The much-heralded decoupling of the financial markets between developed, emerging and frontier markets met its nemesis in the 2008/9 global financial crisis- The Great Recession. For the believer in a diversified global basket of stocks or indices this came as a crushing blow. This notwithstanding, we still believe that a globally-diversified passive buy-and-hold strategy provides the best chance at maximising net investment return. We test this empirically.

1. GLOBAL INVESTMENT OPPORTUNITIES- A LOW-COST
PASSIVE ‘BUY-AND-HOLD’ STRATEGY

2. Backdrop
The much-heralded decoupling of the financial markets in the developed economies from those of the emerging and frontier markets found its nemesis in
the 2008/9 global financial crisis- The Great Recession. For the believer in a diversified global basket of stocks or indices that reduces his unsystematic or
idiosyncratic risk (that is the risk associated with investing in individual countries, regions or currencies leaving him solely to worry about his world beta) to
almost nil the events of 2008/9 came as a big blow to his world of comfort.
The above notwithstanding, investing in a world index
like the MSCI index series, specifically the MSCI All
Country World Index (MSCI ACWI)1
comprising as much
exposure as possible to stocks across region, sector and
currency may well provide the most cost-effective way
of maximising net2
investment return. Ideally, the global
fund manager and/or investor will passively invest in an
index fund that tracks the MSCI ACWI. He will invest in
this at the market trough, buy and hold it and only
liquidate his holding when the index peaks in a
subsequent period. He will have augmented this
broader passive strategy with periodic tactical shifts
across region, size and style to bolster his returns. These
tactical shifts are informed by a number of heuristics
that are the subject of study here. We believe this to be
a low-cost winning strategy.
Much academic literature has been written about the fallacy of attempting to generate alpha3
.
With this in mind the much-lauded euphemism in finance that ‘there is no free lunch’ comes to mind; superior return can only come at a premium in added
1
The MSCI ACWI Investable Market Index (IMI) captures large, mid and small cap representation across 23 Developed Markets (DM) and 24 Emerging Markets (EM)
countries. With 8,668 constituents, the index is comprehensive, covering approximately 99% of the global equity investment opportunity set.
2
Costs of research and identifying winning stocks are deducted from the return achieved.
3
Fama (1965) champions a naive ‘buy-and-hold’ strategy based on the notion that stock prices follow a martingale process and any attempt to outperform the market will
lead to excess returns being wiped out by associated costs. Damodaran (2002) argues that not only transaction costs but the research resources needed to identify stock
mispricing would erode any excess returns.

3. cost and/ or risk. Fama (1965) refers to any outperformance through technical analysis as pure luck and not a demonstration of superior knowledge or
expertise referring to such as ‘chance predictions of stock prices’. This is especially so in strong-form efficient markets where a serial dependence of returns
and private information are absent. Assuming this to hold true it would make investment sense to pursue a strategy that combines a low-cost, long-term
buy-and-hold of a global benchmark index (strategic asset allocation) with periodic tactical portfolio shifts towards region, size and style as guided by the
relevant business cycle phase.
The tactical element of this strategy recommends investing in more volatile markets (region), in small-cap stocks (size) and in cyclical sectors (style) during
the bull market phase followed by a 180-degree shift into more stable markets, large-cap stocks and defensive sectors during the bear market phase.
In this article, we test this long-term passive ‘buy-and-hold’ approach interspersed with the appropriate tactical shifts across region, size and style to
establish whether, empirically, it is a winning strategy.
The strategy is depicted diagrammatically as follows.

4. Fig 1: Depiction of our low-cost passive ‘buy-and-hold’ strategy
Cyclicals Underweight Cyclical Defensives Underweight Defensive Cyclical
Small-caps Mid-caps Large-caps Mid-caps Small-caps
Mid-cap
Small-cap
Large-cap
A
Large-cap
Mid-cap
Small-cap
BEAR MARKET BULL MARKET
PEAK PEAK
RISK OFF
RISK ON
RISK OFF
TROUGH
Style
Size

5. Our proposed low-cost investment strategy
A Passive ‘Buy-and-hold’ strategy. For the global fund manager/ investor, allocate money to an appropriate global index
fund that is well-diversified across region (and therefore currency), size and style or sector. (For the country fund
manager/ investor, invest in an index fund that tracks the country’s composite index). Market-timing is perfected here
by investing at the market trough and exiting at the market peak. The difficulty exists, of course, in identifying these
entry and exit points ahead of time to optimise execution. That is, however, not the subject of our study here.
Past data and trends could be instructive but are not a certainty due to the ‘random walk’ nature of the stock market. It
is key here to differentiate between developed, emerging and frontier markets (although these are not in themselves
homogeneous) all with different forms of market efficiency and carry differing levels of market volatility and therefore
return. Volatile markets, small-cap stocks and cyclical sectors are ideal to invest in during a bull market while developed markets, large-cap stocks and
defensive sectors provide a safer haven when there is a sustained market sell-off. Further risk and return advantages can be had by moving money into
other asset classes which, however, is also not the subject of our discussion here.
Market efficiency is key to determining the ability to deliver alpha. Typically, the less efficient
the market the greater the chance of generating abnormal returns (alpha). Frontier markets
would generally be regarded as weak-form efficient, emerging markets as semi-strong form
efficient and developed markets as strong-form efficient, not least due to stricter insider trading
legislation, a large number of market participants and analysts and informational efficiency in
developed markets that enable a near perfect arbitraging away of excess returns.

6. Theoretical backdrop
‘Region and volatility’
Greater volatility of returns persists in frontier markets than in more sophisticated markets due to a number of macro-economic factors (high inflation,
volatile interest rate environments, volatile exchange rate regimes, etc) as well as idiosyncratic factors associated with individual investee companies (poor
corporate governance structures, adverse input price movements, high costs of capital, etc) that impact on stock performance. Stocks in these volatile
markets carry high betas and following CAPM investors will require a higher rate of return to compensate them for this higher risk4
. However, in a bull
market phase it is precisely these high-beta markets that allow an above- average return thereby providing impetus for tactical allocation in their direction
during such market phases.
‘Size effect’5
Low beta large-cap stocks carry less investment risk than high beta small-cap stocks due typically to the business and financial strength of the companies
they represent, the effective governance structures of these companies, greater liquidity of these stocks as well as their efficient pricing brought about by
deep trader and analyst scrutiny. Most large-cap companies also have good disclosures and therefore there is no dearth of information for an investor
looking into them. The converse is the case with small-cap stocks. An absence of these factors leads to price anomalies in small-cap stocks and hence the
prevalence of abnormal returns. Small-cap stocks are therefore potentially big gainers as they are yet to be discovered within the sector and once unfurled
in the market release major growth potential.
‘Informational inefficiencies’
Bull market opportunities in frontier markets are bolstered by informational inefficiencies here that provide for significant price dislocation. These price
anomalies6
present an inviting return opportunity for the above average-risk investor. The holding of private information in these countries, due both to a
lack of robust insider-trading legislation and a lack of full participation by all market participants, means that abnormal returns are a regular feature
alongside the market volatility. Researchers have referred to these markets as carrying a weak-form of market efficiency.
4
This view is widely held by finance researchers including Chiang and Doong (2001) who posit that ‘a higher expected return is required to compensate for risk when
volatility is relatively high.’
5
This phenomenon is associated with the seminal work of Banz (1981) which linked small cap. stocks to high book-to-market ratios and low P/E ratios (and therefore
significant upward potential) and large cap. stocks to low book-to-market ratios and high P/E ratios.
6
Jefferies and Smith (2004) identify efficiency of financial markets as critical in explaining these price anomalies positing that the level of market efficiency will be critical in
determining the extent and speed with which information regarding firms or the market is reflected in stock prices.

7. ‘Sector rotation’7
Cyclical stocks (financials, industrials, consumer discretionary, IT, energy, automobiles, etc) represent firms
whose performance moves in line with that of the wider business cycle. As the economy improves so do profits
of firms represented by these stocks. All else holding constant, their stock prices also rise in line with their
profits. For defensive stocks (healthcare or pharmaceuticals, consumer staples, utilities, telecommunications,
etc), on the other hand, performance will remain relatively unaffected by the stage of the business cycle as the
profitability of firms represented by these stocks is not directly linked to the performance of the economy.
Demand for their products/ services remains relatively constant irrespective of the stage in the business cycle.
Holding these above theoretical bases as true then the following heuristics should apply:
 frontier market stocks outperform emerging market stocks which in turn outperform developed market stocks in global bull market conditions (e.g
those in the periods 1987-1999, 2002-2007 and 2007-2014/5/6 respectively). Conversely, developed market stocks outperform emerging market
stocks which in turn outperform frontier market stocks in global bear markets (e.g those in the periods 1999-2002 and 2007-2009 respectively): this
is tactical asset allocation by region
 small-cap stocks outperform large-cap stocks in a bull market while large-cap stocks outperform small-cap stocks in a bear market: this is tactical
asset allocation by size
 cyclical stocks outperform defensive stocks in a bull market while defensive stocks outperform cyclical stocks in a bear market: this is tactical asset
allocation by style
It follows therefore that:
 as an initial bull market takes hold and gathers pace (confirmed by two quarters of positive returns) the (international) investor should increase his
weighting of small-cap cyclical stocks in frontier or emerging markets with his preference for one or the other guided by his risk tolerance level. As
the bull market peaks and then begins to reverse the investor should start to reduce these holdings.
7
Conover, C.M., Jensen, G.R, Johnson, R.R and Mercer, J.M (2008) identify sector rotation as an effective way of earning consistent and economically-significant excess
returns while requiring only infrequent rebalancing. The strategy places greater emphasis on cyclical stocks during periods of Fed (quantitative) easing and overweight
defensive stocks during periods of Fed tightening (rising interest rates). International investment firm- Fidelity Investments (2014) advocates a business cycle approach to
sector rotation as a way of delivering positive active returns over an intermediate time horizon. This ‘conventional wisdom’ is also referred to in the research by Stangl, J.,
Jacobsen, B. and Visaltanachoti, N. (2009).

8.  as bear market conditions (confirmed by two quarters of negative returns) take root the investor should increase his weighting of large-cap
defensive stocks in safer haven developed markets.
Empirical evidence
Data was gathered on developed markets, emerging markets and frontier markets as a back-test for outperformance of small-cap stocks, cyclical sectors
and frontier market bourses in bull markets and for outperformance of large-cap stocks, defensive sectors and developed market bourses in bear ones.
Compounded annualised percentage returns were therefore gathered for the following 5 market phases between 1987 and 2016 as follows:
 the 1987 – 1999 Bull market
 the 1999 – 2002 Bear market
 the 2002 – 2007 Bull market
 the 2007 - 2009 Bear market
 the 2009 – 2014/5/6 Bull market (different markets in different regions of the world have peaked in different years)
For each stock exchange specific to a region, a national benchmark or composite index was identified to represent constituent stocks on the basis of size
(large-cap or small-cap) or style (cyclical or defensive). Where an appropriate benchmark or composite index was lacking (or sufficient index data lacking) a
relevant constituent or constituents of the benchmark or composite index seen as representative of the wider size or sector category was taken as proxy.
The study attempted to test whether the following heuristics held true:
 volatile markets outperform during the bull phase of a business cycle and developed markets during the bear phase
 small-caps outperform during the bull phase and large-caps during the bear phase
 cyclical sectors outperform during the bull phase and defensive sectors during the bear phase.
The methodology is summarised in the table below.

9. Table 1: Regional markets and their proxies by size and style
MARKET
SOPHISTICATION
REGION COUNTRY BOURSE INDEX ASSET
CLASS
(EQUITIES)
BY SIZE
PROXY PROXY ASSET
CLASS
(EQUITIES)
BY STYLE
PROXY PROXY
Developed
markets
N.
America
US NYSE S&P
Composite
1500
Large-
cap
S&P 500 Cyclical8
S&P Composite
1500 IT
Apple Inc.
S&P Composite
1500 Financials
JP Morgan
Chase & Co.
Small-cap S&P Small-cap
600
Defensive9
S&P Composite
1500 Healthcare
Johnson &
Johnson
Europe UK LSE FTSE All-
share
Large-
cap
FTSE 100 Cyclical10
FTSE All-share
Financials
FTSE All-share
Consumer Goods
British
American
Tobacco Plc
Small-cap FTSE Small-cap Defensive11
FTSE All-share
Healthcare
GlaxoSmithKline
Plc
8
As of December 2016, Information Technology (IT) and Financials sectors comprised 48.9% of the total market capitalisation of all cyclical stocks within the S&P
Composite 1500 index and are here considered adequate proxies for all cyclical stocks within the S&P Composite 1500.
9
As of December 2016, the Healthcare sector represented 47.5% of the total market capitalisation of all defensive stocks within the S&P Composite 1500 index and is here
considered an adequate proxy for all defensive stocks within the S&P Composite 1500.
10
As of December 2016, Financials and Consumer Goods sectors comprised 48.07% of the total market capitalisation of all cyclical stocks within the FTSE All-share index
and are here considered adequate proxies for all cyclical stocks within the FTSE All-share.
11
As of December 2016, the Healthcare sector represented 54.64% of the total market capitalisation of all defensive stocks within the FTSE All-share index and is here
considered an adequate proxy for all defensive stocks within the FTSE All-share.

10. Emerging
markets
Asia China SSE SSE
Composite
Large-
cap
SSE 180 Cyclical12
SSE Composite
Financials
SSE Composite
Industrials
Sinochem
Corporation
Small-cap SSE Small-cap Wanhua
Chemical
Group Co.
Ltd.
Defensive13
SSE Composite
Telecommunications
China Mobile
Ltd.
Fuyao
Glass
Industry
Group Co.
Ltd.
SSE Composite
Healthcare
Guangzhou
Pharmaceutical
Holdings Ltd.
India NSE Nifty 500 Large-
cap
Nifty 50 Cyclical14
Nifty 500 Financials State Bank of
India
Nifty 500 Consumer
Goods
ITC Ltd.
Small-cap Nifty Small-
cap 250
South
Indian
Bank Ltd.
Defensive15
Nifty 500
Healthcare
Sun Pharma
Ltd.
12
As of December 2016, Financials (incl. Banks, Insurance and Investment) and Industrials sectors comprised 88.6% of the total market capitalisation of all cyclical stocks
within the SSE-50 index and are here considered adequate proxies for all cyclical stocks within the SSE Composite.
13
As of December 2016, Telecommunications and Healthcare sectors comprised 53.9% of the total market capitalisation of all defensive stocks within the SSE-50 index and
are here considered adequate proxies for all defensive stocks within the SSE Composite.
14
As of March 2017, Financials (incl. Banks) and Consumer Goods sectors comprised 47.2% of the total market capitalisation of all cyclical stocks within the Nifty 500 index
and are here considered adequate proxies for all cyclical stocks within the Nifty 500.
15
As of March 2017, Healthcare (Pharmaceuticals and Healthcare Services) represented 74.54% of the total market capitalisation of all defensive stocks within the Nifty
500 index and together with Telecommunications are here considered adequate proxies for all defensive stocks within the Nifty 500.

11. Blue Star
Infotech
Ltd.
Nifty 500
Telecommunications
Bharti Airtel
Ltd.
Frontier markets Africa Nigeria Nigeria
SE
Nigeria SE
All-share
Large-
cap
Nigeria SE-30 Cyclical16
Nigeria SE All-share
Financials
Zenith Int. Bank
Plc
Nigeria SE All-share
Consumer Goods
International
Breweries Plc
Small-cap Nigeria SE All-
share
Construction/
Real Estate
Julius
Berger
Nigeria Plc
Defensive17
Nigeria SE All-share
Healthcare
GSK Nigeria Plc
Nigeria SE All-
share
Conglomerates
UACN Plc Neimeth
International
Plc
Kenya Nairobi
SE
Kenya NSE
All-share
Large-
cap
Kenya NSE-20 Cyclical18
Kenya NSE All-share
Financials
Equity Bank Ltd.
Kenya
Commercial
Bank Ltd.
Small-cap Limuru Tea
Co. Ltd.
Defensive19
Kenya NSE All-share
Telecommunications
Safaricom Ltd.
16
As of December 2016, Consumer Goods and Financial Services sectors comprised 53.3% of the total market capitalisation of all cyclical stocks within the Nigeria SE All-
share index and are here considered adequate proxies for all cyclical stocks on the Nigeria SE.
17
As of December 2016, the Healthcare sector comprised 40% of the total market capitalisation of all defensive stocks within the Nigeria SE All-share index and is here
considered an adequate proxy for all defensive stocks on the Nigeria SE.
18
As of December 2015, the Financials (comprising Banking, Insurance, Investment and Investment Services) sector comprised 61.32% of the total market capitalisation of
all cyclical stocks within the Kenya NSE All-share index and is here considered an adequate proxy for all cyclical stocks on the Kenya NSE.
19
As of December 2015, the Telecommunications sector represented 88.1% of the total market capitalisation of all defensive stocks within the Kenya NSE All-share index
and is here considered an adequate proxy for all defensive stocks within the Kenya NSE. Due to lack of sufficient data from Safaricom Ltd. (Telecommunications), additional
data from Kenya Power & Lighting Company Ltd. (Utilities) has been included.

12. Williamson
Tea (K)
Ltd.
Kenya NSE All-share
Utilities
Kenya Power &
Lighting
Company Ltd.
Car &
General
(K) Ltd.
Developed
markets and
Emerging
markets
Global MSCI All
Country
World
Index

13. Figure 2: Market period annualised percentage returns (1987 -99)
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
1987-1999 BULL MARKET ANNUALISED % RETURNS
Developed markets Emerging markets Frontier markets

14. Figure 3: Market period annualised percentage returns (1999 - 02)
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
1999-2002 BEAR MARKET ANNUALISED % RETURNS
Developed markets Frontier marketsEmerging markets

15. Figure 4: Market period annualised percentage returns (2002 - 07)
-100.00
-50.00
0.00
50.00
100.00
150.00
2002-2007 BULL MARKET ANNUALISED % RETURNS
Developed markets Emerging markets Frontier markets

16. Figure 5: Market period annualised percentage returns (2007 - 09)
-100.00
-90.00
-80.00
-70.00
-60.00
-50.00
-40.00
-30.00
-20.00
-10.00
0.00
2007-2009 BEAR MARKET ANNUALISED % RETURNS
Developed markets Emerging markets Frontier markets

17. Figure 6: Market period annualised percentage returns (2009 – 14/15/16)
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
2009-14/15/16 BULL MARKET ANNUALISED % RETURNS
Developed markets Emerging markets Frontier markets

18. Figure 7: Sample period annualised percentage returns (1987 – 14/15/16)
-40.00
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
50.00
SAMPLE PERIOD (1987-2014/15/16) ANNUALISED % RETURNS
Developed markets Emerging markets Frontier markets

19. Results
Table 2: Results from testing the heuristics under study
Heuristic Market
sophistication
Country 1987-1999
BULL MARKET
1999-2002
BEAR MARKET
2002-2007 BULL
MARKET
2007-2009
BEAR MARKET
2009-2014/15/16
BULL MARKET
1987-2014/15/16
BULL RUN
In a bull
market,
frontier
markets
outperform
emerging
markets
which in
turn
outperform
developed
markets
Frontier
markets
outperformed
emerging
markets
Insufficient
data
Yes Yes No
Emerging
markets
outperformed
developed
markets
Yes Yes Yes Yes
In a bear
market,
developed
markets
outperform
emerging
markets
which in
turn
outperform
frontier
markets
Developed
markets
outperformed
emerging
markets
Yes (period
positive returns
in emerging
markets- see
diagram can be
considered
outlier results)
Yes N/A
Emerging
markets
outperformed
frontier
markets
Yes (period
positive returns
in frontier
markets- see
diagram can be
considered
outlier results)
No N/A
In a bull
market,
Developed US No Yes Yes Yes

20. small-cap
stocks
outperform
large-cap
stocks
UK Insufficient
data
Yes Yes No
Emerging China Insufficient
data
Yes Yes Yes
India Insufficient
data
No Yes Yes
Frontier Nigeria Insufficient
data
Insufficient data Yes No
Kenya Insufficient
data
Mixed results Yes Yes
In a bear
market,
large-cap
stocks
outperform
small-cap
stocks
Developed US No Yes
UK Yes Yes
Emerging China Mixed results Yes
India Mixed results Mixed results
Frontier Nigeria Insufficient
data
Insufficient data
Kenya Yes Mixed results
In a bull
market,
cyclical
stocks
outperform
defensive
stocks
Developed US No Yes Yes Mixed results
UK No Yes Yes Mixed results
Emerging China No data No Mixed results Mixed results
India No Mixed results Mixed results No
Frontier Nigeria No data Yes Mixed results Mixed results

21. Kenya Insufficient
data
No Mixed results Mixed results
In a bear
market,
defensive
stocks
outperform
cyclical
stocks
Developed US Yes Yes
UK No Yes
Emerging China Yes Mixed results
India Mixed results Mixed results
Frontier Nigeria Mixed results Mixed results
Kenya No Yes
Outperformance by region
Apart from the bear market of 2007-9, frontier markets showed the most volatility in annualised returns of the three regions for all the periods covered.
During the 2007-9 bear market, the emerging markets bucked this trend to register greater volatility than even the frontier markets.
Outperformance by size
The heuristics that:
 small-cap stocks outperform large-cap stocks in a bull market, and that
 large-cap stocks outperform small-cap stocks in a bear market
largely did not bear out for either the emerging or frontier markets. It was difficult to establish this ‘rule of thumb’ especially in the earlier bull and bear
markets between 1987 and 2002, either due to a lack of sufficient study data on market performance during this period or because the study produced
mixed results for the period.
However, during the later periods under study, notably the bull markets of 2002-7 and 2009-14/15/16 (national bourses in different regions have peaked in
different years) and the bear market of 2007-9, the heuristics appeared to largely hold true for the developed markets of the US and UK.

22. During the bull market of 2009-14/15/16 the heuristics appeared to hold true for all 3 regions and 6 bourses.
Outperformance by sector
The heuristics that:
 cyclical stocks outperform defensive stocks in a bull market, and that
 defensive stocks outperform cyclical stocks in a bear market
largely did not bear out20
for either the emerging or the frontier markets for any of the periods under study.
However, for the developed markets of the US and the UK this set of heuristics did indeed hold true for the 2002-07 and 2009-14/15/16 bull markets and
the 2007-9 bear market.
Analysis
Exogenous factors21
(Monetary policy)
Extended Quantitative Easing (QE) in developed markets (US, UK and Europe) has meant that, while markets in both emerging and frontier markets peaked
in 2014 and 2015 respectively and entered a mean-reverting phase, the bull phase that started in 2009 has persisted in developed economies and
continued to see all-time highs. Both low base interest rates and (corporate) bond-buying by central banks in the developed economies of the West have
extended corporate profits beyond the traditional business cycle upswing.
This means that global investors who in ordinary times would have shifted their portfolio allocations away from emerging and frontier markets and into
‘safe haven’ trades with the onset of a bear phase now have added impetus to increase their weighting in developed markets. What ordinarily would have
been a mean-reverting bear phase of the business cycle in global markets has instead been replaced by a trajectory of extended growth in developed
20
These results are consistent with Stangl, J., Jacobsen, B. and Visaltanachoti, N. (2009) who posit that, even if one gave sector rotation the benefit of the doubt and
assumed that investors can perfectly time business cycles, returns are only marginally higher than the market. Thus, according to the three, no sector performs consistently
and significantly better in different business cycle stages.
21
Fiscal Policy. Robust taxation policies to combat tax inversion during the Obama years as well as proposed infrastructure spending by the Trump administration should
serve to boost economic performance and that of certain affected sectors. However, it remains to be seen how the Trump administration will reconcile reduced taxation
on both corporate and small business with increased infrastructure spending outside of further ballooning the USA’ s national debt. Taxation in Europe is more of a mixed
picture with different governments implementing different and often contrasting national fiscal policies. This, notwithstanding, different tax policies across different
regions and countries in the world should serve as a way of identifying opportunity- strong corporate performance, strong market fundamentals and hence return
opportunity.

23. markets brought about by ‘easy money’ (QE and low interest rates). However, taking into account annualised percentage returns up until 2016 (and
ignoring annualised percentage returns beyond this period) the heuristic that returns in emerging and frontier markets outmatch those in developed
markets in a bull market phase and vice versa in a bear market phase does indeed hold true as is evidenced in Table 2.
Volatility
The developed markets exhibit the least amount of volatility in returns for the periods under consideration. This is in line with the lower investment risk in
these regions. By listing on these developed market bourses, multinational companies (which already enjoy the diversification benefits of global operations)
are setting up shop in stable and predictable economic environments as well as profiting from the constant glare of stock market regulators, analysts and
‘activist’ investors. By contrast frontier market companies escape this scrutiny and this, coupled with their relatively small size, means that profits are easily
impacted by changing market conditions and cost dislocations. This leads to volatile profits for them.
The preceding narrative serves to explain the markedly less volatility observed in Figures 1 – 6 above in developed markets as opposed to their
counterparts. Indeed, a ‘rule of thumb’ that advocates investing in emerging and frontier markets during a bull market to benefit from the high beta of
resident companies and switching to safer havens during bear market conditions to escape these very same high beta companies is clearly a winning
strategy.
The same logic also applies to the large-cap versus small-cap story with the advocacy that high-beta small-cap companies are more volatile and hence ideal
investment ‘destinations’ during cyclical upswings but not so during market slumps. As evident from Table 2 above this heuristic plays out well in the more
efficient developed markets where asset pricing is at its most optimal22
.
Illiquidity
Researchers23
have identified shallow markets and low trading volumes24
as partly to explain for pricing inefficiencies that result in less than full asset-
pricing observable in emerging and developed markets. In these markets small-cap stocks, in particular, may not adjust to their full pricing in either bull or
bear markets leading to the breakdown of the heuristic governing large-cap versus small-cap performances. By the same token this less than optimal pricing
of assets in these illiquid markets may also explain the breakdown of the cyclical versus defensive sector play.
22
Claessens et al. (1995) sum this volatility up in the remark ‘the size effect found in many industrial economies does not prevail as systematically in the emerging markets.’
23
See Claessens et al., (1995) and Poshakwale and Theobald (2004)
24
Thinly-traded markets are also associated with serial autocorrelation that leads to extended losses or profits beyond the conventional business cycle logic.

24. Secular trends and stocks
Secular trends are those not considered seasonal or cyclical instead remaining consistent over time, while secular stocks maintain a certain trajectory
regardless of existing economic conditions. Most sectors in both emerging and frontier markets may not meet the strict definition of cyclical but rather
continue on a growth path over time and hence remain secular25
in nature. This is so since most industries in these regions (including healthcare/
pharmaceuticals, telecommunications, financials, consumer discretionary, etc) are barely in their infancy stages and providers are still able to capture an
ever-growing market within their national borders. This means that the heuristic surrounding cyclical versus defensive sector play, in particular, breaks
down as evidenced in the tabulated results.
Market dislocation
The ‘rule of thumb’ that small-caps and cyclicals outperform in
bull markets and large-caps and defensives in bear ones did not
hold true for any of the market periods for the either the
emerging or frontier markets. Serial autocorrelation26
of returns
in these markets introduces predictability of returns coupled
with a persistence of arbitrage opportunities27
.
However, due to large bid-ask spreads and high transaction costs
in these markets, arbitrageurs are unable to take advantage of
the available value propositions. Positive returns for size or
sector companies may, therefore, persist beyond their
favourable business cycle phase. As an example, positive returns for a given small-cap stock or cyclical sector may extend beyond the traditional period of
market exuberance leading to a breakdown of the above heuristic.
25
Note, however, that secular movements can proceed in either a positive or negative direction; so the term is not necessarily associated with growth. The defining
characteristic involves the long-term nature of the movement and that the associated activity is not highly impacted by short-term trends.
26
Research work by Lo and MacKinslay (1988) and Bollerslev et al. (2001) looked at serial autocorrelation in thinly-traded markets which induced predictability of returns in
those markets.
27
Commenting on the Bolsa Mexicana de Valores (Mexico) and the National Stock Exchange (India) respectively, Prather-Kinsey (2006) and Theobald (2004) attribute the
persistence of these arbitrage opportunities to a lack of market integrity that restricts full market participation by non-insider institutions and investors not least by giving
insiders prior knowledge of event announcements before they are made.

25. By contrast, efficient pricing of stocks in the developed markets means that predictability is no longer possible and arbitrage opportunities are eliminated
on a timely basis. As a result, the heuristics governing size and sector appear to be vindicated in developed markets as evidenced in Table 2.
The ETF as the perfect tracker tool
According to some research28
about 50 percent of the performance of any individual stock comes from its sector—technology, healthcare, energy, etc.; and
within sectors much of their performance is determined by their nature as either cyclical or defensive. This means the ability to outperform, on the basis of
stock-picking, is reduced to 50 per cent in any successful trade. By merely investing in a sector ETF, for example, the successful trader has already captured
at least 50 per cent of any positive move that would result from a successful stock pick. Any attempt to successfully pick winners beyond this point to
outperform the sector average can only come at an added cost. Often times, it is difficult to justify this cost especially where the pick is not the winning one
among the sector pile.
Investing in an ETF that tracks the benchmark index or sector index enables a further cost reduction to our suggested strategy. ETFs act as a single security
that tracks an index and trades intraday enabling investors to buy and sell, in essence, at a go all the securities that make up the entire index with a single
trade. The added advantage is that, unlike index funds which by their nature trade only once per day, ETFs provide the flexibility to get in and out of a
position at any time throughout the day allowing the investor to take advantage of value buys when they appear. ETFs are therefore an added feature of
deep financial markets and their widespread use in developed markets adds to the price efficiency found here. They may in part also explain the success of
the ‘rules of thumb’ in these markets as identified in Table 2.
In sum, ETFs provide a convenient and low-cost way to implement indexing, or passive
management.
28
See CNBC (2012)

26. Smart beta
A recent market development- smart beta29
takes an index and refines it with the aim of improving
risk-return outcomes. Smart beta strategies have been used to identify other factors as
contributing to excess returns other than the traditional market-capitalisation weighted indices. To
this extent smart beta can be viewed as the antithesis of the above -mentioned ETFs as it looks
elsewhere, notably to rising dividends, minimised volatility, market momentum, value and quality
to deliver alpha.
Within the context of the heuristics under study here smart beta can be viewed as yet another
component of deep markets which further helps enhance pricing efficiency. This means prices of
financial assets correctly reflect their underlying investment risk and therefore size and sector
become key considerations when allocating money. It follows, therefore, that the heuristics would
hold true for these deep developed markets. (see Table 2)
Long-term themes
It is important for investors to identify secular trends in markets, not just short-term ones, in order to develop a long-term investment strategy. We
consider the following two secular trends and their possible impact on the heuristics under study here.
 Ageing populations in the West
Ageing of Western populations may mean defensive sectors like healthcare and/or pharmaceuticals will start to outperform well beyond the
traditional bear market phases. Sectors that traditionally outperform during bullish market conditions (consumer discretionary in the main) may
also witness a secular slump (the UK has seen a number of closures of clothing retail outlets although this may be a product of growing online
shopping than it is a structural shift) due to falling demand from an ageing population. Both these factors would delegitimise the heuristics under
study here, in the longer term, even in developed markets where hitherto they appear to hold validity.
29
According to a publication of The Review by CISI (2014) the smart beta concept has been around for decades having been pioneered by early researchers like Eugene
Fama and Ken French. The two found factors like size and value explained excess returns over long time frames. Later Mark Cahart found that momentum also explained
these excess returns.

27.  Digital revolution (Blockchain technology and the cryptocurrency space)
The massive disruption potential of blockchain
technology to all spheres of life from government,
industry, defence, law (‘smart contracts’) and finance
to name a few, means that the investment world may
now have a new asset class whose dynamics and
potential we have barely scratched the surface of.Given
the potential secular growth trend of blockchain technology and cryptocurrencies the returns observable within the heuristics under study here
may pale in significance to the exponential returns possible with blockchain technology and investment in cryptocurrencies.
Warren Buffet
A famous quote by investment guru Warren Buffet summarises our low-cost ‘buy-and-hold’ strategy.
"Long ago, Sir Isaac Newton gave us three laws of motion, which were the work of genius. But Sir Isaac’s talents didn’t extend to investing: He lost a bundle
in the South Sea Bubble, explaining later, ‘I can calculate the movement of the stars, but not the madness of men.’ If he had not been traumatized by this
loss, Sir Isaac might well have gone on to discover the Fourth Law of Motion: For investors as a whole, returns decrease as motion increases."
Source: Letters to shareholders, 2005
Simply put the more you trade, the more you underperform!

28. Caveats
Inadequate proxies
Where data on specific benchmark or composite indices was lacking (for example, on the Nigeria NSE-30
index), an appropriate constituent of the benchmark or composite index was taken as proxy. This was
provided it was a top constituent of that index in terms of market capitalisation and contained enough
back data to cover the period under study. However, this approach is not without drawback. Price
movements of a proxy firm will not wholly replicate those of the relevant benchmark. Idiosyncratic
factors may affect its performance in a material fashion away from that of the broader index. However,
care was taken here to select proxies that are significant blue-chip companies and that form a
substantial part of the size or sector total market capitalisation to better reflect the performance of the
benchmark.
Restricted diversification
The study covered only shares as an appropriate asset class. Of course, returns can be enhanced and better portfolio protection achieved by diversifying
across asset class in addition to that across region, size, sector or style. A study of the correlation between the traditional asset classes- shares, bonds, cash,
property and commodities in addition to that between alternative investments (notably private equity, derivatives, hedge funds) would serve to inform an
even more successful investment strategy to that under study here.
Hidden passive fund costs
Passive portfolios incur covert costs when a stock is added to the benchmark with a corresponding removal of another. When the addition of a stock to the
benchmark is announced the price of that particular stock goes up and the passive portfolio manager is forced to buy it at a higher price. Likewise, these
portfolios are forced to sell stocks that are removed from the index and realise their profit on the sale (if at all) only after the announcement and hence the
price fall. These costs are not readily apparent and may be ignored in comparing passive performance with that of the actively-managed funds.
Concentration risk
Some practitioners argue that inflows into passive funds merely in an attempt to hold the benchmark index leads to an overvaluation of the shares of some
if not all of the index constituents. This rise in the price of some or all of these index constituents may significantly surpass their intrinsic values and lead
later to a larger than anticipated correction. Further, the herding of passive investors all trying to buy the ‘new’ constituent into the index or sell the ‘old’
constituent leaving the index at the time of an index change may lead to an overshot of the stock price in the upward or downward direction respectively.
This phenomenon adds a further hidden cost to passive funds.

29. Diminished corporate governance
One of the strongest case for index investing being much cheaper than active investing is that it avoids the often substantial research and trading costs
involved in seeking alpha while taking advantage of the fair price established by the trading of these active managers. However, this lack of ‘activism’
impacts negatively on corporate governance in constituent firms of the index as the ability to discipline management through the power of large-scale
selling of underperformance or bad governance is diminished.
The last word
It is indeed difficult to identify the precise reason for the heuristics under study appearing to play out in developed markets but not so in the other two
markets, namely the emerging and frontier markets. However, pricing efficiency, greater breadth and depth, high levels of market integrity and high market
liquidity (volumes traded) all work together to ensure return in developed markets is perfectly matched to inherent asset risk. By contrast, in emerging and
more so frontier markets, these influencers are lacking and there is a persistent imperfect match between asset risk and return. Specifically, illiquidity and a
persistence of private information ensure that market participation is sub-optimal. The end product is that the heuristics under study here break down as is
evidenced in the results shown in Table 2.
To the extent that the heuristics appear to carry more validity in developed markets the adoption of a low cost ‘buy-and-hold strategy in these markets,
intertwined with tactical shifts that seek to take advantage of large-cap versus small-cap and cyclical versus defensive plays to bolster returns and/or
minimise downside risk, may very much prove invaluable.
By contrast the unworkability of these very same heuristics, in either the emerging or frontier markets, does not by itself entirely deride this strategy but
rather reveals that, given the study here with all its associated caveats, the strategy would need to be adopted with a whole lot deal of caution.
The world of investment analysis and portfolio management has indeed undergone tremendous change and upheaval since the formative years of Eugene
Fama (and his work) not least with the advent of sophisticated predictive models and algorithmic trading. However, Fama (1965) lays a solid foundation in
favour of passive ‘buy-and-hold’ strategies (now morphing into ETFs) which appear to continue to hold favour with many a successful fund manager and
investor.

30. Table 3: Bourse sector weightings
Developed markets Emerging markets Frontier markets
US UK China India Nigeria Kenya
Benchmark
index
S&P
Composite
1500
index (as
at Dec
2016)
% of
total
mkt.
cap.
FTSE All-
share index
(as at Dec
2016)
% of
total
mkt.
cap.
SSE-50
index (as
at Dec
2016)
% of
total
mkt.
cap.
NSE Nifty
500 index
(as at
Mar
2017)
% of
total
mkt.
cap.
Nigeria SE
All-share
index
(as at Dec
2016)
% of
total
mkt.
cap.
Nairobi
SE All-
share
index (as
at Dec.
15*)30
% of
total
mkt.
cap.
Cyclicals IT 20.3% Fin. 25.7% Fin.** 65.5% Fin. 31.5% Ind. 35.3% Fin. 38.8%
Fin. 15.1% CG 14.4% Ind. 16.3% CG 12.1% CG 27.0% Ind. 19.1%
CD 12.0% O&G 13.2% O&G 2.5% Ind. 11.7% Fin. 26.3% CSe. 2.6%
Ind. 10.9% CSe. 11.6% Auto. 1.9% Eng. 11.3% O&G 6.8% O&G 1.2%
Eng. 7.2% Ind. 10.7% Tran. 1.7% Auto. 9.5% RE 1.0% Agr. 0.6%
RE 3.6% BM 6.9% RE 1.7% IT 9.1% CSe. 1.0% Tran. 0.6%
Mat. 3.3% Tech. 0.8% Min. 1.4% Const. 4.3% Agr. 0.9% CG 0.2%
M&E 0.8% CSe. 1.9% Cong. 0.8% Auto. 0.1%
IT 0.6% M&E 1.1% NR 0.1%
TOTAL 72.4% TOTAL 83.3% TOTAL 92.4% TOTAL 92.5% TOTAL 99.2% TOTAL 63.2%
Defensives HC 13.1% HC 9.1% Tel. 2.8% HC 5.6% ICT 0.5% Tel. 32.4%
CSt. 8.8% Tel. 4.0% CSt. 2.7% Tel. 1.9% HC 0.3% Util. 4.4%
Util. 3.3% Util. 3.6% HC 1.3%
Tel. 2.4% Util. 0.8%
TOTAL 27.6% TOTAL 16.7% TOTAL 7.6% TOTAL 7.5% TOTAL 0.8% TOTAL 36.8%
Source: NYSE; LSE; SSE; NSE (India); Nigeria SE; Kenya NSE
30
Financials are represented by the Banking, Insurance, Investment and Investment Services sectors on the Kenya NSE

31. Key:
Agr.- Agriculture; Auto.- Automobiles; BM- Basic Materials; C&S- Commercial & Services; Cong.- Conglomerates; Const.- Construction; CD- Consumer
Discretionary; CG- Consumer Goods; CSe.- Consumer Services; CSt.- Consumer Staples; Eng- Energy; E&M- Energy & Petroleum; Fin.- Financials; HC-
Healthcare; ICT- Information & Communications Technology; Ind.- Industrials; IT- Information Technology; M&A- Manufacturing & Allied; Mat.- Materials;
M&E- Media & Entertainment; Min.- Mining; NR- Natural Resources; O&G- Oil & Gas; Pharm.- Pharmaceuticals; RE- Real Estate; Tech.- Technology; Tel.-
Telecommunications; Tran.- Transport; Util.- Utilities
*Market capitalisations as at December 2015 for Financials, Oil & Gas, Industrials and Consumer Services
**This includes Banks, Insurance and Investment

32. Resources
Banz, R.W (1981) The relationship between return and market value of common stocks. Journal of Financial Economics. 9(1), p.3-18
Chartered Institute for Securities & Investment (2014) World betas. The Review. London: Chartered Institute for Securities & Investment
Chartered Institute for Securities & Investment (2011) Portfolio Construction Theory. London: Chartered Institute for Securities & Investment
Chiang, T.C. and Doong, S (2001) Empirical analysis of stock returns and volatility: Evidence from seven Asian stock markets based on TAR-GARCH model.
Review of Quantitative Finance and Accounting. 17, p.301-318
CNBC (2012) The Importance of Secular Vs. Cyclical [online] New Jersey: CNBC. Available from: https://www.cnbc.com/id/47266526
Conover, C.M., Jensen, G.R, Johnson, R.R and Mercer, J.M (2008) Sector rotation and monetary conditions. Journal of Investing. 17(1), p.34-46
Damodaran, A. (2002) Investment Valuation: Tools and techniques for determining the value of any asset. 2nd ed. John Wiley and Sons
Fama, E.F. (1965) Random walks in stock market prices. Financial Analysis Journal. 21(5), p.55-59
Fidelity Investments (2014) The Business Cycle Approach to Equity Sector Investing. Boston: Fidelity Investments
Jefferis, K, and Smith, G. (2004) Capitalisation and weak-form efficiency in the JSE Securities Exchange. South African Journal of Economics. 72(4), p.684-
707
Reilly, F.K. and Brown, K.C. (2011) Investment Analysis and Portfolio Management. Tenth Edition. The Dryden Press
Sassetti, P. and Tani, M. (2006) Dynamic Asset Allocation. Using Systematic Sector Rotation. The Journal of Wealth Management. 8(4), p.59-70
Stangl, J., Jacobsen, B. and Visaltanachoti, N. (2009) Sector rotation over business cycles. Massey University.

33. Disclaimer.
The views contained here are those of the author and do not constitute investment (or any other) advice. Any research or analysis used in the preparation of the information
has been procured by ourselves for our own internal use and purpose.
Charts used in this article are for illustrative purposes only and must not be relied upon to make investment decisions.
Past performance is not a guide to future performance. The value of investments, and the income from them, can go up as well as go down and the investor may get back
less than the amount invested.
This is a promotional document and as such the views contained herein are not to be taken as advice or recommendation to buy or sell any investment or interest thereto.
Reliance upon information in this material is at the sole discretion of the reader. Any research in this document has been obtained and may have been acted upon by PK
Mwangi Global Consulting for its own purpose. Any forecasts, figures, opinions, statements of financial market trends or investment techniques and strategies expressed
are, unless otherwise stated, those of PK Mwangi Global Consulting.
Where recourse has been had to academic research this has been appropriately referenced under the ‘Resources’ section.
September 2017

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