The causal linkages between investor sentiment and excess returns on Borsa Istanbul

+

MODEL

Available online at www.sciencedirect.com

Borsa _Istanbul Review _ Borsa Istanbul Review xxx (xxxx) xxx

http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450

Full Length Article

The causal linkages between investor sentiment and excess returns on Borsa Istanbul Efe Caglar Cagli a,*, Zeliha Can Ergu¨n b, M. Banu Durukan a b

a Dokuz Eylu¨l University, Faculty of Business, Buca, Izmir, Turkey Aydın Adnan Menderes University, Faculty of Business, S€oke, Aydın, Turkey

Received 9 August 2019; revised 28 January 2020; accepted 20 February 2020 Available online ▪ ▪ ▪

Abstract The main aim of this study is to analyze the causal relationship between BIST-100 return index and investor sentiment. The investor sentiment is measured by constructing an index comprised of the closed-end fund discount, mutual fund flows, share of equity issues in aggregate issues, repo shares in mutual funds and turnover ratio on a monthly basis for the period from 1997 to 2018. We employ a novel Granger causality test developed by Shi, Phillips, and Hurn (2018) which detects and dates the changes in causal relationships. The results show that the conventional Granger causality test indicates no causality between the sentiment index and BIST-100 return index. However, the recursive evolving window procedure detects Granger causality episodes for the proxies, except repo shares in mutual funds. The findings indicate that considering nonlinearities for the sample period could change the causal relationship between investor sentiment and the market return. _ Copyright © 2020, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classification: G10; G40; G41 Keywords: Behavioral finance; Investor sentiment; Granger causality; Recursive evolving algorithm

1. Introduction The followers of behavioral finance argue that investors have bounded rationality, and psychological and emotional factors are influential in the decision-making process. Behavioral finance theories consider investors’ general feelings of optimism and pessimism levels about the market as an additional source of systematic risk (Kıyılar & Akkaya, 2016, p. 315). Succinctly, these feelings are defined as investor sentiment, and it is argued that unpredictable changes in sentiment may have an impact on stock prices (Verma, Baklacı & Soydemir, 2008, p. 1303).

* Corresponding author. E-mail addresses: [email protected] (E.C. Cagli), [email protected] (Z. Can Ergu¨n), [email protected] (M.B. Durukan). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi.

Investor sentiment cannot be observed directly in the market, and that is why its measurement requires using proxies which are believed to represent investor sentiment. These proxies are divided into two categories as direct and indirect. Direct proxies such as American Association of Individual Investors (AAII) Sentiment Survey (i.e. Brown, 1999), Investors Intelligence (II) Index (i.e. Otoo, 1999), consumer confidence indices (i.e. Fisher & Statman, 2000) are surveybased proxies and they aim to directly measure investor sentiment. On the other hand, indirect proxies such as mutual fund flows (i.e. Neal & Wheatley, 1998), turnover ratio (i.e. Ni, Wang, & Xue, 2015), closed-end fund discounts (i.e. Baur, Quintero, & Stevens, 1998), volatility premium (i.e. Baker, Wurgler, & Yuan, 2012) are market-based measures used as indirect sentiment proxies. Additionally, some researchers such as Baker and Wurgler (2006, 2007) constructed an investor sentiment index by combining various indirect

https://doi.org/10.1016/j.bir.2020.02.001 _ 2214-8450/Copyright © 2020, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). _ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+ 2

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

proxies with the aim to create a better measure. However, there is still no consensus about which measure of sentiment is more accurate and efficient (Uygur, 2015). Although there are number of studies that examine the relationship between stock market and investor sentiment with the aforementioned proxies (i.e. Otoo, 1999; Olgaç & Temizel, 2008; Schmeling, 2009, etc.), the studies that consider structural breaks which cause nonlinearities in the data (i.e. Bolaman & Mandacı, 2014; Ça glı, Ergu¨n & Durukan, 2018) are limited. Structural breaks indicate the instability points, such as war, peace, natural disasters, terrorism, financial crises and policy changes in the parameters of the forecasting model (Valentinyi-Endresz, 2004: 12). Andreou and Ghysels (2009, pp. 839e870, p. 840) emphasized that financial time series are mostly affected by multiple structural breaks and these breaks, in turn, may affect returns and volatility which are the fundamental financial indicators. Therefore, ignoring structural breaks causes inaccurate results about the financial variables (Andreou & Ghysels, 2009, pp. 839e870; ValentinyiEndresz, 2004). In light of the above explanations, the main aim of this study is to extend the literature on the causal relationship between market returns, namely BIST-100 return index, and investor sentiment for the period between July 1997 and November 2018 by considering structural breaks in the data that causes heteroskedasticity and nonlinearity. This paper contributes to the literature in several ways. First, we construct an investor sentiment index for Borsa Istanbul, embodying the information from the closed-end fund discount, mutual fund flows, the share of equity issues in aggregate issues, repo shares in mutual funds and turnover ratio on a monthly basis, using the partial least squares regression suggested by Huang, Jiang, Tu, and Zhou (2015). Each sentiment proxy is also analyzed separately along with the investor sentiment index. Second, the sample period covers significant socioeconomic events, and financial crises, shedding light on the changes in the investor sentiment in Borsa Istanbul. Third, we employ a novel time-varying Granger causality test, developed by Shi et al. (2018), considering the stylized facts of the data (e.g. heteroskedasticity, nonlinearity etc.), and the changes in the causal relationships over time. The rest of the study is structured as follows. Section 2 discusses the literature and Section 3 provides explanations of the methodology. Section 4 presents the data and the construction of the investor sentiment index. Section 5 reports the empirical results and Section 6 concludes with the discussion of the findings. 2. Literature review The previous literature on investor sentiment is generally focused on measuring investor sentiment in stock markets by using variety of investor sentiment proxies in the US (i.e. Otoo, 1999; Fisher & Statman, 2000; Lemmon & Portniaguina, 2006, etc.), in other developed markets such as European and G7 countries (i.e. Jansen & Nahuis, 2003; Schmeling, 2009; Baker et al., 2012; Bathia & Bredin, 2013),

and in developing markets such as Pakistan (i.e. Rehman, 2013), China (i.e. Ni et al., 2015) and Taiwan (i.e. Hu, Huang, Chang & Li, 2015). The common findings of these studies are that there is a strong relationship between investor sentiment and stock market returns. Similarly, in the Turkish stock market (Borsa Istanbul) investor sentiment is investigated by using both direct and indirect proxies which provide supporting evidence for the relationship (i.e. Kandır, 2006; Canbas‚ & Kandır, 2007). Moreover, there are several studies on the causal relationship between stock returns and sentiment with conflicting findings. Brown and Cliff (2004) investigated the causal relationship between US market returns and investor sentiment by constructing a composite sentiment index, and they found a bidirectional relationship between them. On the other hand, in the US (S&P 100 index) by using put-call trading volume, putcall open interest ratio and ARMS index, Wang, Keswani, and Taylor (2006) provided evidence for a unidirectional relationship where investor sentiment is caused by stock returns. In addition, Spyrou (2012) also found a similar unidirectional relationship in the US by constructing an investor sentiment index. In the Chinese stock market, Chu, Wu, and Qiu (2016) documented a unidirectional relationship from stock returns to investor sentiment by using the number of net added accounts as a sentiment proxy, whereas by constructing a sentiment index Yang and Hasuike (2017) found a bidirectional relationship between them. Furthermore, as a preliminary test Schmeling (2009) applied Granger causality between consumer confidence index (CCI) and investor sentiment in G7 countries and provided evidence of a bidirectional relationship. In the Scandinavian stock markets (Sweden, Finland, Norway and Denmark) Grigaliunien_e and Cibulskien_e (2010) found a unidirectional relationship where returns cause sentiment by using CCI and Economic Sentiment Indicator (ESI) as a proxy for investor sentiment. In Turkey, by using the CCI as a direct proxy of investor sentiment; Olgaç and Temizel (2008), Topuz (2011), Kale and Akkaya (2016) and Can€oz (2018) found unidirectional relationship from stock prices to investor sentiment. Moreover, by using the Real Sector Confidence Index, Korkmaz and Çevik (2009) and Kale and Akkaya (2016) provided supporting evidence of a bidirectional relationship between market returns and the confidence index. Furthermore, by using indirect proxies such as closed-end fund discounts, mutual fund flows, odd lot sales ratios, the share of equity issues in aggregate issues, repo shares in mutual fund portfolios, and BIST turnover ratios; Canbas‚ and Kandır (2009) also documented a unidirectional relationship from stock prices to investor sentiment. The results of Kaya (2017) are also consistent with the findings of previous research by using investor sentiment index. Although there is a number of studies examining the linear relationship between the stock market and investor sentiment, the studies that consider structural breaks and financial crises that cause nonlinearities in the data are limited. One of the studies that analyzed the effect of investor sentiment during the financial crisis periods is conducted by Baur et al. (1998).

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

In their study, investor sentiment effect on the stock market crash of 1987 is analyzed. By using the closed-end fund discounts as an investor sentiment proxy, they found that sentiment influences stock prices in the 1987 stock market crash, however, there is no sentiment effect detected in the period surrounding the crash. Moreover, Zouaoui, Nouyrigat, and Beer (2011) investigated the effect of investor sentiment internationally in the crisis periods. As a proxy for investor sentiment they used consumer confidence index, and similar to the findings of Baur et al. (1998), it is found that the effect of investor sentiment on stock markets is significant during the financial crises (Zouaoui et al., 2011). In Turkey, the relationship between the stock market and investor sentiment for the crisis period is examined by Bolaman and Mandacı (2014). They investigated the relationship for the period between 2003 and 2012, and only the 2008 crisis was detected, and they also identified structural breaks at the crisis period. By using the consumer confidence index as a proxy for investor sentiment, a long-term relationship was found between the variables (Bolaman & Mandacı, 2014). Therefore, they concluded that investor sentiment is effective in the crisis periods in Borsa Istanbul. Furthermore, Ergu¨n and Durukan (2017) examined the effect of investor sentiment in the 1998 Asian crisis that is followed by the Russian crisis, the 2001 Turkish financial crisis and the 2008 global crisis periods. They separated these crisis periods as local and international, and they used closed-end funds discounts as a sentiment indicator. Based on their results, a negative relationship between investor sentiment and market returns for the whole and local crisis periods was found. In contrast, their findings showed no effect of investor sentiment on BIST 100 index returns for the no crisis, all crises and global crisis periods (Ergu¨n & Durukan, 2017). Lastly, different from previous studies, Ça glı, Ergu¨n and Durukan (2018) examined the effect of investor sentiment in the presence of multiple structural breaks on BIST-100 index using the volatility premium as a sentiment proxy. They concluded that in the presence of structural breaks there is a long-run and positive relationship between sentiment and market returns, and the causality between BIST-100 index and volatility premium is bidirectional (Ça glı, Ergu¨n and Durukan, 2018). Their findings are consistent with the findings of Bolaman and Mandacı (2014), supporting a long-run relationship between the BIST-100 index and investor sentiment. In sum, studies on sentiment and market return relationship mainly focus on the linear relationship and the existence of a relationship is well documented for developed as well as developing markets. The studies that investigate the effect of structural breaks and the causal relationship between the two are limited. Moreover, the findings of these studies vary based on the investor sentiment proxy used as well as the methodology employed. Hence, this study drives its motivation from these inconclusive results. The present study aims to extend the literature by employing a recent Granger causality test developed by Shi et al. (2018) which considers structural breaks in the data and by applying this methodology to different sentiment proxies to investigate the

3

relationship between investor sentiment and market returns. The findings are expected to shed light on two issues. The first issue to be dealt with is whether a causal relationship between sentiment and market returns exists and its direction. The second issue to be investigated is whether the causal relationship and its direction changes based on the investor sentiment proxy used. Moreover, the present study employs an investor sentiment index constructed for Borsa Istanbul using partial least squares regression suggested by Huang et al. (2015) 3. Methodology For testing conventional Granger (1969, 1988) causality, we estimate the following unrestricted VAR(k) model: yt ¼ a0 þ a1 yt1 þ ::: þ ak ytk þ b1 xt1 þ ::: þ bk xtk þ εt xt ¼ a0 þ a1 xt1 þ ::: þ ak xtk þ b1 yt1 þ ::: þ bk ytk þ εt

ð1Þ

where k is the optimal lag length determined by the Schwarz Information Criterion. We obtain Wald (W ) statistics following c2 distribution, with k degrees of freedom, under the null hypothesis of Granger non-causality against the alternative hypothesis of Granger causality: H0 : b1 ¼ b2 ¼ ::: ¼ bk ¼ 0

ð2Þ

We test the null hypothesis of that x does not Granger cause y in the first regression of Equation (1), and that y does not Granger cause x in the second regression of Equation (1). The standard Granger causality test loses power and suffers from misspecification in case of nonlinearity in the relationship between the time-series. The studies dealing with the nonlinearities in the data develop procedures following various approaches, forward window procedure (Thoma, 1994), rolling window procedure (Arora & Shi, 2016; Balcilar, Ozdemir, & Arslanturk, 2010; Swanson, 1998), nonparametric causalityin-quantiles (Balcilar et al., 2016, 2017) and other moments (Chen, 2016). Recently, Shi et al. (2018) propose a novel recursive evolving window algorithm for detecting changes in causal relationships and thus capturing the nonlinearities and time-varying structure of the data. The recursive evolving procedure is an extension of both the forward expanding window algorithm by Thoma (1994) and the rolling window algorithm by Swanson (1998). We estimate W for each subsample regression in the recursive evolving approach and estimate sup W (SWr) as follows: SWr ðr0 Þ ¼

sup ðr1 ;r2 Þ2L0 ;r2 ¼r

fWr2 ðr1 Þg

ð3Þ

where L0 ¼ fðr1 ;r2 Þ : 0 < r0 þ r1  r2  1; and 0  r1  1  r0 g, r is the observation of interest, r0 is the minimum window size, r1 and r2 are the starting and terminal points of the sequence of regressions, respectively. Origination (re) and termination (rf) dates in the causal relationship are calculated according to the following crossing time equations:

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

4

b r e ¼ inf fr : SWr ðr0 Þ > scvg

ð4Þ

b r f ¼ inf fr : SWr ðr0 Þ < scvg r2½b r e ;1

ð5Þ

r2½r0 ;1

Ri;t ¼

where scv is the sequence of the bootstrapped critical values of the SWr statistics. 4. Data and sentiment proxies The data is obtained from the bulletins of Capital Markets Board of Turkey and the Electronic Data Delivery System of the Central Bank of the Republic of Turkey. Five individual proxies for investor sentiment in Borsa Istanbul are calculated; closed-end fund discounts (CEFD), the share of equity issues in aggregate issues (EQTY), mutual fund flows (FLOW), repo shares in mutual fund portfolios (REPO), and turnover ratio (TURN). The CEFD is calculated following Lee, Shleifer, and Thaler (1991, p. 87) where first the value-weighted index of discounts (VWD) in month t is calculated using the following: VWDt ¼

nt X

Wt DISCit

ð6Þ

i¼1

where NAVit Wt ¼ P nt NAVit

ð7Þ

i¼1

NAVit  SPit  100 ð8Þ NAVit In Equation (6), nt is the number of funds with available DISCit and NAVit data at the end of month t. In Equations (7) and (8), NAVit indicates the per-share net asset value at the end of month t. In Equation (8), SPit shows the stock price at the end of month t. In the second stage, changes in the value-weighted index of discounts (DVWD) are calculated with the following equation and it is used as a proxy for investor sentiment: DISCit ¼

DVWDt ¼ VWDt  VWDt1

ð9Þ

In Equation (9), VWDt-1 is the value-weighted index of discounts in the previous month. The CEFD is negatively related to investor sentiment. The EQTY is calculated by dividing the total share issues at month t to the total issues, and it is positively related to investor sentiment (see Baker & Wurgler, 2000, p. 2248; and Baker & Stein, 2004, p. 288). The FLOW is calculated based on the calculation of Sirri and Tufano (1998, p. 1594) with the following formula: FLOWi;t ¼

TNAi;t  TNAi;t1 ð1 þ Ri;t Þ TNAi;t1

the end of month t, TNAi,t-1 is the total net assets of fund i at the previous month, and Ri,t is the return of fund i at the end of month t which is calculated with the following equation:

ð10Þ

In Equation (10), FLOWi,t shows the flow of fund i at the end of month t, TNAi,t indicates the total net assets of fund i at

Pi;t 1 Pi;t1

ð11Þ

In Equation (11), Pi,t, and Pi,t-1 show the share prices of fund i at the end of the month and the previous month, respectively. In the last stage, the average flow of all mutual funds is computed, and it is used as a proxy for investor sentiment: AFLOWt ¼

n 1X FLOWi;t n i¼1

ð12Þ

In Equation (12), n shows the total number of mutual funds at the end of month t. Because the focus of this study is the stock market, only the A-type mutual funds which must hold at least 25 percent of their holdings as stocks were taken into consideration. In June 2015, the classification of A- and Btype mutual funds were removed, and after that time the funds that hold at least 25 percent of their holdings as stocks were used again, regardless of the type of funds. The FLOW is a positive indicator of investor sentiment. Moreover, similar to Canbas‚ and Kandır (2009:40), the share of reverse repo holdings in mutual fund portfolios is used as a positive indicator of investor sentiment. Finally, the TURN is the positive indicator of investor sentiment and it is calculated by dividing the trading volume of the BIST by the market value of the BIST at month t (see Baker & Stein, 2004, p. 282). After calculating each proxy, Borsa Istanbul 100 Return Index (BIST-100) is used as a proxy for stock market prices, and the aggregate stock market return (D(BIST)) is the logreturn of BIST-100. The excess return on the market (BIST) is calculated by subtracting the risk-free rate from the aggregate stock market return. The sample period spans from July 1997 to November 2018, which includes significant financial crises and socioeconomic events. Table 1 presents descriptive statistics for the log-transformed time series. We check the integration properties of the time series using the M-type unit root tests following the procedures developed by Carrion-i-Silvestre, Kim, and Perron (2009), which test the null hypothesis of unit root allowing up to five (5) structural breaks in the data. Table 2 reports unit root test statistics for Table 1 Descriptive statistics, log series.

Mean Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera

BIST-100

CEFD

EQTY

FLOW

REPO

TURN

10.555 12.147 7.618 1.174 0.727 2.504 25.268a

0.074 3.448 3.685 1.747 0.138 1.921 13.278a

0.253 0.693 0.000 0.246 0.503 1.695 29.059a

0.069 3.754 3.960 0.561 0.830 26.839 6115.018a

0.338 0.621 0.018 0.153 0.532 2.448 15.388a

2.667 3.559 1.824 0.252 0.199 3.997 12.336a

Note: a denotes statistical significance at the 1% level for the Jarque-Bera test of which the null hypothesis is that time series has a normal distribution.

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

5

Table 2 Carrion-i-Silvestre et al. (2009) Unit root test statistics. Asset

MZA

MSB

MZT

TB1

TB2

TB3

TB4

TB5

BIST-100 D(BIST) BIST CEFD EQTY FLOW REPO TURN

39.212 108.704a 93.631a 92.154a 97.749a 70.951a 48.687b 99.828a

0.113 0.068a 0.073a 0.074a 0.072a 0.084a 0.101b 0.071a

4.427 7.369a 6.839a 6.788a 6.991a 5.956a 4.933b 7.063a

Apr-00 Dec-99 Aug-99 Jan-00 Jan-00 Feb-00 Sep-99 Jan-00

Mar-03 Nov-02 Oct-01 Jun-03 Jan-03 Apr-02 Jun-02 Sep-02

Feb-06 Apr-05 Oct-03 Feb-07 Nov-05 Jun-04 Jan-06 Mar-05

Feb-09 Jan-08 Feb-06 Sep-09 Feb-08 Oct-06 Apr-10 Dec-07

Apr-11 Aug-11 Mar-10 Apr-12 May-10 Dec-08 Sep-16 Aug-12

Note: a and b denote statistical significance at the 1% and 5% level, respectively. The null hypothesis of the M-type unit root tests is that the time series contains a unit root.

BIST and each individual sentiment proxy. The M-type unit root statistics suggest rejecting the null hypothesis of unit root for each individual proxy, aggregate stock market return, and excess return on the market, indicating that those series are stationary over time, I (0). We cannot reject the null hypothesis of unit root for the log-price series of BIST-100 return index, however, it becomes stationary when first-differenced, suggesting that the series is integrated of order one, I (1). Next, using the individual proxies described above, we calculate a sentiment index estimating the partial least squares regression (PLS ) suggested by Huang et al. (2015). To remove the effect of business cycle variation, each individual proxy is regressed on the change in industrial production, and a dummy variable for OECD-dated recessions, and smoothed six-month moving average values. Following Huang et al. (2015), the share turnover is lagged 12 months relative to the other proxies. The calculated sentiment index is illustrated in Fig. 1, covering the period between January 1999 and November 2018. The sentiment index tends to decrease before significant economic events (e.g. recessionary periods), indicating the index may be used as a leading indicator. The index, for instance, reached its highest level in 2005 and then ramped down to its lowest level during the global financial crisis, triggered by the mortgage delinquencies in late 2007; the same applies to the devastating domestic banking crises of Turkey in late 2000.

Fig. 1. The Sentiment Index. The shaded areas denote the recessionary period, based on OECD based recession indicators for Turkey from the peak through the period preceding the trough.

We check the integration properties of the sentiment index using the M-type unit root tests. The unit root test statistics, reported in Table 3, suggest that the sentiment index is integrated of order one, I (1); the index has a unit root in its level and becomes stationary when first-differenced. 5. Empirical results We estimate bi-variate Vector Autoregressions (VAR) to test the standard Granger causality between excess return and each individual proxy, respectively, and the results are reported in Table 4. We cannot reject the null hypothesis of no causality for the following pairs, CEFD, EQTY, FLOW, and REPO; however, the Granger causality test suggests rejecting the null hypothesis of no causality at the 5% level, indicating bi-directional Granger causality between TURN and BIST. To check the nonlinearity, which causes the misspecification of the standard Granger causality tests, we estimate the Brock, Dechert, Scheinkman, and LeBaron (1996, BDS ) statistics on the residuals obtained from the VAR systems; the results are reported in Table 5. We can reject the null hypothesis of independent and identically distributed (i.i.d.) residuals, at the 10% level, or better, at various embedding dimensions (m), for all cases except EQTY, for which the BDS statistics are significant at the 1% level, for the fifth and sixth embedding dimensions. The results provide significant evidence of nonlinearity in the relationship between excess returns on BIST and the individual proxies, indicating the necessity of employing a Granger causality test considering nonlinearity in the data. Accordingly, we test Granger causality between the pairs by employing the recursive evolving window procedure of Shi et al. (2018), considering heteroskedasticity and capturing nonlinearities in the data1; and the estimation results are illustrated in Figs. 2e6 which report the MWald test statistics sequence and 90% critical value sequence for each pair. The 1 Not only is recursive evolving procedure implemented, but also the causality tests following forward (Thoma, 1994) and rolling window (Swanson, 1998) procedures, which modify the standard Granger causality test, and consider the nonlinearities in the data are estimated; however, for the sake of space, we do not report the results from the latter, which are similar to those reported in the paper, though, available upon request.

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

6

Table 3 Carrion-i-Silvestre et al. (2009) Unit root test statistics. Asset

MZA

MSB

MZT

TB1

TB2

TB3

TB4

TB5

SENT D(SENT)

28.202 49.269b

0.133 0.101b

3.751 4.962b

Nov-00 Feb-01

May-03 Nov-03

May-05 Nov-05

Dec-07 Feb-08

Jul-11 Mar-11

Note: b denotes statistical significance at the 1% and 5% level, respectively. The null hypothesis of the M-type unit root tests is that the time-series contains a unit root.

Table 4 Standard granger causality between BIST and individual proxies. CEFD s> BIST_RP BIST_RP s> CEFD EQTY s> BIST_RP BIST_RP s> EQTY FLOW s> BIST_RP BIST_RP s> FLOW REPO s> BIST_RP BIST_RP s> REPO TURN s> BIST_RP BIST_RP s> TURN

Test

Prob

0.206 2.198 0.024 0.023 0.022 1.936 0.358 0.363 3.890 10.340

0.650 0.138 0.878 0.879 0.881 0.164 0.549 0.547 0.049 0.001

Note: s> denotes no causality direction, for instance, CEFD s> BIST denotes no causality running from CEFD to BIST. The optimal lag length is determined by the Schwarz Information Criterion as one (1) for all VAR systems.

Table 5 BDS Statistics on Residuals from VAR(1) systems.

BIST CEFD BIST EQTY BIST FLOW BIST REPO BIST TURN

m¼2

m¼3

m¼4

m¼5

m¼6

a

a

a

a

0.097a 0.043a 0.094a 0.046a 0.096a 0.099a 0.091a 0.053a 0.095a 0.049a

0.021 0.015a 0.020a 0.007 0.021a 0.054a 0.020a 0.012c 0.019a 0.017a

0.041 0.027a 0.041a 0.006 0.041a 0.095a 0.039a 0.035a 0.041a 0.030a

0.066 0.037a 0.065a 0.008 0.065a 0.112a 0.061a 0.042a 0.065a 0.042a

0.084 0.042a 0.083a 0.028a 0.084a 0.111a 0.079a 0.049a 0.084a 0.050a

Note: m denotes the embedding dimension, the number of consecutive data points to include in the set. Superscripts a, b, and c indicate statistical significance at the 1%, 5%, and 10% levels, respectively, based on the bootstrapped p-values, computed with 2500 repetitions.

left and right columns of Figs. 2e6 show the results for the causality direction from each individual proxy to BIST, and the causality running from BIST to each individual proxy, respectively. We test the null hypothesis of no causality between the variables and reject it when MWald test statistics exceed the 90% critical value sequence. The shaded areas in the figures denote the recessionary period, based on OECD based recession indicators for Turkey from the peak through the period preceding the trough. Fig. 2 shows the estimation results for the Granger causality between CEFD and BIST. We cannot reject the null hypothesis of no causality from CEFD to BIST as the MWald test statistics are always below the 90% critical value sequence. However, the recursive evolving window procedure detects two episodes

of Granger causality from BIST to CEFD at the 10% level, or better; first lasts four months between April 2008 and July 2008; the second lasts 26 months between August 2015 and September 2017. Fig. 3 shows the estimation results for the Granger causality between EQTY and BIST. The procedure detects two series of Granger causality running from EQTY to BIST at the 10% level, or better; the first series occur between April 2008 and June 2009; and the second series occur between September 2014 and June 2017. We reject the null hypothesis of no causality from BIST to EQTY at the 10% level, or better, during the period between October 2008 and April 2009. Fig. 4 shows the estimation results for the Granger causality between FLOW and BIST. The procedure detects an episode of Granger causality running from FLOW to BIST at the 10% level, or better, during the period between June 2015 and November 2018. For the Granger causality running from BIST to FLOW, the procedure detects two significant episodes at the 10% level, or better; the first lasts 22 months, originated in March 2002 and terminated in December 2003; and the second lasts 31 months between January 2005 and July 2007. Fig. 5 shows the estimation results for the Granger causality between REPO and BIST. We cannot reject the null hypothesis of no causality between REPO and BIST as the MWald statistics reported in both columns of Fig. 5 do not exceed their 90% critical value sequence. Fig. 6 shows the estimation results for the Granger causality between TURN and BIST. The procedure detects a significant episode of Granger causality from TURN to BIST at the 10% level, or better, between December 2002 and December 2007. The procedure also detects the episode of Granger causality from BIST to TURN at the 10% level, or better, starting in November 2002 and lasting until the end of the sample period. Using the sentiment index constructed using the individual sentiment proxies, we employ the standard Granger causality test between BIST and the first differenced sentiment index (DSENT ) and report the results in Table 6. We cannot reject the null hypothesis of no causality between BIST and DSENT. We report BDS statistics on the residuals from VAR(1) models for BIST and the sentiment index in Table 7. Similar to the results reported in Table 5, we can reject the null hypothesis of independent and identically distributed (i.i.d.) residuals, at the 10% level, or better, at various embedding dimensions (m), indicating the existence of nonlinearity in the relationship between excess returns on BIST and the sentiment index. Following the significant evidence of nonlinearity, we test Granger causality between BIST and DSENT employing the recursive evolving window procedure. Fig. 7 reports the

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

7

Fig. 2. Recursive evolving granger causality testing between BIST and CEFD.

Fig. 3. Recursive evolving granger causality testing between BIST and EQTY.

Fig. 4. Recursive evolving granger causality testing between BIST and FLOW.

estimation results for the Granger causality between BIST and DSENT. The procedure detects two main episodes of Granger causality from DSENT to BIST at the 10% level, or better, the first originates in January 2005 and terminates in March 2005; and the second occurs during the global financial crises, between March 2008 and March 2009. However, we cannot reject the null hypothesis of no causality from BIST to DSENT

as the MWald statistics do not exceed the 90% critical value sequence. 6. Concluding remarks Investor sentiment is defined as general feelings of optimism and pessimism levels of investors and behavioral finance

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

8

Fig. 5. Recursive evolving granger causality testing between BIST and REPO.

Fig. 6. Recursive evolving granger causality testing between BIST and TURN.

Table 6 Standard granger causality between BIST and sentiment index. D(SENT) s> BIST_RP BIST_RP s> D(SENT)

Test

Prob

0.927 2.061

0.336 0.151

Note: s> denotes causality direction, for instance, CEFD s> BIST denotes causality running from CEFD to BIST. The optimal lag length is determined by the Schwarz Information Criterion as one (1).

Table 7 BDS Statistics on Residuals from VAR(1) systems.

BIST D(SENT)

m¼2

m¼3

m¼4

m¼5

m¼6

0.028a 0.007

0.051a 0.017c

0.076a 0.023b

0.096a 0.028b

0.105a 0.033a

Note: m denotes the embedding dimension, the number of consecutive data points to include in the set. Superscripts a, b, and c indicate statistical significance at the 1%, 5%, and 10% levels, respectively, based on the bootstrapped p-values, computed with 2500 repetitions.

theories consider investor sentiment as an additional source of systematic risk (Kıyılar & Akkaya, 2016, p. 315). Therefore, investor sentiment and its effect on market returns have been analyzed in various markets whereas the studies on the causal relationship are limited. In this regard, the main aim of this

study was to analyze the causal relationship between BIST100 return index and investor sentiment by taking structural breaks into consideration using different investor sentiment proxies. Different from the previous studies, investor sentiment was measured by constructing an index comprising the closed-end fund discount (CEFD), mutual fund flows (FLOW), share of equity issues in aggregate issues (EQTY), repo shares in mutual funds (REPO) and turnover ratio (TURN) on a monthly basis between July 1997 and November 2018. The sentiment index is constructed by using partial least squares regression suggested by Huang et al. (2015). Each indirect proxy was analyzed separately along with the investor sentiment index in order to contribute to the literature by extending the analysis to investigate whether the causal relationship changes based on the investor sentiment proxy used. The other contribution of the study is achieved by employing a recent Granger causality test developed by Shi et al. (2018) which considers structural breaks in the data that causes heteroskedasticity and nonlinearities with the aim to test for the existence and direction of the causal relationship between sentiment and market returns. The results show that, by using the conventional Granger causality test, no causality between CEFD, EQTY, FLOW, REPO, the sentiment index, and BIST-100 return index is

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

9

Fig. 7. Recursive evolving granger causality testing between BIST and SENT.

found; and there is a bidirectional relationship between TURN and BIST-100. When the recursive evolving window procedure taking nonlinearities into consideration is applied, the causal relationships have changed. While there is a Granger causality from BIST-100 to CEFD and SENT to BIST, there is a bidirectional relationship between EQTY and BIST-100, FLOW and BIST-100, TURN and BIST-100 for the different episodes. No causal relationship between BIST-100 and REPO is found. Therefore, the findings imply that the causal relationship between investor sentiment and stock returns change over time, as well as based on the sentiment proxy employed, and it is crucial to consider nonlinearities over the sample period. For that reason, implementing an econometric framework based on a dynamic setting may be more proper to detect potential changes in causal relationships. Moreover, since the majority of the sentiment proxies are found to have a Granger causality relationship with BIST-100 returns, it can be argued that the effect of investor sentiment on Borsa Istanbul has to be considered as an additional source of systematic risk while making investment decisions by investors, policymakers and portfolio managers. However, it should be underlined that even though the majority of the investor sentiment proxies lead to similar results, the existence and the direction of the causal relationship found are dependent on the methodology used and the investor sentiment proxy employed. Finally, our results should be interpreted with caution since the Granger causality test based on the recursive evolving window procedure provides results for the in-sample predictive ability of the sentiment index, and each sentiment proxy considered. Future research may extend our empirical analyses by investigating the out-of-sample forecasting ability of the investor sentiment index developed in this paper. References Andreou, E., & Ghysels, E. (2009). Structural breaks in financial time series. Handbook of financial time series. Berlin: Springer. Arora, V., & Shi, S. (2016). Energy consumption and economic growth in the United States. Applied Economics, 48(39), 3763e3773. Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271e299.

Baker, M., & Wurgler, J. (2000). The equity share in new issues and aggregate stock returns. The Journal of Finance, 7(99), 2219e2257. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645e1680. Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. The Journal of Economic Perspectives, 21(2), 129e152. Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104(2), 272e287. Balcilar, M., Cakan, E., & Gupta, R. (2017). Does US news impact asian emerging markets? Evidence from nonparametric causality-in-quantiles test. The North American Journal of Economics and Finance, 41, 32e43. Balcilar, M., Gupta, R., Kyei, C., & Wohar, M. E. (2016). Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Economies Review, 27(2), 229e250. Balcilar, M., Ozdemir, Z. A., & Arslanturk, Y. (2010). Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics, 32(6), 1398e1410. Bathia, D., & Bredin, D. (2013). An examination of investor sentiment effect on G7 stock market returns. The European Journal of Finance, 19(9), 909e937. Baur, M. N., Quintero, S., & Stevens, E. (1998). The 1986-88 stock market: Investor sentiment or fundamentals? Managerial and Decision Economics, 17(3), 319e329. € & Mandacı, P. (2014). Effect of investor sentiment on stock Bolaman, O., markets. Finansal Aras‚tırmalar ve Çalıs‚malar Dergisi, 6(11), 51e64. Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197e235. Brown, G. W. (1999). Volatility, sentiment, and noise traders. Financial Analysts Journal, 55(2), 82e90. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1e27. Canbas‚, S., & Kandır, S. Y. (2007). The effect of investor sentiment on ISE _ _ sector indices. Dokuz Eylul Universitesi Iktisadi ve Idari Bilimler Faku¨ltesi Dergisi, 22(2), 219e248. Canbas‚, S., & Kandır, S. Y. (2009). Investor sentiment and stock returns: Evidence from Turkey. Emerging Markets Finance and Trade, 45(4), 36e52. _ (2018). The causality relationship between Istanbul stock exchange Can€oz, I. 100 index and consumer confidence index: The case of Turkey. Fiscaoeconomia, 2(1), 136e153. Carrion-i-Silvestre, J. L., Kim, D., & Perron, P. (2009). GLS-based unit root tests with multiple structural breaks under both the null and the alternative hypotheses. Econometric Theory, 25(6), 1754e1792. Çaglı, E.Ç., Ergu¨n, Z. C., & Durukan, M. B. (2018, October). The effect of investor sentiment on Borsa Istanbul in the presence of structural breaks. In Presentation at the meeting of 22nd finance symposium. Turkey: Mersin.

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001

+ 10

MODEL

_ E.C. Cagli et al. / Borsa Istanbul Review xxx (xxxx) xxx

Chen, Y. T. (2016). Testing for granger causality in moments. Oxford Bulletin of Economics & Statistics, 78(2), 265e288. Chu, X., Wu, C., & Qiu, J. (2016). A nonlinear granger causality test between stock returns and investor sentiment for Chinese stock market: A waveletbased approach. Applied Economics, 48(21), 1915e1924. Ergu¨n, Z. C., & Durukan, M. B. (2017). Investor sentiment in the crisis periods: Evidence from Borsa Istanbul. Journal of Business, Economics and Finance (JBEF), 6(4), 309e317. Fisher, K. L., & Statman, M. (2000). Investor sentiment and stock returns. Financial Analysts Journal, 56(2), 16e23. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424e438. https:// doi.org/10.2307/1912791. Granger, C. W. J. (1988). Some recent development in a concept of causality. Journal of Econometrics, 39(1e2), 199e211. https://doi.org/10.1016/ 0304-4076(88)90045-0.  & Cibulskien_e, D. (2010). Investor sentiment effect on stock Grigali unien_e, Z., returns in scandinavian stock market. Economics and Management, 15, 929e940. Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor sentiment aligned: A powerful predictor of stock returns. Review of Financial Studies, 28, 791e837. Hu, W., Huang, C., Chang, H., & Lin, W. (2015). The effect of investor sentiment on feedback trading and trading frequency: Evidence from taiwan intraday data. Emerging Markets Finance and Trade, 51(1), 111e120. Jansen, W. J., & Nahuis, N. J. (2003). The stock market and consumer confidence: European evidence. Economics Letters, 79(2003), 89e98. Kale, S., & Akkaya, M. (2016). The relation between confidence climate and stock returns: The case of Turkey. Procedia Economics and Finance, 38, 150e162. _ Kandır, S. Y. (2006). Tu¨ketici gu¨veni ve hisse senedi getirileri i?lis‚kisi: IMKB Mali sekt€ or sirketleri u¨zerinde bir uygulama. Ç.U¨. Sosyal Bilimler Enstitu¨su¨ Dergisi, 15(2), 217e230. Kaya, E. (2017, October). Yatırımcı duyarlılıgının hisse senedi getirileri u¨zerindeki etkisinin tespiti ve yatırımcı duyarlılıgı endeksi. Presentation at the 21st finance symposium. Turkey: Balıkesir. _ (2009). Reel kesim gu¨ven endeksi ile IMKB _ Korkmaz, T., & Çevik, E.I. 100 endeksi arasındaki dinamik nedensellik i?lis‚kisi. Istanbul U¨niversitesi Ilahiyat Faku¨ltesi Dergisi, 38(1), 24e37. Kıyılar, M., & Akkaya, M. (2016). Davranıs‚sal finans. Istanbul: Literatu¨r yayınları. Lee, C. M. C., Shleifer, A., & Thaler, R. H. (1991). Investor sentiment and the closed-end fund puzzle. The Journal of Finance, 46(1), 75e109. Lemmon, M., & Portniaguina, E. (2006). Consumer confidence and asset prices: Some empirical evidence. Review of Financial Studies, 19(4), 1499e1529. Neal, R., & Wheatley, S. M. (1998). Do measures of investor sentiment predict returns? Journal of Financial and Quantitative Analysis, 33(4), 523e547.

Ni, Z. X., Wang, D. Z., & Xue, W. J. (2015). Investor sentiment and its nonlinear effect on stock returns - new evidence from the Chinese stock market based on panel quantile regression model. Economic Modelling, 50, 266e274. Olgaç, S., & Temizel, F. (2008). The relationship between stock returns and investor sentiment: Turkish case. TISK Akademi, 2(2008), 225e239. Otoo, M. W. (1999). Consumer sentiment and the stock market. https://doi.org/ 10.2139/ssrn.205028. . (Accessed 10 July 2019). Rehman, M. U. (2013). Investor's sentiments and stock market volatility: An empirical evidence from emerging stock market. Pakistan Journal of Commerce & Social Sciences, 7(1), 80e90. Schmeling, M. (2009). Investor sentiment and stock returns: Some international evidence. Journal of Empirical Finance, 16(2009), 394e408. Shi, S., Phillips, P. C. B., & Hurn, S. (2018). Change detection and the causal impact of the yield curve. Journal of Time Series Analysis, 39(6), 966e987. https://doi.org/10.1111/jtsa.12427. John Wiley & Sons, Ltd (10.1111). Sirri, E. R., & Tufano, P. (1998). Costly search end mutual fund flows. The Journal of Finance, 53(5), 1589e1622. Spyrou, S. (2012). Sentiment changes, stock returns and volatility: Evidence from NYSE, AMEX and NASDAQ stocks. Applied Financial Economics, 22(19), 1631e1646. Swanson, N. R. (1998). Money and output viewed through a rolling window. Journal of Monetary Economics, 41(3), 455e474. Thoma, M. A. (1994). Subsample instability and asymmetries in moneyincome causality. Journal of Econometrics, 64(1e2), 279e306. Topuz, Y. V. (2011). Tu¨ketici gu¨veni ve hisse senedi fiyatları arasındaki nedensellik i?lis‚kisi: Tu¨rkiye €ornegi. Ekonomik ve Sosyal Aras‚tırmalar Dergisi, 7(1), 53e65. Uygur, U. (2015). The effects of investor sentiment on conditional volatility of asset returns: Evidence from international stock markets. (Unpublished PhD dissertation). Istanbul: Istanbul Technical University Graduate School of Science Engineering and Technology. Valentinyi-Endresz, M. (2004). Structural breaks and financial risk management. MNB working papers. https://ideas.repec.org/p/mnb/wpaper/200411.html. (Accessed 5 July 2018). Verma, R., Baklaci, H., & Soydemir, G. (2008). The impact of rational and irrational sentiments of individual and institutional investors on DJIA and S&P500 index returns. Applied Financial Economics, 18(16), 1303e1317. Wang, Y. H., Keswani, A., & Taylor, S. J. (2006). The relationship between sentiment, returns and volatility. International Journal of Forecasting, 22(2006), 109e123. Yang, Y., & Hasuike, T. (2017, July). Construction of investor sentiment index in the Chinese stock market. Presented in the 6th IIAI international congress on advanced applied informatics, Japan. Zouaoui, M., Nouyrigat, G., & Beer, F. (2011). How does investor sentiment affect stock market crises? Evidence from panel data. The Financial Review, 46(2011), 723e747.

_ Please cite this article as: Cagli, E. C et al., The causal linkages between investor sentiment and excess returns on Borsa Istanbul, Borsa Istanbul Review, https:// doi.org/10.1016/j.bir.2020.02.001