From efficient markets to adaptive markets: Evidence from the French stock exchange

Research in International Business and Finance 49 (2019) 156–165

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Research in International Business and Finance journal homepage: www.elsevier.com/locate/ribaf

From efficient markets to adaptive markets: Evidence from the French stock exchange

T

Christophe M. Boya Laboratoire TRIS, Université de Montpellier, Faculté d’économie, Avenue Raymond Dugrand, CS 79606, 34960, Montpellier Cedex 2, France

A R T IC LE I N F O

ABS TRA CT

JEL classification: C12 C14 G14 G15

This paper examines the degree of market efficiency of the French Stock Market and tries to check both the efficient market hypothesis (EMH) and the adaptative market hypothesis (AMH). We use a rolling variance ratio test approach in order to provide an overview of the efficiency behavior from 1988 to 2018. We find that our results are consistent with the AMH. Indeed, it seems that the French stock market presents successive periods of efficiency and inefficiency. Moreover, inefficiency periods coincide with major macroeconomics events.

Keywords: Adaptive markets Efficient markets Rolling variance ratio

1. Introduction The modern efficient market hypothesis (EMH) was given by Fama (1970). Accordingly, a market is said to be efficient if prices at any time fully reflect all available and relevant information. Hence, each new information is instantaneously integrated in prices. This means that stock returns are independent and cannot be predicted. Fama divided the EMH into three categories according to prices to reflect particular subsets of available information. This distinction is threefold: the weak form where the information subset is historical prices; the semi strong form based on publicly available information; and the strong form on private information. The weak form efficiency has been the most widely studied and as well the most controversial. The definition indicates that, if a market is weakly efficient, stock returns should follow a random walk, which causes that prices are serially uncorrelated. As a result, historical prices cannot be used to forecast stock market trend. No investors are able to make abnormal profits over time by using trading rules based on past returns. The empirical literature on EMH studied the weak form1 through a variety of random walk tests (variance ratio, Hurst statistic, run test, …). However, it seems that there is still no consensus on whether financial markets are efficient or not. Indeed, in the one hand, several studies have shown that stock returns follow random walk for different developed and developing markets (Fama, 1970; Chow and Denning, 1993; Poon, 1996; Cheung and Coutts, 2001; Tabak, 2003). On the other hand, literature has highlighted that financial markets are not efficient and possess some components of predictability (Mills, 1993; Huang, 1995; Hoque et al., 2007). With regard to the French stock exchange, results are equally diverse. For instance, Stachowiak (2004) and Worthington and Higgs (2003) have observed the random walk behavior in the equity market using respectively simple and multiple variance ratio tests. More recently, Kim and Shamsuddin (2008) and Borges (2010) have indicated the presence of inefficiency with a variance ratio test based on bootstrap. Mignon (1998) and Matouk and Monino (2005) have used the Hurst’s statistic and confirmed the presence of long memory validating the hypothesis of return predictability. The literature on the field has examined the hypothesis of random walk over some predetermined sample period. Consequently, the efficiency is analyzed as an all or nothing

1

E-mail address: [email protected]. See Boya (2017) for a survey on the field.

https://doi.org/10.1016/j.ribaf.2019.03.005 Received 24 October 2018; Received in revised form 3 March 2019; Accepted 9 March 2019 Available online 12 March 2019 0275-5319/ © 2019 Elsevier B.V. All rights reserved.

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condition, since results can be different depending on the sample period, relaunching the endless discussion between proponents and opponents of the EMH. Lo (2004) has proposed an alternative market theory to the HEM with the Adaptive Market Hypothesis (AMH) that “can be viewed as a new version of the EMH, derived from evolutionary principles”. This framework tries to reconcile EMH with behavioural alternatives, by using the notions of bounded rationality and satisficing derived from Simon (1955).2 According to Lo (2004), many behavioural biases in finance are in fact consistent with an evolutionary model of individuals learning and adapting to a changing environment. Hence, the degree of market efficiency depends on the impact of these evolutionary forces on financial institutions and market participants. Lo (2005) has provided the primary components of the AMH: Individuals act in their own self interest; they make mistakes and thereby, learn and adapt; competition drives adaptation and innovation; natural selection shapes market ecology; evolution determines market dynamics. In contrast to the EMH, AMH suppose that investors can make mistakes, but they are able to learn and adapt their behavior accordingly. Moreover, this new paradigm has several implications (Urquhart, 2013). Firstly, the risk premium changes over time due to the stock market environment and the preferences of individuals in this environment. Secondly, from an evolutionary perspective arbitrage and profit opportunities do appear from time to time in the market. As a result, return predictability can arise time to time due to changing market conditions such as cycles, bubbles, crisis, crashes etc. The third implication arises from the previous, one, investment strategies are successful and unsuccessful, depending on certain market environment. The AMH implies that such strategies can decline for a time, and then return to profitability when environmental conditions change. For instance, using strategies based on historical prices is likely to produce excess returns in a cyclical fashion. The AMH differs from the weak form of efficiency where future prices cannot be predicted from past data. The last implication of the AMH is that characteristics such as value and growth may behave like ‘risk factors’ from time to time. That is to say, stocks with these characteristics may yield higher expected returns during periods when those attributes are in favour. The main consequence of all these elements is that, financial markets are adaptable and switch between periods of efficiency and inefficiency. The AMH has received an increasing amount of attention from literature. Indeed, Lo (2005) has examined the SP index from 1987 to 2003. He has found the existence of a cyclical pattern through time and that AMH provides a better description of the behavior of stock returns. Results have been confirmed by Ito and Sugiyama (2009) for this index. Lo (2005) and Lim et al. (2013) have stated and showed that markets are relatively efficient for a long time, until a market crash or a major event causes a short period of relatively lower efficiency. These findings have been supported for others stock exchanges. Todea et al. (2009) for six Asian stock markets3 have verified the cyclical efficiency pattern as postulated by the AMH, using linear and nonlinear tests. Hiremath and Kumari (2014), and Hiremath and Narayan (2016) have studied the Indian stock exchange and pointed out that the inefficiency is associated with major macroeconomics events. Recently, Smith (2011) investigated fifteen European emerging stock markets from 2000 to 2009, and Urquhart and Hudson (2013) considered three indices (UK, USA and Japan). They have used a rolling window variance ratio and highlighted that the degree of market efficiency varies through time in a cyclical fashion. Results are consistent with the AMH. Smith (2011) upholds the effect of economic events in the degree of return predictability. Neely et al. (2009) analyzed the AMH from exchange rates from 1973 to 2005. Results are consistent with the AMH. Charles et al. (2012) used automatic variance ratio test for five currencies4 from 1974 to 2009. The authors show that return predictability of foreign exchange rates occurs from time to time depending on changing market conditions. These market conditions are altered by specific major events such as financial crises or central bank interventions. These results were confirmed by Katusiime et al. (2015). Khuntia et al. (2018) and Kumar (2018) have lately examined the Indian exchange rate5 from 1999 to 2017 with an identical data sample. Their results show that the AMH offers better and deeper insights into the nuances of efficiency. Khuntia et al. (2018) have also approved the idea that the degree of market efficiency is affected by crisis, or macroeconomics fluctuations. Khuntia and Pattanayak (2018) assessed the AMH in the context of bitcoin from 2010 to 2017, using a time-varying approach. Their findings are in line with Lo (2004) underlying that bitcoin market goes through different levels. As for results in others markets, the authors demonstrate that periods of inefficiency coincide with major political or economic events. The purpose of this article is to stress dynamics to the degree of market efficiency by investigating the major index of the French stock market and check whether its evolution is consistent with the AMH. Thus, the paper complements literature on AMH and provides an overview of the efficiency behavior in the French market. In order to a have better sight of the evolution of the degree of efficiency, I decide to employ a non-parametric multiple variance ratio (VR) that has gained tremendous popularity since the eighties to examine the random walk hypothesis. However, to keep a dynamic approach, a rolling variance ratio test is applied. While, the classic VR test gives just one output, the rolling VR test uses moving sub-samples windows and enables to bring out a set of results. Results confirm the idea that financial markets present successive periods of efficiency and inefficiency. They bring to light that major macroeconomic events create favourable conditions to identify periods of inefficiency. Overall, the results are consistent with the AMH, and even more, it seems the degree of efficiency coincides with major exogenous events striking the financial market. The study contributes to the literature in several ways. To begin with, the sample chosen cover nearly 30 years for the French

Simon (1955) has described the idea of “bounded rationality”, suggesting that individuals do not have all available information (because it is costly) and are not always able to optimize in a rational way because their abilities are limited. To complete the analysis, the author has defined the notion of “satisficing” which explains that individuals make choices that are merely satisfactory, not necessarily optimal. 3 Australia, Honk Kong, India, Malaysia, Singapore and Japan. 4 Australian dollar, British pound, Canadian dollar, Japanese yen, Swiss franc relative to US dollar. 5 Indian rupe and four major currencies US dollar, Euro, Japanese yen and British pound. 2

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stock market using daily data, which has not been previously investigated. Secondly, I use a multiple variance ratio test based on ranks proposed by Belaire-Franch and Contreras (2004) and I apply a rolling window with different length of periods. The literature has largely used parametric variance ratio tests but this approach has had little investigation until now. Furthermore, this rolling method enables to better follow the efficiency. So, I may examine periods of efficiency and inefficiency, and identify what alters market conditions in the index of the French stock exchange. All in all, this will allow us to compare our results with other studies. Following this first introductory section, the paper is organized as follows. Section 2 presents the model of variance ratio test. Section 3 presents the empirical results with data for the study used and findings obtained using a rolling variance ratio test. Finally, a last section concludes the paper. 2. The variance ratio test There are many ways of testing the random walk hypothesis, but the variance ratios are considered as the most powerful. The results have been confirmed by Poterba and Summers (1988), Cochrane (1988), Fama and French (1988), Lo and MacKinlay (1988), Liu and He (1991), Mobarek and Fiorante (2014). Lo and MacKinlay (1988) offered a first version. It then received significant improvements, through multiple variance ratio (Chow and Denning, 1993), automatic variance ratio (Choi, 1999), wild bootstrap test (Kim, 2006); and also sign and rank tests (Wright, 2000). 2.1. From the parametric approach It consists in analysing the random walk hypothesis that a given time series is a collection of independent and identically distributed observations, against stationary alternatives. One important property of the random walk is that the variance of its increments is linear in the observation interval. Hence, the variance of its k differences is k times the variance of its first difference. Lo and MacKinlay (1988) have exploited this property. Suppose that (x t ) is a time series of asset returns with a sample of size T , the variance ratio statistic can be written as: T

VR (k ) =

2 −1 { (Tk ) ∑t = k (x t +…+x t − k + 1 − kμˆ) } T − 1 2 {T ∑t = 1 (x t − μˆ) }

T

Where, μˆ = T −1 (∑1 x t ) . Most commonly, the variance ratio test is calculated for each of the intervals , k = 2, 4, 8, and 16. The statistic should be close to 1, if (x t ) follows a random walk for all horizons k. If the statistic is less than one, then negative correlation is implied (mean reversion), while a statistic greater than one indicates positive serial correlation (mean aversion). The presence of negative or positive serial correlation indicates that future returns may be forecast by using information on past returns. Observations do not follow a random walk and contain a stationary transitory component. Lo and MacKinlay (1988) showed that, if (x t ) is iid and under the assumption of homoscedasticity, then for ≥ 2 :

Z1 (k ) =

T 0.5 (VR (k ) − 1) →d N (0,1) (2(2k − 1)(k − 1)/3k )0.5

The test statistic is asymptotically standard normal under the i.i.d. null hypothesis. In the presence of heteroskedasticity, they have proposed the modified test statistic:

Z2 (k ) =

{

T 0.5 (VR (k ) − 1)

(

k−1

∑ j=1 ⎡ ⎣

2(k − j ) 2 ⎤ . k ⎦

T

δj

)

0.5

→d N (0,1)

}

T

2

Where δj = T ∑t = j + 1 (x t − μˆ)2 (x t − j − μˆ)2 / {∑t = 1 (x t − μˆ)2} . They denoted that, if (x t ) can be described by a martingale difference sequence, and with some more assumptions, then Z2 (k ) is asymptotically standard normal. Lo and MacKinlay (1988) have proposed asymptotic tests, and the distribution depends on the value of the horizon k. The asymptotic theory provides a poor approximation to the small sample distribution of the variance ratio statistic. Consequently, the statistics are biased and right skewed in finite samples. Chow and Denning (1993) have pointed out that the random-walk null hypothesis was rejected too easily. To overcome this problem, the authors decided to use the maximum absolute value of Lo and MacKinlay’s (1988) tests for set ratios of estimated multiple variance {ki |i = 1,2, …, m} . The authors define their statistics:

Z1* (m) = max |Z1* (ki)| 1≤i≤m

Z2* (m) = max |Z2* (ki)| 1≤i≤m

They delimit a confidence level for a set of variance ratio test statistics. The multiple variance ratio test statistics is based on the ‘Studentized Maximum Modulus’ distribution. The choice of the value k is fundamental, but Charles et al. (2012) indicates it is often arbitrary and adopted without any concrete statistical justifications. Choi (1999) has proposed an automatic variance ratio test (AVRT) based on frequency domain to determine the optimal value of k automatically. It is based on a variance ratio estimator related to the normalized spectral density estimator at zero frequency, such as 158

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ˆ (k ) = 1 + 2∑T − 1 k ⎛ j ⎞ ρˆ (j ) VR i=1 ⎝k ⎠ Where ρˆ (j ) = γˆ (j )/ γˆ (0) is the sample autocorrelation of order j, γˆ (j ) is the sample autocovariance of order j, and

k (x ) = 25/12π 2x 2 ⎡ sin(6πx / 5) 6πx / 5 − cos(6πx /5) ⎤ is the quadratic spectral kernel. Under the null hypothesis that ρj = 0 and that the ⎣ ⎦ stock return is identically and independently distributed, Choi (1999) shows that: T

AVR (k ) =

ˆ (k ) − 1]/ 2 →N (0,1) k [VR d

In the presence of heteroskedasticity, Kim (2009) proposes wild bootstrapping of AVR (k ) statistic for improved small sample properties. It can be conducted in three stages: i) Form a bootstrap sample of T observations Xt* = ηt Xt (t = 1, …T ) where ηt is a random variable with zero mean and unit variance, ii) Calculate AVR* (k*) , the AVR statistic obtained from {Xt*}Tt = 1 iii) Repeat (i) and (ii) B times to from a bootstrap distribution {AVR* (k*; j ) Bj = 1 } Kim (2009) has presented that this procedure improves the size and power properties of the test, and compare favorably to the other alternative parametric tests. 2.2. To the non-parametric approach More recently, Wright (2000) proposed alternatives to the parametric variance ratio tests using signs and ranks. He showed that the non-parametric approach may be more powerful than the conventional variance ratio tests against a wide range of models displaying serial correlation, including fractionally integrated alternatives. Let r (x t ) be the rank of x t among x1…xT , under the null hypothesis that x t is generated from an iid sequence, then the standardized ranks r1t and r2t are given by:

(

r1t = r (x t ) −

T+1 2

) {(T − 1)(T + 1)/12}

0.5

r2t = Φ−1 . ⎛ r (x t ) T + 1 ⎞ ⎝ ⎠ Wright (2000) proposed the R1 (k ) and R2 (k ) statistics, defined as: T

R1 (k ) =

2 1 ⎞ ⎛ ∑t = k + 1 (r1, t +…+r1, t − k ) − 1 . ϕ (k )− 2 T 2 ⎟ ⎜ k r . ∑ 1, t t=1 ⎠ ⎝

R2 (k ) =

2 1 ⎞ ⎛ ∑t = k + 1 (r2, t +…+r2, t − k ) − 1 . ϕ (k )− 2 T 2 ⎟ ⎜ k r . ∑ t = 1 2, t ⎠ ⎝

T

Where, Φ−1 is the inverse of the standard normal cumulative distribution function, and ϕ (k ) = 2(2k − 1)(k − 1)(3kT )−1. Tests based on the signs of first differences are given by: T

S1 (k ) =

2 1 ⎞ ⎛ ∑t = k + 1 (st +…+st − k ) − 1 . ϕ (k )− 2 T 2 ⎟ ⎜ k s ∑ t t=1 ⎠ ⎝

S2 (k ) =

2 1 ⎞ ⎛ ∑t = k + 1 (st (μ¯)+…+st − k (μ¯)) − 1 . ϕ (k )− 2 T ⎟ ⎜ 2 k s μ ( ∑ ¯) t=1 t ⎠ ⎝

T

0.5 if x t > q Where, st = 2u (x t , 0) = 2u (εt , 0) , st (μ¯) = 2u (x t , μ) and u (x t , q) = ⎧ . Critical values can be obtained by simulating their ⎨ ⎩ − 0.5 otherwise exact distributions. As Lo and MacKinlay (1988), Wright (2000) used several k values to test the null hypothesis. The tests based on ranks are exact under the i.i.d. assumption, whereas the tests based on signs are exact even under conditional heteroskedasticity. Wright pointed out from Monte Carlo simulations that the size distortions of these tests under heteroskedasticity are small, moreover both statistics follow the same exact sampling distribution. However, he added that S2 test is expected to have lower power. Furthermore, using several k values would lead to an over rejection of the null hypothesis. Consequently, Belaire-Franch and Contreras (2004) developed an extension of Wright’s test to the multiple variance ratio. They pointed out that Wright’s tests suffer size distortions when they are sequentially applied at several k values. They suggested to substitute the variance ratio tests by rank and sign tests in the Chow and Denning procedure. The statistics are defined as: CD(R1) = max |R1 (ki )| 1≤i≤m

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CD(R2) = max |R2 (ki )| 1≤i≤m

CD(S1) = max |S1 (ki )| 1≤i≤m

CD(S 2) = max |S2 (ki )| 1≤i≤m

For the authors, ranks based tests are more powerful than signs-based tests. Their critical values are computed for several combinations of sample size and k values. Colletaz (2006) in his own version of the multiple variance ratio test supports ContrerasFranch and Contreras’ findings. 3. Empirical results 3.1. Data and descriptive statistics This study uses daily closing prices for the French stock market index (CAC40). It is the most important indicator of the Paris stock exchange, representing the forty largest companies by market capitalization and the greatest trading volume in the stock market in France. Data span from December 31, 1987 to January 12, 2018, namely 7610 observations.6 Fig. 1 below presents the time plot of daily index. The starting point of the sample corresponds to the final wave of the financial liberalization process in France. Hence, this reporting period of nearly 30 years includes data belonging to a liberalized market. It is large enough to examine how the efficiency behavior has changed over long term. Also, it enables to observe (through the use of a rolling variance ratio) the length of inefficiency and efficiency episodes, and the modification dates. Table 1 presents descriptive statistics for the daily returns of the CAC40, which are computed as the first differences in the logs of the closing prices (rt = lnPt − lnPt − 1, where Pt is the CAC40 rate at day t ). I use the methodology proposed by Miller et al. (1994) in order to account the presence of thin trading, by estimating an AR(1), such as rtadj = εt /(1 − α1) , where rtadj is the adjusted return for thin trading at day t, rt = α 0 + α1 rt − 1 + εt , and εt the residuals. Returns are non-normal, pointing out evidence of significant negative Skewness and excess Kurtosis. It confirms the leptokurtic behavior of asset returns. The test of Jarque-Bera illustrates the fact that the hypothesis of normality7 is rejected. At last, the LM test presents strong evidence of conditional heteroskedasticity, which is a normal feature of stock prices. Table 2 reports the whole sample results of parametric and non-parametric variance ratio tests.8 Variance ratios are below unity, indicating a mean reversion process which is line with Borges (2010). Stock prices do not follow random walk, there is a difference more or less durable, between the intrinsic value and the market value. The statistic Z * (2) based on the parametric variance ratio test of Chow and Denning (1993) indicates the rejection of the null hypothesis of random walk. The rank-based test results CD . R (1) and CD . R (2) based on Belaire-Franch and Contreras (2004) support those completed with the parametric test. It is confirmed with the signs-based ratios CD . S (1) and CD . S (2) . however, Wright (2000) and Colletaz (2006) explained that signs tests are expected to have lower power than ranks tests (hence, they will not be computed thereafter). Overall, the results based on the whole sample exhibit a rejection of the market efficiency for the French stock exchange. 3.2. Rolling variance ratio test results The next figures present the results of the overlapping moving window analysis based on Belaire-Franch and Contreras rank-based tests (.R (1) and CD . R (2) ), using CAC40 index. The rolling window has a length of T = 250, 500 and 1000 which is equivalent to approximatively 1 year, 2 years and 4 years respectively. Different sizes of window will give a better insight of evolution and point of comparison. Each graph provides statistics for the rank-based tests, and the critical values at 5% level of significance characterized by a straight line. If the value of the CD . R (1) and CD . R (2) statistic exceed the critical values, the null hypothesis of random walk is rejected. If each value indicates a stochastic process, then the chart of the statistic test should lie below the straight line. Using a rolling variance ratio enables us to gain insights into both the EMH and the AMH. Figs. 2 and 3 show plots for CD . R (1) and CD . R (2) with a rolling window T = 250. Graphics for the CAC40 index indicate, that the plots lie below the 95% confidence line. So, the null hypothesis of random walk is accepted. However, it is possible to identify periods when the return predictability is statistically significant which involve the rejection of the null hypothesis. These peaks can be found in 1995, at the beginning and end of the 2000 and in the middle of the 2010, all of which can be related with periods of crisis. The evidence of return predictability in 1995 can be linked to a series of general strikes due to the proposition of prime minister to an extensive program of welfare cutbacks. The peaks in 2007–2008 can be attributed to the subprime mortgage crisis. Results seem to indicate that the French stock market does not remain constant and exhibit dynamic behavior over the period that supports the hypothesis of AMH. Figs. 4 and 5 present results with a rolling window T = 500, and support periods where the null hypothesis is rejected. 6

Daily prices of the index (CAC40) were obtained from yahoo website, and they are free of charge. The test value is greater than the critical value at 5% with two degrees of freedom (5.99). 8 The variance ratios are reported in the main rows in each table and, the statistics are given in the parentheses. We don’t comment them with homoskedasticity, because stock prices do not verify this hypothesis. We adopt the procedure of selecting lags 2, 4, 8, 16. 7

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Fig. 1. Time plot of CAC40 index. Table 1 Statistical analysis of CAC40. CAC40 rtadj Observations Mean Median Std Dev. Minimum Maximum Kurtosis Skewness Jarque Bera test LM test

7610 0.000224 0.000414 0.01355 −0.094485 0.105597 7.69 −0.084 7012 232

Table 2 Results of multiple VR tests - CAC40. t-statistic

VR(2)

VR(4)

VR(8)

VR(16)

Z * (1)

3.56**

0.99

0.95

0.88

0.83

Z * (2) CD . R (1) CD . R (2) CD . S (1) CD . S (2)

2.29*

0.99

0.95

0.88

0.83

2.46* 2.90* 2.64* 2.81*

1.02 1.01 0.99 0.98

0.97 0.96 0.95 0.95

0.92 0.90 0.91 0.90

0.88 0.86 0.89 0.90

**, * Variance ratios are significantly at the 1%, 5% level, respectively. VR(k) is the VR for the kth difference. Z * (1), Z * (2) are the Chow and Denning joint VR test, CD . R (1), CD . R (2), CD . S (1), CD . S (2) are the test for ranks and signs variance test of Belaire-Franch and Contreras.

Fig. 2. CD(R1) test on CAC40. Rolling Window T = 250.

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Fig. 3. CD(R2) test on CAC40. Rolling Window T = 250.

Fig. 4. CD(R1) test on CAC40. Rolling Window T = 500.

Fig. 5. CD(R2) test on CAC40. Rolling Window T = 500.

Comparatively to the previous figures, the periods of inefficiency are longer and more noticeable when the length of the rolling window is wider. Then, it appears that the length has an impact on persistent deviations. Results do not show the evidence of return predictability in 1995. We observe more clearly the main periods where the plot drifts. From 2003–2009, the periods of inefficiency can be explained by the invasion war in Irak in 2003, the general strike in 2006 to protest against the First Job Contract (CPE—Contrat de première embauche) introduced by the government, the subprime mortgage crisis (2007–2008) that has been followed by the European sovereign debt crisis in 2009. Since 2012, stock markets have known alternatively recovery phases and negative events. Indeed, the peaks in 2012–2013 can be related to the Cypriot financial crisis, those in 2014 can be linked to Russian military intervention in Ukraine and the Crimea crisis successive to the Ukrainian revolution. Figs. 6 and 7 present plots with a rolling window T = 1000. Globally, results confirm the previous ones, pointing out the evidence of return predictability which can be attributed to the impact of financial, economic and political crisis, and instability phases. Using a rolling variance ratio test approach to examine the efficiency hypothesis proves to be useful and appropriate. Indeed, this econometric tool is able to show the efficiency evolution, or in other words, the degree of market efficiency. So, our empirical 162

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Fig. 6. CD(R1) test on CAC40. Rolling Window T = 1000.

Fig. 7. CD(R2) test on CAC40. Rolling Window T = 1000.

investigation allows to assert that the market knows long periods of efficiency interrupted by short periods of relative inefficiency. Results have shown that periods of inefficiency are longer and more clearly visible when the window length T is of 500 or 1000 rather than 250. Results confirm the AMH, the dynamic character of efficiency, and the literature on this field which has investigated both for various stock markets, exchange rates and for the Bitcoin market. For instance, Urquhart and McGroarty (2016) have found that return predictability in stock markets does vary over time and that each market adapts differently to certain market conditions. Then, markets are adaptable and they switch between efficiency and inefficiency at different points of time. Our results support past studies, since periods of inefficiency seem to coincide with market turbulences due to international economic events or crisis, but also to domestic economic policy events and to global politics decisions. Charles et al. (2012), Smith (2011), and Ahmad et al. (2012) have also exhibited that global macroeconomics events such the mortgage financial crisis in 2008, have affected the market efficiency. This conclusion seems normal in view of financial markets are now liberalized and globalized. Furthermore, that the French stock exchange is not only sensitive to global factors, but also domestic ones (i.e French or European). They are domestic economic policy events. First, there is a large period of inefficiency in 2009 consecutive to the European sovereign debt crisis (particular in Greece), and in 2012−2012 the Cypriot financial crisis which were European economic problems. Then, there are political events that occurred in France such as strikes in 1995 and 2006, which impacted the level of efficiency. This conclusion concurs with the study of Hiremath and Narayan (2016) about the impact of domestic policies decisions on the Indian stock exchange. Lim et al. (2013) have shown the impact of the Asian financial crisis on US stock market. This event has no impact on the French stock exchange. This may be explained by the fact that the Asian financial crisis was caused by the fixed exchange rate for Asian currencies pegged to the US dollar. It is possible to consider this as a local crisis and its influence limited to Asian countries and some of the peripheral geographic areas. These had no major consequences on the French stock market. Finally, results stress that major international politics events have involved a period of inefficiency, in particular the Russian decision of military intervention in Ukraine in 2014, and the USA invasion in Irak in 2003. That supports Khuntia and Pattanayak (2018) who showed an identical result for the Bitcoin market after the political decisions from Russia, China and Japan to prohibit institutions from dealing in bitcoin. Consequently, results have underlined that major events which alter the market conditions are various and plentiful as has been described by Khuntia et al. (2018) for the foreign exchange market. Theses shocks alter the market conditions and involve a modification in the degree of return predictability which is consistent with the AMH. Globally, stock markets are fully efficient till there is no market shock that causes market ecology to change. When a significant event triggers the process of competition and natural

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selection, the degree of efficiency on stock markets varies. It involves a temporary period of inefficiency (that varies in time). When the new market ecology is formed, efficiency of financial markets returns to pre-shock levels (Lo, 2004). In a recent study, Boya and Monino (2010) have indicated that stock prices are affected by a number of factors and events which have to be relevant, which caused inefficiency. They are classified according to good news and bad news, gathered under the name of “coloration of information”. Following the degree of influence of the informational content event (major crisis), the significance impact on asset returns can be widely different. In conclusion, the more relevant a financial or macro political news is on the stock markets the more significant will the range of inefficiency be. Past and present results (Charles et al., 2012; Lim et al., 2013) support the author’s conclusion since. If a major event such as the subprime mortgage crisis, the European sovereign debt crisis, market bubbles can create a crash on stock exchange and last for a significant period of time, thereby modifying the framework market, it means that events have an informative content that of significant weight sufficient to produce inefficiency pockets. 4. Conclusion This article examined the degree of efficiency of the French stock exchange by using weekly date from 1988 to 2018. A rolling variance ratio has been used to test this hypothesis. This approach has a major advantage since it able to assess the time varying return predictability. Results indicate that French stock exchange is broadly efficient but interrupted by periods of inefficiency. These pockets of inefficiency are statistically significant and characterized by negative serial correlation. Furthermore, episodes of inefficiency are associated with major reliable events. This finding suggests that the AMH gives a better description of the behavior of stock returns for the French stock exchange. Indeed, the AMH denotes that the degree of efficiency is changing over time depending on market conditions and the rolling variance ratio enables to furnish the finest estimate of the efficiency over time. This survey contributes to the present works on the field, by adding the analysis of the French stock exchange. Moreover, results are in line with previous ones, namely that the stock market does switch between periods of efficiency and inefficiency (Hiremath and Kumari, 2014), and consequently confirm the AMH. At last, this study can be a significant addition to the literature because it validates the assumption under which major macroeconomic global events are associated with significant return predictability (Charles et al., 2012; Hiremath and Narayan, 2016). These shocks are linked to financial, economic or political phenomena which can be defined as global and domestics factors. They cause a break in the efficiency trend of the stock market and drive it to an inefficiency episode. Investors might use this conclusion to exploit the occasional opportunities profit existence. Based on these results, we can wonder why do these events cause patterns in the time variation of return predictability and especially negative ones? A first answer to this question, provided by Boya and Monino (2010) specifies that stock prices tend to react excessively to certain news or events provided they are salient. However, the authors have not indicated whether positive events had an impact on stock markets more important than when bad news affect the market. Epstein and Schneider (2008) have shown, on the other hand that asset prices react more strongly to bad news than to good news. Shan and Gong (2012), Huerta and Perez-Liston (2011) have also shown that negative events have a significant impact on stock market. Consequently, future researches on the AMH should study if events (and more specifically the type of events) which change market conditions are only driven by major negative events or whether major positive events can also lead to periods of inefficiency. References Ahmad, R., Rhee, S.G., Wong, Y.M., 2012. Foreign exchange market efficiency under recent crises: Asia-Pacific focus. J. Int. Money Finance 31 (6), 1–26. Belaire-Franch, J., Contreras, D., 2004. Ranks and Signs-Based Multiple Variance Ratio Tests. Working Paper (1–40). Department of Economic Analysis, University of Valencia. Borges, M.R., 2010. Efficient market hypothesis in European stock markets. Eur. J. Finance 16 (7), 711–726. Boya, M.C., 2017. Testing capital market efficiency. Glob. Bus. Econ. Rev. 19 (2), 194–224. Boya, M.C., Monino, J.L., 2010. The impact of exogenous information on stock value through the coloration concept: a test model. J. Innov. Econ. 6 (2), 163–180. Charles, A., Darné, O., Kim, J.H., 2012. Exchange-rate return predictability and the adaptative markets hypothesis: evidence from major foreign exchange rates. J. Int. Money Finance 6, 1607–1626. Cheung, K.C., Coutts, J.A., 2001. A note on weak market efficiency in security prices: evidence from the Hong Kong stock exchange. Appl. Econ. Lett. 8, 47–410. Choi, I., 1999. Testing the random walk hypothesis for real exchange rates. J. Appl. Econom. 14, 293–308. Chow, K.V., Denning, K.C., 1993. A simple multiple variance ratio test. J. Econom. 58 (3), 385–401. Cochrane, J.H., 1988. How big is the random walk in GNP? J. Polit. Econ. 96, 893–920. Colletaz, G., 2006. A Simple Multiple Variance-Ratio Test Based on Ranks, Journée d’économétrie, Développements récents de l’économétrie appliquée à la finance, Working paper. Epstein, L.G., Schneider, M., 2008. Ambiguity, information quality, and asset prices. J. Finance 63 (1), 197–228. Fama, E.F., 1970. Efficient capital markets: a review of theory and empirical work. J. Finance 25, 383–417. Fama, E.F., French, K.R., 1988. Permanent and temporary components of stock prices. J. Polit. Econ. 96, 246–274. Hiremath, G.S., Kumari, J., 2014. Stock returns predictability and the adaptative market hypothesis in emerging markets: a evidence from India. Springerplus 3, 1–14. Hiremath, G.S., Narayan, S., 2016. Testing the adaptive market hypothesis and its determinants for the Indian stock markets. Finance Res. Lett. 19, 173–180. Hoque, H.A., Kim, J.H., Pyun, C.S., 2007. A comparison of variance ratio tests of random walk: a case of Asian emerging stock market. Int. Rev. Econ. Finance 16 (4), 488–502. Huang, B.N., 1995. Do asian stock market prices follow random walks? Evidence from the variance ratio test. Appl. Financ. Econ. 5, 251–256. Huerta, D.E., Perez-Liston, D., 2011. The impact of hurricanes on investor sentiment and stock market returns. Res. Int. Bus. Finance 16 (2), 136–149. Ito, M., Sugiyama, S., 2009. Measuring the degree of time varying market inefficiency. Econ. Lett. 10, 62–64. Katusiime, L., Shamsuddin, A., Agbola, F.W., 2015. Foreign exchange market efficiency and profitability of trading rules: evidence from India. Int. Rev. Econ. Finance 35, 315–335. Khuntia, S., Pattanayak, J.K., 2018. Adaptive market hypothesis and evolving predictability of bitcoin. Econ. Lett. 197, 26–28. Khuntia, S., Pattanayak, J.K., Hiremath, G.S., 2018. Is the foreign exchange market efficiency adaptive? The empirical evidence from India. J. Asia-Pac. Bus. 19 (4),

164

Research in International Business and Finance 49 (2019) 156–165

C.M. Boya

261–285. Kim, J.H., 2006. Wild bootstrapping variance ratio tests. Econ. Lett. 92, 38–43. Kim, J.H., 2009. Automatic variance ratio test under conditional heteroskedasticity. Finance Res. Lett. 3, 179–185. Kim, J.H., Shamsuddin, A., 2008. Are Asian stock markets efficient? Evidence from new multiple variance ratio tests. J. Empir. Finance 15 (3), 518–532. Kumar, D., 2018. Market efficiency in Indian exchange rates: adaptive market hypothesis. Theor. Econ. Lett. 8, 1582–1598. Lim, K.P., Luo, W., Kim, J.H., 2013. Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests. Appl. Econ. 8, 953–962. Liu, C.Y., He, J., 1991. A variance ratio tests of random walks in foreign exchange rates. J. Finance 46, 773–785. Lo, A.W., 2004. The adaptive markets hypothesis: market efficiency from an evolutionary perspective. J. Portfolio Manag. 30, 15–29. Lo, A.W., 2005. Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis. J. Invest. Consult. 7, 21–44. Lo, A.W., MacKinlay, A.C., 1988. Stock market prices do not follow random walks: evidence from a simple specification test. Rev. Financ. Stud. 1, 41–66. Matouk, J., Monino, J.L., 2005. Le Marché de Paris a la Mémoire Courte. Rev. Econ. Financ. 81, 1–25. Mignon, V., 1998. Méthodes d’Estimation de l’Exposant de Hurst: Application aux Rentabilités Boursières. Econ. Prévision 132–133, 193–214. Miller, M.H., Mustaswamy, J., Whaley, R.E., 1994. Mean reversion of SP500 index basis changes, arbitrage-induced or statistical illusion? J. Finance 49, 479–513. Mills, T.C., 1993. Is there long term memory in UK stock returns? Appl. Financ. Econ. 3, 303–306. Mobarek, A., Fiorante, A., 2014. The prospects of BRIC countries: testing weak-form efficiency. Res. Int. Bus. Finance 30, 217–232. Neely, C.J., Weller, P.A., Ulrich, J.M., 2009. The adaptive markets hypothesis: evidence from the foreign exchange market. J. Financ. Quant. Anal. 44 (2), 467–488. Poon, S.H., 1996. Persistence and mean reversion in UK stock returns. Eur. Financ. Manag. 2 (2), 169–196. Poterba, J.M., Summers, L.H., 1988. Mean reversion in stock prices: evidence and implications. J. Financ. Econ. 22, 27–59. Shan, L., Gong, S.X., 2012. Investor sentiment and stock returns: Wenchuan earthquake. Finance Res. Lett. 9 (1), 36–47. Simon, H., 1955. A behavioral model of rational choice. Quaterly J. Econ. 69, 99–118. Smith, G., 2011. The changing and relative efficiency of European emerging stock markets. Eur. J. Finance 8, 689–708. Stachowiak, C., 2004. Prévisibilité des Rentabilités Boursières: une Etude Empirique du Marché Boursier Français sur Données Intra Quotidiennes. Econ. Prévision 166, 71–85. Tabak, B.M., 2003. The random walk hypothesis and the behaviour of foreign capital portfolio flows: the Brazilian stock market case. Appl. Financ. Econ. 13, 369–378. Todea, A., Ulici, M., Silaghi, S., 2009. Adaptive markets hypothesis: evidence from Asia Pacific financial markets. Rev. Finance Bank. 1, 7–13. Urquhart, A., 2013. An Empirical Analysis of the Adaptative Market Hypothesis and Investor Sentiment in Extreme Circumstances. University of Newcastle, pp. 1–231. Urquhart, A., Hudson, R., 2013. Efficient or adaptive markets? Evidence from major stock markets using very long run historic data. Int. Rev. Financ. Anal. 28, 130–142. Urquhart, A., McGroarty, F., 2016. Are stock markets really efficient? Evidence of the adaptive market hypothesis. Int. Rev. Financ. Anal. 47, 39–49. Worthington, A.C., Higgs, H., 2003. Weak-form market efficiency in European emerging and developed markets. Discussion paper 159, School of Economics and Finance. Queensland University of Technology, Brisbane, Australia. Wright, J.H., 2000. Alternative variance-ratio tests using ranks and signs. J. Bus. Econ. Stat. 18, 1–9.

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