1. Welcome!
My name is <Name> and I’m <5tle> with Voya Investment Management.
Over the next 30 minutes or so, I’m going to introduce you to Voya’s differen5ated
consistency lens. We’ve developed methods to help you iden5fy fund managers who
have delivered consistent performance with lower vola5lity. Our method will help
assist you in managing client fears and expecta5ons, lower stress across your prac5ce,
and poten5ally improve re5rement outcomes.
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2. 3

3. As you work to manage the fears and expecta5ons of your re5rement clients, a big
challenge is to find funds that have produced excess returns but that also have kept
vola5lity to a manageable level.
What are some of the fund evalua5on methods you currently use?
<Encourage audience responses>
4

4. You’re also facing a lot of vola5lity in fund returns over 5me. For example, in the total
large cap growth fund universe, the low to high range of returns jumped from minus 1
percent to plus 17 percent per year between 2005 and 2015. This is enough vola5lity to
create quite a bit of uncertainty when selec5ng funds.
Voya Investment Management has found a different way. It’s a uniquely applied
methodology that can be summed up in a single word…CONSISTENCY.
METHODOLOGY: The analysis was based on monthly returns of the A-share class at net
asset value for 196 large-cap growth funds from 2005-2015. The upper and lower grey
lines represent the high and low rolling 3-year returns from the Morningstar Large-Cap
Growth Fund universe of funds that are not index funds and had at least a 3-year return
history and $1 million in assets as of 9/30/2015. Past performance is not a guarantee of
future results.
5

5. Far and away the most common fund evalua5on measures are some combina5on of
historical performance data, generally over fixed 1, 3, 5- and 10 year periods. There’s
nothing inherently wrong with this approach…
…but as you know, these tradi5onal methods have some limita5ons.
As typically applied, tradi5onal performance evalua5ons just give you a snapshot view
of a par5cular point in 5me over the past 1, 3, 5- and 10 year periods. They all include
the most recent 1-year period. Similarly, the most recent 3-year period appears in the
3-year, the 5-year and the 10-year results, and so on. This known as “end period
dominance” a problem that actually limits the amount of informa5on and focuses it on
the most recent periods.
And the raw returns alone can’t tell you if a fund manager’s success is due to skill or
mere luck. Or how a fund behaves when markets are going down. Or if a fund manager
can regularly iden5fy undervalued securi5es that will appreciate faster than market
averages.
6

6. Ager months of extensive research and sophis5cated analysis, we’ve developed a
proprietary process that iden5fies ac5vely-managed mutual funds with historically
consistent and sustained performance characteris5cs through all market cycles.
Using Voya’s fund evalua5on consistency analysis, you can iden5ty funds across major
asset classes that have demonstrated consistent excess returns with lower downside
vola5lity over rolling 5me periods, compared to the averages in the total fund universe.
__
Our “consistent” approach to fund evalua5on isn’t meant to replace what you’re
currently doing. It’s designed to complement and enhance your exis5ng investment
strategies and client management prac5ces.
You won’t find this methodology organized in such a prac5cal and useful manner
anywhere else. You can use our consistency tools and resources to demonstrate your
value to exis5ng clients and prospects in a powerful new way that will help you stand
apart from the compe55on.
The bokom line benefit for you and your clients is less stress across the board and the
poten5al for improved re-rement outcomes.
7

7. [Sec5on divider slide]
Our analysis of the past performance of funds across ten asset classes may be an
excellent complement to the complete due diligence process you use to help clients
select mutual funds.
While past returns can never be a guarantee of future outcomes, we believe the
comprehensive nature of the analysis, including rolling periods, ten asset classes, and a
focus on excess returns in rela5on to downside vola5lity, is robust enough to provide
genuine insights into some of the enduring characteris5cs of fund performance.
Nevertheless, it is intended only as fund evalua5on to assist advisers as part of a
broader due diligence process and is not intended as investment advice.
8

8. I’ll briefly explain the ra5onale behind our use of six specific performance factors and
then explain how these factors iden5fy funds with a history of sustained excess returns
and lower downside vola5lity over the long term.
Finally, I’ll highlight addi5onal criteria that can help s5mulate a value-added
consistency conversa5on with your clients and help you customize historically
consistent funds for clients in specific life stages.
9

9. First up is informa5on ra5o, which measures manager skill.
• It answers the ques5on: Do managers have a track record of excess returns with
controlled risk?
• We want to discover if a fund’s record of success is the result of inten5onal,
deliberate and skillful management, or mere luck.
• Skill is repeatable, luck is not. We rank the degree of skill in terms of the rela5onship
between the excess return managers produced and the risk they accepted in order
to produce it. Please understand that when we say a fund shows no evidence of
skill, we’re not sugges5ng the managers should be looking for work in another
industry. We’re simply talking about the history of repea-ng good performance
consistently.
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10. Sor5no ra5o measures how returns relate to downside risk.
It answers the ques5on: Has a manager mi5gated the possibility of large losses in
rela5on to total return? Once again, this speaks directly to client fears of big losses,
which you are akemp5ng to avoid.
The Sor5no ra5o is a form of the “Sharpe ra5o”, also known as a return to risk ra5o.
The Sor5no ra5o resolves the problem inherent in “standard devia5on” as a risk
measure, which is that it converts up-market returns into “risk.” I think you’ll agree that
clients don’t generally view above average returns in a rising market as a form of “risk.”
The Sor5no ra5o corrects this problem
11

11. Number 3 is R-Squared versus benchmark, which measures fund style purity.
This metric answers the ques5on, “Do managers manage assets the way they claim on
a consistent basis?”
Most mutual funds are managed against and compared to performance benchmarks,
which are usually familiar indexes like the S&P 500 or the Barclays Bond Indexes.
To outperform a benchmark, a manager must build a porsolio that is similar to but
different from the benchmark. R-Squared gives us an objec5ve measure that reveals
the degree to which the fund’s performance resembles the returns of the benchmark
or is different from it.
The best managers can outperform their benchmarks without devia5ng wildly from it.
When this happens, it usually means they have an effec5ve security selec5on process.
Security selec5on is the “safest” way to beat a benchmark, and it can be repeatable.
12

12. Next is Upside capture—one of the more obviously named factors. It measures a
manager’s historical ability to realize gains when the market is going up.
This factor answers the ques5on: How is fund performance 5ed to the managers’ ability
to pick securi5es that have appreciated faster than market averages?
It’s interes5ng to note that since 1926, the stock market has gone up about 65% of the
5me. In fact, the market has gone up about 40% of the 5me even during bear markets.
[Source: Voya Investment Management. Based on monthly U.S. stock market returns,
Fama-French data, 1926-2014, 655 up months of 1052 total months, 15% bear market
periods, defined as any 12-month period in which the U.S. stock market averages
declined by at least 10%.]
In order to realize gains and outperform the benchmark, a manager’s strategy needs to
include ways to iden5fy securi5es that represent value. Favorable upside capture —
meaning greater than 100%—provides a valuable clue that a fund manager has a buy
discipline that can find undervalued securi5es.
13

13. The flip side to Upside capture is Downside capture, which measures a manager’s
historical ability to avoid losses.
Downside capture answers the ques5on: Do managers have an investment process that
has mi5gated losses in difficult markets?
I think you will all agree that clients see the risk of real losses as the risk that really
makers. Beta, standard devia5on and tracking error can be genuinely valuable risk
measures but when markets are rising, they may be less relevant. Downside capture
tells us explicitly how a fund has behaved when the market has gone down, which is
when clients are really paying aken5on and feeling stress.
A fund’s par5cipa5on in down-market moves should be less than 100%, which would
indicate the inclusion of a principal protec5on feature within the manager’s process.
14

14. The last factor is Overall Capture, which is the ra5o of upside capture to downside
capture.
Overall capture narrows and clarifies evidence of manager skill. Our analysis shows that
high rankings in both upside and downside capture are very difficult to achieve.
What’s more relevant in the real world is a favorable ra5o between the two.
15

15. To ensure validity and credibility in our measurement and analysis, we took a broad
perspec5ve by analyzing rolling 3-year periods rather than the more common fixed
term periods of 1, 3, 5 or 10-years.
As typically applied, tradi5onal performance evalua5ons just give you a snapshot view
of a par5cular point in 5me over the past 1, 3, 5- and 10 year periods. They all include
the most recent 1-year period. Similarly, the most recent 3-year period appears in the
3-year, the 5-year and the 10-year results, and so on. This is known as “end period
dominance,” a problem that actually limits the amount of informa5on and focuses it on
the most recent periods.
Rolling periods eliminate the tendency to over-emphasize recent returns, which is
par5cularly important today given the bull market we’ve experienced over the past 6
years.
16

16. Fund manager style preferences and investment techniques can only be es5mated over
reasonably long 5me periods. Rolling periods provide a more realis5c viewpoint, by
covering mul5ple market cycles, equally weigh5ng results across both up and down
markets, and minimizing any bias inherent in recent events.
More than 90% of a stock fund’s performance comes from the manager’s “style,” such
as growth or value, or the fund’s category, such as large cap or small cap. Styles go in
and out of favor, and on average, the in-favor periods tend to last about 2½ years. So
it’s quite possible that all of a par5cular fund’s “excess returns” over any specific three-
year period could simply be due to that style being in favor, rather than any repeatable
manager processes and philosophies.
[Source: Style generaliza5on based on monthly rela5ve performance of Russell U.S.
Large-cap Growth vs. Russell U.S. Large-Cap Value, 1979-2014. Source: Russell
investments, Voya Investment Management.]
17

17. No one lives by excess returns alone. Risk is part of this rela5onship and it’s important
to try to minimize the likelihood of experiencing large losses and to provide a less
vola5le ride over 5me.
We’re looking for funds that have exhibited a narrower range between lowest
performance and highest performance over long 5me periods.
18

18. Did our approach tell us anything significant?
19

19. Focusing more closely on high-performing funds, we see in this chart that funds which
ranked in the first quar5le for consistency also ranked highly in terms of excess returns.
The average excess return ranking for funds ranked in the 1st quar5le by consistency
was the 17th percen5le. Among those ranked in the top quar5le by consistency
rankings, 75% of them also ranked in the top quar-le in terms of excess returns. For
this high-performing group of funds, the average excess return ranking was the 11th
percen5le—higher than 89% of the ini5al universe.
The remaining 25% of funds ranked in the 2nd quar5le by excess returns, with an
average percen5le ranking of 33rd. None of the funds in the first quar5le for
consistency were ranked in the 3rd or 4th quar5les in terms of excess return.
20

20. Let’s first set some context: The overall average excess return versus the benchmark
among the total Morningstar ac5ve fund universe for all asset classes is actually a
nega-ve point 91 percent.
The funds that go through our six performance factor consistency evalua5on improved
on the overall average excess return by an average of about 1.33 percent a year.
It’s important to note that it’s our proprietary combina5on of six factors that produces
the posi5ve results shown here. Using a single evalua5on factor may fall short of this
mark.
METHODOLOGY: See appendix for addi5onal detail. Morningstar Direct data for ten
asset classes from October 2005-September 2015, source, Morningstar Direct, Voya
Investment Management.
21

21. The data points on this chart place the historical excess returns into a two-dimensional
space with the applicable downside risk data on the horizontal axis. The data points are
averages for the excess returns and risk for the funds that ranked in the top half of
their respec5ve categories in terms of both excess returns and the selected
performance factor.
The benefits of iden5fying funds on the basis of these measurements are evident: all
factors demonstrated the ability to enhance excess returns, and all but up-capture
(because is falls to the right of the “Overall Average” point) poten5ally supported risk
reduc5on.
METHODOLOGY: The analysis was based on rolling 3-year returns with the rolling
window advanced one month at a 5me for the A-share class at net asset value for 1,046
funds over the period 2005-2015. The fund categories were Large-Cap Growth, Large-
Cap Blend, Large-Cap Value, Mid-Cap Growth, Mid-Cap Blend, Small-Cap Growth, Small-
Cap Blend, Intermediate Bond, Senior Bank Loans and High Yield Bond. All funds had at
least a 3-year return history and $1 million in assets as of 9/30/2015.
Past performance is not a guarantee of future results
22

22. While the average excess return is nega5ve for the ten asset classes shown here,
Voya’s six-factor evalua5on improved the return profiles across every one of them.
The grey bars, which are all in nega5ve territory, represent the average excess return
for all funds in each category. The colored bars represent funds in Voya’s analysis as
evaluated by each of the six performance factors we’re using to analyze excess return
and consistent performance. No5ce almost all of them are in plus territory, and even
those that show nega5ve returns greatly improved on the nega5ve returns of the
average funds in that category.
Let’s take a closer look at two of these categories…
• I’ve sliced out just the Large Cap Value category here. The overall average excess
return is about nega5ve point forty one percent, while all of our performance factors
—all show more than 1% of excess return.
• Even when posi5ve excess return does not show up, as you see here with high yield
bond funds, our factor analysis provides about twice the rela5ve performance—in
the neighborhood of nega5ve .54%— as the overall average indicated by the grey
bar at about nega5ve 1.63%.
METHODOLOGY: See appendix for addi5onal detail.
23

23. What’s really important is these excess returns have been repeatable over -me.
For example, on average, there was over a 71 percent chance that the funds which
ranked high in terms of consistency would repeat their excess return performance over
rolling periods for the ten years ending in 2015 vs. their performance at the end of
2012. This is much higher than than the persistence reported by observers using other
analy5cal techniques.
Spotligh5ng just three categories as typical examples, the 181 funds in the Large-Cap
Growth category repeated their excess return 70 percent of the 5me, the 78 Small-Cap
Growth funds repeated 59% of the 5me, and the 109 funds in the Intermediate-Term
Bond category showed an 85% repeat rate.
METHODOLOGY: **Percentage of funds for each of ten asset classes which ranked
above the median (top 50th percen5le) for excess returns for all rolling 3 year periods
ending 9/2012 and 9/2015. See appendix for addi5onal detail.
24

24. Back at the beginning of this presenta5on, I men5oned that the total fund universe has a
vola5lity range of low to high returns from 2005 to 2015 of 18 percent. That’s a lot of vola5lity
and uncertainty.
Total Fund Universe: –1% to 17% per year
• A vola5lity range of: 18%
The variance of rolling returns over the same 5me frame for large cap growth funds that rank
highly on our consistency factors is much narrower—from 9 percent to 13 percent, which is just
a 4 percent average difference between lowest and highest returns compared to 18 percent for
all funds.
Funds that rank highly on Consistency Factors: 9% to 13% per year
• A vola5lity range of: 4%
METHODOLOGY: The analysis was based on monthly returns of the A-share class at net asset
value for 196 large-cap growth funds from 2002-2015. The upper and lower grey lines
represent the high and low rolling 3-year returns from the Morningstar Large-Cap Growth Fund
universe of funds that are not index funds and had at least a 3-year return history and $1
million in assets as of 9/30/2015. The upper and lower orange lines represent the high and low
rolling 3-year returns from the same universe that rank in the top 50% by the consistency
analysis. Past performance is not a guarantee of future results.
25

25. As you narrow the gap between historically high and low returns, you can seek more
methodical and consistent results, which helps to keep clients feeling more posi5ve
about the future and may lessen their tendency to make emo5onal decisions. The
narrow return ranges exist in all “consistent” funds across all analyzed asset classes,
giving you maximum flexibility to select funds that match your other evalua5on criteria.
26

26. We tested various combina5ons of our six performance factors and found that funds
showing excess returns were easily aligned into three broad and familiar risk tolerance
categories 5ed to client life stages…
27

27. …which, for simplicity’s sake, we’ll label re5rees, pre-re5rees, and accumulators.
While all six analysis factors are always employed in Voya’s consistency lens fund
evalua5ons, we assign more weigh5ng to a subset of relevant factors for each of the
three life stages to beker customize the fund evalua5on results in a meaningful way for
advisors.
I’ll quickly run down the performance factor combina5ons we use for each life stage
category.
28

28. By categorizing funds into one of three life stage buckets, you can show your clients
how a par5cular fund has delivered consistent excess returns with risk management
5ed to their par5cular risk profile.
Consistency can mean different things to different clients. For some, it may mean
excess returns above a benchmark. For others, it indicates the lowest possible exposure
to big losses. S5ll others may be seeking returns that are a close fit to an index so they
can be comfortable that a fund’s behavior within an asset alloca5on plan is rela5vely
predictable (because the returns look much like the indexes on which asset alloca5on
plans are based).
Our factor combina5ons result in just a 25 percent fund average overlap between
re5rees and pre-re5rees, and re5rees or pre-re5rees to accumulator. The lower the
fund overlap, the purer each risk category becomes. This makes it possible for
you to customize fund selec5on to the unique needs of each client.
29

29. Some factors are included in all the evalua5ons—notably the overall ra5o between
upside and downside capture—but varia5on in factor choices and weights permiked
customized results for each of our three risk tolerance groups, as follows:
Re-rees
• The most conserva5ve investors, presumably sa5sfied by funds that closely track
benchmarks and have lower downside risk than other funds, on average.
• Sor5no Ra5o, Down Capture, and R-Squared are emphasized to iden5fy funds with
very low poten5al for actual losses and a return pakern that is close to a chosen
benchmark.
Pre-Re-rees
• These investors are assumed to prefer a secure, balanced, middle-of-the-road
approach, offering consistent, above-benchmark returns with average downside
vola5lity.
• Informa5on Ra5o, Upside Capture, and R-Squared are heavily weighted to iden5fy
funds that show above average skill, risk-adjusted returns, and steady, above-
benchmark results.
Accumulators
• The youngest investor group is characterized by an expecta5on of the highest
poten5al returns and a willingness to accept typical or above average vola5lity.
• Informa5on Ra5o and Upside Capture are heavily weighted to iden5fy funds that
show above average returns in up markets—to deliver more return poten5al than
funds in the other groups but with commensurately higher risk of loss.
30

30. [Transi5on slide]
I imagine you are already thinking of ways to use this powerful new approach.
31

31. This is not a “one size fits all” solu5on. Our fund evalua5on process can be tailored to
suit different client goals and risk tolerances.
Historical consistency analysis provides clients with more informa5on about their
investments and their future re5rement security which improves trust and lowers
stress for clients, for you, and across your prac5ce.
Historically consistent excess returns with lower vola5lity and controlled risk offers the
poten5al to improve re5rement outcomes for clients in every life stage.
32

32. Exis5ng clients will appreciate learning about a new approach to managing their
re5rement porsolios in a way that helps to alleviate concerns about vola5lity and
poten5al large losses, especially in the years leading up to re5rement.
Prospects will be impressed with a new approach to re5rement inves5ng that isn’t
available anywhere else.
33

33. Reach out to Voya Investment Management or contact your wholesaler to find out how
you can tap into the power of consistency in your ac5ve mutual fund evalua5on
processes and your client management ini5a5ves.
34

34. [no narra5on]
35

35. [Sec5on divider slide: Appendix]
36

36. 37

37. Our consistency analysis is based on historical returns. While past returns can never be
a guarantee of future outcomes, we believe the nature of the analysis — rolling
periods, ten asset classes, focus on excess returns in rela5on to downside vola5lity — is
robust enough to provide genuine insights into some of the enduring characteris5cs of
fund performance. Nevertheless, it is intended only as a method of fund evalua5on to
assist advisers in a broader due diligence process of fund selec5on and is not intended
as investment advice.
38

38. 39

39. 40