiccups over the decade prior.

Another result of low variability has been low absolute (but not relative) business risk. This decade, Cal Water

has achieved lowest relative sales volatility and business risk, but Aqua continues to lead in marginal profitability by a

wide margin. While Cal Water sported average operating margins of 12% and American States Water 18%, Aqua set

pace at 40%. CWT and AWR also experienced negative growth in three years since 1999 versus Aqua’s unscathed

earnings record. To differentiate, then, I investigated operating leverage amongst the firms. WTR earned a group low,

1.003, implying that changes in sales and operating earnings follow a nearly one-to-one relationship. CWT scored

highest, 4.67, likely due to negative growth of operating income in 2001, 2004, and 2008; likewise, American States

scored 2.575, likely due to similarly volatile operating income.6

1 “Beta” alone denotes raw beta, not adjusted beta.

2 See Exhibit A for Regression Results summary

3 See Exhibit B for Beta Results summary

4 See Exhibit C for comparisons to Published Beta Estimates and Competitor Beta Estimates

5 See Exhibit D for Sales & EBITDA Variation Charting

6 See Exhibit E for Business Risk summary

Aqua America’s operating performance leans heavily on its ability to produce return on fixed assets. While

capital asset turnover has slipped slightly, Aqua America has managed annual turnover variance relatively well. Next-of-

kin metric fixed asset turnover, a key figure for Aqua due to high levels of fixed plant, has also fallen, though not

significantly. The one-to-one operating leverage relationship has carried over to fixed plant and its turnover, and Aqua

appears to have managed the relationship between its assets and earnings better than AWR and CWT. Declining

turnovers, then, should not be worrisome.7

WTR and the water utilities are simultaneously improving as cash managers. On a relative basis, Aqua America

monetizes its current assets (customer billing) and pays suppliers in about 48 days – nearly twice as fast as Cal Water and

over 5 times faster than American States. Annually, Aqua has reduced its cash cycle by 2.75% since 2004, and while the

industry has tended towards faster collections – a trend expected to slow during the recession – Aqua America has

managed to extend supplier payments by 2.5%.

While solvency and liquidity are closely related, Aqua America is justifiably not as concerned about keeping a

liquid book. Since the company grows inorganically, invests heavily in infrastructure, and is so heavily regulated (and

subsidized), it does not keep much cash on hand. Accounts receivable, particularly unbilled customer accounts, have risen

5.5% on an absolute basis but have dropped nearly 4% relative to capital growth. Since inventory, consisting primarily of

materials and supplies, has de minimus operational impact, billing activities are responsible for most of Aqua’s liquid

assets. These results are fair given Aqua’s customer base growth since 1999. Besides the recession, which likely will hurt

collection activities, Aqua faces risk in regulatory lag. Regulatory lags may hamper a utility’s ability to raise cash through

operations on a timely basis, making administration of rate cases one of the most critical aspects of utility financial

management.8

On the other side of the balance sheet, Aqua America has reduced short-term debt by nearly 3% annually since

1999, shifting its debt load out on the curve to more closely match asset lives. (Long-term debt rose 11.7% and subsidies,

or “contributions in aid of construction”, rose 14.3% over the period.) Aqua’s working capital position, then, is rosier

prima facie but perhaps less so when one considers the strategic shift. By the same token, debt and leverage ratios have

remained stable, if not slightly sloped upwards since 2004. Increases in debt have not outpaced those of equity, and Aqua

has experienced low relative capital base variation. The latter two seem to be playing catch-up, as their times interest

earned have spiked and caught Aqua’s downward trend.

Unfortunately, Aqua America’s enviable fundamental positions have not translated into higher returns on

investment. In fact, ROE is trending downward. WTR currently returns less investment than do AWR and CWT; WTR,

though, began the millennium from a point of significant marginal advantage. In fact, Aqua’s net and operating margins

still outpace AWR’s and CWT’s by nearly 6% and 19%, respectively. Clearly, time has conspired to allow agile aspirants

to become more productive, while Aqua seems either to have entered a maturity stage, has been mismanaged, or has

grown too fast (affecting integration of new assets ). At the same time, Aqua America has increased its customer base by

5% annually since 2004 (adjusted for divestures).9 So, despite significant market power, Aqua America has struggled to

increase returns on capital and, as a result, equity investment. By nearly every functional element of ROE – ROC, net

7 See Exhibit G for Asset Turnover summary

8 See Exhibit F for Liquidity summary

9 CWT increased its customer base by 0.6% in 2008. AWR did not provide customer data.

profit margin, and capital turnover – Aqua fared worse in 2008 than it did in 2004. Only financial leverage rose, and

while that may have been at management’s behest, that factor alone proved not to be enough to prevent ROE from sinking

3.3% over the period. Categorical results are flatter over the 10-year period, during which ROE “only” fell 0.3%, but

signs point to a company at a crossroads. In the past, WTR has divested under- or non-performing assets, including water

systems, activity I expect to see in the coming periods as the firm prepares to refocus in this stimulus era.10

In the nearer term, just as systems integration bogs down capital returns, technical factors should weigh on share

price and prevent significant breakout. For months, analysts, commentators, and pundits alike have repeatedly touted

Aqua America as a “can’t miss” in this economy. Indeed, Aqua has outperformed major benchmarks over five-, three-,

and one-year periods, yet fundamentals tell a somewhat different story going forward. Technical indicators do, too. The

Relative Strength measurement (RSI) and Williams %R each tell a story – albeit differently – of an overbought stock.

RSI compares stock returns to benchmark returns, and WTR easily outran the S&P 500 from 2004-2009. I see signs of

fatigue, or mean regression. WTR stock actually had positive returns in October 2008, so a major (2x) swing over the last

quarter, on high volume, may confine WTR to the Yogism “That restaurant’s so popular, no one goes there anymore.”11

Williams %R gives explicit under/overbought signals to investors, generally over 14 days. The WTR stock price has

bounced a great deal lately, and as recently as early March was thought to be oversold. It has since moved into

overbought, bearish territory (> -20).12

Similarly, the Commodity Channel Index (CCI) tells investors when a stock has

moved significantly enough away from its 20-day, adjusted moving average. CCI only indicates directionality 20-30% of

the time, and currently it indicates that investors should, in fact, sell WTR (or to continue not to own it at all).13

MACD, standard SMA, and Fibonacci Extension functions proved helpful in assigning momentum and shape to

WTR’s chart. Since October, WTR looks to have taken on a head-and-shoulders shape, a leading indicator of reversion.

The MACD path suggests that a downward trend began to develop when WTR touched near the resistance level of $20 on

March 23rd

. The 50-day SMA passed the 200-day SMA in October, and while it remains above, the two look to be

converging. Consequently, MACD diverged from its relative gravity and indicates current downward momentum.

Finally, Fibonacci Extensions helps explain curve resistance, support, and shape. Fibonacci draws conclusions

from boundaries, expressed as percentage, derived from retracement levels between two “swing points.” Since reaching

the 100% line (near $20) late on March 23rd

, WTR has retraced back through the 76.4% line, bounced off of the 61.8%

line (a support level), advanced back to 76.4% but turned back and blew through the 61.8% resistance – a decidedly

negative trend. Going through a level is supposed to predict further surge or recession to the next Fibonacci level, at

which point investors should buy or sell.14

This will remain a stock of great interest as long as the government’s economic stimulus concentrates on

infrastructure. Integration will continue to be challenging but do not expect it to stop soon. Said CEO Nick Benedictis on

March 23rd

, when asked about the company’s direction: “Investing in the future of the country by improving infrastructure

and buying up all these small, undercapitalized water companies.” For his sake, Aqua America will hopefully have turned

around operations before investors will have ever noticed anything was amiss.

10

See Exhibit I for DuPont summary 11

See Exhibit J for Classical Technical analyses 12

See Exhibit K for Williams %R explanation 13

See Exhibit L for Commodity Channel Index explanation 14

See Exhibit M for Fibonacci Extensions explanation

§1: Beta Analyses

EXHIBIT A

Regression Results

Monthly Results

5 yr monthly vs S&P 400 Midcap

Regression Statistics

Multiple R 19.3%

R Square 3.7%

Adjusted R Square 2.1%

Standard Error 6.6%

Observations 60

ANOVA

df SS MS F Significance F

Regression 1 0.01 0.01 2.25 0.14

Residual 58 0.25 0.00

Total 59 0.26

Coefficients Standard Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.01 0.01 0.67 0.50 -0.01 0.02 -0.01 0.02

Beta -0.21 0.14 -1.50 0.14 -0.49 0.07 -0.49 0.07

5 yr monthly vs S&P 500


Multiple R 13.1%

R Square 1.7%


Standard Error 6.7%

Observations 60

ANOVA


Regression 1 0.00 0.00 1.02 0.32

Residual 58 0.26 0.00

Total 59 0.26

Coefficients Standard Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.01 0.01 0.62 0.54 -0.01 0.02 -0.01 0.02

Beta -0.17 0.17 -1.01 0.32 -0.52 0.17 -0.52 0.17



Multiple R 28.0%

R Square 7.9%


Standard Error 6.8%

Observations 36

ANOVA


Regression 1 0.01 0.01 2.90 0.10

Residual 34 0.16 0.00

Total 35 0.17

Coefficients

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -0.01 0.01 -0.89 0.38 -0.03 0.01 -0.03 0.01

Beta -0.27 0.16 -1.70 0.10 -0.59 0.05 -0.59 0.05



Multiple R 17.1%

R Square 2.9%


Standard Error 7.0%

Observations 36

ANOVA


Regression 1 0.01 0.01 1.03 0.32

Residual 34 0.17 0.00

Total 35 0.17

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept -0.01 0.01 -0.78 0.44 -0.03 0.02 -0.03 0.02

Beta -0.20 0.19 -1.01 0.32 -0.59 0.20 -0.59 0.20



Multiple R 29.0%

R Square 8.4%

Adjusted R Square -0.8%

Standard Error 9.4%

Observations 12

ANOVA


Regression 1 0.01 0.01 0.92 0.36

Residual 10 0.09 0.01

Total 11 0.10

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.03 -0.12 0.90 -0.07 0.06 -0.07 0.06

Beta -0.23 0.24 -0.96 0.36 -0.77 0.31 -0.77 0.31



Multiple R 21.0%

R Square 4.4%

Adjusted R Square -5.1%

Standard Error 9.6%

Observations 12

ANOVA


Regression 1 0.00 0.00 0.46 0.51

Residual 10 0.09 0.01

Total 11 0.10

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.03 -0.12 0.90 -0.07 0.07 -0.07 0.07

Beta -0.21 0.31 -0.68 0.51 -0.91 0.48 -0.91 0.48

Weekly Results

5 yr weekly vs S&P 400 Midcap


Multiple R 8.1%

R Square 0.7%


Standard Error 4.0%

Observations 260

ANOVA


Regression 1 0.00 0.00 1.72 0.19

Residual 258 0.41 0.00

Total 259 0.42

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 0.79 0.43 0.00 0.01 0.00 0.01

Beta -0.10 0.07 -1.31 0.19 -0.25 0.05 -0.25 0.05

5 yr weekly vs S&P 500


Multiple R 8.6%

R Square 0.7%


Standard Error 4.0%

Observations 260

ANOVA


Regression 1 0.00 0.00 1.93 0.17

Residual 258 0.41 0.00

Total 259 0.42

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 0.76 0.45 0.00 0.01 0.00 0.01

Beta -0.12 0.09 -1.39 0.17 -0.30 0.05 -0.30 0.05



Multiple R 14.8%

R Square 2.2%


Standard Error 4.4%

Observations 156

ANOVA


Regression 1 0.01 0.01 3.43 0.07

Residual 154 0.30 0.00

Total 155 0.31

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 -0.44 0.66 -0.01 0.01 -0.01 0.01

Beta -0.16 0.09 -1.85 0.07 -0.34 0.01 -0.34 0.01



Multiple R 15.7%

R Square 2.5%


Standard Error 4.4%

Observations 156

ANOVA


Regression 1 0.01 0.01 3.89 0.05

Residual 154 0.30 0.00

Total 155 0.31

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 -0.47 0.64 -0.01 0.01 -0.01 0.01

Beta -0.20 0.10 -1.97 0.05 -0.41 0.00 -0.41 0.00



Multiple R 19.2%

R Square 3.7%


Standard Error 6.4%

Observations 51

ANOVA


Regression 1 0.01 0.01 1.87 0.18

Residual 49 0.20 0.00

Total 50 0.21

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.01 0.14 0.89 -0.02 0.02 -0.02 0.02

Beta -0.21 0.15 -1.37 0.18 -0.51 0.10 -0.51 0.10



Multiple R 20.3%

R Square 4.1%


Standard Error 6.4%

Observations 51

ANOVA


Regression 1 0.01 0.01 2.10 0.15

Residual 49 0.20 0.00

Total 50 0.21

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.01 0.07 0.95 -0.02 0.02 -0.02 0.02

Beta -0.25 0.17 -1.45 0.15 -0.60 0.10 -0.60 0.10

Daily Results

5 yr daily vs S&P 400 Midcap


Multiple R 56.7%

R Square 32.1%


Standard Error 1.5%

Observations 1257

ANOVA


Regression 1 0.13 0.13 593.22 0.00

Residual 1255 0.28 0.00

Total 1256 0.42

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%

Intercept 0.00 0.00 1.06 0.29 0.00 0.00 0.00 0.00

Beta 0.66 0.03 24.36 0.00 0.61 0.72 0.61 0.72

5 yr daily vs S&P 500


Multiple R 56.2%

R Square 31.6%


Standard Error 1.5%

Observations 1257

ANOVA


Regression 1 0.13 0.13 579.43 0.00

Residual 1255 0.28 0.00

Total 1256 0.42

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%

Intercept 0.00 0.00 1.27 0.21 0.00 0.00 0.00 0.00

Beta 0.71 0.03 24.07 0.00 0.66 0.77 0.66 0.77



Multiple R 58.7%

R Square 34.5%


Standard Error 1.6%

Observations 753

ANOVA


Regression 1 0.11 0.11 395.78 0.00

Residual 751 0.20 0.00

Total 752 0.31

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 0.18 0.86 0.00 0.00 0.00 0.00

Beta 0.63 0.03 19.89 0.00 0.56 0.69 0.56 0.69



Multiple R 59.7%

R Square 35.6%


Standard Error 1.6%

Observations 753

ANOVA


Regression 1 0.11 0.11 415.77 0.00

Residual 751 0.20 0.00

Total 752 0.31

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 0.26 0.79 0.00 0.00 0.00 0.00

Beta 0.68 0.03 20.39 0.00 0.62 0.75 0.62 0.75



Multiple R 63.8%

R Square 40.7%


Standard Error 2.1%

Observations 251

ANOVA


Regression 1 0.08 0.08 170.77 0.00

Residual 249 0.11 0.00

Total 250 0.19

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 1.18 0.24 0.00 0.00 0.00 0.00

Beta 0.60 0.05 13.07 0.00 0.51 0.69 0.51 0.69



Multiple R 66.4%

R Square 44.1%


Standard Error 2.0%

Observations 251

ANOVA


Regression 1 0.08 0.08 196.75 0.00

Residual 249 0.10 0.00

Total 250 0.19

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.00 0.00 1.39 0.16 0.00 0.00 0.00 0.00

Beta 0.66 0.05 14.03 0.00 0.57 0.76 0.57 0.76

EXHIBIT B

Beta Results & Estimation Methodology

Observations t-stat R2 β Adj β Weight Factor

Daily 1257 24.36 32.1% 0.66 0.78

Weekly 260 -1.31 0.7% -0.10 0.27

Monthly 60 -1.50 3.7% -0.21 0.19

Daily 753 19.89 34.5% 0.63 0.75

Weekly 156 -1.85 2.2% -0.16 0.22

Monthly 36 -1.70 7.9% -0.27 0.15

Daily 251 13.07 40.7% 0.60 0.73 50% 0.37

Weekly 51 -1.37 3.7% -0.21 0.20

Monthly 12 -0.96 8.4% -0.23 0.18

Observations t-stat R2 β Adj β

Daily 1257 24.07 31.6% 0.71 0.81

Weekly 260 -1.39 0.7% -0.12 0.25

Monthly 60 -1.01 1.7% -0.12 0.25

Daily 753 20.39 35.6% 0.68 0.79

Weekly 156 -1.97 2.5% -0.20 0.20

Monthly 36 -1.01 2.9% -0.20 0.20

Daily 251 14.03 44.1% 0.66 0.78 50% 0.39

Weekly 51 -1.45 4.1% -0.25 0.16

Monthly 12 -0.68 4.4% -0.21 0.19

Beta 0.75

S&

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Figure 1. Since weekly and monthly data were so significantly different and nonsensical, frankly, those data were tossed. The R2 statistic

was the primary determinant of which beta to use: 1-, 3-, or 5-year regressions. The adjustment is the standard Bloomberg adjustment:

𝜷𝒂𝒅𝒋 = 𝟐

𝟑 𝜷 +

𝟏

𝟑.

Then, I merely weighed the S&P 500 and S&P 400 Midcap indices at 50% and summed the beta factors.

EXHIBIT C

Published Beta Estimates & Industry Beta Estimates

Source Date Beta

Yahoo! Financial n/a 0.07

Thomsen-Reuters 3/25/2009 0.32 i

Standard & Poors 3/21/2009 0.29

Google Finance n/a 0.29

AOL Finance n/a 0.29

TheStreet.com n/a 0.07 ii

Company/Fund Symbol Beta

American States Water Company AWR 0.48

California Water Services Group CWT 0.64

Artesian Resources A ARTNA 0.33

Connecticut Water Services CTWS 0.44

ConsolidatedWater CWCO 1.50

MiddlesexWater MSEX 0.50

Pennichuck Corporation PNNW 0.41

SJW Corporation SJW 0.99

SouthwestWater Company SWWC 0.84

YorkWater YORW 0.62

PFW Water A PFWAX 1.02 iii

iUpdated from 0.29 0n 3/24/2009iiTheStreet.com uses 3 years of data to estimate betaiiiWTR is a member of this fund

Competitor, Industry, and Utility Fund Betas

Published Estimates of WTR Beta

§2: Fundamental Analysis

EXHIBIT D

Models of Sales & EBITDA Variation

NAICS # :

Model WTR 2 digit 3 digit 4 digit WTR 2 digit 3 digit 4 digit

Mean

VM-est 0.3232 0.3227 0.3227 0.2726 0.2902 0.2360 0.2360 0.3486

Std Dev n.a. 0.2056 0.2056 0.1368 n.a. 0.1501 0.1501 0.2137

Linear

VM-est 0.0743 0.2492 0.2492 0.1293 0.0251 0.1590 0.1590 0.2798

Std Dev n.a. 0.3227 0.3227 0.1038 n.a. 0.1578 0.1578 0.4608

Constant Growth

VM-est 0.0490 0.2514 0.2514 0.1005 0.0611 0.1582 0.1582 0.2757

Std Dev n.a. 0.3406 0.3406 0.0846 n.a. 0.1612 0.1612 0.4692

N = n.a. 126 126 12 n.a. 126 126 12

Source: AIM Variation Model V2 and COMPUSTAT 2006.

Growth rate = 10.40% Growth rate = 10.26%

NAICS NAICS

Variation Model Estimates

Company : AQUA AMERICA INC 221310

Industry : WATER SUPPLY

Sales EBITDA

Average growth = $41,071.78 Average growth = $20,794.78

Figure 2. Using the lowest variation estimate - e.g. 0.049 in the case of Sales Variation figures - I compare at the most detailed level, in this

case the 4-digit NAICS. My VM is about have the industry's, indicating that WTR's sales do not vary much annually.

EXHIBIT E

Business Risk Summary

Sales Operating Income Operating Margin

1999 257,326 101,045 39%

2000 274,014 116,789 43%

2001 307,280 134,340 44%

2002 322,028 140,504 44%

2003 367,233 153,561 42%

2004 442,039 177,234 40%

2005 496,779 196,507 40%

2006 533,491 205,547 39%

2007 602,499 216,016 36%

2008 626,972 225,801 36%

Standard Deviation 136,699 43,641

Mean 422,966 166,734

Sales Volatility: 0.323 Business Risk: 0.262


1999 206,440 30,610 15%

2000 244,806 33,196 14%

2001 246,820 25,151 10%

2002 263,151 30,297 12%

2003 277,128 30,234 11%

2004 315,567 41,483 13%

2005 320,728 39,810 12%

2006 334,717 40,306 12%

2007 367,082 44,170 12%

2008 410,312 57,469 14%


Mean 298,675 37,273



1999 173,421 28,514 16%

2000 183,960 32,307 18%

2001 197,514 36,692 19%

2002 209,205 37,648 18%

2003 212,669 33,605 16%

2004 228,005 36,090 16%

2005 236,197 40,444 17%

2006 268,629 56,606 21%

2007 301,370 67,732 22%

2008 318,718 54,806 17%


Mean 232,969 42,444


Aqua America

Cal Water

American States Water

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐑𝐢𝐬𝐤 = 𝒇(𝐂𝐨𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐨𝐟 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐄𝐚𝐫𝐧𝐢𝐧𝐠𝐬)

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐑𝐢𝐬𝐤 =𝐒𝐃 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐄𝐚𝐫𝐧𝐢𝐧𝐠𝐬

𝛍 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐄𝐚𝐫𝐧𝐢𝐧𝐠𝐬

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐑𝐢𝐬𝐤 =

𝐎𝐄 − 𝐎𝐄 𝟐

𝐧

𝐎𝐄𝐧

Operating Income %ΔOE Sales %Δsales

(%ΔOE) /

(%Δsales)

1999 101,045 257,326

2000 116,789 15.6% 274,014 6.5% 2.403

2001 134,340 15.0% 307,280 12.1% 1.238

2002 140,504 4.6% 322,028 4.8% 0.956

2003 153,561 9.3% 367,233 14.0% 0.662

2004 177,234 15.4% 442,039 20.4% 0.757

2005 196,507 10.9% 496,779 12.4% 0.878

2006 205,547 4.6% 533,491 7.4% 0.623

2007 216,016 5.1% 602,499 12.9% 0.394

2008 225,801 4.5% 626,972 4.1% 1.115

1.003


(%ΔOE) /

(%Δsales)

1999 30,610 206,440

2000 33,196 8.4% 244,806 18.6% 0.455

2001 25,151 -24.2% 246,820 0.8% 29.458

2002 30,297 20.5% 263,151 6.6% 3.092

2003 30,234 -0.2% 277,128 5.3% 0.039

2004 41,483 37.2% 315,567 13.9% 2.682

2005 39,810 -4.0% 320,728 1.6% 2.466

2006 40,306 1.2% 334,717 4.4% 0.286

2007 44,170 9.6% 367,082 9.7% 0.991

2008 57,469 30.1% 410,312 11.8% 2.557

4.670


(%ΔOE) /

(%Δsales)

1999 28,514 173,421

2000 32,307 13.3% 183,960 6.1% 2.189

2001 36,692 13.6% 197,514 7.4% 1.842

2002 37,648 2.6% 209,205 5.9% 0.440

2003 33,605 -10.7% 212,669 1.7% 6.486

2004 36,090 7.4% 228,005 7.2% 1.025

2005 40,444 12.1% 236,197 3.6% 3.358

2006 56,606 40.0% 268,629 13.7% 2.910

2007 67,732 19.7% 301,370 12.2% 1.613

2008 54,806 -19.1% 318,718 5.8% 3.315

2.575

Aqua America

Operating Leverage:

Cal Water

Operating Leverage:

American States Water

Operating Leverage:

EXHIBIT F

Liquidity Summary

0.0

0.5

1.0

1.5

2.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

liqu

id c

urr

en

t as

sets

/cu

rre

nt

liab

iliti

es

Acid Test(Liquid Current Assets)

(Current Liabilities + ST Debt)

Aqua America California Water Service American States Water

0.0

0.5

1.0

1.5

2.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

curr

en

t as

sets

/

curr

en

t lia

bili

tie

s

Current Ratio(Current Assets inc. Inventory)(Current Liabilities + ST Debt)


0.0

0.5

1.0

1.5

2.0

2.5

3.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

liqu

id c

urr

en

t as

sets

/cu

rre

nt

liab

iliti

es

Quick Ratio(Cash & A/R)

Current Liabilities


EXHIBIT G

Cash Cycle Summary

0

20

40

60

80

100

120

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

day

s (3

65

pe

r ye

ar)

Average Collection Period(365 days)*(A/R)/(Sales)


0

50

100

150

200

250

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

day

s (3

65

pe

r ye

ar)

Inventory Turnover(Cost of Sales)/(Inventory)


0

20

40

60

80

100

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

day

s (3

65

pe

r ye

ar)

Average Payment Period(365 days)*(A/P)/(Cost of Sales)


0

50

100

150

200

250

300

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

day

s (3

65

pe

r ye

ar)

Cash CycleTurnover Days (A/R + Inventory - Payables)


EXHIBIT H

Financial Risk Summary

0.45

0.50

0.55

0.60

0.65

0.70

0.75

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

%

Debt RatioLiabilities/Capital


2.75

3.00

3.25

3.50

3.75

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

tim

es

leve

red

Financial LeverageCapital/Common Equity


1.0

1.5

2.0

2.5

3.0

3.5

4.0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

tim

es

ear

ne

d

Times Interest Earned(Operating & Non-operating Income)

Interest Expense


EXHIBIT I

DuPont Analysis

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

10-year

CAGR

5-year

CAGR

Return on Equity 9.93 13.29 13.32 13.92 12.30 11.39 11.69 10.61 10.01 9.62 -0.31% -3.31%

Financial Leverage 3.31 3.21 3.13 3.23 3.14 3.01 3.07 3.06 3.11 3.20 -0.35% 1.20%

Return on Capital 3.00 4.14 4.25 4.31 3.92 3.78 3.81 3.47 3.22 3.01 0.03% -4.45%

Net Profit Margin 14.14 19.30 19.56 20.87 19.28 18.10 18.35 17.25 15.77 15.62 1.00% -2.91%

Capital Turnover 0.21 0.21 0.22 0.21 0.20 0.21 0.21 0.20 0.20 0.19 -0.96% -1.59%

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

0.00

5.00

10.00

15.00

20.00

25.00

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

DuPont Analysis

Return on Equity Net Profit Margin Financial Leverage

Return on Capital Capital Turnover

ROE CAGR5-yr -3.31%10-yr -0.31%

§3: Technical Analysis

EXHIBIT J

Classical Technical Analyses

Figure 3 Even though the shorter (50-day) SMA moved above the

200-day SM A during WTR's October rally, the averages seem to

be re-converging - a fact amplified when looking at the MACD.

Figure 4 October's tremendous rally against the market has

created an aura of invincibility around WTR. That may be short-

lived; however, as business integration and technical trends

weigh heavily on the share price back down.

EXHIBIT K

Williams %R Explained

Definition: A technical analysis oscillator showing the current closing price in relation to the high and low of the past N

days (for a given N). It was developed by trader and author Larry Williams.

The oscillator is on a negative scale, from -100 (lowest) up to 0 (highest). A value of -100 is the close today at the lowest

low of the past N days, and 0 is a close today at the highest high of the past N days.

Williams used a 10 trading day period and considered values below -80 as oversold and above -20 as overbought. But

they were not to be traded directly, instead his rule to buy an oversold was

%R reaches -100%.

Five trading days pass since -100% was last reached

%R rises above -95% or -85%.

or conversely to sell an overbought condition

%R reaches 0%.

Five trading days pass since 0% was last reached

%R falls below -5% or -15%.

Equations

Assumptions: Generally run over a 7- to 14-day period.

Example

Figure 5. These Williams %R data were run on 3/24/2009 using WTR pricing data from Bloomberg.

http://en.wikipedia.org/wiki/Technical_analysis

http://en.wikipedia.org/wiki/Larry_Williams_(trader)

3-day Williams %R

1-year Williams %R

Figure 6. The 1-year chart indicates recent movement above the -20 threshold; therefore, SELL

EXHIBIT L

Commodity Channel Index (CCI) Explained

Definition: The Commodity Channel Index is often used for detecting divergences from price trends as an

overbought/oversold indicator, and to draw patterns on it and trade according to those patterns. In this respect, it

is similar to Bollinger bands, but is presented as an indicator rather than as overbought/oversold levels.

The CCI typically oscillates above and below a zero line. Normal oscillations will occur within the range of

+100 and -100. Readings above +100 imply an overbought condition, while readings below -100 imply an

oversold condition. As with other overbought/oversold indicators, this means that there is a large probability

that the price will correct to more representative levels.

Methodology

1) Calculate Typical Price ("TP"):

2) Calculate TPMA, a 20-day simple moving average of TP.

3) Subtract TPMA from TP.

4) Apply the TP, TPSMA, the Mean Deviation & a Constant (0.015) to the following formula:

Example

Figure 7. An investor would want to be long WTR in the red areas (> +100), and short in the green (< -100) areas. The most recent data, at

the far right, appears to be in the green - a bearish sentiment.

http://en.wikipedia.org/wiki/Bollinger_bands

EXHIBIT M

Fibonacci Extensions Explained

Definition: Fibonacci levels are a standard measure for support and resistance levels within the market. These levels are

calculated by analyzing the retracement levels between two swing points.

Mechanics

What happens when price exceeds the very swing points we use to calculate our Fibonacci levels?

At what point do we look to exit our position?

The key to these questions are Fibonacci extensions. Fibonacci extensions provide price targets that go beyond a 100%

retracement of a prior move. The levels for Fibonacci extensions are calculated by taking the standard Fibonacci levels

and adding them to 100%. Therefore, the standard Fibonacci extension levels are as follows: 138.2%, 150%, 161.8%,

231.8%, 261.8%, 361.8% and 423.6%.

The first step in drawing Fibonacci extension levels is to identify two clear swing points. These points should be in

relation to both your current timeframe and length of trend.

The last part of the Fibonacci extension equation, is what to do when the asset hits the respective target. The first

inclination is to immediately close your position at the next Fibonacci level. Traders will have to fight this urge and wait

to see how the stock reacts at these Fibonacci extensions. Remember, the stock has exceeded previous swing highs and

could very well start an impulsive move.

Example

Figure 8. Retracement through a level indicates a downward trend to the next Fibonacci level (50.0%); therefore, SELL on downtrend.

http://www.mysmp.com/technical-analysis/support-and-resistance.html

http://www.mysmp.com/technical-analysis/fibonacci-day-trading.html

Aqua America beta estimation, fundamental/technical analyses

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