YieldCos in the U.S. Final AN

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Table of Contents
Table of Tables....................................................................................................... 3
Table of Figures ..................................................................................................... 4
Declaration ............................................................................................................ 5
Abstract ................................................................................................................. 6
Glossary of Terms and Abbreviations..................................................................... 7
Chapter 1 ̶ INTRODUCTION .................................................................................. 9
1.1 Investment Climate.............................................................................................................................9
1.2 YieldCos.............................................................................................................................................10
1.3 Background and Purpose of Research ..............................................................................................11
1.4 Problem Definition and Importance of Research .............................................................................12
1.5 Goals, Objectives and Propositions to be Examined or Tested ........................................................12
Chapter 2 ̶ LITERATURE REVIEW..........................................................................13
2.1 YieldCos Asset Base...........................................................................................................................13
2.2 Underlying Risks................................................................................................................................17
2.2.1 Leverage.....................................................................................................................................17
2.2.2 Legal Risk....................................................................................................................................17
2.2.3 Interest Risk ...............................................................................................................................18
2.2.4 Energy Price Risk........................................................................................................................20
2.2.5 Counterparty & Parent Default Risk ..........................................................................................22
2.3 Valuation Methods ...........................................................................................................................23
2.3.1 Historical Stock Price and Dividend Based Valuation ................................................................23
2.3.2 Financial Statement Based Valuation ........................................................................................23
2.3.3 Ratio Based Valuation................................................................................................................25
Chapter 3 ̶ RESEARCH APPROACH .......................................................................27
3.1 Research Method..............................................................................................................................27
3.2 Data Reliability..................................................................................................................................28
3.3 Data Analysis Procedures..................................................................................................................28
3.3.1 Sharpe Ratio Procedures............................................................................................................28
3.3.2 GGM Procedures........................................................................................................................29
3.3.3 Discounted Cash Flows Procedures ...........................................................................................29
3.3.4 Simple Adjusted Present Value Procedures...............................................................................30
3.3.5 Initial Growth Adjusted Present Value Procedures ...................................................................31
3.3.6 Scenario Analysis Procedures ....................................................................................................31
3.3.7 Ratio Analysis Procedures..........................................................................................................32
Chapter 4 ̶ RESULTS, ANALYSIS AND INTERPRETATION .......................................33
4.1 Stock Price and Dividend History Analysis........................................................................................33
4.1.1 CAPM..........................................................................................................................................33
4.1.2 GGM Valuation...........................................................................................................................34
4.1.3 Other Correlations .....................................................................................................................37
4.2 Financial Statement Based Valuation Methods:...............................................................................37

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4.2.1 Discounted Cash Flow Method..................................................................................................37
4.2.2 Simple Adjusted Present Value Method....................................................................................39
4.2.3 Complex Adjusted Present Value Method.................................................................................40
4.3 YieldCo Valuations Using Ratios........................................................................................................41
4.3.1 Enterprise Value/Sales Ratio......................................................................................................42
4.3.2 Sales After Capital Costs.............................................................................................................42
4.3.3 P/E Ratio.....................................................................................................................................43
4.3.4 Price-to-Book Ratio....................................................................................................................43
4.3.5 Price-to-Sales Ratio....................................................................................................................44
4.3.6 EV/EBIT Ratio .............................................................................................................................45
Chapter 5 ̶ CONCLUSONS AND RECCOMENDATIONS...........................................46
5.1 Conclusions .......................................................................................................................................46
5.2 Recommendations............................................................................................................................47
5.3 Summary...........................................................................................................................................48
5.4 Originality..........................................................................................................................................49
5.5 Contribution to the Body of Knowledge in the Field ........................................................................49
5.6 Limitations.........................................................................................................................................50
5.7 Scope for Future Research................................................................................................................50
Appendix A ...........................................................................................................51
Appendix B............................................................................................................52
Appendix C............................................................................................................54
Appendix D ...........................................................................................................56
Appendix E............................................................................................................57
Bibliography & References....................................................................................58

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Table of Tables
TABLE 4.1 CAPM RISK RETURN TRADEOFF. YIELDCOS US. DATA SOURCE: CALCULATIONS BASED ON YAHOO
FINANCE DATA ....................................................................................................................................................34
TABLE 4.2 GMM INTRINSIC STOCK VALUE CALCULATION DATA SOURCE: CALCULATIONS BASED ON YAHOO
FINANCE DATA ....................................................................................................................................................36
TABLE 4.3 GGM INTRINSIC STOCK VALUES MAR-MAY ‘16. DATA SOURCE: CALCULATIONS BASED ON YAHOO
FINANCE..............................................................................................................................................................36
TABLE 4.4 DCF METHOD VALUATION OF NEP AND NYLD. 3% VARIABLE RATE INCREASE. DATA SOURCE: 2015 10-KS
............................................................................................................................................................................38
TABLE 4.5 SIMPLE APV VALUATION. DATA SOURCE: 2015 10-K REPORTS.................................................................40
TABLE 4.6 COMPLEX APV VALUATION. DATA SOURCE: 2015 10-K REPORTS.............................................................41
TABLE 4.7 U.S. YIELDCO EV/SALES RATIO AND CCS. DATA SOURCE: MOST RECENT SEC FILINGS..............................42
TABLE 4.8 U.S. YIELDCO P/E AND P/B RATIOS. DATA SOURCE: MOST RECENT SEC FILINGS......................................43
TABLE 4.9 U.S. YIELDCO EV/EBIT. DATA SOURCE: MOST RECENT SEC FILINGS ..........................................................45
APPENDIX A: U.S. YIELDCOS’ INFORMATION AND A DETAILED ASSET BREAKDOWN. ................................................51
APPENDIX B: NRG YIELD (TICKER: NYLD) PRO-FORMA INCOME STATEMENTS (2016-2025) DATA SOURCE: 2015
NYLD 10-K ...........................................................................................................................................................52
APPENDIX B: NRG YIELD (TICKER: NYLD) PRO-FORMA CASH FLOW STATEMENTS (2016-2025) DATA SOURCE: 2015
NYLD 10-K ...........................................................................................................................................................52
APPENDIX B: NRG YIELD (TICKER: NYLD) PRO-FORMA BALANCE SHEET (2016-2025)DATA SOURCE: 2015 NYLD 10-K
............................................................................................................................................................................53
APPENDIX C: NEXTERA ENERGY PARTNERS (TICKER: NEP) PRO-FORMA INCOME STATEMENTS (2016-2025) DATA
SOURCE: 2015 NEP 10-K .....................................................................................................................................54
APPENDIX C: NEXTERA ENERGY PARTNERS (TICKER: NEP) PRO-FORMA CASH FLOW STATEMENTS (2016-2025).
DATA SOURCE: 2015 NEP 10-K............................................................................................................................54
APPENDIX C: NEXTERA ENERGY PARTNERS (TICKER: NEP) PRO-FORMA BALANCE SHEETS (2016-2025). DATA
SOURCE: 2015 NEP 10-K .....................................................................................................................................55
APPENDIX D: U.S. YIELDCO VALUATION RESULTS. SUMMARY. DATA SOURCE: RECENT 10-K REPORTS & YAHOO
FINANCE..............................................................................................................................................................56

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Table of Figures
FIGURE 1.1 DAILY STOCK PRICES OF YIELDCOS IN THE US. DATA SOURCE: YAHOO.COM...........................................11
FIGURE 2.1 RENEWABLE ENERGY ASSETS OF YIELDCOS IN THE US. DATA SOURCE: FINANCIAL REPORTS 10-K, 10-Q,
20-F .....................................................................................................................................................................13
FIGURE 2.2 CALIFORNIA DUCK CURVE. CALIFORNIA INDEPENDENT SYSTEM OPERATOR(CAISO). SOURCE: “FAST
FACTS” 3..............................................................................................................................................................15
FIGURE 2.3 NYLD EBIT-INTEREST EXPENSE. CURRENT INTEREST RATE. DATA SOURCE: APPENDIX B.......................19
FIGURE 2.4 NYLD EBIT - INTEREST EXPENSE. FLOATING INTEREST RATE +3%. DATA SOURCE: APPENDIX B.............19
FIGURE 2.5 NEP EBIT - INTEREST EXPENSE. CURRENT INTEREST RATE. DATA SOURCE: APPENDIX C .......................20
FIGURE 2.6 NEP EBIT - INTEREST EXPENSE. FLOATING INTEREST RATE +3%. DATA SOURCE: APPENDIX C...............20
FIGURE 2.7 CRUDE OIL TO YIELDCO PRICE CORRELATION. DATA SOURCES: EIA, YAHOO..........................................21
FIGURE 2.8 PPA EXPIRATIONS. DATA SOURCES: 2015 10-K REPORTS NYLD (PP.35-37), NEP (P.6)............................22
FIGURE 4.1 EFFICIENT FRONTIER, CAL &GMV. DATA SOURCE: CALCULATIONS BASED ON YAHOO FINANCE DATA .33
FIGURE 4.2 U.S. YIELDCO ROE TO PRICE/BOOK. DATA SOURCE: YAHOO FINANCE AND RECENT 10-K REPORTS. .....44
FIGURE 4.3 U.S. YIELDCOS NPM TO P/S RATIO: YAHOO FINANCE AND RECENT 10-K REPORTS. ................................44
APPENDIX E: S&P 500 P/B RATIO VS ROE. SOURCE: SCHMIDLIN 206. DATA SOURCE: BLOOMBERG........................57
APPENDIX E: S&P 500 CONSUMER PRODUCTS COMPANIES: P/S RATIO VS NET PROFIT MARGIN. SOURCE:
SCHMIDLIN 214. DATA SOURCE: BLOOMBERG..................................................................................................57

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Declaration
I grant powers of discretion to the Department, the School of Business, and Manhattanville College to
allow this final project to be copied in part or in whole without further reference to me. This permission
covers only copies made for study purposes or for inclusion in Department, School of Business, and
Manhattanville College research publications, subject to normal conditions of acknowledgement.

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Abstract
YieldCo stocks have been a topic of controversy since middle of 2015. Stocks with high volatility
and little history often get mispriced. At that time, investors realized how little they knew about the
future of these companies and sold their stocks causing an industry-wide equity collapse. Now, almost a
year later, additional performance data for these companies is available. However, the market has not
become less volatile. Majority of investors don’t see YieldCos as lucrative investment opportunities.
Based on the additional data, a fair value of these companies can be determined with more precision.
This project uses financial statements and past stock prices of YieldCos in the United States, as
well as, additional industry data researched from studies, articles, web-based databases and
conferences. The paper studies the risks and the underlying assets associated with these organizations
and analyzes relevance of application of the common valuation methods to them. The results of this
project provide investors with a supported study on investment opportunities in specific YieldCos.

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Glossary of Terms and Abbreviations
ABY – Atlantica Yield, formerly Abengoa Yield. Name was changed in January 2016.
ACF – Average Capacity Factor – ratio of actual energy asset output to their maximum output
AEP – Annual Energy Production
APV – Adjusted Present Value – a model which determines the value of the company dividing FCF by
growth adjusted required return(CAPM) and then adjusting for tax shield.
CAFD – 8 point 3 energy YieldCo
CAL – Capital Allocation Line – straight line which originates at risk free rate of return and is tangent to
the efficient frontier under CAPM.
CAPEX – Capital Expenditures
CAPM – Capital Asset Pricing Model –model which determines the risk-return tradeoff for an asset
CELS – NASDAQ Clean Edge Green Energy
CEV – Continuation Enterprise Value – value of stock given perpetual cash flow characteristic
DCF – Discounted Cash Flows – a model which determines value of a company based on FCF, WACC,
growth and tax rates. This model adjusts for tax within WACC.
Distributed generation – rooftop solar panel leasing program for small energy consumers
EBIT – Earnings Before Interest and Taxes
EBITDA – Earnings before Interest, Taxes, Depreciation & Amortization
Efficient frontier – a graph that represents the minimal risk which can be achieved for a certain amount
of required return given a portfolio of stocks.
ETF – Exchange Traded Funds
EV – Enterprise Value - sum of market equity and debt minus the cash and off-market assets.
FA – Fixed Asset – non-financial asset quantity of which does not increase with increase in sales.
FCFF – Free Cash Flow to the Firm – cash available after operations and CAPEX
GGM – Gordon Growth Model – model which evaluates value of a stock based on the dividend growth.
GLBL- Terraform Global YieldCo
GMV – Geometric Minimum Variance – minimum risk-return trade off portfolio under CAPM.
GSPC – Ticker of the index which mirrors S&P 500 index, but does not pay dividend
HASI – Hannon Armstrong Sustainable Infrastructure capital YieldCo
ITC – Investment Tax Credit – federal tax credit which allows to deduct 30% of the original solar
investment from the tax bill for the company to which it belongs.
IXE – Energy Select Sector S&P
LIBOR – London Interbank Offered Rate
MW – megawatt

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MWh – megawatt-hour
NEP – NextEra Energy Partners YieldCo
NMC – Net Metering Credit – largely debated solar incentive which allows solar panel owners to sell
energy they produce on the market without having to pay for the infrastructure which makes the
delivery possible at the market energy prices.
NREL – National Renewable Energy Laboratory
NWC – Net Working Capital
NYLD – NRJ Yield YieldCo
P/B ratio – price-to-book ratio
P/E ratio –Price-to-earnings ratio
P/S ratio – price-to-sales ratio
PP&E – Property, Plant and Equipment
PPA – Power Purchase Agreement - an agreement between the supplier and the energy consumer to
purchase electricity at a set price for a set period of time.
PV – photovoltaic: deriving electricity from light
RECs – renewable energy credits – tradable claims to produced renewable energy, commonly purchased
by organizations to offset CO2 emissions.
ROA – Return on Assets
ROE – Return on Equity
SACC– Sales After Capital Costs – percentage which reflects sales remaining after financial obligations of
the company have been met.
SEC – Securities and Exchange Commission
SEIA - Solar Energy Industry Association
S&P –Standard and Poor’s
SH – shareholders
Short position – sale of a borrowed security with expectation of the security price decline.
SREC – Solar Renewable Energy Credits – tradable claims to produced solar energy, commonly
purchased by organizations to offset CO2 emissions.
Tax benefit from borrowing - variable which equals tax subtractions due to interest payments
TERP – Terraform Partners
Unlevered – not accounting for debt
WACC – Weighted Average Cost of Capital – sum of products of costs and weighted quantities of
common equity, debt (tax adjusted) and preferred equity.

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Chapter 1 ̶ INTRODUCTION
The group of renewable energy stocks called YieldCos has little historical data. In 2015,
Marathon Capital released the only scientific YieldCos market overview and analysis based on the
information available at the time (Grant and Cornfeld 1). In the publication, Marathon Capital provided
up-to-date information about the YieldCos and its proposed solutions for increased volatility in the
YieldCos market and their future outlook (Grant and Cornfeld 6). This project introduces more recent
YieldCos performance information, analyzes common methods of valuation, shares their results and
makes recommendations to investors. In order to understand the value of YieldCos, it is important to
know what they are and what has made them possible.
1.1 Investment Climate
Over the course of this decade, the world witnessed major catastrophic weather events which
caused billions of dollars in property damage and the countless loss of human lives. These events have
escalated conversations regarding global warming and theories of how humans have contributed to it.
The scientific consensus is that “global warming since the mid-20th
century can be attributed to human
induced increases in atmospheric greenhouse gas concentrations” (Strengers, Verheggen and Vringer 8).
Recently, multiple government policies were enacted for the purpose of decreasing greenhouse gas
emissions. Among them is the encouragement of solar and wind energy production. Solar and wind
energy assets generate energy which does not produce greenhouse gas emissions and can serve as
substitutes for coal and natural gas. In the past, raising funds for solar energy development in the U S
was challenging because of low profit margins. However, the profit margins have been increasing due to
technological advancement and because of three main incentives (Burns and Kang 223). First, and
largest incentive is an investment tax credit in the amount equal to 30% of the solar panel cost, which is
deducted directly from owner’s tax bill (Feld 1). Secondly, the availability of renewable energy
certificate (REC) programs which further incentivize corporate electricity consumers to install solar and

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wind energy assets by awarding them tax credits for the electricity produced (Burns and Kang 217).
Third incentive is net metering credits (NMCs) which allow consumers to sell their surplus electricity to
other consumers on the grid and receive energy credits (Burns and Kang 218). Individual states
commonly have other less significant tax incentives (Argo 2 min). Currently, together with financial
incentives, decreasing costs of solar panels, increasing availability and accessibility, drive the current
surge in wind and solar energy asset demand.
1.2 YieldCos
YieldCos originate as subsidiaries of energy companies. They buy up and operate installed
energy assets from their parent companies or related organizations. YieldCos are in-part publicly
owned. For holding their stock, investors are promised a growing quarterly dividend. Even though the
majority of assets they own and operate generate renewable energy, conventional energy assets as well
as power lines and pipelines make up part of some of their properties (see Appendix A). For example:
NRG Yield, Inc. (Ticker: NYLD) is one of many subsidiaries of NRG Energy Inc. NRG Energy owns 55.1%
voting interest in NRG Yield. NRG Yield, Inc. is a “growth oriented company formed to serve as a primary
vehicle through which NRG owns, operates and acquires contracted renewable and conventional
generation and thermal infrastructure assets” (10-K NYLD 8). They currently own 2GW of wind energy,
0.5GW of solar energy and 2GW of conventional energy generation. Most of the other YieldCos do not
hold conventional generation and thermal infrastructure.

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1.3 Background and Purpose of Research
Current investor perceptions of YieldCos are reflected in their stock prices (see Figure 1.1). The
extent to which these perceptions change can be tracked with the volatility of YieldCos’ stock prices (see
Figure 1.1). Between May and October 2015, YieldCo stocks have lost more than 50% of their value.
Investors were promised a high rate of growth from YieldCos and in 2015 the “markets have realized
that they can’t grow at nearly the rates that they have originally expected” (Mendelsohn, Senior
Director of Project Finance and Capital Markets at SEIA, SEIA PV Conference 23min).
Figure 1.1 Daily Stock Prices of YieldCos in the US. Data source: Yahoo.com.
It is common for new industries to initially experience volatility. YieldCos are both new and
volatile. The purpose of this project is to create a knowledgeable estimation of market values for the
YieldCos and determine the most appropriate approaches for this evaluation based on the latest data.
The most recent market data comes from May 1, 2016. Two YieldCos: NextEra Energy Partners(NEP)
and NRG Yield(NYLD) were chosen as examples for financial analysis due to their consistency in
presenting financial data and sufficiency in historical data. Understanding individual YieldCo’s asset base
and the extent to which risks impact it differently from other YieldCos is important for accurate
valuation.

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1.4 Problem Definition and Importance of Research
Estimating value of a new and rapidly growing firm is difficult, due to the shortage of historical
data. Until recently, there was not enough data to make an accurate value assessment of any YieldCo.
Current YieldCo prices are hinged on the past fluctuations within the market which may not be reflective
of their future performance. The lack of understanding within financial markets of the significance of
YieldCos’ asset bases and their risks causes investors to react to unrelated changes in the market.
Assessment of the asset base, relevant risks and methods of valuation is required in order to analyze the
gap between the fair values of YieldCos and the market prices. This gap is reflective of the investment
opportunities related to YieldCos.
1.5 Goals, Objectives and Propositions to be Examined or Tested
The proposition that there is a gap between the fair value of YieldCos and the current market
price is presented and tested in this project by comparing results of different valuation methods. This
paper sets out to evaluate the relevance of multiple risks associated with YieldCos, valuate YieldCos
using a variety of methods, align these methods in order of effectiveness and provide investors with
tools for selecting certain YieldCos as investments. Some methods require more data and therefore can
only be applied to NEP and NYLD.

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Chapter 2 ̶ LITERATURE REVIEW
2.1 YieldCos Asset Base
YieldCos in the U.S. rely significantly on the performance of their wind and solar power plants.
Figure 2.1 analyzes renewable asset foundations of U.S. YieldCos. The amounts of megawatt (MW)
capabilities are drawn from firms’ latest financial reports. Specific numbers, names of the latest
financial reports and full detailed asset breakdowns for U.S. YieldCos can be found in “Appendix A”.
Figure 2.1 Renewable Energy Assets of YieldCos in the US. Data Source: financial reports 10-K, 10-Q, 20-F
Wind and solar energy assets vary greatly in their limitations and benefits. The foundation of a
company’s performance lies in its operating revenues and sales. In the case of YieldCos, amount of sales
is proportional to megawatt hours(MWh) of sold energy. The 10-K annual reports present the electrical
power of the owned assets in MW rather than in MWh of energy output. Power represents the amount
of energy an asset can generate in one hour when its output is at its maximum. The time of average
maximum output per year is different for wind energy assets and solar energy assets, because the time
the wind blows and the time the sun shines are two independent variables. Therefore, an average wind
energy asset with a power rating of 1 MW does not have the same output as an average solar energy
asset with the same power rating. This paper refers to existing literature to define the productive

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capacity for different assets. Average capacity factor(ACF) is a ratio of actual energy asset output to
their maximum output (Bolinger, Wiser vii). Several reports produced by Berkley National Lab in 2015,
kept track of past ACF for both solar and wind. The ACF of wind energy in the U.S. has been consistent
at around 32% from 2004 to 2014 (Wiser, Bolinger 43). As a benchmark, energy asset with 100% ACF
produces 8,760 MWh/MW/year, therefore the average per MW per year output of wind energy
expressed by that study was 2,800 MWh. The average ACF of solar has increased from 24.1% in 2010 to
29.9% in 2012 (Bolinger, Weaver, Zuboy 12). This is translated to the average of 2,619 MWh per MW
per year. Aside from the difference in ACF of wind and solar assets due to technological imperfections,
it is also a result of different exposure. This difference occurs because wind energy is produced when
the wind is blowing and solar energy is produced when the sun is active and unobstructed. These are
two unrelated variables and thus the length of time of average maximum energy production varies.

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Figure 2.2 California Duck Curve. California Independent System Operator(CAISO). Source: “Fast Facts” 3
Meanwhile ACF assesses the average annual output, there is some value attached to the lack of
deviation from the average energy production levels and the asset’s longevity. It is easier to predict
when the sun is going to shine, than, when the wind is going to blow. Predictable output of solar energy
assets in combination with energy storage is beneficial for “frequency response, avoiding demand
charges, increasing T&E deferral value and conducting time of use rate arbitrage” (Roberts, Executive
director of Energy Storage Association, SEIA conference 1 min). As the U.S. approaches an increased
production and use of solar energy, as it is in California, the downside of solar becomes more apparent
as shown in Figure 2.2. Duck curve occurs when a large daytime supply of solar energy skews regular
energy demand towards nighttime (“Fast facts” 1). Delivery charge is the amount the utility charges
energy consumer for infrastructure, based on the highest level of energy consumption. Because of the
uneven consumption, the energy consumer ends up paying the infrastructure charges (Denholm,
O'Connell, Brinkman, and Jorgenson iii). Solar and wind energy also have a frequency which is more

16
compatible with the grid than that of the biofuel and geothermal energy. Solar and energy storage
installations are helping to solve frequency problem in Hawaii (Harney, business development director
at STEM, SEIA conference 11 min). Hydroelectricity requires a much larger upfront investment and
maintenance than both solar and wind. From 2004 to 2014, wind ACF has been frequently diminished
due to unsold electricity (Wiser, Bolinger 38). For instance, 0.5% of potentially produced wind energy
was curtailed because energy was overproduced during the time when the grid did not require that
amount of energy (Wiser, Bolinger 38). The expected lifetime of a wind turbine is 25 years, while solar
effectiveness deteriorates with an average speed of 0.7% per year because unlike wind turbines, solar
panels don’t have any moving parts (“Data Proves Wind Turbines Productive For Full Lifespan” 5; Dirk 6).
Solar energy assets can last significantly longer than the wind energy assets.
When considering ACF, the variability of energy output and asset longevity, 1 MW of solar
energy capacity is more valuable over time, than 1 MW of wind energy capacity. In the short-term,
there is a 2.1% difference in ACF between wind and solar energy, however, the wind energy output has
a higher variability which makes the short term benefit of either one unapparent.
NEP asset base varies greatly from that of NYLD. All of the electricity produced by NEP is solar
or wind based, meanwhile only 55% of power produced by NYLD is based on renewable resources
(“Appendix A”). However, their renewable energy asset composition is similar. NYLD has 491 MW of
solar capacity while NEP has 324 MW (“Appendix A”). NEP owns 1,761 MW of wind capacity and NYLD
owns an equivalent 1,999 MW (“Appendix A”). For a term of up to 25 years, wind energy assets and
solar energy assets will produce a similar output per MW of capacity (“Data Proves Wind Turbines
Productive For Full Lifespan” 5). After 25 years, their average solar panel will still have most of its
original productive capacity, while an average wind turbine will have been retired.

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2.2 Underlying Risks
2.2.1 Leverage
All U.S. YieldCos have a high degree of leverage. They have promised their shareholders rapid
dividend growth. For the exception of Atlantica Yield(ABY), all of them have paid dividends in a timely
fashion. Owning and operating solar and wind energy assets produces a slim profit margin. Analysis of
corporate 10-K reports has shown that in all case of YieldCos debt was cheaper than equity and was
chosen more frequently to produce further growth (see Appendix B; Appendix C). This is discussed
further in Chapter 4.2.1 with calculation examples. High leverage ratio signals that the firm may not
make enough money to satisfy bond obligations (Bodie, Kane, Marcus 470). Same to a greater extent
applies to dividends, because debt payments come first. In case of YieldCos, this can happen in two
ways: either returns on assets decrease or the debt obligations become higher.
US YieldCo assets are mostly energy assets (“Appendix A”). Revenue from an energy asset can
decrease because of a decrease in energy output, energy price or tax incentives. The risk of the
unanticipated energy output decrease is low because every portfolio consists of many small assets. This
study will evaluate a magnitude of impacts different risk factors can have on the profitability of NYLD
and NEP because existing literature currently lacks this.
2.2.2 Legal Risk
Legal risk is reduced because YieldCos receive a majority of tax incentive benefits when the
energy assets are complete. There are three main tax incentives for solar energy production in the U.S.
Investment tax credit(ITC) was extended to projects started before 2024 ("Global Solar Industry Saved
from 2017 Cliff-Edge as U.S. Set to Extend Solar ITC." 2015), thus the risk associated with this incentive is
eliminated. Net metering legislation differs greatly among individual states. Previously, renewable
energy producers in Nevada have been fully compensated annually for the rollover energy at the retail
rates without having to bear the cost of maintaining the grid. In 2015, after a lobbying effort by the

18
utilities, legislation was passed that ended net-metering in the state. Shortly thereafter, NMC incentives
were eliminated in Oklahoma and modified in Wisconsin, Minnesota, Mississippi and Hawaii (“Nevada's
Solar Flare” 1;“Freeing the Grid” 1). This risk impacts both new solar energy assets and the ones
currently in operation by increasing the prices of renewable energy. Reduction of NMC would
discourage energy consumers from renewing their power purchase agreements(PPAs) and would
encourage low credit entities to terminate their contracts. Most of YieldCos’ energy consumers, except
for rooftop solar panel leasing energy consumers are creditworthy because those consumers involved in
solar leasing are individuals. Rooftop solar leasing program is referred to as “distributed generation” of
financial fillings (2015 Q3 10-Q TERP 43). The only two YieldCos which own distributed generation are
NYLD with 9 MW and TERP with 399 MW of solar energy asset capacity (“Appendix A”). Similar to
NMCs, RECs affect both new and operating renewable energy assets by changing the price of renewable
energy production. However, RECs are minor incentives and therefore, the impact of their elimination
would be smaller. RECs are regulated by the Environmental Protection Agency (EPA), which aims to
bring incentives to the states which create most pollution (“Renewables Advocates Seek EPA Guide on
REC Value” 2016). Green house emissions by territory don’t change rapidly and can be tracked.
Therefore, changes of EPA REC policy are easily predictable.
2.2.3 Interest Risk
YieldCos have a narrow profit margin. A slight change in the average cost of capital can signify
the difference between profit and loss. London Interbank Offered Rate (LIBOR) based debt is a large
portion of all YieldCos’ capital structures. Currently, LIBOR is 0.66% for 3 months and 0.91% for 6
months. The extent to which changes in LIBOR can affect the firm’s cost of capital is defined by firm’s
debt to equity ratio and the proportion of the debt with variable interest rates. NextEra Energy Partners
has a debt-to-equity ratio of 3 and proportion of variable interest rate debt of 59% (“2015 NEP 10-K”
77). NRG Yield has the debt-to-equity ratio of 2 and 52% of floating rate debt (“2015 NYLD 10-K” 105).

19
NYLD has a history of paying off its long term debt, which was considered in the pro-forma statements
(“Appendix B”). Even though this reduces leverage in the long term, the short term profitability
becomes more narrow.
Figure 2.3 NYLD EBIT-Interest expense. Current Interest Rate. Data source: Appendix B
Figure 2.4 NYLD EBIT - Interest Expense. Floating interest rate +3%. Data source: Appendix B
Figures 2.3 and 2.4 above describe a scenario analysis for NYLD. In this analysis, the spread
between EBIT and interest expense which represents the income before tax increases over time. This
happens because during the past 2 years, NYLD has been paying a significant portion of their long term
debt. Pro-forma income statements were made on the basis of that assumption (“Appendix B”). Figure
2.4 above reflects an assumption that the variable interest rate will increase by 3% in 2016 to assess the
impact of interest rate risk on profitability. Consequently, the net loss would be incurred by NYLD
during the years 2016, 2017 and 2018. During these years, the company would not be able to pay
dividends. Although, this would cause the loss of quarterly dividends, the value of the firm would
remain positive, since the long term projections would show that the firm will be profitable (see Figure
2.4).

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Figure 2.5 NEP EBIT - Interest Expense. Current Interest Rate. Data source: Appendix C
Figure 2.6 NEP EBIT - Interest Expense. Floating Interest Rate +3%. Data source: Appendix C
In 2015, NEP increased its LIBOR based debt by $1.3 billion. 59% of NEPs current debt is based
on a variable rate. Figure 2.5 above shows the spread between EBIT and interest expense. The width of
the spread reflects a large income before tax. Figure 2.6 above reflects an assumption that the variable
interest rate will increase by 3% in 2016. Based on Figure 2.6, the change in variable interest rate would
not eliminate the profitability of NEP or its ability to issue dividend. It can therefore be concluded that
interest rate risk has a lesser impact on operations of NEP when compared to that of NYLD, despite the
fact that NYLD has less variable rate debt.
2.2.4 Energy Price Risk
Returns on assets(ROAs) of YieldCos have a high positive correlation with prices of the
conventional energy. Currently, power purchase agreements are participating in a “race to the bottom”
(Arad, vice president, GreenSkys Renewable Energy, SEIA Conference 13min). NYLD decreased the size
of its PPAs by $55M in 2015 despite a significant growth of the portfolio of assets (“2015 NYLD 10-K”
100). This got their clients shorter term contracts and gave NYLD access to the more creditworthy
energy consumers. Additionally, the cost of hedging decreased.

21
In case of an energy price decrease, renewable energy would become less attractive, thus
discouraging consumers with expiring PPAs to renew their contracts. The average credit quality of
energy consumers will be diminished, thus giving rise to credit risk. Equity investors will regard this
investment to be riskier and get rid of this stock or require a higher rate of return thus increasing the
cost of capital. The news articles such as “Rising Electricity costs: A challenge for consumers, regulators,
and utilities” suggest that the prices of electricity are bound to go up. This is another argument to
support the decrease in the size of PPAs.
Figure 2.7 Crude Oil to YieldCo price correlation. Data sources: EIA, Yahoo
Energy price risk also manifests itself through volatility of oil prices. YieldCo stocks prices tend
to increase when the oil prices go up and fall when the oil prices decrease (see Figure 2.7, previous
page). Strong correlation is partially illogical because solar energy is a long way from being a direct
substitute for oil. In 2015, almost 85% of crude oil was used to produce gasoline, diesel and jet fuel (EIA
“Refinery Yield” 1). Therefore 85% of the use of crude oil can be impacted by renewable energy through
electric vehicles. The electric vehicle market share in the U.S. has increased from 1.2% in 2013 to 1.5%
in 2014 (IEA 1). There is also no reason to assume that electricity which electric vehicles utilize comes
from renewable sources, because despite strong growth energy generated by using solar and wind
energy assets make up less then 5% of electricity in the grid today ("Monthly Energy Review May 2016"
1). Therefore, changes in oil prices have a small effect on actual demand for renewable energy.

22
In December 2015, all renewable energy produced by NYLD and NEP was being sold through
PPAs (“2015 10-K NYLD” 35-37; “2015 10-K NEP” 6). Both sold produced energy at a fixed price. Figure
2.8 below reflects the size and the expiration year of these renewable energy PPAs. According to this
graph, negative movements in price are well hedged for the next 10 to 20 years. However, PPAs are
susceptible to the risk of counterparty default.
Figure 2.8 PPA Expirations. Data sources: 2015 10-K reports NYLD (pp.35-37), NEP (p.6)
2.2.5 Counterparty & Parent Default Risk
SunEdison is the parent company of Terraform Global (GLBL) and Terraform Power (TERP).
SunEdison’s bankruptcy has negatively effected the obligations that the parent company has had to its
subsidiaries such as contracts on which the subsidiaries have already posted deposits or delivered
payments (" TerraForm Global Distances from SunEdison on Bankruptcy Risk” 1). It has also negatively
impacted the creditworthiness and the ability to raise funds for these organizations. Nevertheless, the
source of funding for their current operations is secured. GLBL and TERP 10-Q reports reflect a high
paid-in capital mix in their equities. This suggests that even when the parent company stock holdings
get sold during the bankruptcy proceedings, the equity will not be close to being wiped out. SunEdison’s
bankruptcy represented a tail risk, because their credit rating was medium before SunEdison declared
technical bankruptcy associated with the fact that they could not provide financial statements.
Technical bankruptcies are difficult to predict. All of the YieldCos except HASI and CAFD have
considerable risk of parent company’s default. NYLD’s PPA energy buyers are a variety of independent
0 MW
500 MW
1,000 MW
1,500 MW
2,000 MW
2,500 MW
3,000 MW
'16 '21 '26 '31 '36 '41
Capacity(MW)
NEP PPA
NYLD PPA

23
utilities, with just a few contracts tied to the parent (“2015 NYLD 10-K” 35-37). NEP did not disclose the
counterparties to their PPAs. NEP 10-K report mentions that PPAs are only established with
“creditworthy counterparties” (“2015 NEP 10-K” 5). Any counterparty default, in the case of reduced
energy prices, will have a direct impact on revenues and the net income.
2.3 Valuation Methods
This project valuates companies based on their expected cash flows, past stock prices and
dividend trends as well as by stacking up their ratios against others in the industry.
2.3.1 Historical Stock Price and Dividend Based Valuation
Capital asset pricing model(CAPM) describes a risk return relationship of an asset in relation to
the market. It takes a number of assumptions such as a rational and risk-averse investor, fully
diversified portfolios and fluid markets. A shortcoming of this model for this particular valuation is the
insufficiency of historical data. Longer periods of data would provide a better approximation. For CAPM
model, 3 month LIBOR is used as the risk free rate and uses the ^GSPC S&P index is used to trail market
performance and evaluate the 5-year average market rate of return.
On its own CAPM only describes the risk return trade off associated with the investing activities.
Gordon Growth Model(GGM) uses the beta of the stock in relation to market (S&P 500) to identify the
risk, the expected dividend and expected return to calculate the intrinsic value of the stock.
2.3.2 Financial Statement Based Valuation
Free cash flow to the firm (FCFF) represents the value that the company brings in during each
reporting period. For the purpose of YieldCos analysis, FCFF is preferable to free cash flows to the
equity (FCFE) because the interest payments which are accounted for in FCFE are high and rapidly
growing. This skews the numbers and makes them inconclusive. Having calculated this amount enables
the use of FCFF valuation models: the model of discounted cash flows (DCF) and the adjusted present
value (APV) model.

24
DCF uses weighted average cost of capital (WACC), growth rate and FCFF to estimate the fair
value of the firm. DCF is particularly suitable for analyzing YieldCos, because the income from fixed
assets (FA) is steady and there is no variability in sales or costs because the electricity produced by solar
energy assets can be easily forecasted. This cost model is volatile because FCFF for the latest period
may not be accurate. It also puts a lot of emphasis on WACC, which can change easily in this industry
because of high debt to equity ratios and majority of debt financed using variable rates.
APV method which calculates the value of the firm based on the constant perpetual growth rate
will be referred to as the “simple APV method”. APV method which calculates the value of the firm and
takes into account the rapid growth during the first 10 years will be referred to in this paper as the
“complex APV method”. Fair values of the firms determined by both APV methods consist of unlevered
cost of equity, unlevered value of the company and the cost of potential risk of bankruptcy. Simple APV
method calculation assumes a constant rate of growth. In the case of NEP and NYLD, however it would
be helpful to make an adjusted calculation with the rapid growth during the first 10 years which
approaches and becomes 2%. Ten years of pro-forma financial statements are created based on a
number of assumptions and past financial trends in order to calculate FCFFs during these years.
When estimating the value of the company using Future Cash Flows to the Firm (FCFF), accurate
estimation of growth becomes essential. This is a young industry which is currently experiencing strong
growth, however in perpetuity growth of 20-80% is not sustainable. Renewable energy is becoming
cheaper. Energy generated from both sun and wind is already cost competitive in some regions of the
United States with the energy generated by conventional energy sources. Currently, the growth is high
due to the previous lack of integration. Solar utility growth is assumed to become equivalent to that of
the other utilities in 10 years. Average annual capital growth rate of utilities in U.S. between 2010 and
2014 was 2% (MarketLine 7). Therefore, growth of YieldCos in perpetuity is assumed to be 2%.

25
An assumption is taken that the growth decrease is a regression from current value to 2% over
the course of 10 years. It decreases faster in the beginning because new competitors enter the market
and with additional competition the inflow of competitors will decrease. It is assumed that old debt will
be replaced with new debt at the same rate at the time of maturity. New debt is assumed to be
financed at the current cost of debt. Cost of equity is assumed to remains the same. Proportions of
debt and equity financing presumed unchanged. For example, NEP historically funded 26% of its growth
with equity, therefore all of today’s equity will account for 33% of equity in 10 years. Proportions of
capital expenditures to equity investments are assumed to remain unchanged.
The term “unlevered” in the context of this paper describes variables which do not account for
debt. Tax benefit from borrowing is a variable which equals tax subtractions due to interest payments.
2.3.3 Ratio Based Valuation
Another way to valuate firms is by comparing their financial ratios. Figures 2.3 and 2.5 (pp. 18-
19) display the profit margins of NEP and NYLD. From these figures, it is evident that NYLD has a thinner
profit margin than NEP. Profit margins are important to maintain in order for the firm to pay dividend
even in case of negative events. An example of a risk event is displayed in figures 2.4 and 2.6 (pp 18-19),
where due to a 3% variable rate increase NYLD pro-forma statements showed net losses, meanwhile
NEP had reduced gains. Another way to analyze profit margin is by using enterprise value(EV)/sales
ratio and the sales after capital costs (SACC) ratio. EV/sales demonstrates the possible gain on the scale
of the enterprise. The SACC ratio subtracts mandated costs of capital to show how close the
performance is to the required profit margin.

26
EV is relevant because it determines how much capital is being used to make profit. Sales
numbers are hard for companies to manipulate, which makes them helpful during analysis. Dividing EV
by sales determines company’s ability to turn assets into sales. EV/sales ratio provides an effective
valuation when comparing companies which own similar types of assets, because different types of
asset will produce different amounts of sales.
SACC shows the relationship between the quantity of sales and the amount that will be paid to
the lenders and shareholders. This relationship defines the amount remaining after the company’s
financial obligations have been met.
Price-to-Earnings (P/E) ratio shows the proportion of annual earnings per share to the stock
price. Investors generally accept higher P/E ratios for companies with higher expected growths.
Price-to-Book ratio (P/B) shows how profitable the business is by evaluating the market
expectation of the firm’s ability to increase shareholders’ equity and pay dividends. Consequently, if the
return on investment is high, the expectation is that the stock price will increase.
Price-to-Sales (P/S) ratio like EV/sales ratio remains meaningful even during a net loss. P/S ratio
and the net profit margin generally have a linear relationship.
EV/EBIT ratio assesses the firm’s ability to turn assets into earnings before financial obligations
and taxes. EBIT is better than EBITDA for cross industry comparison because of the different amounts of
fixed assets(FA) required for operations in other industries. Higher EV/EBIT ratio, the smaller the profit
margin. The shortcoming of this ratio is that it doesn’t account for interest payments which can be
significant for YieldCos.

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Chapter 3 ̶ RESEARCH APPROACH
This chapter describes the methodology of this project which includes market research, stock
price history analysis and assessments of YieldCos’ SEC filings. YieldCo stocks are unique securities
which at the time of the study haven’t been recently scientifically analyzed. The researcher approached
this project with the hypothesis that YieldCos present a number of investment opportunities due to a
variety of factors such as the decrease in prices of YieldCo stocks, effective risk management, reliability
of the assets and the observed decrease in volatility (see Figure 1.1). This study set out to analyze
YieldCos financial data through the utilization of multiple valuation techniques and through the
evaluation of effectiveness of these techniques.
3.1 Research Method
The purpose of the research was to gain understanding of the renewable energy markets and
specifically YieldCos to preform asset analysis, risk determination, valuation method assessment and an
accurate valuation.
Methodology of this paper is based on a compilation of recent scientific journal publications,
reports and video recordings made during Boston’s SEIA Solar Conference and Expo in February 2016.
During the conference, speakers shared their enthusiasm for the solar energy industry due to cost
reductions and tax incentives (Argo 1 min, Ashai 4 min, Belair 9 min), identified distinguishing qualities
of solar energy assets (Roberts 1 min; Harney 11 min) and stated that YieldCos have dropped in price in
2015 due to the inability to grow fast enough (Mendelsohn 23 min). The inability to maintain predicted
dividend growth was also stated in Marathon Capital’s white paper publication regarding YieldCos
securities (Grant and Cornfeld 30). The forward looking benefits of solar energy incentives and cost
reductions, as discussed at the conference have also been reiterated in scientific publications (Bolinger,
Weaver and Zuboy 12; Burns and Kang 217). Based on these resources, the projection was made that
the growth rate will be reduced to the utility industry average by 2025. The differences between wind

28
energy assets and solar energy assets were reaffirmed in scientific literature (Bolinger and Weiss 43;
Bolinger, Weaver and Zuboy 12). NEP and NYLD YieldCos were chosen for in-depth analysis and
projected financial statement development in part due to the matching composition of assets.
3.2 Data Reliability
Yahoo Finance stock history data was used because it is reliable, publicly available and easily
exported to excel. Additionally, Yahoo Finance gets majority of its stock and dividend data in the United
States directly from the stock exchange and from interactive real-time data services (“Exchanges and
Data Providers of Yahoo Finance” 1). EDGAR database for SEC filings was used to access the most recent
financial reports because EDGAR performs automatic collection and validation of such reports and
makes it available to the public (“Important Information About EDGAR” 1). EIA website was used to
obtain crude oil price data because of its timeliness and reliability. Notably, EIA obtains its data from
Thomson Reuters and regularly verifies it (“Today in Energy” 1).
3.3 Data Analysis Procedures
3.3.1 Sharpe Ratio Procedures
Monthly stock price histories for the NYLD, NEP, TERP, GLBL, ABY, HASI and CAFD YieldCos and
the S&P ^GSPC index were uploaded from Yahoo Finance to Microsoft Excel. The procedures described
below were conducted for each firm using Microsoft Excel. Average rate of return and standard
deviation were calculated for the period of existence of the YieldCos and for 5 years for the S&P index.
In order to calculate correlations between these firms, the index matrices of correlations and
covariances of these securities were created. Dividend data for YieldCos was uploaded from Yahoo
Finance to Microsoft Excel. Average dividend was added to average rate of return from stock to
calculate the total average rate of return. Solver tool was used to determine the Geometric Mean
Variance(GMV) portfolio of YieldCos. GMV reflects the portfolio of YieldCos with minimal risk and
maximum return. Ten additional YieldCo portfolios with minimal risk values and given rates of return

29
were calculated using the solver tool. Risks and returns were connected together on a scatter graph to
form the efficient frontier. A Capital Allocation Line(CAL) which originated from the risk free rate of
return and was tangent to the efficient frontier was drawn. Sharpe ratio is the slope of the CAL. Sharpe
ratio was calculated by dividing CAL’s rise over its run.
Monthly index price histories for Energy Select Sector S&P (IXE) and NASDAQ Clean Edge Green
Energy (CELS) were downloaded from Yahoo Finance to Microsoft Excel. ^GSPC formulas were copied to
apply to the IXE and CELS data in order to identify average risks, average returns, covariances,
correlations and betas of YieldCos in relation to these indexes.
3.3.2 GGM Procedures
Following procedures were applied to individual YieldCos in order to obtain the intrinsic values
of their stocks. Beta for an individual YieldCo was calculated by dividing the covariance of the YieldCo
stock price and ^GSPC by the variance of ^GSPC. Risk free rate plus market return margin multiplied by
the YieldCo’s beta resulted in a required rate of return. By averaging the past dividend growth,
expected dividend growth estimate was established. The expected dividend was divided by the required
rate of return minus the dividend growth rate, which resulted in a Gordon Growth Model(GGM)
approximation of an intrinsic value of that stock.
3.3.3 Discounted Cash Flows Procedures
Balance sheets, income statements and cash flow statements for NEP and NYLD were
transposed from their 2015 10-K reports (“2015 NEP 10-K” 59-63; “2015 NYLD 10-K” 70-75). The
average income tax paid was obtained from the income statement. Interest rate paid on debt was
obtained from 2015 10-K reports, the recent LIBOR was factored into the floating rates and summarized
into a total cost of debt (“2015 NEP 10-K” 77; “2015 NYLD 10-K” 105). Total debt and total equity were
obtained from the balance sheet. Cost of equity was obtained from the statements of shareholders’
equity (“2015 NEP 10-K” 62; “2015 NYLD 10-K” 75). Ratio of debt was multiplied by the cost of debt and

30
multiplied by one minus the average tax rate and then added with the product of cost of equity and the
ratio of equity resulting in the WACC. Unlevered net income was obtained from the income statement
by adding the net income and the after-tax interest expense. Change in net working capital(NWCs) and
net investment were obtained from the statement of cash flows. Increase in NWC and net investment
were subtracted from the unlevered net income and resulted in FCFF. FCFF was divided by the WACC
minus the long-term growth rate to calculate the continuation enterprise value (CEV). Next, the debt
was subtracted from the CEV and divided by the amount of shares outstanding to determine the fair
share value.
For the purpose of testing the exposure of DCF valuation method to changes of interest rates,
WACCs were recalculated using increased variable interest rates. WACCs were then plugged into the
calculations to determine the updated fair share values.
3.3.4 Simple Adjusted Present Value Procedures
The financial statements used for DCF method were used to calculate the simple APV method
fair share values of NEP and NYLD stocks. This section describes the procedure used to calculate fair
share value for an individual stock. Unlevered beta was calculated by dividing CAPM beta by 1 plus the
debt to equity ratio multiplied by one minus the tax rate. Unlevered cost of equity was calculated by
adding the risk free rate and the product of unlevered beta and risk free rate subtracted from the
market rate. APV FCFF is DCF FCFF plus the depreciation. Unlevered value of the company was
calculated by dividing FCFF by the unlevered cost of equity minus the terminal growth rate. The benefit
from borrowing was calculated by multiplying outstanding debt by the tax rate. The likelihood of
bankruptcy was derived from the company’s credit rating and converted into a percentage (Damodaran
30). Accounting for bankruptcy was calculated by multiplying unlevered value of the company by 30%
bankruptcy cost and then by the likelihood of bankruptcy. Unlevered value of the company plus the tax
benefit from borrowing and less the accounting for bankruptcy has resulted in fair enterprise value. The

31
debt was then subtracted from it and the result was divided by the amount of outstanding shared, thus
calculating the fair share value.
3.3.5 Initial Growth Adjusted Present Value Procedures
APV method requires development of pro-forma financial statements in order to account for the
fast YieldCo growth during the first 10 years. Equity reinvested in business was calculated by combining
capital expenditures, equity investment and repaid debt and subtracting from that the sum of
depreciation, debt issued, equity issued and change in NWC. Current growth was determined by
dividing equity reinvested in business by the net income and then multiplying this quotient by the ROE.
Ten-year regression of the growth rate was created. Percentages of funds allocated toward cap-ex and
equity investments were calculated by adding equity capital allocations in the CFS and dividing by the
total capital allocation. Percentages of capital raised by issuing debt or equity were calculated by adding
up the past capital raised using each method and dividing by the total capital raised. All types of
operating costs were multiplied by the growth factor and then by the cap-ex portion of capital allocation
to determine future costs. Other costs were multiplied by the growth factor unless a different trend was
in place historically. Payments of long term debt, if previously in place, were continued at a rate that did
not diminish the ability to pay dividend and grow net income. The APV during the first 10 years was
calculated by adding up the annual FCFF and the tax shield discounted by the unlevered cost of equity
and the diluted equity effect. The benefit from borrowing and the FCFF after 10 years were both
discounted by the unlevered cost of equity less the terminal growth rate. The sum of two APVs minus
the accounting for bankruptcy resulted in the total APV. Total APV minus the debt and divided by the
amount of outstanding shares resulted in the fair share value.
3.3.6 Scenario Analysis Procedures
The risk analysis in Chapter 2 required data analysis. Lists of assets were extracted from every
YieldCos 10-K report. NEP and NYLD PPA data was transposed to excel from their property summary 10-

32
K report pages. Pro-forma income statements for NEP and NYLD were adjusted to reflect the interest
rate cost associated with the increased variable rates. WACCs were recalculated by multiplying the
variable portions of debt by the rate increase and adding that to the previous WACCs. To calculate the
new interest rate expenses, the amounts of debt outstanding on the pro-forma balance sheets were
multiplied by the new WACCs.
3.3.7 Ratio Analysis Procedures
The following ratio procedures were performed on all YieldCos to whom these ratios were
applicable. Enterprise value was determined by subtracting cash and other highly liquid assets from the
sum of debt and equity. Liquid assets, debt total and equity total were found on the balance sheet of
the most recent SEC filing. Sales and EBIT were obtained from the income statements. Sales after
capital costs (SACC) ratio was calculated by dividing sales minus the sum of total equity cost and total
debt cost by sales. Total debt cost or interest rate expense and total equity cost were obtained from the
income statement. The stock price for P/E and P/B ratios was obtained from Yahoo Finance. Earnings
were obtained from the income statement. Book value was obtained from the balance sheet.
3.4 Summary:
Based on the industry information and data collected prior to financial analysis, this project set
out to analyze this new and volatile group of securities. Data procedures were outlined to open the way
for calculations and analysis presented in the Results, Analysis and Interpretation chapter.

33
Chapter 4 ̶ RESULTS, ANALYSIS AND INTERPRETATION
4.1 Stock Price and Dividend History Analysis
CAPM puts forth a theory about the risk return tradeoff connected to the investment activities
based on which an optimal portfolio of stocks can be developed. Intrinsic value of the stock can then be
estimated by using the beta found in the CAPM through the application of GGM.
4.1.1 CAPM
CAPM was previously discussed in Chapter 2.3.1 where the theory was explained and its
assumptions listed. In Chapter 3.3.1, the calculation procedures were detailed. Three months of USD
LIBOR for this study equaled 0.66% (“3 Month US Dollar LIBOR Interest Rate” 1). The last data points for
average rates of return and risks associated with YieldCo stocks and the ^GSPC index were used from
May 1st
, 2016. Five-year average ^GSPC index rate of return was calculated to be 8.51%. The risks in
this project were derived from the volatility of the stock prices.
Figure 4.1 Efficient frontier, CAL &GMV. Data source: calculations based on Yahoo Finance data
Global Minimum Variance(GMV) portfolio was the least risky portfolio of YieldCo stocks which
had an expected return of 22% (See figure 4.1). It consisted of 94% Hannon Armstrong Sustainable
Infrastructure capital, Inc.(HASI) and 6% NEP stocks. Sharpe ratio is the slope of the CAL in Figure 4.1. It
is the risk-return tradeoff for an optimal portfolio which in this case was 1.33. Therefore, for every 1%
of expected profit the investor must have tolerated at least 1.33% of risk increase. According to the
Vanguard Group website Sharpe ratio of a well diversified and safe investment, such as Vanguard 500

34
ETF(VOO) is 0.96. Notably, both NYLD and NEP have had low betas of about 5% to 7% of their expected
stock values (see Table 4.1). Beta is the extent to which the risk of the market portfolio correlates with
the risk associated with the stock. Since unsystematic risks were excluded due to CAPM assumptions,
stocks with lowest betas also produced the lowest risks, provided a better risk return tradeoff and had a
higher chance of being included in the GMV portfolio. Low expected dividend of 5%-7% on the other
hand directly causes a lower expected return and therefore a worse risk-return tradeoff. Based on this
contradiction, a hypothesis can be made that among the results of this project, the stocks with the
lowest expected dividend such as NYLD, NEP and HASI will be regarded as better investments.
Table 4.1 CAPM risk return tradeoff. YieldCos US. Data source: calculations based on Yahoo Finance
data
Even though the stocks with the highest expected returns were HASI with 30.55%, CAFD with
24.80% and GLBL with 37.11%, the stocks with the best risk-return tradeoffs were GLBL, HASI & NEP (see
Table 4.1). Subsequently, all minimal risk for a given level of return portfolios consisted only of these
three stocks. CAFD stock didn’t have an advantageous risk return tradeoff because it had a risk of
63.83% associated with it. GLBL stock had a high risk associated with it, but also a very high expected
rate of return. Even though GLBL stocks were not included in the optimal portfolio, due to high returns
they were included in the minimal risk, higher return portfolios.
4.1.2 GGM Valuation
GGM is used for the purpose of identifying the intrinsic value of the stock based on the required
rate of return and dividend. GGM valuation method was described in Chapter 2.3.1 and the procedures
were explained in Chapter 3.3.2. Due to 2015 YieldCo market volatility, betas for NYLD and CAFD were

35
abnormally high (see Table 4.1 previous page). This has negatively impacted the required return,
therefore the value of the company using GGM (see Table 4.2). According to the valuation results, CAFD
and NYLD were highly overvalued despite having a high dividend and already having devaluated by more
than half of their value.
In contrast TERP, ABY, NEP and GLBL were undervalued by more than 60% of the current stock
price. Their values have been inflated due to high promise of dividend growth (Mendelsohn 23 min). If
this dividend growth is sustainable, the prices of these stocks should be increasing significantly. GLBL
stock has a shorter history than most other YieldCo stocks and has experienced strong volatility recently
due to the struggles experienced by its parent, SunEdison (Gatlin 1). From August 2015 to March 2016,
GLBL stock lost 85% of its value. After the SunEdison bankruptcy was announced during April 2016,
GLBL stock has experienced a 57% increase thus signifying an improvement in stockholders’ perceptions
(see Figure 1.1 page 10). Within this project’s GGM, April 2016 data decreased the average risk and
increased the expected return of all YieldCo stocks, thus increasing their GGM intrinsic stock value (see
Table 4.3 next page). If GLBL stock price remains constant or increases, GLBL GGM intrinsic value will
increase further.
HASI intrinsic stock value was within 30% of its recent stock price. GGM intrinsic stock value
determines present value of the future dividends. Due to low volatility, the promise of dividends had a
higher value to the investor. Thus the stock price was similar to the present value of the future
dividends.

36
Table 4.2 GMM intrinsic stock value calculation Data source: calculations based on Yahoo Finance data
Table 4.3 GGM Intrinsic stock values Mar-May ‘16. Data source: calculations based on Yahoo Finance
The shortcoming of finding intrinsic stock value through GGM is that GGM’s calculations are
based on the past stock pricing and dividend data, which do not span too far in this case. Data from
every new period had a strong impact on the GGM intrinsic stock value (see Table 4.3). Table 4.3
summarizes the volatility of GGM intrinsic stock values during the past 3 month. During their short
history, YieldCos have always paid a quarterly dividend to their investors. GGM only relies on the
historical data for its calculations and only uses the required rate of return to adjust for the possibility
that the dividend will not be paid. Based on the GGM calculation results, majority of YieldCos are
undervalued (see Table 4.2 previous page). The extent to which investors assessment of firm’s ability to
pay dividends was not reflected in the required rate of return can be seen in the difference between the
intrinsic stock value and current stock price. For example, NEP’s intrinsic stock value was $98.85 and the
recent stock price was $28.89 (see Table 4.2 previous page). From this difference, it was concluded that
NEP’s required rate of return was 11.87%. It was lower than the investor consensus of what required
rate of return should be. On the other hand, the results for NYLD have shown that it was overvalued
due to a 25.38% required rate of return.

37
4.1.3 Other Correlations
S&P’s IXE energy sector index and NASDAQ’s CELS diversified renewable energy index
correlation calculation procedures were discussed in Chapter 3.2.1. Both SunEdison YieldCos: TERP and
GLBL have shown a negative correlation to the IXE energy index: -0.19 and -1.14 respectively.
Therefore, if IXE’s price will experience a significant decrease, it can be expected that TERP and GLBL will
soar. All YieldCos aside from HASI had a strong correlation with CELS, the renewable energy index.
Events in this industry effect most of these companies in the same way. Accordingly, if an event such as
a decrease in conventional energy price were to decrease the value of all YieldCos, HASI value would be
expected to increase.
4.2 Financial Statement Based Valuation Methods:
Both DCF and APV calculate the present value of future cash flows. The differences between the
two methods and their alterations were discussed in Chapter 2.3.2. The detailed method procedures
through which fair share values are calculated are explained in Chapters 3.3.3, 3.3.4 and 3.3.5. This
section will analyze fair share values of NYLD and NEP using DCF, simple APV and complex APV methods.
4.2.1 Discounted Cash Flow Method
DCF model used FCFF, constant growth rate of 2% and WACC to estimate the CEV of the firm.
DCF method was described in Chapter 2.3.2 and the procedures were explained in Chapter 3.3.3.
NYLD cost of debt and equity were identified to be 3.93% and 4.85% respectively. After
factoring in the weights of 59% for debt and 41 % for equity and the average tax rate of 8%, WACC was
calculated to be 3.9%. This in in conjunction with $219 million in FCFF caused CEV to be $7,469 million.
Given the amount of debt and 97 million shares outstanding, fair share price was calculated to be
$27.38. This is 1.8 times the recent NYLD share price of $16.18.
NEP capital structure was made up of 62% debt with average cost of 3.2% and 38% equity with
the cost of 3.03%. Adjusted for 23% average tax rate, WACC equaled 2.57%. NEP FCFF was calculated to

38
be $117 million. DCF CEV for NEP was evaluated at $6,096M. Minus $4 billion in debt divided by the 31
million outstanding shares has resulted in the fair share value of $86.66 which is 2.9 times higher than
its current price of $29.85.
DCF model NEP fair stock value was within 15% proximity of the GGM NEP intrinsic stock value
(see Appendix D). NYLD DCF method results were contradictory to GGM valuation findings. Both of
these results supported the hypothesis stated in Chapter 4.1, that as a result of this study stocks with
lowest expected dividends will be regarded as valuable investments.
Table 4.4 DCF method valuation of NEP and NYLD. 3% variable rate increase. Data source: 2015 10-Ks
As mentioned earlier, these companies are exposed to the interest rate risk. 62% of NEP
financial structure was debt and 59% of NEP’s debt had variable interest rate. To determine magnitude
of this risk, this study raised risk free rate by 3%(see Table 4.4). The projected WACC for NEP increased
from 2.57% to 3.68%. The fair share value dropped immediately from $86.66 to $18.54, past the recent
stock price of $29.85. Same scenario was used for NYLD. Because 66% of new funds were raised
through debt and that 52% of current debt had a variable rate, 3% increase in variable rate has resulted
in a WACC increase of 1.03%. Consequently, fair price of NYLD dropped to $0.35(see Table 4.4). It was
concluded that DCF fair share value formula gets significantly impacted by changes in WACC. It is
unlikely that NYLD stock price would drop so low because of a 1.03% WACC change. Figure 2.4 displays
the potential impact a 3% variable rate increase would have on the NYLD pro-forma income statements.
APV methods are not subject to the same risks because they use unlevered cost of equity instead of
WACC.

39
4.2.2 Simple Adjusted Present Value Method
Simple APV valuation was used to determine fair share values of NEP and NYLD stocks at a
continuous growth. Simple APV method was described in Chapter 2.3.2 and the procedures were
explained in Chapter 3.3.4. NEP CAPM beta of 1.43 was translated into unlevered beta of 0.44 and the
unlevered cost of equity of 4.15%. NEP APV FCFF was calculated to be $117 million. FCFF divided by
unlevered cost of equity minus the 2% growth rate came out with the unlevered value of the company
of $5,432 million. Tax benefit from borrowing was $781 million because of the 23% average tax rate.
Due to the credit rating of A- the possibility of bankruptcy was estimated at 1.41% and subsequently the
cost of accounting for bankruptcy was $23 million (Damodaran 30). APV was calculated to be $6,190
million. APV minus the debt and divided by the 30.7 million outstanding common shares resulted in a
fair share value of $89.72 which was 3 times higher than the recent share value (see Table 4.5). NYLD
CAPM beta of 3.15 was translated into the unlevered beta of 1.13 and the unlevered cost of equity of
9.5%. FCFF of $373 million resulted in $4.973 unlevered company value. Tax benefit from borrowing
was $383 million because of the 8% average tax rate. Accounting for bankruptcy costs was $149 million
because of the BBB credit rating with the possibility of bankruptcy equaling 10%. Hence, APV was
calculated to be $5,207 million. This amount minus the debt and divided by the 97 million outstanding
shares resulted in the fair share value of $4.15. This is 4 times lower than the current NYLD stock price
of $16.18 (see Table 4.5).
Simple APV fair share values for NEP and NYLD were within a 5% range of the GGM intrinsic
share values (see Appendix D). APV fair share value of NEP was with a 10% range of the DCF fair share
value. Similarly to GGM, simple APV method puts a lot of attention on the CAPM beta. High CAPM beta
of NYLD has caused its unlevered cost of equity to be two times higher than that of NEP. This method is
valuable for companies with short history and high volatility as it accounts for a large number of factors
and only requires one SEC filing to be preformed. This method, however, ignores the initial high growth

40
and the dilution of equity which is projected to occur during the next 10 years (see Appendix B:
Appendix C).
Table 4.5 Simple APV valuation. Data Source: 2015 10-K reports
4.2.3 Complex Adjusted Present Value Method
Complex APV evaluation accounts for the rapid growth during the first 10 years. NEP growth in
2015 was calculated to be 57%. Growth regression to 2% and the assumptions discussed in Chapter
2.3.2 were used to create pro-forma financial statements. Complex APV procedures were discussed in
the Chapter 3.3.5. Annual NEP FCFFs were discounted by the unlevered cost of equity of 4.15% and the
equity dilution factor. Ten-year NEP APV was $1,514 million and perpetual APV after 10 years was
$4,636 million. Total APV of $6,149 million minus debt and accounting for bankruptcy, divided by 30.7
million shares yielded a fair share value of $87.64. This was 3 times higher than the recent NEP stock
price of $29.85 (see Table 4.6). NYLD’s 2015 growth rate was 24%. Annual NYLD FCFFs from the first 10
years were discounted using the unlevered cost of equity of 9.5% and the equity dilution factor. Due to
a high unlevered cost of equity adjustment rate most of NYLD’s value came during the first 10 years.
Ten-year NYLD APV was $3,966 million and perpetual APV after 10 years was $1,801 million. Total APV
of $5,767 million minus debt and bankruptcy chance cost adjustment, divided by 97 million shares

41
resulted in a fair share value of $8.36. This was 2 times lower than the recent NYLD stock price of
$16.18 (see Table 4.6).
Table 4.6 Complex APV valuation. Data Source: 2015 10-K reports
Accounting for the period of rapid growth during the first 10 years has increased the fair share
value of the NYLD stock, despite the dilution and high rate of discount. Similar to GGM and the simple
APV method, complex APV calculations concluded that NYLD stocks were overvalued. Unlike the
previous calculations, complex APV method has concluded that NYLD stocks are overvalued only by a
factor of 2. Complex APV method for NEP fair share value was within a 15% range of all the results
produced by calculations in previous sections.
Both of the APV methods have the advantage of accounting for debt and the cost of potential
bankruptcy separately. This is helpful to YieldCos because they have high debt to equity ratios.
4.3 YieldCo Valuations Using Ratios
This section is going to calculate key YieldCo financial ratios, compare them to each other,
compare some to the S&P 500 averages and analyze the impact such ratios have on the value of
YieldCos. This chapter will calculate ratios for all YieldCos, however the analysis will be centered around
NYLD and NEP. Logistics behind valuation using ratios was previously described in Chapter 2.3.3. Some
ratios TERP, GLBL, CAFD were excluded due to a lack of 2015 10-K filings.

42
Table 4.7 U.S. YieldCo EV/sales Ratio and CCS. Data Source: Most recent SEC filings
4.3.1 Enterprise Value/Sales Ratio
NYLD had an EV/sales ratio of 5.21, therefore it was able to produce more dollars worth of
electricity per dollar invested than NEP, whose ratio is 8.87 (see Table 4.7). To make a dollar of sales
NYLD only required $5.21, meanwhile NEP needed $8.87. NYLD had a low EV/sales ratio because it held
2 GW of conventional energy generation (see Appendix A). Conventional energy generation assets are
much cheaper per MW, but they require continuous reinvestment.
NEP held a similar mix of assets to TERP, therefore their EV/sales ratios were reflective of their
ability to generate sales using assets (see Appendix A). TERP had an EV/sales ratio of 7.33 (see Table
4.7). This shows that using the same types of assets TERP was able to generate more sales per dollar of
assets than NEP. Other things equal, TERP would be perceived as a better investment. Having a higher
EV/sales ratio may impact NEP’s ability to recover from financial loss.
ABY had a mix of energy assets similar to GLBL (see Appendix A). ABY had a EV/sales ratio of
8.79 meanwhile GLBL had a ratio of 4.97. Even though, this implies that GLBL could conduct more sales
given the same amount of assets, some of the difference could be attributed to the fact that GLBL was
started in August 2015 and thus the reported sales were annualized and do not reflect seasonal
fluctuations.
4.3.2 Sales After Capital Costs
SACC for ABY was 41%, while SACC was 64% for NEP and 65% for NYLD (see Table 4.7). This
suggests that NEP and NYLD paid less than ABY in interest and dividend as a percentage of sales.
Subsequently, if ABY, NEP and NYLD optimize their operations, NEP and NYLD would have a larger profit
margin. HASI had a SACC of -2% (see Table 4.7). HASI provides debt and equity to the renewable energy

43
markets, and maintains the same dividend approach as other YieldCos. SACC of -2% shows that the sum
of interest expense and dividend actually exceeds the current revenue from operations. HASI’s interest
expense is projected to be $37 million in 2016 and the dividends are promised to increase as well (2015
10-K HASI 117). Due to the tax benefit from borrowing, this allows for a small profit margin.
Nevertheless, due to an increased volatility in the renewable energy markets, a chance of negative
events for HASI has increased. In an event of a default by a borrower, HASI’s profit margin would easily
be eliminated, thus making HASI unable to pay dividends. GLBL and TERP haven’t yet filed their 4th
quarter results, therefore, SACC ratio may not yield accurate results.
Based on the 48.31 EV/sales ratio, HASI holds very long term investments. Generally, the longer
the investment the higher is the risk associated with it. After combining the results of SCC and EV/sales
ratios, it was concluded that HASI was not a very reliable investment. This was contradictory to the
findings of the GGM valuation and the proposition that YieldCos with the lowest expected dividends will
be regarded as better investments (see Appendix D).
4.3.3 P/E Ratio
Table 4.8 U.S. YieldCo P/E and P/B ratios. Data Source: Most recent SEC filings
High YieldCo P/E ratios reflect strong growth in the industry. However, for TERP, GLBL and ABY
the profits could not be properly evaluated because the most current SEC filings have shown negative
earnings.
4.3.4 Price-to-Book Ratio
Except for HASI, all P/B ratios were below 1 (see Table 4.8). Therefore, YieldCos are valued at
less than what their assets are worth. If YieldCos sold all their assets and paid back their debt, the cash
that would remain on their balance sheets at more than the recent market cap. TERP and ABY have

44
encountered a net loss with negative ROE. Figure 4.2 below displays the correlation between ROE and
P/B ratio of YieldCos in the United States. Although the relationship is not apparent for YieldCos, NYLD
and NEP graphically are aligned with S&P 500 companies (see Appendix E). This suggests that given the
ROE, P/B is sufficient and therefore, recent NEP and NYLD stock prices were accurate.
Figure 4.2 U.S. YieldCo ROE to Price/Book. Data Source: Yahoo Finance and recent 10-K reports.
4.3.5 Price-to-Sales Ratio
Figure 4.3 below displays the relationship of net profit margin and P/S ratio for U.S. YieldCos.
The ratio is not apparent due to negative annual earnings by TERP and ABY. HASI’s P/S ratio was much
higher than those in S&P 500 (see Appendix E). This suggests that HASI has not been bringing in a
sufficient amount of profit. NEP and NYLD P/S ratio was twice that of an average S&P 500 company with
the same profit margin (see Appendix E). This suggests that NEP and NYLD were overvalued.
Figure 4.3 U.S. YieldCos NPM to P/S ratio: Yahoo Finance and recent 10-K reports.

45
4.3.6 EV/EBIT Ratio
Table 4.9 U.S. YieldCo EV/EBIT. Data Source: Most recent SEC filings
Most YieldCos had a higher EV/EBIT ratio than the average S&P 500 EV/EBIT ratios of 12-16
(Schmidlin 153). NYLD had a low ratio of 14.86 (see Table 4.9). This may not be reflective of NYLD
overall performance, because NYLD has a large amount of debt. Therefore, it has a significant interest
expense which EBIT does not account for. Based on GLBL EV/EBIT ratio of 30.61 and the previously
discussed EV/sales ratio of 4.91, it can be concluded that the margin has narrowed significantly due to
the cost of operations and Depreciation and Amortization (see Table 4.9). This may have been due to
the GLBL initiation costs. However, it also may have reflected a flaw in the efficiency of operations.

46
Chapter 5 ̶ CONCLUSONS AND RECCOMENDATIONS
5.1 Conclusions
This study indicated that the most significant risk to YieldCos is interest rate risk. A 3% increase
in the risk free rate was projected to cause net losses for NYLD and reduced profit for NEP (see Figure
2.3 and Figure 2.4). Secondly, although ITC incentive is not exposed to legal risk, NMCs and RECs
changes may impact profits. Lastly, YieldCos are protected from energy price risk with PPAs, unless
counterparties default of which the risk is low and diluted (see Figure 2.8).
All YieldCos were valuated using the GGM and financial ratios. NEP and NYLD were valuated
using the DCF, simple APV and complex APV methods. All methods have suggested that NEP is a more
valuable investment than NYLD (see Appendix D). DCF method has proposed that both stocks were
undervalued (see Appendix D). A 3% increase in variable rates has caused the DCF fair share value of
NYLD to tumble to $0.35 (see Table 4.4). This has demonstrated that DCF method has too much
exposure to the variable interest rate. Three months of GGM results were presented. They showed
how volatile and unreliable GGM valuation is for this industry (see Table 4.3). Differences between
simple and complex APV models were discussed. It was determined that the complex APV model
reflects growth better. Based on P/B and P/S ratios this project was able to compare YieldCos to the
S&P 500 averages (see Figure 4.2 and Figure 4.3). EV/sales and SACC were more effective at comparing
YieldCos with each other.
Despite recent NEP stock price being $28.89, all the valuations have estimated a fair share value
between $87 and $99 (see Appendix D). Recent NYLD stock price was $16.18. GGM and simple APV
methods have valuated NYLD stock’s intrinsic value at $4 (see Appendix D). The complex APV method
has yielded fair share values of $8.36 for NYLD stocks and $87.64 for NEP stocks (see Appendix D).

47
Ratios and the expected dividend proposition have provided contradicting results, thus supporting the
notion that YieldCos securities need to be valuated using more complex methods (see Appendix D).
5.2 Recommendations
Based on the research and analysis performed for this project, investment opportunities have
been identified and most effective valuation methods and caveats suggested. First, all valuation
methods have suggested that NEP is largely undervalued, therefore ownership of NEP stock is highly
recommended. In contrast, NYLD was valued 2-4 times less than its current stock price. Hence, the
suggestion of this study is to recommend acquiring a short position in NYLD. A short position requires
sale of a borrowed security. GLBL, TERP and CAFD results were inconclusive due to the lack of 2015 10-K
SEC filings. Despite ratios having contradicting results for other YieldCos, they have all suggested that
HASI was overpriced. Therefore, a short position is recommended in HASI stock. In Chapter 4, the
complex APV method of valuation was credited as the method which includes the most diverse list of
variables, which is particularly useful for this industry because it accounts for the current unusually high
annual growth of 20-60%. Its results were consistent with the averages of the other methods. As a
result, it is recommended that the complex APV method is used for future YieldCo valuations.
Additionally, particular attention should be paid to the variable interest rate debt as a portion of capital
structure because changes in that value bring additional risk to the operations of that particular YieldCo.
Lastly, it is recommended that the combination of energy assets on YieldCos’ lists of properties are given
additional attention during the next few years due to the increasing difference between the
effectiveness of new solar energy assets and new wind energy assets.

48
5.3 Summary
YieldCos are publicly traded energy company subsidiaries which own and operate renewable
energy projects. In the U.S. they were made possible through the advancement of solar technology and
the introduction of solar energy incentives. Recently, YieldCos’ stock prices have experienced a
significant decline and their growth business model lost its previous investor appeal. This study sets out
to define YieldCos’ asset bases, analyze risks related to these securities, valuate them and assess the
effectiveness of valuation methods. The attention of this study was centered around NYLD and NEP.
Asset analysis has revealed that NYLD and NEP have a similar renewable energy asset base and
therefore are looking at comparable short-term and long-term prospects. Risk analysis has indicated
that interest rate risk is the most significant risk YieldCos face today and that NYLD profit margin would
be impacted more then NEP profit margin in case of a variable interest rate increase (see Figure 2.3 and
Figure 2.4). The energy price risk was projected to have a smaller influence on YieldCos’ profit margins.
The values of YieldCo stocks were evaluated using GGM, DCF, simple APV and complex APV valuation
methods and through ratio comparisons. All the valuation methods have suggested that NEP was
undervalued and all except DCF valuation method have proposed that NYLD is overvalued. Through
month-to-month comparisons GGM valuation method was concluded to be too volatile (see Table 4.3).
From an interest rate risk scenario analysis, it was demonstrated that DCF method is too exposed to
interest rate changes (see Table 4.4). The results of ratio comparisons were contradictory to each other,
due to which it was recommended that a more complex method of valuation is required (see Appendix
D). The complex APV method of valuation was considered a better method then the simple APV
valuation method because it factored the rapid growth during the first 10 years into the calculation. The
complex APV method has approximated that the fair share values were $8.36 for NYLD stocks and
$87.64 for NEP stocks.

49
5.4 Originality
The previous scientific study which assessed the YieldCos market was a web-based white paper
published in 2015 by Marathon Capital which had significant investments in the industry at the time and
therefore could be considered biased (Grant and Cornfeld 6). Other than that and news articles on the
subject, there hasn’t been any significant scientific work in regards to this young market. This study is
the first to compare and assess the risk factors associated with YieldCos. It was the first body of work to
indicate that the risk YieldCos are most susceptible to is the interest rate risk. It was original at pointing
out the meaninglessness of the correlation of YieldCo stock prices and prices of oil. It used the latest
data to perform multiple valuations, asses the valuation processes and the yielded results to
recommend the most effective method and investment possibilities. Although, there have been articles
which recommended investments in certain YieldCos in the past, this project supports its
recommendations with calculations and current data.
5.5 Contribution to the Body of Knowledge in the Field
YieldCos have appeared in the U.S. recently and the body of knowledge surrounding them is
limited. The conclusions of this project include unique ratings of risks effecting YieldCos, evaluation of
valuation methods, indication that there are safe investments within YieldCo markets and challenges to
simple valuation techniques. It suggests that the most significant risk that YieldCos face is the interest
rate risk. Stock holders in possession of that information will react more strongly to an announcement
of Federal Reserve rates changes. This study suggests that energy price risk is not significant for
YieldCos. This study proposes that YieldCos revenues are not exposed to fluctuations in oil, coal and
natural gas prices (Figure 2.7 and Figure 2.8). An investor in possession of such information will not
react strongly to changes in coal or natural gas prices and will not react at all to the changes in oil prices.
This study merits the more complex methods such as complex and simple APV methods and dismisses

50
the results of the simpler methods such as GGM or valuation through ratios. New data may eventually
come out in relation to all US YieldCos and the researchers will be advised on the most efficient method
for determining the value of particular YieldCo securities. An investor in possession of that information
will be informed not to react strongly to changes in ratios or the security value determined through
GGM valuation method. This project has used numerous valuation techniques to valuate NEP and has
come up with a result that this security is a safe investment, thus challenging the perception that
YieldCos are inherently risky. This information may be helpful to an investor who would like to invest in
a YieldCos but can only tolerate a certain amount of risk.
5.6 Limitations
YieldCos have limited history. As new information becomes available and the possibility of new
risks impacting YieldCos arises, price correlations may change and the use of new valuation methods
may become applicable. Potential risks can manifest themselves and diminish value of stocks. YieldCos’
stock prices may lean toward values forecasted by simple methods of valuation, however, on average
complex APV valuation method can benefit calculation of the fair values for YieldCos’ stocks.
5.7 Scope for Future Research
Future research will have a larger sample of financial data for analysis. The average expected
return and the volatility will become more accurate. Additional data can provide more accurate
predictions about the short and long term market trends and correlations. Future research may draw
correlations between foreign and U.S. markets. This will provide better insight into renewable energy
market fluctuations globally.
It may be valuable for investors to know more about other investment opportunities such as
YieldCos stock options strategies. A study may be conducted to analyze how a YieldCos investment can
be made safe with forward or futures contracts in correlated commodities.

51
Appendix A
Appendix A: U.S. YieldCos’ information and a detailed asset breakdown.
Ticker NYLD TERP GLBL ABY NEP HASI CAFD
Company Name
NRG
Yield,
Inc.
TerraForm
Power
Terraform
Global
Atlantica
Yield Plc
NextEra
Energy
Partners
Hannon
Armstrong
Sustainable
Infrastructure
8point3
Energy
Partners LP
Parent
NRG
Energy,
Inc.
SunEdison SunEdison
Abengoa
SA
NextEra
Energy,
Inc.
First Solar
&
SunPower
Type Multiple Wind/Solar
Wind/
Solar
Multipl
e
Wind/Solar
Equity & Debt
Wind/Solar
Solar
New Solar 0 431.5 344.8 380 34 432
Distributed
generation
9 399.9 0 0 0 39
Solar U.S. MW 491 872.7 0 560 284 N/A 432
Solar Global 491 1417.8 344.8 1341 324 N/A 432
New Wind 252 500 460.7 50 598 0
Wind US 1999 500 0 0 1405 0
Wind Global 1999 500 460.7 100 1761 $ 319 M 0
Conventional
Global
1945 0 0 300 0 0
Natural Gas 124 0 0 0 0 0
Total MW 4559 1917.8 805.5 1741 2085 N/A 432
Transmission(mi) 0 0 0 1099 0 0
Water(M ft3/d) 0 0 0 10.5 0 0
Market Cap($M) $882 $1,390 $562 $1,780 $810 $718 $1,080
Corp Formed 12/20/12
IPO Date 5/15/15 7/18/14 8/3/15 6/13/14 6/27/14 4/18/13 6/19/15
Source
10-K '15 10-Q 15Q3
10-Q
15Q3 20-F '15 10-K '15 10-K '15 10-Q 15Q3

52
Appendix B
Appendix B: NRG Yield (Ticker: NYLD) Pro-Forma Income Statements (2016-2025) Data source: 2015
NYLD 10-K
Appendix B: NRG Yield (Ticker: NYLD) Pro-Forma Cash Flow Statements (2016-2025) Data source: 2015
NYLD 10-K

53
Appendix B: NRG Yield (Ticker: NYLD) Pro-Forma Balance Sheet (2016-2025)Data source: 2015 NYLD 10-K

54
Appendix C
Appendix C: NextEra Energy Partners (Ticker: NEP) Pro-Forma Income Statements (2016-2025) Data
source: 2015 NEP 10-K
Appendix C: NextEra Energy Partners (Ticker: NEP) Pro-Forma Cash Flow Statements (2016-2025). Data
source: 2015 NEP 10-K

55
Appendix C: NextEra Energy Partners (Ticker: NEP) Pro-Forma Balance Sheets (2016-2025). Data source:
2015 NEP 10-K
Data source: 2015 NEP 10-K

56
Appendix D
Appendix D: U.S. YieldCo valuation results. Summary. Data source: Recent 10-K reports & Yahoo
Finance.
Notes:
*Light green cells imply that according to this valuation (row), this stock (column) is undervalued.
*Light red cells imply that according to this valuation (row), this stock (column) is overvalued.
*Light orange cells imply that according to this valuation (row), this stock (column) is priced correctly.

57
Appendix E
Appendix E: S&P 500 P/B ratio vs ROE. Source: Schmidlin 206. Data Source: Bloomberg.
Appendix E: S&P 500 consumer products companies: P/S ratio vs net profit margin. Source: Schmidlin
214. Data Source: Bloomberg.

YieldCos in the U.S. Final AN

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