Asset pricing factors and future economic growth

Accepted Manuscript Asset pricing factors and future economic growth Vaibhav Lalwani, Madhumita Chakraborty

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S0165-1765(18)30168-X https://doi.org/10.1016/j.econlet.2018.04.031 ECOLET 8030

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Economics Letters

Received date : 6 April 2018 Revised date : 22 April 2018 Accepted date : 26 April 2018 Please cite this article as: Lalwani V., Chakraborty M., Asset pricing factors and future economic growth. Economics Letters (2018), https://doi.org/10.1016/j.econlet.2018.04.031 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

*Highlights (for review)

Highlights    

Do common asset-pricing factors such as size, value, etc. predict economic growth? We test this hypothesis in 4 emerging and 5 developed stock markets. Overall stock market returns are related to economic growth. Other asset-pricing factors do not contain any information about economic growth.

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Asset pricing factors and future economic growth Vaibhav Lalwani1 Indian Institute of Management Lucknow

Madhumita Chakraborty Indian Institute of Management Lucknow

Abstract We test if seven commonly used asset-pricing factors predict future growth in GDP and industrial production. There is minimal evidence that asset-pricing factors other than the market factor predict future economic growth. Predictive power of these factors reported in earlier literature seems to have attenuated. JEL Codes: G12, G15 Keywords: asset pricing; risk factors; economic growth

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Corresponding Author : Vaibhav Lalwani, Room no 23, FPM Hostel, Indian Institute of Management Lucknow, Lucknow-226013, India Phone: +91-9311572672. E-mail – [email protected]

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Introduction In the study of finance, considerable attention has been paid to common factors that seem to be related to stock returns. Hundreds of these factors have mushroomed over the last few decades, each with a unique claim of explaining movements in stock returns. Most popular and empirically robust among these factors are the Size, Value and Momentum factors explored in Fama & French (1993) and Jagadeesh and Titman (1993). Fama and French (1993, 1998) (Henceforth FF) argue that the size and value factors in their 3-factor asset-pricing model proxy for risks that are not captured by the capital asset pricing model. In line with the risk-based explanation, they argue that the size and value factors, SMB and HML respectively act as proxies for state variables in the context of the intertemporal capital asset pricing model (ICAPM) of Merton (1973). While there is a consensus that the use of additional factors such as size, value etc. does tend to improve the explanatory power of asset pricing models, the economic rationale behind this observation remains contentious. Two schools of thought emerge from the literature: one argues that the explanatory power of additional factors is because they capture undiversifiable risks not incorporated in the market factor of CAPM while the other argues that the existence of additional factors is anomalous and originates due to behavioral biases by investors. Our study attempts to contribute to this debate by exploring whether a risk-based explanation can be provided for commonly used asset pricing factors. For this purpose, we study whether past returns on asset pricing factors have predictive power concerning future economic growth. For asset-pricing factors to be considered as proxies for risk, they should ideally have some relationship with the states of the economy. One of the first studies to test for a relationship between asset pricing factors and economic growth is Liew and Vassalou (2000) (Henceforth LV). They find that HML and SMB factors contain information about future economic growth apart from what is already contained in the market factor. Their study provided a pedestal for a risk based explanation for the size and value factors. LV (2000) is a widely referred study (900+ cites as per google scholar) and is often cited in literature as evidence for a risk based explanation of the size and value factors. For example, Cochrane (2008) writes that LV (2000) show that size and book-to-market factors are “business cycle” variables. Hodrick and Zhang (2001) refer to it in a similar fashion. However, we have reasons to believe that this study should be revisited out-of-sample (with respect to time as well as markets) for a variety of reasons. Firstly, LV (2000) themselves state that their results for some markets may not be robust due to low number of stocks in the sample. Even in the USA, they observed that their results were weaker when applied to an extended period using data from the FF data library. Given the data-snooping concerns and “statistical biases” highlighted in Lo and Mckinlay (1990) and McLean and Pontiff (2016), it will be interesting to know if the results of LV (2000) hold up in out-of-sample sample testing on about 2 decades of additional better quality data. In addition, a lot has changed in the field of multifactor asset pricing since this study. New factors have been proposed; For example, FF (2015) propose a new 5-factor model that contains two additional factors based on profitability and investment (asset growth) of firms. FF (2015) do not provide a risk based motivation for these new factors. It will be interesting to know if these additional factors also pass the test as SMB and HML did in LV (2000).

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Further, given the rising interest in emerging markets as attractive investment destinations, it makes sense to test if there is congruity between the status of factors as state variables in emerging and developed markets. In the light of the risk vs mispricing debate and LV’s (2000) evidence, our study aims to test whether six asset-pricing factors, namely size, value, profitability, momentum, investment and quality can predict future economic states. Future economic states are characterized as good or bad based on change in GDP and Industrial production index. The fundamental idea is to test if the returns on asset pricing factors act as leading indicators for future economic activity. The broad findings of our study suggest that none of the six asset pricing factors considered have any significant explanatory power in predicting future economic growth.

Data and Methods Our study comprises data from 4 emerging and 5 developed countries for 25 years from July 1992 to June 2017. The sample of countries consists of Australia, Canada, Japan, UK and the USA as developed markets and China, India, South Korea and Taiwan as emerging markets. We use Thomson Reuters Datastream and Worldscope service to collect accounting and returns data for all current as well as dead stocks listed in the markets that we study. Further, we apply data screening procedures such as those in Hanauer and Linhart (2015) to ensure data quality. The details of screening procedures are given in the supplementary appendix. Our factor construction method closely follows that of FF (2012). We use accounting data for year t to construct rankings of stocks on various characteristics and form value-weighted portfolios using these rankings on 1 July of year t+1. These portfolios are held until June of the year t+2 and then rebalanced using latest accounting data. For momentum sorts, the portfolios are formed monthly after sorting on cumulative returns of month t-12 to t-2. We calculate continuously compounded monthly returns on seven asset-pricing factors for each of the nine markets. The factors are MKT (market factor), SMB (size), HML (value), RMW (profitability), CMA (Investment), WML (Momentum) and QMJ (quality). These returns are then converted to quarterly frequency by adding monthly returns for each quarter. Detailed explanation of factor construction is provided in the supplementary appendix. For calculating the MKT factor, the quarterly series of interbank overnight rates collected from datastream is taken as the risk free rate. We also collected quarterly seasonally adjusted nominal GDP data for all the countries in our sample from the Federal Reserve economic database2. All the returns and GDP series are continuously compounded and denominated in local currencies. For the quality factor, we use the definition proposed by Zaremba (2016). Our methodology is similar to LV (2000). We calculate quarterly (q-o-q) growth in seasonally adjusted GDP for each market in our study. We then classify the quarters with highest 25% of GDP growth as good states and lowest 25% of GDP growth as bad states. For all quarters that lie in good and bad states, we then calculate lagged annual returns on asset pricing factors and test whether the returns for these factors are different in anticipation of good and bad states. The data for these 2

The quarterly GDP of China in the FRED database is actually not seasonally adjusted. To remove the seasonal effects from this series, we use the standard X11 procedure developed by the US census bureau. We have used an R programming language package called ‘seasonal’ to implement this procedure.

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statistical tests start from Q3 of 1994 until Q2 of 2017, except for India and China, where the starting period is Q3 of 1996. The difference of timelines is due to GDP and factor data availability. Table 1 provides the results of these tests.

Results Table 1 : Lagged annual returns on Asset pricing factors in good and bad economic states (GDP growth) Country MKT HML SMB RMW Good-Bad

t-stat

Good-Bad

t-stat

Australia

11.01

2.18

5.63

Canada

15.82

2.70

-10.12

1.25

7.66

-1.20

10.47

China

22.35

1.88

3.61

0.88

India

18.08

1.72

12.62

1.50

Japan

14.47

South-Korea

8.06

2.04

-7.57

-1.73

2.53

0.79

-2.18

-0.39

-0.23

Taiwan

21.69

2.57

-1.96

-0.32

1.35

UK

10.65

2.12

9.32

2.30

USA

17.52

3.29

3.07

0.60

WML

Good-Bad

t-stat

CMA

Good-Bad

t-stat

2.16

-2.50

-0.89

2.49

-10.95

-2.09

-10.16

-1.91

-0.11

-0.03

-3.38

-0.51

-4.23

-0.76

0.90

2.57

1.01

-0.04

10.55

2.66

0.28

0.42

0.11

5.87

2.12

-3.97

-1.54

1.40

0.48

-3.55

-1.55

QMJ

Good-Bad

t-stat

Good-Bad

t-stat

Good-Bad

t-stat

Australia

-3.08

-0.33

-8.55

-2.38

6.19

1.38

Canada

-8.74

-0.66

-3.25

-0.68

4.43

1.40

China

3.27

0.59

-1.23

-0.35

3.25

0.84

India

-0.18

-0.02

8.50

1.18

6.21

0.86

Japan

-4.30

-0.81

-3.70

-1.07

-3.33

-1.16

South-Korea

-19.48

-2.97

-2.95

-0.57

-5.04

-0.93

Taiwan

1.25

0.26

-10.49

-2.43

-0.77

-0.25

UK

-4.96

-0.70

-2.74

-0.88

6.71

1.49

USA

-9.12

-1.04

-2.29

-0.72

10.38

3.04

Notes : Good-Bad column refers to the difference between the annual returns of a factor in good and bad economic states t-stat is the t statistic of the Welch test of equality of means testing whether returns of a factor are equal in good and bad states.

For all countries in our sample, the difference in the returns on market factor is unfailingly higher in good economic states. In all the developed markets and Taiwan, the t-statistic is greater than 2.0, suggesting highly significant positive returns leading a good state. This finding is not surprising given that a large body of literature has already established the overall stock market as a leading indicator of real economic activity. For other factors, the direction of returns in good vs bad states is not consistent among markets. In addition, the difference between factor returns in good and bad states is insignificant in most cases. LV (2000) report that in 9 out of ten countries in their study, the SMB factor yields positive returns in the lead up to higher economic growth. In our case, seven countries have positive returns for SMB and only three; Australia, Canada and UK have significantly higher returns. For now, it seems that there is moderate evidence that SMB contains information about

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future economic growth. Later in the next set of tests based on multiple regression, we will test whether this information is independent of what is already contained in the market returns. The probability factor RMW and quality QMJ have no clear pattern of returns in good and bad economic states. Most values are insignificant and there is little consistency in the signs among different countries. The value factor, HML shares a similar story. Four countries have positive HML returns while 5 have negative returns in good economic states. LV (2000) argue that both SMB and HML should have positive returns in good economic states and report that it is the case in many countries. Our evidence on SMB and HML is much weaker than reported in their study. For the investment factor CMA, we observe lower returns in good states for all countries except India. Though there is a consistency in the sign of the coefficients, the returns are significant only in Australia and Taiwan. The results observed for CMA echo those of Grobys (2016). He reported that the asset growth anomaly is significant during times of stress. Therefore, CMA factor yields positive returns in bad states, as observed here. The results for CMA are in line with a risk-based explanation based on the q-theory of investment, which, according to Grobys (2016) suggests a higher asset growth premium when the economy is weak. Except for South Korea, there is an insignificant difference in returns on momentum factor for good and bad economic states. In South Korea, the WML factor has performed significantly poorly in the lead up to higher economic activity. Although momentum has negative returns in seven of the nine markets, we would still not like to make ad-hoc generalizations about such an observation given that it is significant only in one country. Also, momentum is somewhat distinct from other factors in our study as it is subject to monthly rebalancing as opposed to annually. Given its short-term nature visà-vis other factors, taking one year lagged returns of this factor may bias our results. In unreported results, we take 1-quarter lagged returns and find that difference in WML is insignificant for good and bad economic states. There is also no consistency in the sign of return difference. In the line of LV (2000), we conclude that momentum does not have any predictive power for future economic growth.

We complement the results discussed above by using additional regression analysis based tests. The quarterly regressions we run are of the form:

Where GDP growth is the one period ahead quarterly growth rate of GDP for a country. MKT refers to annual returns on the market portfolio less the risk free rate. Factor is the lagged annual return on one of the six asset pricing factors considered in our study. These tests allow us to check whether asset-pricing factors contain unique information (i.e. information not already contained in the overall market returns) for predicting future economic growth. Table 2 contains the results of the OLS regressions discussed. Table 2: Bivariate regressions of future GDP growth on lagged annual returns on market and other asset pricing factors Country

MKT

HML

t(MKT)

t(HML)

AdjR2

Country

MKT

RMW

t(MKT)

t(RMW)

AdjR2

Australia

0.03

0.01

2.90

0.43

0.11

Australia

0.03

-0.01

3.25

-0.66

0.11

Canada

0.04

0.00

3.37

-0.06

0.27

Canada

0.04

0.00

2.95

-0.10

0.28

China

0.02

0.01

3.13

0.69

0.15

China

0.02

0.02

3.30

0.56

0.16

India

0.02

0.01

2.38

1.56

0.11

India

0.02

-0.01

2.13

-0.50

0.07

5

Japan

0.02

0.00

2.33

-0.36

0.14

Japan

0.02

0.02

3.09

1.37

0.16

South.Korea

0.03

-0.01

2.41

-0.81

0.14

South.Korea

0.03

0.07

3.05

5.07

0.37

Taiwan

0.03

-0.01

4.10

-0.91

0.08

Taiwan

0.03

0.02

4.02

0.81

0.08

UK

0.02

0.02

2.51

2.75

0.19

UK

0.02

-0.02

2.10

-1.52

0.15

USA

0.03

0.00

3.69

0.35

0.29

USA

0.03

0.00

3.06

-0.01

0.29

Country

MKT

SMB

t(MKT)

t(SMB)

AdjR2

Country

MKT

CMA

t(MKT)

t(CMA)

AdjR2

Australia

0.02

0.03

1.56

1.55

0.14

Australia

0.02

-0.03

1.45

-1.36

0.16

Canada

0.03

0.03

3.78

2.32

0.35

Canada

0.04

0.00

4.15

0.26

0.28

China

0.02

-0.03

3.65

-1.93

0.25

China

0.02

0.00

3.21

0.01

0.14

India

0.02

-0.02

2.66

-1.57

0.10

India

0.02

0.02

2.49

2.32

0.15

Japan

0.02

0.01

2.93

0.77

0.14

Japan

0.02

-0.02

2.61

-1.20

0.17

South.Korea

0.02

0.02

2.53

0.55

0.14

South.Korea

0.03

0.01

2.16

0.37

0.13

Taiwan

0.03

0.01

4.64

0.34

0.07

Taiwan

0.02

-0.05

3.41

-3.32

0.16

UK

0.02

0.02

1.87

1.77

0.17

UK

0.03

0.00

1.90

0.16

0.13

USA

0.03

-0.01

3.68

-0.42

0.29

USA

0.03

0.00

3.31

-0.44

0.29

Country

MKT

WML

t(MKT)

t(WML)

Country

MKT

QMJ

t(MKT)

t(QMJ)

Australia

0.03

0.00

3.17

0.14

0.10

Australia

0.03

0.01

3.39

1.24

0.13

Canada

0.04

0.00

3.62

-0.24

0.28

Canada

0.04

0.01

3.28

0.65

0.28

China

0.02

0.02

3.98

0.97

0.18

China

0.02

0.01

3.27

0.22

0.14

India

0.02

0.00

2.43

-0.36

0.07

India

0.02

0.00

2.09

0.21

0.06

Japan

0.02

-0.01

2.92

-1.13

0.16

Japan

0.02

-0.02

2.77

-0.86

0.15

South.Korea

0.02

-0.03

1.94

-4.68

0.23

South.Korea

0.03

-0.03

3.20

-2.11

0.17

Taiwan

0.03

-0.02

4.92

-1.00

0.08

Taiwan

0.03

-0.04

3.25

-1.22

0.10

UK

0.02

0.00

2.22

-0.73

0.13

UK

0.02

0.01

2.10

1.43

0.15

USA

0.03

0.00

3.58

-0.65

0.29

USA

0.02

0.01

2.32

1.08

0.30

AdjR2

AdjR2

Notes: The table presents the slope coefficients of Market and another factor in the bivariate regressions. Also provided are the Newey West (1987) t-statistics (4 lags) of those coefficients and the adjusted R2 of the regression

The results of bivariate regressions confirm the results of the t-tests. Market returns act as a leading indicator of future GDP growth. High returns on the overall market leads to higher GDP growth in the future. We cannot say the same about any other asset pricing factors. All asset-pricing factors are significant only in one or two countries at a time. Further, the signs of slope coefficients are conflicting. We see no clear trend to suggest that multifactor asset pricing factors predict economic growth. These results are contrary to LV (2000) who conclude their study by suggesting that HML and SMB factors contain economic growth information and can be used as state variable proxies in the context of the ICAPM. Additional tests for robustness and Concluding remarks Our out-of-sample analysis suggests that none of the six common factors we have studied provide strong evidence of predictability of future GDP growth. For robustness, we conduct the same tests with industrial production (IDP) index in all countries but China. We do not have a seasonally adjusted IDP series for China. The results are provided in the supplementary index and the summary of findings is same as that for GDP growth. Only the market 6

factor leads economic growth and can be said to have predictive power for future growth in industrial production. We also conducted our analysis on two additional factors based on earnings to price and cash flow to price ratios3. These two factors were also insignificant in predicting future economic growth. Further, we tested if returns on asset pricing factors contain information for predicting economic growth beyond that of 1 quarter ahead. We repeated our tests using up to 4-quarter ahead GDP growth and find similar results. Unlike LV (2000), we find no evidence that any one of the six asset pricing factors predict future economic growth. Any power that these factor-mimicking portfolios had in predicting economic growth seems to have attenuated in the last 20 years or so. This suggests that these asset-pricing factors may not have a definitive risk-based explanation. We conclude our study by suggesting that researchers providing motivation for their multifactor models by invoking ICAPM or a similar risk oriented reasoning should provide explicit evidence that the underlying factors indeed satisfy the criteria to qualify as risk factors.

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These results are not reported here but available with the authors upon request.

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