Predicting the performance of equity anomalies in frontier emerging markets: a Markov switching model approach

Figures & data

Table 1. Geographical coverage of the research sample.

Country	Number of firms	Number of monthly observations	Country	Number of firms	Number of monthly observations
Argentina	98	11,977	Morocco	91	12,048
Bahrain	48	5146	Mauritius	83	7882
Bangladesh	260	10,912	Nigeria	54	2989
Bulgaria	156	8709	Oman	120	14,127
Croatia	235	16,227	Pakistan	305	23,736
Estonia	29	2699	Romania	272	9549
Jordan	226	21,719	Serbia	258	11,337
Kazakhstan	32	1152	Slovenia	99	7516
Kenya	54	5742	Sri Lanka	205	12,824
Kuwait	221	24,754	Tunisia	84	8647
Lebanon	11	1481	Vietnam	623	28,862
Lithuania	57	5044	Total	3621	255,079

Note: This table provides an overview of the geographical distribution of the sample. The number of monthly observations refers to periods for which both return and capitalisation data was available.

Figure 1. The research sample. Note. The figure presents the changes in the research sample over time.

Table 2. Performance of equity meta-anomalies.

No.	Meta-anomaly	Panel A: Equal-weighted portfolios				Panel B: Value-weighted portfolios
		R		α		R		α
		Value	t-stat	Value	t-stat	Value	t-stat	Value	t-stat
1.	Value	0.93**	(6.66)	0.93**	(6.14)	0.48*	(2.55)	0.49*	(2.46)
2.	Dividends	0.19	(1.38)	0.19	(1.36)	0.33	(1.26)	0.33	(1.51)
3.	Profitability	0.37	(1.88)	0.36	(1.53)	0.47	(1.35)	0.46	(1.27)
4.	Credit risk and indebtedness	0.43**	(3.75)	0.43**	(3.07)	0.29	(1.66)	0.29	(1.15)
5.	Investment and intangibles	0.02	(0.32)	0.02	(0.10)	–0.14	(–0.34)	–0.14	(–0.49)
6.	Accruals	0.31	(1.86)	0.31	(1.26)	0.04	(0.12)	0.05	(0.13)
7.	Fundamental analysis	0.26*	(2.29)	0.26*	(2.31)	0.24	(1.88)	0.23	(1.66)
8.	Issuance	0.34*	(2.43)	0.34*	(2.45)	0.07	(0.32)	0.07	(0.33)
9.	Liquidity	0.29	(1.01)	0.28	(0.98)	–0.11	(–0.36)	–0.12	(–0.46)
10.	Low-volatility	0.63*	(2.19)	0.63	(1.95)	0.84*	(2.17)	0.83*	(1.98)
11.	Extreme and downside risk	–0.13	(–0.79)	–0.12	(–0.76)	–0.51*	(–2.03)	–0.49	(–1.58)
12.	Skewness	0.13	(1.00)	0.13	(1.00)	0.38	(1.69)	0.38	(1.55)
13.	Long-term reversal	0.41	(1.86)	0.41	(1.61)	0.31	(0.99)	0.31	(0.98)
14.	Short-term reversal	–1.17**	(–4.94)	–1.17**	(–4.78)	–0.94*	(–2.43)	–0.94**	(–2.58)
15.	Momentum	1.29**	(5.66)	1.28**	(5.34)	0.92**	(3.59)	0.91**	(2.90)
16.	Trend	1.15**	(3.88)	1.15**	(3.08)	0.42	(0.97)	0.42	(0.82)
17.	52-week high	1.31**	(5.55)	1.31**	(4.97)	1.07**	(3.88)	1.05**	(3.17)
18.	Seasonalities	0.72**	(4.14)	0.72**	(4.77)	0.49	(1.59)	0.49	(1.73)
19.	Analyst coverage	0.70**	(4.19)	0.70**	(2.75)	0.49*	(2.57)	0.49	(1.58)
20.	Market frictions and others	0.29**	(2.59)	0.29*	(2.47)	0.17	(0.79)	0.17	(0.81)

Note: This table reports the performance for meta-anomalies, i.e., portfolios that equally-weight all long-short anomaly portfolios from one-way sorts with a 30%-breakpoint according to the classifications in Table A1. No. is the number of anomalies considered. Meta-anomaly refers to groups of anomalies in Table 3. R is the mean monthly return, α is the intercept from the CAPM, and t-stat are corresponding t-statistics. The asterisks, * and **, indicate values that are significantly different from zero at the 5% and 1% levels of significance, The numbers in brackets are bootstrap (Newey and West [1987] robust standard error adjusted) t-statistics for the means (alphas).

Table 3. Performance of meta-anomalies under both regimes.

No.	Strategy group	Regime 1					Regime 2					LM
		µ		b		E(R)	µ		b		E(R)
		Value	t-stat	Value	t-stat		Value	t-stat	Value	t-stat
Panel A: Equal-Weighted Portfolios
1.	Value	0.97**	(4.71)	0.01	(0.15)	0.98**	0.44**	(2.90)	0.30*	(2.19)	0.63**	0.00
2.	Dividends	0.36**	(3.26)	0.21	(1.79)	0.45**	0.11	(0.65)	0.06	(0.72)	0.12	0.00
3.	Profitability	0.53**	(4.48)	0.23**	(2.83)	0.69**	–0.10	(–0.19)	0.07	(0.64)	–0.10	0.00
4.	Credit risk and indebtedness	0.31**	(3.51)	0.18*	(2.03)	0.38**	0.39	(1.14)	0.21	(1.80)	0.49	0.00
5.	Investment and intangibles	0.07	(0.16)	0.27*	(2.31)	0.10	–0.05	(–0.43)	0.03	(0.33)	–0.05	0.00
6.	Accruals	0.35	(0.90)	0.20*	(2.05)	0.43	0.08	(0.79)	0.28**	(3.19)	0.11	0.00
7.	Fundamental analysis	0.22**	(4.04)	0.02	(0.25)	0.23**	0.39	(1.10)	–0.12	(–0.98)	0.35	0.00
8.	Issuance	0.12	(0.70)	0.31	(1.33)	0.17	1.05	(0.61)	–0.69	(–1.14)	0.63	0.04
9.	Liquidity	0.29	(1.35)	0.16*	(2.34)	0.35	–0.24	(–0.08)	–0.03	(–0.11)	–0.23	0.00
10.	Low-volatility	0.30	(1.94)	0.27**	(3.64)	0.42	0.92	(1.24)	0.16	(1.38)	1.10	0.00
11.	Extreme and downside risk	0.05	(0.29)	0.25*	(2.13)	0.07	–0.18	(–0.70)	0.31**	(3.65)	–0.26	0.00
12.	Skewness	0.05	(0.68)	0.19**	(2.76)	0.07	0.52	(0.30)	0.06	(0.22)	0.56	0.00
13.	Long-term reversal	1.20	(1.94)	0.15	(1.27)	1.41	–0.04	(–0.22)	0.03	(0.36)	–0.04	0.00
14.	Short-term reversal	–1.02	(–1.93)	0.18	(1.62)	–1.25	–0.93**	(–3.33)	0.15	(1.31)	–1.09**	0.81
15.	Momentum	1.63**	(5.99)	–0.19	(–1.52)	1.37**	–0.33	(–0.38)	0.91**	(6.00)	–3.59	0.01
16.	Trend	1.22**	(2.94)	0.56**	(4.50)	2.81**	–0.02	(–0.03)	–0.28	(–1.24)	–0.02	5.21
17.	52-week high	1.55**	(6.00)	–0.09	(–0.82)	1.42**	0.25	(0.34)	0.80**	(3.95)	1.26	0.01
18.	Seasonalities	0.66**	(5.04)	0.10	(1.40)	0.73**	0.48	(0.33)	0.06	(0.22)	0.51	0.00
19.	Analyst coverage	0.42**	(4.46)	0.05	(0.52)	0.44**	0.89*	(2.07)	0.11	(1.15)	1.00*	0.00
20.	Market frictions and others	1.29**	(3.61)	–0.45**	(–3.17)	0.89**	0.00	(–0.01)	0.35**	(4.06)	0.00	0.00
Panel B: Value-Weighted Portfolios
1.	Value	1.01	(1.76)	0.04	(0.29)	1.05	0.18	(1.04)	–0.05	(–0.56)	0.17	0.00
2.	Dividends	0.79	(1.45)	0.01	(0.13)	0.80	0.05	(0.26)	–0.04	(–0.46)	0.05	0.00
3.	Profitability	0.46**	(2.80)	–0.12	(–1.38)	0.41**	0.53	(0.63)	0.07	(0.74)	0.57	0.00
4.	Credit risk and indebtedness	0.43	(0.95)	0.09	(0.90)	0.48	0.10	(0.81)	0.11	(1.23)	0.11	0.00
5.	Investment and intangibles	–0.17	(–0.22)	0.19	(1.56)	–0.21	–0.17	(–1.16)	0.11	(1.35)	–0.19	0.00
6.	Accruals	0.19	(0.32)	0.15	(1.60)	0.23	–0.31	(–1.92)	0.04	(0.45)	–0.32	0.00
7.	Fundamental analysis	0.14	(1.88)	–0.09	(–1.00)	0.12	0.39	(1.30)	–0.02	(–0.17)	0.38	0.00
8.	Issuance	0.01	(0.16)	0.26**	(2.86)	0.02	–0.03	(–0.05)	0.01	(0.16)	–0.03	0.00
9.	Liquidity	0.15	(0.72)	0.03	(0.26)	0.16	–0.59	(–0.89)	–0.12	(–1.07)	–0.53	0.00
10.	Low-volatility	0.58**	(2.90)	0.24	(1.93)	0.76**	0.95	(1.56)	0.09	(1.02)	1.04	0.00
11.	Extreme and downside risk	–0.19	(–0.94)	0.08	(1.03)	–0.21	–1.19	(–1.33)	0.17	(1.28)	–1.44	0.00
12.	Skewness	1.75*	(2.23)	–0.01	(–0.11)	1.73*	–0.10	(–0.99)	0.11	(1.33)	–0.11	0.00
13.	Long-term reversal	0.24	(0.48)	–0.33*	(–2.55)	0.18	0.23	(0.43)	0.70**	(4.83)	0.78	0.68
14.	Short-term reversal	–1.24	(–1.41)	0.10	(0.84)	–1.37	–0.61*	(–2.42)	–0.04	(–0.45)	–0.59*	0.00
15.	Momentum	1.05**	(4.48)	0.01	(0.07)	1.06**	0.40	(0.36)	0.27	(1.74)	0.55	0.00
16.	Trend	0.54	(1.51)	0.03	(0.31)	0.56	0.07	(0.07)	0.40**	(2.60)	0.11	0.00
17.	52-week high	1.17**	(5.53)	0.03	(0.33)	1.20**	0.66	(0.49)	0.30*	(1.99)	0.95	0.00
18.	Seasonalities	0.60**	(3.92)	–0.05	(–0.61)	0.57**	0.49	(0.69)	0.15	(1.33)	0.57	0.00
19.	Analyst coverage	0.41**	(2.81)	0.08	(0.87)	0.45**	0.47	(1.09)	0.14	(1.58)	0.55	0.00
20.	Market frictions and others	0.25	(1.14)	–0.26**	(–2.81)	0.19	0.06	(0.15)	0.89**	(5.60)	0.56	0.00

Note: This table reports the performance for the meta-anomalies, i.e., portfolios that equally weight the long-short anomaly portfolios according to the classification in Table A1 in the supplementary material, for two regimes as in Equation (4)(4) $$R_t={\mu }_{\left(s_i\right)}+b_{\left(s_i\right)}{R}_{t-1}+{\epsilon }_t\text{\hspace{0.5em}and\hspace{0.5em}}{\epsilon }_t\sim N(0,{\sigma }_{\left(s_i\right)}).$$(4) . No. is the number of anomalies considered. Meta-anomaly refers to groups of anomalies in Table 3, µ and b are parameters in Equation (3)(3) $$E\left(R_t\right)=\frac{\mu }{1-b},$$(3) and (4)(4) $$R_t={\mu }_{\left(s_i\right)}+b_{\left(s_i\right)}{R}_{t-1}+{\epsilon }_t\text{\hspace{0.5em}and\hspace{0.5em}}{\epsilon }_t\sim N(0,{\sigma }_{\left(s_i\right)}).$$(4) . L.M. is the p-value associated with the L.M. test statistic in Equation (10)(10) $$\mathit{LM}=2\mathrm{*}\left({\ell }_c\left(\mathit{\theta }\right)-\ell \left({\mathit{\theta }}_1\right)\right)$$(10) . The asterisks, * and **, indicate values that are significantly different from zero at the 5% and 1% levels of significance. The numbers in brackets are t-statistics.

Figure 2. Time series probabilities of remaining in a regime that results in significant positive returns. Note: This figure presents the probabilities of remaining in a regime that yields significant positive returns on meta-anomalies. Shaded areas represent the periods during which probability exceeds 0.5.

Table 4. Performance of portfolios of meta-anomalies formed using Markov switching model predictions.

	50% probability forecast			70% probability forecast			90% probability forecast			Benchmark portfolio
	Pro	Unprof	P-U	Prof	Unprof	P-U	Prof	Unprof	P-U
R	0.50**	0.10	0.40**	0.55**	0.13	0.43**	0.46**	0.04	0.42**	0.31**
	(4.94)	(1.74)	(3.20)	(6.12)	(1.81)	(3.61)	(7.10)	(0.48)	(3.66)	(5.61)
Vol	0.69	0.47	0.82	0.61	0.53	0.73	0.47	0.78	0.83	0.42
αFF5	0.46**	0.06	0.41**	0.53**	0.08	0.45**	0.47**	0.06	0.41**	0.28**
	(4.07)	(1.05)	(2.67)	(5.26)	(1.14)	(2.95)	(7.34)	(0.70)	(3.20)	(5.80)
MKT	–0.03	–0.01	–0.03	–0.02	–0.04	0.02	0.01	–0.08	0.10	–0.02
	(–0.85)	(–0.30)	(–0.52)	(–0.68)	(–1.29)	(0.40)	(0.47)	(–1.95)	(1.71)	(–0.90)
SMB	0.02	0.04	–0.02	0.00	0.00	0.00	–0.01	–0.03	0.02	0.03
	(0.40)	(1.30)	(–0.29)	(0.10)	(0.01)	(0.06)	(–0.30)	(–0.42)	(0.19)	(1.00)
HML	0.00	0.14**	–0.15	0.00	0.12*	–0.12	–0.05	0.10	–0.15	0.06
	(–0.03)	(3.36)	(–1.60)	(0.00)	(2.15)	(–1.69)	(–1.71)	(1.60)	(–1.90)	(1.80)
RMW	0.10*	0.08**	0.02	0.10**	0.11**	0.00	0.10**	0.13**	–0.03	0.09**
	(2.47)	(3.31)	(0.42)	(3.13)	(3.43)	(–0.04)	(3.93)	(3.34)	(–0.69)	(4.17)
CMA	0.04	0.08	–0.04	0.06	0.07	–0.01	0.07	0.14*	–0.08	0.07*
	(0.65)	(1.84)	(–0.39)	(1.08)	(1.54)	(–0.15)	(1.83)	(2.18)	(–0.90)	(2.54)
R^2	7.40	30.50	1.40	13.90	29.30	4.40	34.40	35.40	7.20	27.90

Note: This table reports the monthly returns on portfolios of equal-weighted meta-anomalies reported in Panel A in Table 2. Prof (Unprof) is an equal-weighted portfolio of meta-anomalies that is predicted to be in a regime with a positive significant (insignificant) µ parameter in Equation (4)(4) $$R_t={\mu }_{\left(s_i\right)}+b_{\left(s_i\right)}{R}_{t-1}+{\epsilon }_t\text{\hspace{0.5em}and\hspace{0.5em}}{\epsilon }_t\sim N(0,{\sigma }_{\left(s_i\right)}).$$(4) with a probability of profitability of at least 50% (Panel A), 70% (Panel B), or 90% (Panel C). The required significance level is 1%. P-U is a long-short portfolio which is long (short) in the Prof (Unprof) portfolios. The table also reports results for a benchmark portfolio that equal-weights all 20 meta-anomalies. R is the mean monthly return, Vol is the monthly standard deviation of returns, and α_FF5 is the intercept of the Fama and French (2015) five-factor model. The numbers in brackets (for R) are bootstrap (Newey and West (1987) robust standard error) adjusted (for α_FF5) t-statistics. Means, volatilities, and intercepts are expressed as percentages. The asterisks * and **, indicate values significantly different from zero at the 5% and 1% levels of significance.

Table 5. Monthly returns on alternative portfolios of meta-anomalies formed using Markov switching model predictions.

	50% probability forecast			70% probability forecast			90% probability forecast
	Prof	Unprof	P-U	Prof	Unprof	P-U	Prof	Unprof	P-U
Panel A: Equal-Weighted Portfolios
10% level of significance	0.48**	0.03	0.46**	0.55**	0.06	0.48**	0.50**	0.03	0.47**
	(4.50)	(0.59)	(3.22)	(5.78)	(0.94)	(3.27)	(7.95)	(0.41)	(3.96)
1% level of significance	0.48**	0.11*	0.37*	0.57**	0.09	0.48**	0.49**	0.07	0.43**
	(3.84)	(2.32)	(2.30)	(5.33)	(1.40)	(2.99)	(7.21)	(0.82)	(3.30)
Panel B: Value-Weighted Portfolios
10% level of significance	0.39**	0.11	0.28	0.44**	0.10	0.33	0.47**	0.22**	0.25
	(2.87)	(1.71)	(1.38)	(2.74)	(1.28)	(1.50)	(2.98)	(3.19)	(1.67)
5% level of significance	0.25	0.17*	0.08	0.27	0.18	0.09	0.46**	0.22**	0.24
	(1.23)	(2.10)	(0.28)	(1.31)	(1.85)	(0.29)	(3.35)	(3.66)	(1.69)
1% level of significance	0.52*	0.14**	0.38	0.52*	0.13*	0.39	0.56*	0.23**	0.34
	(2.39)	(3.42)	(1.69)	(2.40)	(2.33)	(1.72)	(2.39)	(3.83)	(1.31)

Note: This table reports the means of monthly returns with corresponding bootstrap t-statistics (in brackets) for portfolios of equal-weighted (Panel A) and value-weighted (Panel B) meta-anomalies reported in Panel A of Table 2. Prof (Unprof) is an equal-weighted portfolio of meta-anomalies that is predicted to b in a regime with a significant (insignificant) µ parameter from Equation (4)(4) $$R_t={\mu }_{\left(s_i\right)}+b_{\left(s_i\right)}{R}_{t-1}+{\epsilon }_t\text{\hspace{0.5em}and\hspace{0.5em}}{\epsilon }_t\sim N(0,{\sigma }_{\left(s_i\right)}).$$(4) and a probability of profitability of at least 50%, 70%, or 90%. P-U is a long-short portfolio which is long (short) in the Prof (Unprof) portfolios. We consider three different significance levels for expected returns on the meta-anomalies; 10%, 5%, and 1%. Means are expressed as percentages. The asterisks, * and **, indicate values significantly different from zero at the 5% and 1% levels of significance.

Newey, W. K., & West, K. D. (1987). A simple positive-definite heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. doi:10.2307/1913610

 Web of Science ®Google Scholar

Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. doi:10.1016/j.jfineco.2014.10.010

 Web of Science ®Google Scholar