Markit dividend forecasts and their value - cdn.ihs.com · It is also worth mentioning that the...

Markit dividend forecasts and their

value

Markit dividend forecasts and

their value Research / November 2015

Shan Gao Thomas Matheson Neerav Aggarwal

[email protected] [email protected] [email protected]

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Abstract It is well established that dividends are an important component of total returns for an investor. Dividend yield

is a key metric for identifying value stocks, and extent literature suggests that stocks with high and

sustainable dividend yields outperform the overall stock market and stocks with low dividend yield. However

the increased market volatility since the financial crisis has caused greater variability in dividend payments.

Previously “safe” dividend payers have had to cut or suspend policies, meaning selection on the backward

looking trailing dividend yield no longer offers the insight it once did.

With this in mind, we investigated whether Markit’s Dividend Forecasting forward looking dataset can be

used to create income portfolios that can lead to higher returns. We created a high forward dividend yield

portfolio in the US market. We adjusted the baskets to add basic constraints to remove micro-caps and

illiquid stocks, and sector capping to remove a heavy bias on the financial sector.

Our analysis indicates that i) total returns using Markit forecasts outperformed the portfolio created using

announced historical dividends by 38% in the sample period, ii) total returns using Markit forecasts

outperformed the S&P High Yield Dividend Aristocrats index by 24% in the sample period, iii) dividend yield

was similar for both forecast and trailing baskets, indicating that the primary benefit was as a value indicator

and the positive signalling value on future earnings and iv) using Markit forecasts, the portfolio was less

volatile than trailing yield, but more volatile than the S&P High Yield Dividend Aristocrats index.

This test was confined to the US where dividends are paid quarterly, meaning they are relatively stable

compared to other global markets. We believe the benefits of using Markit forecasts could be even more

pronounced in Europe and Asia where dividend payments are more variable.

1 Introduction

1.1 Introduction

Dividends have enjoyed a remarkable rise to prominence since the financial crisis, shedding their reputation

as steady and unexciting. Increased market volatility coupled with a low interest rate environment has

diverted the investment community towards high dividend yield strategies. As seen in Chart 1, assets under

management for dividend focused exchange traded funds (ETFs) have seen a rising trend since the financial

crisis, highlighting the increasing use of backward looking methodologies and the increased number of

dividend negative surprises this year: contemporaneous index methodologies based on trailing yield take no

account of future prospects and as such can fall foul to “yield traps”.

In this research paper, we investigate the value of forecast dividend yield as an investment strategy. Our

hypothesis is based on empirical evidence that suggests high dividend yield companies are value companies

with stable businesses and strong fundamentals, and generally outperform low dividend yield companies

and the overall market. Academic evidence also suggests that high dividend yield stocks generally have

lower price-to-earning and price-to-book multiples when compared to stocks with a low dividend yield.

Indeed, new research from Bilinski and Bradshaw (2015) contests the “sticky” dividends theory and posits

conversely that dividend payments across stocks have high variability which thus increases investor demand

for dividend information. The report goes further to indicate that analysts’ dividend estimates are useful to

investors because they: “(i) are more accurate and better aligned with market dividend expectations than

other estimates, such as standard time-series modelling approaches, (ii) convey incremental information to

the market beyond that contained in other fundamentals, and (iii) help investors interpret the persistence of

earnings news.”

We tested Markit’s proprietary forecast dataset to see whether we could quantify the value compared with

two alternatives: i) selecting stocks on a historical, trailing yield basis and ii) one of the popular incumbent

dividend indices. Results were gathered and refined in several guises, by looking at a quarterly rebalance,

annual rebalance, and also applying rudimentary capping. The methodology employed for the study was not

devised under the premise of simply maximising dividend returns. What we wanted to investigate was the


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signalling value of dividend forecasts and whether total returns supported this factor. It takes into account

observations in a study by Andres, Betzer, Bongard, Haesner, Theissen (2013) that when dividend and

earnings announcements are made on the same day, the dividend surprise has, if anything, higher

explanatory power for the share price reaction than the earnings surprise. Our research tested whether using

forecast dividend yield can be more beneficial for the value effect, and boost share price growth.

Chart 1: Rise of dividend ETFs

1.2 Introduction to Markit Dividend Forecasting

Markit’s Dividend Forecasting service provides independent, discrete forecasts for dividend amounts and

dates up to four years in the future. It covers over 8,000 stocks globally, including emerging markets, ADRs,

and US listed ETFs. Dividend forecasts benefit uniquely from of the team’s specialisation, size and service

level. The product was established over ten years ago, and we now employ over 30 analysts across different

time zones to provide local market expertise. A key benefit of the service is that this analyst team is available

to answer bespoke research requests, investigate data queries and provide support for all our forecasts.

Dividend forecasts are created using a bottom-up, research led methodology to provide the highest level of

accuracy for both amount and dates. They are based on the factors in Chart 2, including latest market news

and direct company guidance, combined with fundamental analysis, historical observations, conference call

statements and peer group trends. Forecasts are further enhanced by a number of proprietary datasets

available within Markit, from consensus OTC implied dividends and short interest data to macro PMI and

credit spread data.

Markit Dividend Forecasting is the benchmark forecasting service used by major derivatives exchanges for

pricing instruments, including Eurex, Euronext, ICE, MEFF and ASX. Customers can also subscribe to a

dividend point service, which provides insight into the expected impact on equity index values.


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Chart 2: Factors that estimate Markit’s dividend forecasts

2 Data and methodology

2.1 Universe and coverage

As our investment universe, we used the Russell 3000 index, which tracks the performance of the largest

3,000 companies and represents around 98% of the investable market in the US. Our backtesting period ran

quarterly1 from October 2011 through June 2015. We used data for the constituents of the index on the first

trading day of the sample period to remove survivorship bias. We had 100% coverage in terms of forecasted

dates, amounts and historical dividends paid. Typically there were around 1,700 stocks that paid dividends in

this universe over the sample period and the dataset. We collected this data for each dividend paying stock

from Markit’s Dividend Forecasting dataset. Pricing and returns data for individual stocks were sourced from

FactSet. Our benchmark S&P High Yield Dividend Aristocrats were sourced from the S&P Dow Jones

Indices website.

2.2 Portfolio strategy

We implemented our portfolio strategy using the following procedure:

Step 1: At the beginning of our first reference date in October 2011, we collected data on dividends paid in

the four quarters preceding the reference date and also on the forecasted dividends that would be paid in the

next four quarters from the reference date. Then we created two factors for every dividend paying stock in

our universe:

— Trailing dividend yield: sum of dividends paid over the last four quarters relative to the stock price on the

reference date

— Forward dividend yield: sum of dividends forecasted over the next four quarters relative to the price on

the reference date

1 *We have also implemented annual rebalancing approach. Please see appendix session.


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Step 2: We ranked the stocks in descending order for both factors and selected the top 10% stocks

according to factor ranks to create our high forward dividend yield portfolio HFDY and our high trailing

dividend yield portfolio HTDY.

Step 3: We held our two portfolios for a quarter and assessed their one quarter forward equally weighted

price return, dividend return and total return.

Step 4: We rebalanced our portfolios on the next rebalance date, i.e. next quarter, and repeated the steps

above.

3 Portfolio performance and characteristics

3.1 Base case: Unconstrained portfolios

In this section we look at the performance of our two portfolios and also the S&P High Yield Dividend

Aristocrat Index (SPDA)2 in terms of total returns. Chart 3 shows the cumulative total returns (including price

return and dividends) over the sample period, assuming an investment of $100 at the beginning of October

2011 (Q3 2011).

Chart 3: Cumulative total returns, unconstrained portfolios, October 2011 – June 2015

Our HFDY portfolio constructed using Markit’s Dividend Forecasting dataset did a better job of selecting

outperforming stocks in terms of total returns over the sample period in comparison to both the HTDY

portfolio and the SPDA benchmark. It outperformed the HTDY portfolio by 4.6% and the SPDA benchmark

by 3.1% on an annual basis. Also, HFDY outperformed HTDY in 13 out of 15 quarters (87% hit rate) and

SPDA in 9 quarters (60% hit rate). It is also worth mentioning that the SPDA benchmark has strict

construction rules as they only include those stocks that have consistently increased their dividends over the

last twenty years. The stocks are also weighted according to their dividend yield, and it ensures that the

stocks are well diversified across sectors, among other construction rules. Even though the construction

2 We also added Russell 3000 index performance for comparison purpose. Please see appendix session.

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High Forward Dividend Yield Portfolio (HFDY)

High Trailing Dividend Portfolio (HTDY)

S&P High Yield Dividend Aristocrats (SPDA)


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rules were different, both of our portfolios tracked the benchmark and each other well as the correlation

between the three return series was high (>0.85).

Detailed performance statistics are presented in Table 1. Annual volatility is the annualised standard

deviation of returns, which measures the degree of return variation. Information ratio is the ratio between

annual return and annual volatility, which measures return earned per unit of risk. Therefore a higher

information ratio is desirable.

Out of the three, the HFDY portfolio offered the highest information ratio. Although the return of HFDY was

more volatile than the SPDA benchmark, it earned more return per unit of risk taken. HFDY portfolio

outperformed the HTDY portfolio on all the performance metrics reported.

HFDY HTDY SPDA

Cumulative return (%) 110.4 82.1 91.1

Annual return (%) 21.9 17.3 18.9

Annual volatility (%) 11.0 11.3 10.3

Information ratio 2.0 1.5 1.8

Table 1: Total returns performance statistics, October 2011 – June 2015

We also looked at the performance in terms of price returns (dividend return excluded) and found that the

SPDA benchmark outperformed our HFDY portfolio by 1.9% and our HTDY portfolio by 6.7% on an annual

basis. HFDY outperformed HTDY by 4.8% annually with a hit rate of 80%.The cumulative price returns and

the performance statistics are presented in Chart 4 and Table 2, respectively.

Chart 4: Cumulative price returns, unconstrained portfolios, October 2011 – June 2015

Our HFDY portfolio outperformed the HTDY portfolio in terms of all the performance metrics reported in

Table 2. It offered more return with less risk taken than HTDY. The SPDA benchmark had the highest

information ratio due to the lowest volatility and highest return out of the three portfolios.

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Annual return (%) 13.4 8.6 15.3



Table 2: Price returns performance statistics, October 2011 – June 2015

Finally, we looked at the performance of our portfolios and the benchmark in terms of dividend generated

over time. Both HFDY and HTDY have significantly outperformed SPDA with a slightly higher annual

dividend yield of 8.2% in HTDY. As mentioned previously, the selection criteria of SPDA ensures a more

sustainable dividend pay-out from its constituents, which explains the underperformance in dividend yield.

The cumulative dividend returns and the performance statistics are presented in Chart 5 and Table 3,

respectively.

The dividend yield was similar for both forecast and trailing baskets, indicating that the primary benefit of

using Markit dividend forecasts as a selection input was as a value indicator and a positive signalling effect

on future earnings. The methodology employed was not designed to test dividend maximisation. We believe

that the benefit of forecast to this end is intuitive. We believe yield was so similar for HFDY and HTDY

because stocks were ranked on yield over the whole forecast/trailing year, not in respect to dividends in the

immediate three months.

Chart 5: Cumulative dividend returns, unconstrained portfolios, October 2011 – June 2015

Table 3: Dividend returns performance statistics, October 2011 – June 2015

HFDY HTDY SPDA


Annual return (%) 7.8 8.2 3.2



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4 Portfolio characteristics In this section, we analyse our portfolios in terms of market cap, sector and liquidity exposures to see if any

bias was driving the return differential between HFDY and HTDY.

4.1 Market cap exposure

Table 4 shows the market cap exposure of the two portfolios for the analysed period. It is based on the % of stocks appearing within each market cap bucket. We see that although both portfolios have a bias towards small cap stocks, HFDY has a smaller exposure to small caps than HTDY.

% of stocks in different market cap range HFDY HTDY

Large cap (>$10 billion) 11% 8%

Medium cap ($2billion-$10billion) 25% 22%

Small cap (<$2 billion) 65% 70%

Table 4: Market cap exposures, October 2011 – June 2015

For comparison purposes, we looked at those portfolio characteristics of SPDA from the S&P high yield

dividend aristocrats’ month end factsheet as of September 30th 2015. SPDA has different and stricter stock

selection rules. Their constituents must have a float adjusted market cap of at least $2bn as of the

rebalancing reference date, which eliminates all small caps.

4.2 Liquidity exposure

We also examined the liquidity exposure of the portfolios. Table 5 shows the % of stocks appearing within

each liquidity bucket based on the average daily value traded for the past month prior to the rebalancing

reference date. We see that although both portfolios have a bias towards less liquid stocks, HFDY has a

higher exposure to more liquid stocks than HTDY.

Average daily value traded for the past 30 days HFDY HTDY

>$80 million 11% 9%

$10 million-$80 million 35% 34%

<$10 million 53% 57%

Table 5: Liquidity exposures, October 2011 – June 2015

Stocks within the SPDA benchmark must have an average daily value traded of at least $5m for the three

months prior to the rebalancing reference date. The minimum initial portfolio size that can be turned over in a

single day is $2bn. The sector breakdown for SPDA is shown in Chart 6.


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Chart 6: S&P high yield dividend aristocrats sector breakdown as of September 30th 2015

4.3 Sector exposure

Looking at the sector exposure of the portfolios in Table 6, both HFDY and HTDY have a high exposure to

financial stocks, although HFDY has a slightly lower exposure than HTDY.

Sector

HFDY HTDY

Financials 61% 63%

Utilities 7% 5%

Consumer Discretionary 7% 6%

Industrials 6% 7%

Telecommunication Services 5% 6%

Energy 5% 4%

Consumer Staples 4% 3%

Health Care 2% 2%

Information Technology 2% 2%

Materials 1% 1%

Table 6: Sector exposures, October 2011 – June 2015

4.4 Difference between HFDY and HTDY

75% of the stocks on average are common in our HFDY and HTDY portfolios. This means that using

dividend forecasting allows a selection of 25% different stocks. Stocks in the HTDY but not in the HFDY had

an average PE ratio of 45 over the subsequent year from the portfolio construction date. Stocks in the HFDY

but not in the HTDY had an average PE ratio of 32 during the same period. This indicates that HFDY is

better at picking value stocks.


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5 Adjusting for market cap, liquidity and sector bias In this section, we analyse our portfolios after applying restrictions on market cap, sector and liquidity

exposures to see how performances differ from previous unconstrained portfolios. The restrictions also

brought our portfolio constructions closer to SPDA.

5.1 Criteria

We applied the following criteria to both HFDY and HTDY portfolios.

— Removed micro market cap stocks (i.e. < $300m)

— Removed illiquid stocks (average daily value traded for the past 30 days < $10m)

— Applied a 30% cap on each sector

5.2 Constrained portfolios

We looked at the performance of our two constrained portfolios and the S&P High Yield Dividend Aristocrat

Index (SPDA) in terms of total returns. Chart 7 shows the cumulative total returns over the sample period

with an assumed investment of $100 at the beginning of October 2011 (Q3 2011).

Chart 7: Cumulative total returns, constrained portfolios, October 2011 – June 2015

Overall performance stayed robust with a small improvement. Our HFDY portfolio constructed using Markit’s

Dividend Forecasting dataset still did a better job of selecting outperforming stocks in terms of total returns

over the sample period in comparison with both the HTDY portfolio and the SPDA benchmark. It

outperformed the HTDY portfolio by 5% and the SPDA benchmark by 3.4% on an annual basis. The

outperformance was 4.6% and 3.1%, respectively, before adjustments.

As before, HFDY outperformed HTDY in 13 out of 15 quarters (87% hit rate) and SPDA in 9 quarters (60%

hit rate). We also found a slightly higher correlation between HFDY, HTDY and the benchmark, which

increased from 0.85 to 0.9.

Detailed performance statistics are presented in Table 7. HFDY had a slightly higher total return and lower

volatility, which resulted in a higher information ratio after applying the constraints. The information ratio of

the HTDY portfolio also increased marginally due to the slightly lower volatility.

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HFDY HTDY SPDA


Annual return (%) 22.3 17.3 18.9



Table 7: Total returns performance statistics, October 2011 – June 2015

We also looked at the performances in price returns. The cumulative price returns and the performance

statistics are presented in Chart 8 and Table 8, respectively. By making adjustments on market cap, liquidity

and sector exposure, we found better performances from both HFDY and HTDY in capital appreciation.

Before making the constraining adjustments outlined previously, the annual return of HFDY was 13.4%

whereas it increased to 15% after adjustments. It outperformed HTDY by 5.4% compared to 4.8%

previously. Performance of HTDY also improved slightly with information ratio increasing from 0.8 to 0.9.

Chart 8: Cumulative price returns, unconstrained portfolios, October 2011 – June 2015

Our HFDY portfolio outperformed the HTDY portfolio in terms of all the performance metrics reported in

Table 8. The same as the total return, the volatilities of the price returns of HFDY and HTDY declined after

making adjustments. With small increases in price returns, the information ratio of both portfolios improved

slightly.

HFDY HTDY SPDA


Annual return (%) 15.0 9.6 15.3



Table 8: Price returns performance statistics, October 2011 – June 2015

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Finally, we looked at the performances of our portfolios and the benchmark in terms of income/dividend

generated over time after adjustments. The cumulative dividend returns are presented in Chart 9 and Table

9. Dividend return decreased in both HFDY and HTDY. HTDY still generated the highest dividend yield with

7.1% per year, which declined from 8.2 before adjustments. That is slightly higher than the annual dividend

yield from HFDY (6.6%), which was 7.8% before adjustments.

Chart 9: Cumulative dividend returns, unconstrained portfolios, October 2011 – June 2015

Table 9: Dividend returns performance statistics, October 2011 – June 2015

6 Conclusions In this research note, significant value was found in using Markit's forward looking dividend forecasts over

historical dividends paid. We created a high forward dividend yield portfolio (HFDY) and high trailing dividend

yield portfolio (HTDY) based on the Russell 3000 index. We compared performance between these two

portfolios as well as with the S&P High Yield Dividend Aristocrats index (SPDA).

On a like-for-like comparison, we found that both total returns and price returns of HFDY were 34% higher

than those of HTDY. HTDY had a slightly higher dividend yield than HFDY. Dividend yield was very similar

for both forecast and trailing baskets, indicating that the primary benefit was as a value indicator and a

positive signalling effect on future earnings. The methodology employed was not designed to test dividend

maximisation. We believe that the benefit of forecast to this end is intuitive.

HFDY also showed outperformance of 21% in total return and dividend yield when compared with SPDA. It

is important to note that SPDA has a number of screening criteria which aim to provide a basic test for

HFDY HTDY SPDA


Annual return (%) 6.6 7.1 3.2

Annual Volatility (%) 0.3 0.5 0.3

Information Ratio 20.6 13.8 10.7

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sustainability. This limits its stock selection universe and leads to a lower dividend yield. While these screens

may be successful in making the basket less volatile, it can lead to underperformance compared to the use

of forecasts.

Using dividends forecasts instead of historical dividends resulted in a different stock selection of 25% on

average across both portfolios. The stocks in the HFDY portfolio but not in the HTDY had a much lower P/E

ratio in the following year when compared to stocks in the HTDY portfolio but not the HFDY. This indicates

that using forecasted dividend is more suited to picking value stocks compared with using historical dividend.

The final stage of our research was to apply constrains to the baskets. We wanted to test whether the results

held-up when micros caps and illiquid stocks were removed. We observed a high proportion of these stocks

in the initial baskets, although the HFDY portfolio had a slightly lower exposure to stocks with small market

cap and lower liquidity than the HTDY portfolio.

By adding the constraints to remove illiquid and micro market cap stocks and also put a limit on selecting

only 30% of the stocks from each sector, the performance actually improved. Sector capping was the reason

for this, by removing stocks that have a relative low dividend yield compared with their direct competitors

within the high yield sector, which underperformed later in the next period. Overall the HFDY portfolio now

outperformed the HTDY portfolio by 38% in the sample period. It outperformed the SPDA by 24%. The

outperformance in price return slightly improved and that in dividend yield diminished a little.

Our research shows that value investors should pay attention to dividend forecasts to form their views about

the firms’ dividend prospects, not their dividend past. Our test was conducted on a US universe, where

dividends are paid quarterly and as such more stable. We believe the benefits of using Markit forecasts

could be even more pronounced in Europe and Asia where dividend payments are more variable.


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Appendix

Portfolio total returns- Annual rebalance3

3 Annual rebalance with quarterly performance reported for both HFDY and HTDY.

4 SPDA index rebalances quarterly. It is only shown here for comparison purposes.

HFDY HTDY SPDA4


Annual return (%) 16.5 12.5 16.3



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High Forward Dividend Yield Portfolio

High Trailing Dividend Portfolio

S&P High Yield Dividend Aristocrats


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Portfolio comparisons with Russell 3000

Total return

HFDY HTDY SPDA

R3000

Cumulative return (%) 110.4 82.1 91.1 108.0

Annual return (%) 21.9 17.3 18.9 21.6

Annual volatility (%) 11.0 11.3 10.3 10.7

Information ratio 2.0 1.5 1.8 2.0

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High Forward Dividend Yield Portfolio High Trailing Dividend Portfolio

S&P High Yield Dividend Aristocrats Russell 3000


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Price return

HFDY HTDY SPDA

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Annual return (%) 13.4 8.6 15.3 19.2



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Dividend return

HFDY HTDY SPDA

R3000


Annual return (%) 7.8 8.2 3.2 2.1



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S&P High Yield Dividend Aristocrats

Russell 3000

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