Discussion Papers In Economics
And BusinessHistorical Relationships and International Market Return Predictability:
The Role of the UK in the Former British Colonies, Protectorates and Mandates

Takuro Hidaka, Yuta Saito, Jun Sakamoto

Discussion Paper 21-08-Rev.

October 2023

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANHistorical Relationships and International Market Return Predictability: The Role of
the UK in the Former British Colonies, Protectorates and Mandates *Takuro Hidaka †, Yuta Saito ‡, Jun Sakamoto §Abstract
This study demonstrates that lagged UK stock returns predict stock returns in emerging markets
that were previously colonies, protectorates, and mandates (CPMs). First, we evaluate thepredictive power of lagged market returns from various advanced countries for the former
CPMs. The results show that the UK ’s lagged market returns have the highest predictive power.
We then examine the performance of investment strategies that use the returns of advanced
countries as predictors. We find that a strategy that uses UK returns significantly outperforms
those that use returns from other advanced countries as predictors. We also analyze a model that
includes a dummy variable for recessions. The results show that the strategy using UK returns
is the best for terminal wealth, while the strategy using U.S. returns is the best for the Sharp
ratio. These findings suggest that lagged UK returns have strong predictive power for former
CPMs in normal times, while lagged U.S. returns are better predictors during recessions.
Keywords: Return predictability, Emerging market, Investment strategy
JEL Codes: G11, G15, G17*

We are indebted to Chiaki Yamamoto, Yuichi Fukuta, Kenta Kudo, seminar participants at Osaka Gakuin
University and Tokyo Keizai University for their helpful comments and acknowledge the support of the
JSPS KAKENHI No 20H01515.
† Department of Economics, Rikkyo University, 3-34-1, Nishi Ikebukuro, Toshima-ku, Tokyo 171-8501,
Japan.
‡ Faculty of Economics and Business, Hokkaido University, Kita 9, Nishi 7, Kita-ku, Sapporo, 060-0809,
Hokkaido, Japan.
§ Department of Economics, Osaka Gakuin University, 2-36-1 Kishibeminami, Suita, Osaka 564-8511,
Japan.

1. Introduction
The predictability of stock returns is of interest to economists. Financial and macroeconomic variables have been the primary predictors in the literature, while another set ofstudies shows that the lagged market returns of advanced countries are good predictorsof returns in other countries. These studies tend to emphasize that lagged U.S. returns aregood predictors of returns in other countries, consistent with the leading role of the U.S.
in international stock markets.
These results are consistent with Rizova (2010), theoretical analysis that shows thatthe market return in one country can predict that of its economic partner using a twocountry Lucas tree model with gradual information diffusion. Since the U.S. stock market
is the largest in the world, investors may focus more on it than on others. Consequently,
global market-relevant information gradually diffuses from the U.S. to other countries.
This explanation also implies that information related to stock markets spills over fromone country to another if they have close socioeconomic ties; that is, investors in one
country are more likely to focus on the market of another country if these two countries
are economically or culturally linked.
This study contributes to the literature on the predictability of international market returns by examining the role of UK markets in emerging markets, with particular focus ondeveloping countries heavily influenced by the UK: former colonies, protectorates, andmandates (CPMs) of the British Empire or the UK. We evaluate the lagged stock market
returns of the UK as a predictor of the market returns of former CPMs. For comparison,
we also consider the predictive power of other advanced countries: the U.S., France, Germany, Japan, China, and the European Union (EU) excluding the UK. The list includes
Egypt, India, Malaysia, Pakistan, Qatar, South Africa, the United Arab Emirates (UAE),
Bahrain, Jordan, Kenya, Kuwait, Mauritius, Nigeria, Oman, and Sri Lanka.
We consider two models for the predictive regression: a baseline model and a modelwith a recession dummy variable. Using the results of these predictive regression analy2
ses, we analyze these in two ways. First, we use ROS; that is, the out-of-sample R2 statistic

studied by Campbell and Thompson (2008). Second, based on the results of the predictive
regression, we examine the performance of the investment strategy using short-term op1

timal investment vectors. We employed terminal wealth (TW) and the Sharpe ratio (SR)as performance measures.
2
In the analysis of ROS, the baseline model shows that the lagged returns of the UK isthe highest predictor, followed by those of the EU. Contrastingly, a model with a recession
dummy shows that all predictors have negative values, and the highest among them is the
EU. The results of the performance evaluations show that the investment strategy in thebaseline model with lagged UK returns as predictors significantly outperforms the otherinvestment strategies in terms of both TW and SR. The results of the recession dummy
model show that the UK strategy has the highest TW, while the U.S. strategy has the best
SR.
These results have key implications. First, lagged UK returns have strong predictive
power for CPMs, especially in periods when the economy is not in a recession. Second,
during recessions, lagged U.S. returns have strong predictive power for former CPMs.
This finding is consistent with previous studies (e.g., Rapach et al. 2013), which showsthat the predictive power of U.S. market returns becomes stronger during recessions. Our
study contributes to the literature by demonstrating the importance of historical socioeconomic relationships in explaining variations in returns.
1.1. Literature Review
Empirical finance research has identified numerous predictors of future market returns. Much of the literature focuses on financial and macroeconomic variables (e.g., Ang
and Bekaert 2007, Black et al. 2014 , Jacobsen et al. 2019, Campbell and Thompson 2008,
Welch and Goyal 2008, Devpura et al. 2018, Wang et al. 2019). A branch of the literature(such as Appiah-Kusi and Menyah 2003, Bannigidadmath and Narayan 2016, and Sharma
et al. 2019) focuses on developing countries’ markets..
This study relates to the literature on predicting stock returns across different countries. Previous studies demonstrate that stock returns in the global market can predict
stock returns in emerging markets (e.g., Harvey 1995, and Rahman et al. 2017). Masih and
Masih (1999) provide evidence of dynamic linkages between leading and emerging Asianstock markets.

2

Several existing studies show the strong predictive power of U.S. market returns forother markets. Rapach et al. (2013) show that (i) lagged U.S. stock returns can predict
stock returns in other markets, and (ii) their predictive power increases during recessions.
Siliverstovs (2017) and Wen and Li (2020), support the findings of Rapach et al. (2013).
Wen et al. (2015) examine the predictability of stock returns in the South African market
using lagged returns from the U.S., the UK, Germany, and Japan from 1973 to 2014. Theyshow that lagged U.S. market returns were the best predictor of the South African market
from 1973 to 1996, while the predictive power declined after 1996. Contrastingly, thisstudy provides evidence that UK stock returns play a significant role in former CPMs.
Finally, this study is also related to studies that examine the impact of cultural distance on stock markets. Flavin et al. (2002) examine the influence of geographic variables,
including common language, on international stock market price correlations and findthat overlapping borders and business hours positively affect correlations. Portes andRey (2005) analyzed the determinants of international stock flows and showed that capital markets are fragmented by information asymmetries and familiarity effects and thatthe geographic location of information affects flows. Lucey and Zhang (2010) find thatthe smaller the cultural distance between two countries, the greater the stock market covariance. The primary method of examining the impact of cultural distance is to include
a variable indicating cultural distance in the model.2 In contrast to this literature, which
examined contemporaneous correlations, this study examines the predictability of future
market returns.
1.2. Historical Background
Many emerging markets are located in countries that once had close political ties with
Western nations as CPMs. Economists have studied the impact of such historical experiences on contemporary economic conditions (e.g., Acemoglu et al. 2001). Britain is one
such country that has exercised extensive political control and their economic influenceon others. Britain established economic ties with dependent territories through economic

2

However, Tihanyi et al. (2005) argues that the concept of cultural distance is ambiguous and there is noconsensus on which variables should be included in the model.

3policies and corporate activities. For economic gain, Britain controlled the trade and currencies of dependent countries and invested in them (Brown and Louis 1999; Hopkins
1993). British capital engaged in a wide range of activities in the territories it controlled
such as the acquisition of natural resources, trade, railroads, and mining.
British rule expanded in the 19th century and declined after World War II. After thewar, British colonies gradually gained independence (e.g., India, Pakistan, Ceylon, andBurma in the 1940s, Nigeria and Kenya in the 1960s). To establish a national identity, thegovernments of these newly independent countries restricted foreign investment, particularly in natural resources and public utilities. Nevertheless, links between the former
colonies and the UK continued through trade, investment, and business activities. Companies, such as the United Africa Company and Lonrho, continued to operate extensivelyin Africa after World War II (Jones 2000).
While previous studies have shown economic links between the UK and dominant
countries, the impact of this historical experience on the current stock market remains unclear. The relevance of global stock markets has been studied previously (Arshanapalli
and Doukas 1993), and a link between price movements in developed and emerging markets has been identified (Wong et al. 2004). However, the relationship between the market
returns of formerly colonized and developed countries has not been well studied.
2. Data and Methodologies
2.1. Data
We collected our market return data from the MSCI (Morgan Stanley Capital International). The predictor countries were the UK, the U.S., France, Germany, Japan, China, andthe EU (excluding the UK). The former CPMs are Egypt, India, Malaysia, Pakistan, Qatar,
South Africa, the UAE, Bahrain, Jordan, Kenya, Kuwait, Mauritius, Nigeria, Oman, andSri Lanka. The sample period is from January 2006 to February 2021. We use the risk-freerate obtained from the K. French’s website, specifically, the one-month T-bill rate. OECD
Composite Leading Index (CLI) as an indicator of recession.
Table 1 presents the summary statistics of excess market returns. The mean excess
market returns vary across countries, with the highest being 0.011 (China) and the lowest
4

being -0.008 (Jordan). The standard deviations varied from 0.043 (Japan) to 0.096 (Nigeria).
Table 2 shows the correlation matrices of excess market returns.
2.2. Methodology
We run the following two predictive regression analyses for all former CPMs and predicting countries:p
rCP M,t =α + βrOther,t−1+ εt ,(1)

p
rCP M,t =α + βrOther,t−1+ λDReccesion,t−1 × rOther,t−1 + εt ,(2)pin which rCP M,t is the excess market return of a former CPM, rOther,t−1
is the lagged

excess market return of an advanced country that is used as a predictor, DRecession,t−1 is adummy variable that takes 1 if the value of the CLI is below the time-series average and 0
otherwise, and εt is the error term.
Eq. (1) is our baseline predictive regression model with lagged returns. Eq. (2) isanother model with a dummy variable that controls for the predictive effect of a recession.
The latter equation is considered because it is well studied in the literature that lagged
U.S. market returns have stronger predictive power during recessions (e.g., Rapach et al.
(2013), Siliverstovs (2017), and Wen and Li (2020)). If there is a similar phenomenon exists
between the excess returns of the UK and the former CPMs, the predictive power of Eq.
(2) should be greater than those in Eq. (1). The initial forecasting window was set at 120
months and the regression window was expanded thereafter.
2.2.1. Validation of out-of-sample predictive power
2
Following Cambell and Thompson (2008), we calculate ROS, the out-of-sample R2
2
statistics for each predictor country and for predictive regression analyses (1) and (2). ROS

is computed as follows:∑T(rCP M,k − r̂CP M,k )2
2
ROS = 1 − ∑Tk=1212
k=121 (rCP M,k − r̄CP M,k )5(3)

in which (r̂CP M,k ) is the fitted value from the predictive regression estimated throughperiod k − 1, and r̄CP M,k is the historical average excess returns estimated through periodk − 1. If the independent variable used in the regression analysis has useful information
for future casting, the error from the realized value will be smaller r̂CP M,k than r̄CP M,k .
2
Thus, ROS
is positive.If historical relationships have led to the UK market having highly
2
predictive power for the returns on former CPMs, we expect a high ROS
when the UK isthe predicting country.
2.2.2. Investment Performance Evaluation
Given the results of the predictive regression analyses, we examine the performance ofinvestment strategies that use the market returns of advanced countries as predictors ofthe market returns of former CPMs. Consider the short-term optimal investments of individuals with a constant relative risk-averse utility function. Suppose the returns follow a
lognormal distribution. The optimal short-term portfolio weights are as follows:3

αt =

1 −1
Σ (Ert+1 − rf,t+1 ι + σt2 /2),
γ t

(4)

in which αt is the vector of the optimal weight, γ is the relative risk aversion, Σt is thevariance-covariance matrix of returns, Ert is the vector of expected returns, rf,t+1 is therisk free rate, ι is the vector with all elements equal to 1, and σ is the variance of the
returns (i.e., the diagonal components of Σ).
We set the risk aversion to 5 (γ = 5). We approximate Ert+1 − rf,t+1 ι with the predictedvalues of the regressions, and Σt is the variance-covariance matrices of the error term of
the predictive regression. The initial assets at the time of investment strategy execution
are assumed to be 1. To evaluate investment performance, we consider the following
common measures: the TW and the SR.
In our analysis, we first randomly select n = {4, 5, 6, 7} countries from the 15 CPMs
that comprise the portfolio. Then, we implement the investment strategy in Eq. (4) to

3

The details of this optimal investment vector are explained in Chapter 2 of Campbell and Viceira 2002.

6

obtain wealth in the final period T , that is TW, and the SR of the monthly returns. This
procedure was repeated 1000 times to obtain the distributions of TW and SR for eachpredictor. Distributions were used to test for differences in the means. For comparison,
we consider a buy-and-hold strategy in which the portfolio comprises equally weighted
market returns for the n countries extracted.
3. Results
3.1. Baseline Model
3.1.1. Predictive Regressions
Table 3 presents the results of the baseline predictive regression. The last column in the2bottom row shows the mean of ROS
for each advanced country. The UK has the highest at

1%, implying that UK lagged returns have the strongest predictive power for the returnsof the overall CPMs. The results also showed some cross-country heterogeneity. For someformer CPMs – Bahrain, Jordan, Kuwait, Mauritius, Nigeria and Sri Lanka – the estimated2
is positive for all predictors. For the returns of these former CPMs, each withvalue of ROS

advanced market returns could have predictive power. By contrast, for some other former
CPMs, such as Egypt, Malaysia, Pakistan, Qatar, South Africa, the UAE, and Kenya, theestimated value was negative for all predictors. The results imply that for the returns ofthese former CPMs, each of the advanced market returns did not have strong predictive
power.
3.1.2. Terminal Wealth
We now examine the performance of investment strategies using developed market returns as predictors of CPMs. We call the strategy that uses advanced country i as predictorthe ”i strategy.”
Table 4 lists the TW for each investment strategy. E[wi ] is the mean of TW with the istrategy. The ”t-stat of E[wU K ] − E[wi ]” shows the t-test statistic of the difference of theTWs between the UK and i strategies.
The TW of the UK strategy is significantly higher than any other predictors regardlessof the number of assets comprising the portfolio; that is, n. For instance, when n = 4,7

the UK strategy is 36% higher than the second-best strategy (Germany). This is consistent
with any other n, and all differences from the other strategies are significant (p < .01).
These result imply that the UK strategy significantly outperformed the other strategies interms of TW.
Of the investment strategies, the TW of the buy-hold strategy was the lowest, witha net return of approximately 17%. Among the strategies with a predictor, the China
strategy has the lowest TW, with a net return of approximately 20 to 30%. In addition, forany predictor, TW increased as n increased. This could be because a higher n implies that
more assets are available for portfolio composition, allowing for the selection of assets
with higher performance in the predictive regressions.
3.1.3. Sharp Ratio
While it is a common measure of investment strategies, evaluation with TW does notassess the risk in investments. To assess the strategies that consider risks, we use theSR of monthly returns as a risk-adjusted performance measure. Table 5 presents the estimated SR for each investment strategy. E[SRi ] is the mean SR of i strategy. ”t-stat of
E[SRU K ] − E[SRi ]” represents the t-value of the difference of the SR between UK strategy
and i strategy. Overall, the results are similar to those obtained using TW. The SR of the
UK strategy was significantly higher than that of any other predictors for all n, and alldifferences from the other strategies were significant (p < .01). Therefore, the UK strategy
significantly outperformed the other strategies, even in terms of SR.
Similar to the analysis, the buy-hold strategy was the lowest, and China strategy was
the worst predictor. In addition, the SR values generally increased as n increased. A key
difference between the results of TW and SR is the second-best strategy: Germany in termsof the TW and the U.S. in terms of SR. This might be owing to the lower return volatilityof the U.S. strategy relative to that of German strategy.
3.2. Model with the Recession Dummy
3.2.1. Predictive Regressions
Table 6 lists the results of predictability with the recession dummy. The column ”Mean”
2
displays the mean of ROS
for each advanced country. Unlike the results obtained for the8

2baseline regressions, the mean of ROS
for each advanced country is negative, indicatingthat none of the advanced market returns have any predictive power for the returns of theformer CPMs. Although there are some positive values, that is, UK returns for Egyptian
returns, the overall results suggest that in the sample of this study, including the recession
dummies does not increase the powers of predictors.
3.2.2. Terminal Wealth
Table 7 lists the TW for each investment strategy. Similar to the results of the baselinemodel, the TW of the UK strategy is significantly higher than that of the other strategies
for all n. For example, in the case of n = 4, the UK strategy is approximately 20% higher
than the second-best strategy (U.S.). All the differences from the other strategies weresignificant (p < .01).
Compared to the results of the baseline model, TW is higher for all investment strategies with a predictor. In particular, the TW of the U.S. strategy is much higher than that of
the baseline case, which is consistent with the findings in the literature (e.g., Rapach et al.
(2013)) that the predictive power of U.S. returns becomes stronger during recessions.
3.2.3. Sharp Ratio
Table 8 lists the SR of each investment strategy. In contrast to the baseline model, theU.S. strategy is the best, and the difference in means between the U.S. and UK strategies issignificant (p < .01) for any n. The performance of the UK strategy is significantly lower
than that of the strategies other than the U.S. strategy (e.g., the Japan strategy outperforms
the UK strategy for all n). Compared to the estimated SR of the baseline model, the performance of the UK strategy is virtually unchanged, whereas the strategies with most otherpredictors significantly increases with the recession dummies. This finding suggests thatthe predictive power of the UK strategy is constant, regardless of economic conditions(whether it is in a recession or not). Thus, the results imply that the UK market has predictive power through a mechanism different from that of the rest of the leading markets:
historical socioeconomic influence on the former CPMs.

9

4. Conclusion
This study contributes to the literature on the predictability of market returns by demonstrating the importance of historical ties between countries in explaining the predictabilityof market returns in emerging markets. We examine the predictive power of the lagged
market returns of the UK and other advanced countries for the market returns of the former British CPMs.
We find that the market returns of former CPMs can be significantly predicted bylagged UK returns relative to those of other leading markets, including the U.S. We also
show that an investment strategy using lagged UK returns as a predictor yields the highest TW and SR. We also examine a model with recession dummies and show that the UK
strategy yields the highest TW, while the U.S. strategy yields the highest SR.
The current empirical results have several implications. First, lagged UK market returns have high predictive power for the returns of former CPMs in normal times. Second, the investment performance of the UK strategy does not improve when the effects
of recessions are considered, suggesting that the predictive power of UK market returns
is owing to a different mechanism (i.e., a historical relationship) compared to other leading markets. Third, the investment performance of the U.S. strategy improves when theeffects of recessions are considered. This finding is consistent with the leading role of
the U.S. in the international financial market and with previous empirical studies (e.g.,
Rapach et al. 2013).
For future work, analyzing the financial market linkages between former sovereigns(including countries other than the UK) and their colonies could shed light on the role ofhistorical relationships in the current international financial market.
Author statement
Takuro Hidaka: Conceptualization, Methodology, Formal analysis, Investigation, Data
Curation, Writing - original draft, Writing - review & editing, Project administration.
Yuta Saito: Conceptualization, Methodology, Formal analysis, Investigation, Writing original draft, Writing - review & editing, Project administration.
Jun Sakamoto: Conceptualization, Methodology, Software, Validation, Formal analysis,
10Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration, Funding acquisition.
Declaration of competing interest
The authors state that there are no competing interests to declare.

11

Table 1: Summary Statistics of Excess Market Return

UK
USA
FRANCEGERMANYJAPAN
CHINA
EU.ex.UK
EGYPT
INDIA
MALAYSIAPAKISTAN
QATAR
SOUTH.AFRICA
UAE
BAHRAIN
JORDANKENYAKUWAIT
MAURITIUS
NIGERIAOMAN
SRI.LANKA

N Mean Std,dev Max
182 0.003
0.052 0.168
182 0.008
0.044 0.131
182 0.005
0.062 0.229
182 0.006
0.066 0.171
182 0.003
0.043 0.125
182 0.011
0.072 0.196
182 0.003
0.058 0.170
182 0.004
0.092 0.289
182 0.009
0.081 0.367
182 0.005
0.049 0.161
182 0.002
0.086 0.234
182 0.004
0.072 0.232
182 0.005
0.075 0.178
182 0.000
0.088 0.252
182 -0.007
0.067 0.215
182 -0.008
0.058 0.150
182 0.010
0.072 0.241
182 0.002
0.062 0.210
182 0.007
0.070 0.244
182 0.003
0.096 0.474
182 0.001
0.053 0.143
182 0.005
0.087 0.601

Min-0.190-0.172-0.225-0.230-0.149-0.228-0.224-0.335-0.286-0.176-0.500-0.266-0.263-0.334-0.277-0.369-0.288-0.229-0.378-0.413-0.299-0.266

The table reports summary statistics of excess market
returns in the advanced and former CPM countries. The
sample period is from January 2006 to February 2021.

12

Table 2: Correlation Matrix of Excess Market Returns

UK
USA
UK
1.000
0.855
USA
0.855
1.000
FRANCE
0.910
0.842
GERMANY
0.875
0.861
JAPAN
0.743
0.741
CHINA
0.663
0.608
EU.ex.UK
0.917
0.865
EGYPT
0.490
0.454
INDIA
0.651
0.651
MALAYSIA
0.652
0.589
PAKISTAN
0.396
0.354
QATAR
0.456
0.445
SOUTH.AFRICA
0.737
0.691
UAE
0.548
0.514
BAHRAIN
0.388
0.418
JORDAN
0.166
0.158
KENYA
0.470
0.503
KUWAIT
0.485
0.478
MAURITIUS
0.542
0.484
NIGERIA
0.383
0.290
OMAN
0.491
0.462
SRI.LANKA
0.431
0.347
QATAR SOUTH.AFRICA
UK
0.456
0.737
USA
0.445
0.691
FRANCE
0.439
0.715
GERMANY
0.472
0.729
JAPAN
0.407
0.637
CHINA
0.429
0.699
EU.ex.UK
0.468
0.739
EGYPT
0.505
0.539
INDIA
0.458
0.700
MALAYSIA
0.397
0.662
PAKISTAN
0.160
0.291
QATAR
1.000
0.491
SOUTH.AFRICA
0.491
1.000
UAE
0.678
0.441
BAHRAIN
0.384
0.298
JORDAN
0.344
0.130
KENYA
0.277
0.458
KUWAIT
0.447
0.392
MAURITIUS
0.423
0.472
NIGERIA
0.281
0.210
OMAN
0.490
0.374
SRI.LANKA
0.154
0.314

FRANCE GERMANY
0.910
0.875
0.842
0.861
1.000
0.943
0.943
1.000
0.721
0.741
0.618
0.657
0.981
0.966
0.498
0.541
0.655
0.695
0.631
0.663
0.301
0.318
0.439
0.472
0.715
0.729
0.516
0.504
0.363
0.340
0.108
0.159
0.496
0.514
0.436
0.434
0.537
0.538
0.348
0.341
0.459
0.439
0.341
0.301
UAE
BAHRAIN
0.548
0.388
0.514
0.418
0.516
0.363
0.504
0.340
0.373
0.274
0.393
0.294
0.529
0.366
0.515
0.399
0.501
0.233
0.406
0.231
0.397
0.282
0.678
0.384
0.441
0.298
1.000
0.510
0.510
1.000
0.334
0.216
0.297
0.306
0.530
0.586
0.435
0.375
0.411
0.336
0.586
0.453
0.378
0.163

JAPAN
0.743
0.741
0.721
0.741
1.000
0.557
0.753
0.470
0.566
0.514
0.203
0.407
0.637
0.373
0.274
0.129
0.444
0.387
0.427
0.286
0.433
0.328
JORDAN
0.166
0.158
0.108
0.159
0.129
0.109
0.156
0.261
0.200
0.120
0.070
0.344
0.130
0.334
0.216
1.000
0.147
0.193
0.214
0.243
0.220
0.162

CHINA EU.ex.UK
0.663
0.917
0.608
0.865
0.618
0.981
0.657
0.966
0.557
0.753
1.000
0.662
0.662
1.000
0.468
0.536
0.658
0.696
0.597
0.674
0.229
0.318
0.429
0.468
0.699
0.739
0.393
0.529
0.294
0.366
0.109
0.156
0.459
0.514
0.320
0.462
0.374
0.553
0.283
0.344
0.358
0.466
0.294
0.345
KENYA KUWAIT
0.470
0.485
0.503
0.478
0.496
0.436
0.514
0.434
0.444
0.387
0.459
0.320
0.514
0.462
0.400
0.363
0.372
0.385
0.352
0.330
0.153
0.377
0.277
0.447
0.458
0.392
0.297
0.530
0.306
0.586
0.147
0.193
1.000
0.321
0.321
1.000
0.415
0.457
0.242
0.345
0.371
0.519
0.264
0.327

EGYPT
0.490
0.454
0.498
0.541
0.470
0.468
0.536
1.000
0.557
0.471
0.288
0.505
0.539
0.515
0.399
0.261
0.400
0.363
0.407
0.304
0.466
0.263
MAURITIUS
0.542
0.484
0.537
0.538
0.427
0.374
0.553
0.407
0.478
0.441
0.270
0.423
0.472
0.435
0.375
0.214
0.415
0.457
1.000
0.308
0.499
0.426

INDIA
MALAYSIA PAKISTAN
0.651
0.652
0.396
0.651
0.589
0.354
0.655
0.631
0.301
0.695
0.663
0.318
0.566
0.514
0.203
0.658
0.597
0.229
0.696
0.674
0.318
0.557
0.471
0.288
1.000
0.646
0.274
0.646
1.000
0.303
0.274
0.303
1.000
0.458
0.397
0.160
0.700
0.662
0.291
0.501
0.406
0.397
0.233
0.231
0.282
0.200
0.120
0.070
0.372
0.352
0.153
0.385
0.330
0.377
0.478
0.441
0.270
0.321
0.304
0.299
0.411
0.384
0.376
0.448
0.293
0.290
NIGERIAOMAN
SRI.LANKA
0.383
0.491
0.431
0.290
0.462
0.347
0.348
0.459
0.341
0.341
0.439
0.301
0.286
0.433
0.328
0.283
0.358
0.294
0.344
0.466
0.345
0.304
0.466
0.263
0.321
0.411
0.448
0.304
0.384
0.293
0.299
0.376
0.290
0.281
0.490
0.154
0.210
0.374
0.314
0.411
0.586
0.378
0.336
0.453
0.163
0.243
0.220
0.162
0.242
0.371
0.264
0.345
0.519
0.327
0.308
0.499
0.426
1.000
0.349
0.320
0.349
1.000
0.337
0.320
0.337
1.000

This table reports correlation matrix of excess market returns in the advanced and former CPM countries. The sample period is from January 2006 to February 2021.

13

2
Table 3: ROS: Baseline Model

EGYPT
INDIA MALAYSIA PAKISTANQATAR SOUTH.AFRICA
UAE
BAHRAIN-0.0030.003-0.010-0.016-0.045-0.013-0.0670.042-0.008-0.017-0.010-0.021-0.048-0.013-0.1140.041-0.015-0.010-0.009-0.009-0.042-0.009-0.0810.030-0.015-0.006-0.017-0.009-0.038-0.009-0.0950.021-0.020-0.006-0.032-0.013-0.024-0.012-0.0930.006-0.001-0.030-0.029-0.002-0.023-0.019-0.0420.008-0.011-0.002-0.010-0.008-0.048-0.009-0.0830.038
JORDAN KENYAKUWAIT
MAURITIUS NIGERIAOMANSRI.LANKA
M ean
UK
0.053
-0.004
0.030
0.060
0.045
-0.012
0.089
0.010
USA
0.005
-0.026
0.037
0.047
0.014
0.036
0.037
-0.003
FRANCE
0.014-0.021
0.016
0.018
0.016
-0.0030.065-0.003
GERMANY
0.045-0.025
0.018
0.017
0.024
-0.0150.083-0.001
JAPAN
0.026-0.028
0.005
0.026
0.057
-0.0300.043-0.006
CHINA-0.022-0.000-0.003-0.0170.010-0.0050.004-0.011EU.ex.UK
0.031-0.022
0.023
0.024
0.027
-0.003
0.084
0.002
UK
USA
FRANCEGERMANYJAPAN
CHINA
EU.ex.UK

2
2This table reports ROS
using equation (1) for the forecast model. ROS
is calculated for each former CPM. M ean in the lastcolumn of the bottom row shows the arithmetic mean. The sample period is from January 2006 to February 2021.

Table 4: Terminal Wealth: Baseline Model
wU S
wF R
wGE
wJP
wCH
wEU.ex.U K
n=4
2.214 1.787
1.683 1.857 1.792 1.192
1.856
NA9.796 11.891 7.046 8.331 25.564
7.512
n=5
2.468 1.919
1.819 2.073 1.966 1.234
2.045
NA 11.081 12.817 6.759 8.704 27.038
7.78
n=62.703 2.032
1.938 2.281 2.143 1.271
2.225
NA 12.702 14.178 6.668 9.018 29.261
8.169
n=7
2.939 2.139
2.063 2.517 2.301 1.317
2.419
NA 14.186 15.237 6.176 9.728 30.912
8.291
wU K

E[wj ]
t-stat of E[wU K ] − E[wj ]E[wj ]
t-stat of E[wU K ] − E[wj ]E[wj ]
t-stat of E[wU K ] − E[wj ]E[wj ]
t-stat of E[wU K ] − E[wj ]wbuy−hold1.169
27.202
1.174
29.872
1.172
33.1991.172
35.954This table reports the mean (E[wj ]) for the distribution of generated TW (Terminal Wealth).
The t-stat of E[wU K]−E[wj ] shows the t-test statistic of the difference between the mean of the
UK strategy and the other strategies. The calculation of the TW is as follows (1): Calculate
the expected value of the excess market returns and the variance-covariance matrix of therandomly selected CPMs from the regression results of Eq. (1). (2): Using the results of(1), calculate the TW when the investment strategy is implemented based on Eq. (4). (3):Repeat this process 1000 times to generate the distribution of the TW. The sample period of
the predictive regressions are recursively extended. The sample period of the first predictive
regression is 120 months. All sample periods used for forecasting and evaluation are fromJanuary 2006 to February 2021.

14

E[SRj ]t-stat of E[SRU K ] − E[SRj ]E[SRj ]
t-stat of E[SRU K ] − E[SRj ]E[SRj ]
t-stat of E[SRU K ] − E[SRj ]E[SRj ]
t-stat of E[SRU K ] − E[SRj ]Table 5: Sharp Ratio: Baseline ModelSRU K SRU S SRF R SRGE SRJP SRCH
n=40.138 0.129 0.107 0.105 0.111 0.055
NA
2.802
8.24
8.687 6.217 19.556
n=50.149 0.135 0.115 0.115 0.122 0.061
NA
4.296 9.209 8.951 6.222 20.489
n=6
0.16
0.14
0.123 0.127 0.134 0.065
NA
6.228 10.573 9.184 6.289 22.383
n=70.167 0.144 0.129 0.136 0.142
0.07
NA
7.575 11.423 8.784 6.441 23.35

SREU.ex.U K

SRbuy−hold

0.123
4.157

0.056
26.482

0.131
4.745

0.059
30.782

0.141
5.28

0.06
37.534

0.149
5.308

0.061
42.449This table reports the mean (E[SRj ]) for the distribution of generated SR (Sharp Ratio). The t-stat of
E[SRU K] − E[SRj ] shows the t-test statistic of the difference between the mean of the UK strategy
and the other strategies. The calculation of the SR is as follows (1): Calculate the expected valueof the excess market returns and the variance-covariance matrix of the randomly selected former
CPMs from the regression results of Eq. (1). (2): Using the results of (1), calculate the SR when theinvestment strategy is implemented based on Eq. (4). (3): Repeat this process 1000 times to generate
the distribution of the SR. The sample period of the predictive regressions are recursively extended.
The sample period of the first predictive regression is 120 months. All sample periods used forforecasting and evaluation are from January 2006 to February 2021.

2: Recession Dummy
Table 6: ROS

EGYPTINDIA MALAYSIA PAKISTANQATAR SOUTH.AFRICA
UAE
BAHRAIN
0.004-0.011-0.036-0.018-0.102-0.028-0.2770.031-0.019-0.013-0.041-0.037-0.078-0.026-0.238-0.020-0.031-0.007-0.022-0.011-0.038-0.020-0.2100.033-0.024-0.026-0.029-0.012-0.031-0.028-0.1840.015-0.0480.027-0.075-0.011-0.058-0.020-0.2390.000-0.020-0.031-0.047-0.005-0.063-0.043-0.1120.026-0.021-0.008-0.027-0.010-0.045-0.025-0.181
0.042
JORDAN KENYAKUWAIT
MAURITIUS NIGERIAOMANSRI.LANKA
M ean
UK
0.048
-0.040
0.020
0.054
0.010
-0.0300.017-0.024USA
0.001-0.0500.016
0.056-0.005-0.0040.045-0.028FRANCE
0.010-0.0490.021-0.0130.008-0.023-0.017-0.025
GERMANY
0.041-0.0500.033-0.0000.020-0.0340.034-0.018JAPAN
0.015-0.106
0.030
0.022
0.063
-0.0620.031-0.029CHINA-0.051-0.068-0.010-0.0400.009-0.008-0.032-0.033EU.ex.UK
0.029-0.041
0.030
0.007
0.023
-0.0170.037-0.014UK
USA
FRANCEGERMANYJAPAN
CHINA
EU.ex.UK

2
2This table reports ROS
using equation (2) for the forecast model. The ROS
is calculated for each CPM country, and the M ean
in the last column of the bottom row shows the arithmetic mean. The sample period is January 2006-February 2021.

15

Table 7: Terminal Wealth: Recession Dummy
wU S
wF R
wGE
wJP
wCH
wEU.ex.U K
n=4
2.812 2.594 2.012 2.297 2.575 1.417
2.405
NA 3.331 13.094 7.254 3.062 25.433
6.043
n=5
3.321 3.018 2.249 2.652 2.986 1.522
2.76
NA 3.865 14.908 7.898 3.627 27.499
7.025
n=6
3.809 3.454 2.462 3.001 3.454 1.613
3.116
NA 4.131 17.231 8.595 3.433 30.572
7.873
n=7
4.348 3.932 2.692 3.394 3.931
1.72
3.512
NA 4.311 19.263 9.102 3.645 32.966
8.559
wU K

E[wj ]
t-stat of E[wU K ] − E[wj ]E[wj ]
t-stat of E[wU K ] − E[wj ]E[wj ]
t-stat of E[wU K ] − E[wj ]E[wj ]
t-stat of E[wU K ] − E[wj ]wbuy−hold1.169
31.076
1.174
34.3561.172
38.805
1.172
42.292This table reports the mean (E[wj ]) for the distribution of generated TW (Terminal Wealth).
The t-stat of E[wU K] − E[wj ] shows the t-test statistic of the difference between the mean of
the UK strategy and the other strategies. The calculation of the TW is as follows (1): Calculate the expected value of the excess market returns and the variance-covariance matrixof the randomly selected CPMs from the regression results of Eq. (2). (2): Using the resultsof (1), calculate the TW when the investment strategy is implemented based on Eq. (4). (3):Repeat this process 1000 times to generate the distribution of the TW. The sample period of
the predictive regressions are recursively extended. The sample period of the first predictive
regression is 120 months. All sample periods used for forecasting and evaluation are fromJanuary 2006 to February 2021.

E[SRj ]
t-stat of E[SRU K ] − E[SRj ]E[SRj ]
t-stat of E[SRU K ] − E[SRj ]E[SRj ]
t-stat of E[SRU K ] − E[SRj ]E[SRj ]
t-stat of E[SRU K ] − E[SRj ]Table 8: Sharp Ratio: Recession DummySRU K SRU S SRF R SRGE SRJP SRCH
n=4
0.134
0.16
0.119 0.127 0.139 0.102
NA-9.532 5.245 2.255 -1.521 9.409
n=5
0.146
0.175
0.131
0.14
0.155 0.112
NA-11.098 5.626 1.879 -2.718 10.001
n=6
0.157
0.188
0.142 0.154 0.172 0.117
NA-12.917 6.118 1.099 -4.864 11.921
n=7
0.165
0.198
0.15
0.165 0.185 0.123
NA-14.102 6.422 0.149 -6.757 12.608

SREU.ex.U K

SRbuy−hold0.145-3.9590.056
30.906

0.158
-4.32

0.059
38.7220.17-5.1120.06
49.6820.18-5.961

0.061
58.81

This table reports the mean (E[SRj ]) for the distribution of generated SR (Sharp Ratio). The t-stat of
E[SRU K] − E[SRj ] shows the t-test statistic of the difference between the mean of the UK strategy
and the other strategies. The calculation of the SR is as follows (1): Calculate the expected value ofthe excess market returns and the variance-covariance matrix of the randomly selected former CPMs
from the regression results of Eq. (2). (2): Using the results of (1), calculate the SR when the investment strategy is implemented based on Eq. (4). (3): Repeat this process 1000 times to generate the
distribution of the SR. The sample period of the predictive regressions are recursively extended. The
sample period of the first predictive regression is 120 months. All sample periods used for forecastingand evaluation are from January 2006 to February 2021.

16

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