Switching Between the Banking and Metals and Mining Sectors of Australia

Switching Between the Bankingand Metals and Mining Sectors

of Australian

TARIQ HAQUE

Department of Finance, The University of Adelaide, Adelaide, SA, Australia

ABSTRACT

Using the Australian banking and metals and mining industries as thecategories in the Barberis and Shleifer model, this study demonstrates switchingin the Australian stock market. Switching occurs when investors move into anindustry by selling off stocks of an alternate industry, thus causing negativelagged cross-correlation between those industries. Our results, based on dailyreturns, suggest that category-level investor sentiment may drive observedswitching patterns in the Australian stock market and not fundamental riskfactors. Our results also show that switching does not necessarily only occurbetween value and growth stocks or large-cap and small-cap stocks.

I. INTRODUCTION

This study is an empirical application of the Barberis and Shleifer (2003) model,BS hereafter. The BS model, a theoretical model, suggests that investorscategorize similar stocks into groups and that investors transfer funds betweengroups based on their recent relative performance. The BS model is aboutmaking a difficult investment decision process simpler by grouping similarstocks together and moving into groups that have performed relatively stronglyin the recent past (switching into a group) by simultaneously withdrawingfunds from groups that have performed relatively poorly (switching out of agroup).1 An important feature of the model is that investors focus on and tradegroups of stocks simultaneously, rather than focusing on individual stocks.

The BS model implies that whenever a particular group earns strong returnsrelative to another group some investors systematically withdraw funds fromthe latter and invest in the former. For example, investors may switch intometals and mining stocks from banking stocks when metals and mining stocksare performing relatively well and then switch back into banking stocks whenmetals and mining stocks are forecast to drop in value. The action of investors

n I am grateful to Paul Kofman and Kim Sawyer for their numerous comments andsuggestions. I also thank seminar participants at the 2008 AFAANZ conference and 2008QUT Seminar Series and participants at the 2005 FIRN Doctoral Colloquium, 2006Melbourne-Monash PhD symposium and 2007 AFAANZ Doctoral Colloquium.

1 The terms group and category refer to a set of stocks regarded by investors as being similar, for

example investors may consider stocks belonging to the same industry to be a group because

their fundamental cashflows are expected to be highly correlated.

r 2009 The Author. Journal compilation r International Review of Finance Ltd. 2009. Published by BlackwellPublishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

International Review of Finance, 9:4, 2009: pp. 387–403DOI: 10.1111/j.1468-2443.2009.01097.x

demanding stocks of the in-favor category causes the prices of these stocks toincrease while the simultaneous selling of stocks of the out-of-favor categorycauses the prices of these stocks to decrease. The model suggests there isnegative lagged cross-correlation between particular groups or segments ofequity markets where the lag is due to the assumption that switching takesplace after a shock to a particular category.

In empirical analysis of switching models some important issues includewhich categories of stocks are expected to give rise to switching effects and howlong after a shock to a particular category is switching expected to be observed?Does a switch occur immediately or is there a lag before the switch isimplemented? A fast switch could imply a behavioral trend chasing explanationwhere investors believe the strong performance of a category will continue inthe short run while a slower switch could be the result of a careful decision toalter exposure to different categories. The analysis also needs to control for firm-specific factors to ensure that category attributes are driving the results, in linewith predictions of the BS model.

This paper has two major objectives. First, it will investigate the effect ofusing industry categories in the BS model instead of using the widely usedsmall-cap and large-cap and value and growth categories. It is argued here,following the works of Moskowitz and Grinblatt (1999) and Hou (2007) that therelatively high correlation of cashflows and similarity in fundamental opera-tions of stocks in the same industry implies that investors can consider thesestocks to be similar and therefore a category as defined by the BS model. Thiscontrasts with the value and growth or small-cap and large-cap categoriesusually used in the current switching literature. We argue that those categoriesmight not give rise to trend chasing switches because the constituent stocksmay come from a variety of different industries and therefore informationpertaining to some stocks in a category cannot be easily extrapolated to theother stocks in the category.

Second, we want to see whether switching between Australian industryportfolios can be attributed to relative investor sentiment toward categories andnot due to fundamental risk factors. We do this by searching for switchingbetween the banking and metals and mining sectors, in the 5 days following apositive shock to an industry on a given day and by controlling for firm-specificfactors in our analysis to ensure our results are driven by a category-levelsentiment factor. Our use of daily returns differs significantly from the monthlyand quarterly returns that have usually been used in the switching literature todate. It allows us to conclude that there is evidence of short-term sentiment-based switching in the Australian stock market.

The remainder of the paper is structured as follows. Section II reviewsthe existing literature on switching in equity markets. Section III describesthe data used in this study. Section IV describes the methodology used.Section V presents our results and discusses some limitations of our empiricalwork while Section VI, the final section, makes some important concludingremarks.

International Review of Finance

r 2009 The AuthorJournal compilation r International Review of Finance Ltd. 2009388

II. LITERATURE REVIEW

The Barberis and Shleifer (2003) model differs from other theoretical models oftrend chasing or momentum because it involves an analysis of two categories Xand Y while other models involve only one stock or category. The analysis oftwo categories means that concepts such as cross-correlation, relative returnsand relative shocks can be considered. In particular, if investors move into acategory X, there is an emphasis on which category they simultaneously moveout of. By comparison, other models assume that a shift into X can be financedbut do not specify from where these additional funds would come from.

The main empirical implication of the Barberis and Shleifer model is thatthere should be negative lagged cross-correlation between categories.2 Thisnegative cross-correlation may be observed in raw returns if there are asufficient number of investors who do not take into account common riskfactors in their trend-chasing activities. On the other hand, if a sufficientnumber of investors take into account these common risk factors, then thenegative cross-correlation predicted by the BS model may be observed in risk-adjusted returns.

The empirical testing of the BS model has highlighted the importance ofcategories in asset-pricing models. For example, the lagged average return tovalue, growth, small-cap and large-cap funds (Teo and Woo 2004) or the laggedchange in demand for value stocks relative to growth stocks or for small-capstocks relative to large-cap stocks (Kumar 2009) have been shown to provideadditional explanatory power for the cross-section of stock returns, over andabove the four factors of Carhart (1997).

Additionally, Pomorski (2004) and Kumar (2009) find that demand for aparticular category is both positively related to prior returns to that categoryand negatively related to prior returns to an alternate category, again consistentwith the predictions of the BS model. Teo and Woo (2004) and Kumar (2009)also show that returns for a particular category are both positively related toprior returns to that category and negatively related to returns to an alternativecategory.3

Finally, Barberis et al. (2005)4 and Boyer (2004) shows that the correlation ofreturns to a reclassified stock increase with the category it has just joined anddecrease with the category it has just left, again in line with the hypothesis inthe BS model that investors trade categories of stocks.

2 The De Long et al. (1990) and Hong and Stein (1999) models that precede Barberis and Shleifer

(2003) share some common features with that model. For example, all of these models involve

the interaction of two types of traders, trend chasers who trade on momentum, and rational

traders who know the true fundamental value of stocks and in all models prices can differ

consistently from true fundamental value due to limits to arbitrage (Shleifer and Vishny 1997).

3 Pomorski (2004), Teo and Woo (2004) and Kumar (2009) all find this relationship even after

allowing for the four factors of Carhart (1997).

4 Barberis et al. (2005) uses the S&P 500 and non S&P 500 stocks as the categories in its analysis.

They find evidence of switching in daily, weekly and monthly returns.

Banking and Metals and Mining Sectors of Australia

r 2009 The AuthorJournal compilation r International Review of Finance Ltd. 2009 389

A. Summary of literature review

Empirical testing of the BS model has largely focused on the value and growthcategories or the small-cap and large-cap categories. The results suggest, forexample, that one possible strategy could be to increase exposure to valuestocks and simultaneously decrease exposure to growth stocks at a time whenvalue stocks are expected to outperform growth stocks. However, this strategy isalready implied by the Fama and French (1993) and Carhart (1997) asset pricingmodels which both contain a factor related to the relative performance of valueand growth stocks (usually referred to as the HML factor) and the relativeperformance of small-cap and large-cap stocks (usually referred to as the SMBfactor).

In addition, because most asset-pricing models using monthly returns, theempirical tests of switching have also used monthly returns, which is equivalentto searching for switching in the month following the release of relevantinformation. The empirical literature on switching has also focused on findingswitching after taking into account common risk factors although the BS modeldoes not necessarily require investors to take into account risk factors in theirinvestment allocations.

We believe that there is scope for investigating faster switches than thosedocumented in the current literature. These faster switches could be consistentwith trend chasing where investors expect strong returns from a particularcategory to continue in the short term. In this study, we propose using dailyreturns, rather than monthly returns to investigate these faster switches. Thereis also scope for investigating whether categories other than value and growthare appropriate and relevant. We believe that for faster switches industrycategories rather than value and growth categories may be more likely to givebetter results as the constituent stocks are then more similar to one another andinformation about a particular stock can be more accurately extrapolated to theother stocks in the category. Our selection of industry categories is discussedfurther in the next section.

III. DATA

This study uses Australian equity data for the 15 years from January 1, 1990, toDecember 31, 2004. Daily returns to stocks are analyzed using data from theIRESS database. Our dataset differs from other papers on switching in threeimportant ways.

The first is the use of Australian stock market data compared with US equityor funds data. The Australian equity market is heavily influenced by two sectors:resources and industrials. The resources sector comprises mainly metals andmining companies while the industrials sector comprises mainly banks andfinancial institutions. Switching between these sectors may be expected becausethe major banking stocks in Australia are a very safe investment and providestable franked dividends while the more volatile mining stocks can provide very



high returns in certain periods of the business cycle. However, when thesevolatile stocks are forecast to depreciate investors can move back into the saferbanking stocks. Importantly, there are many Australian equity funds that focuson resources and industrials,5 and hence there is scope for switching betweenthese groups as investors in mutual equity funds switch between these fundshoping to maximize returns.

The second major difference is the sampling frequency. We use daily returnsin this study whereas prior studies have used monthly and quarterly returns.This is because we believe switching should occur relatively soon after goodnews to a particular industry, for example. One month or one quarter betweenobservations may be too long between observations as a switch may haveoccurred soon after a particular event and then the effects of the switch mayhave dissipated by the time the next observation is noted.

Finally, we use industry categories in this study. An advantage of definingtwo categories X and Y in this way is that stocks within groups are expected tohave highly correlated fundamental cashflows owing to their exposure tosimilar risk factors (Moskowitz and Grinblatt 1999). Investors are thus relativelymore likely to use industries as categories, at least in a short-term context. Thereis also a growing body of literature documenting the significance of industriesin asset pricing (Moskowitz and Grinblatt 1999; Grundy and Martin 2001;Swinkels 2002; Hurn and Pavlov 2003; Hou and Robinson 2006; Hou 2007).

In this study, we examine daily returns to stocks in the banking and metalsand mining industries, which dominate the Australian industrials and resourcessectors, respectively. The main objective is to determine whether investors canconsistently make risk-adjusted profits taking from taking a long position ineither of these industries and simultaneously taking a short position in thealternate industry whenever there is a positive shock to the former. If there is aconsistent ability to make risk-adjusted profits in this way, then this returnpredictability can be exploited by investors. Where appropriate we calculatereturn series for each industry portfolio by taking an equally weighted averageof the constituent stocks for which a valid return is available.6

Stocks in our industry portfolios also come from the ASX 200 index and aretherefore expected to be reasonably liquid. Concerns that trading strategiesinvolving our industry portfolios are impractical, owing to illiquidity of somestocks within the industry portfolios, are thus alleviated.

Table 1 presents summary statistics on the industry portfolios in our analysis.It shows that banks and metals and mining together constitute nearly 40% of

5 Examples of Australian Industrials funds include the Wholesale Australian Equity – Industrials

Fund offered by AXA Australia and the Investors Mutual Limited Industrial Share Fund.

Australian Resources funds include The Australian Resources fund by SAS Global Investment

Group, LSC Australian Resources Hi – Alpha Fund, Aviva Investors Australian Resources Fund,

Goldman Sachs JBWere Asset Managament Resources Fund.

6 All industry return series are tested for stationarity using three versions of the augmented

Dickey–Fuller test. All series are found to be stationary with a p-value of o1%. We also use

value-weighted industry return series when analyzing the economic significance of switching

later in the study.



the ASX 200 by market capitalization. These are also the largest industriesrepresented on the ASX 200. Table 1 also shows that there is significant positivefirst-order autocorrelation in returns to banks and metals and mining. This isnecessary for switching to be observed as one element of switching is consistentwith momentum investing or trend chasing while the other element involvesselecting from where the funds required for those momentum trades will come.

Table 1 also shows that the first three autocorrelations for metals and mininghave alternate signs indicating highly volatile returns. As detecting switching isequivalent to finding evidence of return predictability, this should imply itshould be difficult to detect switching involving the metals and miningindustry. However, while the unconditional autocorrelations vary significantly,the autocorrelations conditional on a particular shock occurring may notbehave in the same way. These are more relevant in determining whetherevidence of switching can be found.

IV. METHODOLOGY

We use a similar methodology to Froot and Teo (2008) to test the BS model.Specifically, for each stock i in the banking and metals and mining sector in theAustralian ASX 200 index, we estimate the following regret:

Ri;t ¼ ai þ biMKTt þ gi ln MEi;t

� �þ di

BE

ME

� �i;t

þX5

k¼1

yi;kRi;t�k þX5

k¼1

fi;kRB;t�k

þX5

k¼1

Zi;kRM;t�k þ ei;t ð1Þ

where Ri,t is the return to stock i on a given day t, MKTt is the return to themarket portfolio on a given day t, MEi,t is the market capitalization of stock i ona given day t, BE

ME

� �i;t

is the ratio of the book value of equity per share to themarket value of equity per share, for firm i on a given day t, RB,t is the return tothe banking industry on a given day t, RM,t is the return to the metals andmining industry on a given day t.

Here, we define the return to the market portfolio to be the return to the ASX200 index on a given day. We also do not explain returns to stocks in excess ofthe risk-free rate as the daily risk-free rate is assumed to be zero. The return tothe banking industry on a given day is the average of the returns to all stocks inthat industry in the ASX 200 on that day. Returns to the metals and miningindustry are defined similarly. This reflects the assumption in the BS model thatinvestors regard all stocks in a category as being of equal importance.7

Equation (1) aims to identify whether category-level returns can explainfirm-level returns after allowing for factors known to have explanatory power

7 We also use value-weighted industry returns when analyzing the economic significance of

switching later.



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for firm returns, namely the three factors of the Fama and French (1993) three-factor model. We also include lagged returns to the firm to cover any other firm-specific factors that the Fama and French (1993) model might miss. Then,following Barberis and Shleifer (2003), we say that category factors haveinduced a switch from the banking sector to the metals and mining sector if fora sufficient number of firms the following conditions hold:

X5

k¼1

fi;k > 0;X5

k¼1

Zi;k < 0: ð2Þ

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X5

k¼1

fi;k < 0;X5

k¼1

Zi;k > 0: ð3Þ

V. RESULTS

An overview of the switching patterns found is given in Table 2. This tableshows the significance of the category terms in Equation (1) and thesignificance of the Fama and French (1993) factors in our firm-level regressions.There are eight banking stocks in our sample and 22 metals and mining stocksin our sample.8 This table shows that in six out of eight banking stocks, returnsin the preceding 5 days to the banking category overall have a positive andstatistically significant effect on a stock’s return today. This is even after takinginto account the risk factors in the Fama and French (1993) model and pastreturns to the stock itself in the preceding 5 days. This ‘own-category’ effect isthus very significant given the risk factors that have already been taken intoaccount and is consistent with Barberis and Shleifer (2003).

In addition to this ‘own-category’ effect, Table 2 also shows an ‘alternate-category effect’ where returns in the preceding 5 days to the metals and miningindustry overall have a negative and statistically significant effect on a bankingstock’s return today. Thus, if returns to metals and mining have been high,investors may buy stocks in this category funded by the sale of banking stocks.Barberis and Shleifer (2003) explain that this may occur if investors compare therelative performance of categories so that when one category has beenoutperforming the other, investor sentiment toward that category relative tothe other also rises. Investors then buy stocks of the now in-favor categoryfunded by the sale of stocks in the now out-of-favor category.

8 There are 26 firms in the metals and mining sector of the ASX 200 as at February 28, 2005;

however, for four of those firms Datastream could not provide a valid book-to-market ratio for

the dates we required.



Table 2 shows that in five out of eight banking stocks, alternate categoryreturns negatively and significantly affect the returns of banking stocks. Againthis effect is found after controlling for the Fama and French (1993) risk factors

Table 2 Effect of category-level attributes on firm returns

Stock ai bi gi di

P5k¼1

fi;k

P5k¼1

Zi;k

Banking stocksCBA 1.32nn 0.83nnn �0.05nn �0.32nn 0.05 �0.06nn

NAB 4.53nnn 0.99nnn �0.17nnn �0.59nn 0.23nnn �0.09nnn

ANZ 3.16nn 1.16nnn �0.12nn �0.43nnn 0.16nn �0.02WBC �0.03 1.05nnn 0.00 �0.07 0.07 �0.05n

SGB 0.92 0.58nnn �0.03 �0.31nn 0.14nn �0.09nn

BEN 4.51nnn 0.48nnn �0.17nnn �1.70nnn 0.37nnn �0.10nn

ADB 1.53 0.62nnn �0.05 �0.90nn 0.33nnn �0.01BOQ 0.36 0.32nnn �0.01 �0.16 0.22nnn 0.01

Metals and mining stocksBHP 3.59nn 1.19nnn �0.14nn �0.81nn �0.15nnn 0.06n

RIO 8.13nn 1.14nnn �0.33nn �1.14nn �0.08n 0.05n

WMR 15.91 1.76nnn �0.56 �3.95nnn �0.16 0.39nn

BSL 33.12n 1.01nnn �1.42n �1.79n 0.09 0.43nnn

AWC �2.53n 1.18nnn 0.11n �0.12 �0.16nn 0.13nnn

NCM �0.61 0.82nnn 0.04 �0.52 0.06 0.06ZFX �19.41 1.81nnn 0.97 �0.55 �0.62n 0.19OST 53.14nnn 0.78nnn �2.43nnn �2.04nnn 0.44nnn 0.15ILU 0.35 0.52nnn �0.01 �0.28nn 0.06 0.14nnn

LHG �4.75n 0.51nnn 0.23n �0.29n 0.24n 0.05CEY 0.02 0.37nnn 0.01 �0.12n 0.07 0.33nnn

SSX 22.92n 0.65nnn �1.01n �2.28nn 0.37nn 0.40nnn

PMM 4.76n 1.02nnn �0.22n �0.69nnn �0.19n 0.36nnn

EXL 15.27n 1.12nn �0.72n �0.83 �0.25 1.08nn

CSM �3.08n 0.90nnn 0.26nn �2.99nn �0.15 0.20MRE �0.01 1.14nnn 0.00 0.20 0.30n 0.21GRD �0.31 0.32nnn 0.03 �0.23n 0.34nn 0.62nnn

AQP 7.46nn 0.62nnn �0.33n �1.78nnn �0.19 0.40nnn

KIM 2.98 1.05nnn �0.12 �2.91n �0.07 0.49n

LSG �11.87 �0.03 0.62 0.48 0.11 0.77nnn

KCN 1.33 0.05 �0.04 �1.40nn 0.26 0.36nn

CRS 0.83 0.34nnn �0.02 �0.76nn 0.14 0.49nnn

The following regression is estimated for each stock in the banking industry and each stock in themetals and mining industry for which valid data on the dependent and independent variablescan be found:

Ri;t ¼ ai þ biMKTt þ gi ln MEi;t

� �þ di

BE

ME

� �i;t

þX5

k¼1

yi;kRi;t�k þX5

k¼1

fi;kRB;t�k þX5

k¼1

Zi;kRM;t�k þ ei;t

where Ri,t, MKTt, MEi,t,BEME

� �i;t

, RB,t and RM,t are, on day t, the return to stock i, the return to themarket portfolio, the market capitalization of stock i, the ratio of the book value of equity pershare to the market value of equity per share for firm i and the return to the banking industry andreturn to the metals and mining industry, respectively.nnn, nn and n denote statistical significance at the 1%, 5% and 10% levels, respectively.



and other firm-specific factors showing that it is likely to be a genuinedeterminant of returns to banking stocks.

When looking at daily returns to metals and mining stocks, we find that ofthe 22 stocks in our sample, 16 are positively and statistically significantlyaffected by returns to the metals and mining industry as a whole over thepreceding 5 days. Again this is after taking into account the Fama and Frenchrisk factors and the previous 5 days of returns to the stock in question. Thisresult is consistent with the Barberis and Shleifer (2003) argument thatsentiment pertaining to categories as a whole can affect individual stockreturns.

However, in metals and mining stocks only five out of 22 firms are negativelyand significantly affected by returns overall to the alternate category. It may besignificant, however, that for the seven largest metals and mining firms, five ofthem are negatively affected by the alternate category’s returns and four of theseare statistically significant including the two largest stocks. This suggests thatswitching may be a phenomenon involving trades between the largest or mostliquid and heavily traded stocks of particular categories.

Overall these results suggest that the own-category effect is important forstocks in both the banking and metals and mining industries. The alternatecategory effect seems to be important in many banking stocks suggesting thatwhen the metals and mining industry performs well, many investors switchaway from banking stocks and into metals and mining stocks. The alternatecategory effect seems to be important for fewer metals and mining stocks,although there are significant results for the larger metals and mining stocks.This suggests switches from the metals and mining stocks into banking stocksmay be restricted to the largest most liquid stocks in this category.

The coefficients in Table 2 show, as expected, that the market betas forvirtually all stocks are positive and statistically significant. Interestingly, thecoefficient on size is positive and statistically significant for three metals andmining stocks in our sample but negative and statistically significant for fourbanking stocks and for eight metals and mining stocks. The coefficients onthe book-to-market ratio are negative for all eight banking stocks andstatistically significant for six of them while these coefficients for the metalsand mining stocks are statistically significant and negative for 16 of the 22stocks.

Also of interest is that the abnormal returns for four of the eight bankingstocks and for eight of the 22 metals and mining stocks are both positive andstatistically significant. The abnormal return is negative and statisticallysignificant for three of the 22 metals and mining stocks. These abnormalreturns probably reflect the fact that daily stock returns are quite noisy andthat in daily returns some extreme observations are recorded. Nevertheless,it is necessary to attempt to adjust for known risk factors to ensure ourresults on category-level determinants of stock returns do provide additionalexplanatory power over and above that provided by current asset-pricingmodels.



A. The economic significance of switching

We estimate the following equations for the banking industry overall and forthe metals and mining industry overall:

RB;t ¼ aB þ bBRMKT;t þ gB ln MEð ÞB;tþdBBE

ME

� �B;t

þeB;t ð4Þ

RM;t ¼ aM þ bMRMKT;t þ gM ln MEð ÞM;tþdMBE

ME

� �M;t

þeM;t ð5Þ

where RB,t and RB,t are returns to the banking and metals and mining industrieson day t; MKTt is the return to the market portfolio on day t; MEB,t and MEM,t arethe market capitalizations of the banking and metals and mining industries onday t; BE

ME

� �B;t

and BEME

� �M;t

are the ratios of the book value of equity to the marketvalue of equity for the banking and metals and mining industry on day t.

We then take the residuals from each of these regressions, that is we take theabnormal daily returns to the banks and metals and mining industries, andestimate a bivariate vector autoregression on these residuals. The systemestimated is given below:

eB;t ¼ mB;t þX10

k¼1

cB;keB;t�k þX10

k¼1

cM;keM;t�k þ zB;t ð6Þ

eM;t ¼ mM;t þX10

k¼1

tB;keB;t�k þX10

k¼1

tM;keM;t�k þ zM;t : ð7Þ

Two vector autoregressions are run, the first one uses abnormal returnsassuming equal-weighting of constituent stocks in the calculation of industryreturns and the calculation of industry book-to-market ratios. The secondvector autoregression uses abnormal returns where value-weighting is used tocalculate these series.

Tables 3 and 4 show the cumulative impulse response of abnormal returns tothe two industries in response to (i) a 1% shock to abnormal returns to thebanking industry and (ii) a 1% shock in abnormal returns to the metals andmining industry. In both tables, we assume there is no simultaneous shock tothe second industry.

From Table 3, using equally weighted category returns, it can be seen that a100 basis point shock to the abnormal returns of the banking industry impliesthat cumulative abnormal returns to the banking industry in excess of thatshock, continue to rise for a further 14 basis points over the succeeding 10 days,of which 13 basis points is attributable to the first 2 days. At the same time, overthese 2 days, abnormal returns to the metals and mining industry fall by 7 basispoints and this is statistically significant. These results may be consistent withBarberis and Shleifer (2003) as these abnormal returns cannot be explained bywell-known fundamental risk factors but perhaps could be explained by strong



investor sentiment toward the banking industry relative to the metals andmining industry.

These results are even stronger using value-weighted category returns wherea 100 basis point shock to banks leads to a further cumulative response of 17basis points over the following 10 days, most of which is attributable to the firstday response. At the same time the value-weighted metals and mining returnfalls by 13 basis points over the next 10 days and this is still statisticallysignificant 10 days after the shock. The stronger results from using value-weighted returns suggest that switching may be concentrated in the largestmost liquid stocks of the banking and metals and mining industries.

From Table 4, using equally weighted category returns, a 100 basis pointshock to the metals and mining industry surprisingly does not induce further

Table 3 Effect of 100 basis point shock in abnormal returns to the bankingindustry

Lag

Response of banks Response of metals and mining

Cumulative responsein excess of shock(equal weighting)

Cumulative responsein excess of shock(value weighting)

Cumulativeresponse

(equal weighting)

Cumulativeresponse

(value weighting)

0 0.00 0.00 0.00 0.001 0.12nnn 0.18nnn �0.06nn �0.05nnn

2 0.13nnn 0.17nnn �0.07nn �0.08nnn

3 0.13nnn 0.17nnn �0.06 �0.13nnn

4 0.13nnn 0.18nnn �0.06 �0.17nnn

5 0.16nnn 0.20nnn �0.03 �0.16nnn

6 0.15nnn 0.18nnn �0.08 �0.15nnn

7 0.14nnn 0.17nnn �0.03 �0.14nnn

8 0.15nnn 0.16nnn �0.04 �0.12nn

9 0.16nnn 0.18nnn �0.04 �0.11n

10 0.14nnn 0.17nnn �0.05 �0.13nn

The following regressions are estimated and a vector autoregression with 10 lags is estimated onthe residuals from each regression:


ME

� �B;t

þeB;t


ME

� �M;t

þeM;t

where RB,t and RM,t are equally weighted or value-weighted returns to the banking and metals andmining industries on day t, MKTt is the return to the market portfolio on day t, MEB,t and MEM,t

are the market capitalizations of the banking and metals and mining industries on day t, BEME

� �B;t

and BEME

� �M;t

are the ratios of the book value of equity to the market value of equity for the bankingand metals and mining industry on day t using equal or value weighting of the constituent stocks.The cumulative impulse response of abnormal returns to banks and to metals and mining, as aresult of a 100 basis point shock to abnormal returns for banks, is shown.nnn, nn and n denote statistical significance at the 1%, 5% and 10% levels, respectively, where the p-values calculated are for the cumulative response of banks being significantly positive and thecumulative response of metals and mining being significantly negative.



significant abnormal returns to that industry, as Barberis and Shleifer (2003)might predict. It does however lead to a significant decrease in abnormalreturns for the banking industry of three to four basis points over the 5 daysfollowing the shock to the metals and mining industry.

When using value-weighted returns, however, we find stronger evidence infavor of the Barberis and Shleifer (2003) model. Now a 100 basis point shock tothe abnormal returns of metals and mining leads to a further increase of 13 basispoints on the next day and overall a further increase that is statisticallysignificant for up to 3 days after the shock. The same shock causes value-weighted returns to banking stocks to fall by up to five basis points and this fallis statistically significant up to 7 days after the shock. The stronger results forvalue-weighted returns reflect that switching probably occurs between the most

Table 4 Effect of 100 basis point shock in abnormal returns to metals and miningindustry

Lag

Response of banks Response of metals and mining

Cumulative responsein excess of shock(equal weighting)

Cumulative responsein excess of shock(value weighting)

Cumulativeresponse

(equal weighting)

Cumulativeresponse

(value weighting)

0 0.00 0.00 0.00 0.001 �0.02nn �0.02nn 0.02 0.13nnn

2 �0.02 �0.04nn �0.02 0.11nnn

3 �0.04nnn �0.05nn 0.00 0.06nn

4 �0.04nn �0.04n 0.00 0.045 �0.03n �0.05n 0.01 0.016 �0.02 �0.05n 0.01 �0.037 �0.02 �0.05n �0.03 �0.058 �0.01 �0.05 �0.07 �0.059 0.01 �0.05 �0.05 �0.03

10 0.01 �0.04 �0.04 �0.05

The following regressions are estimated and a vector autoregression with 10 lags is estimated onthe residuals from each regression:


ME

� �B;t

þeB;t


ME

� �M;t

þeM;t

where RB,t and RM,t are equally weighted or value-weighted returns to the banking and metals andmining industries on day t, MKTt is the return to the market portfolio on day t, MEB,t and MEM,t

are the market capitalizations of the banking and metals and mining industries on day t, BEME

� �B;t

and BEME

� �M;t

are the ratios of the book value of equity to the market value of equity for the bankingand metals and mining industry on day t using equal or value weighting of the constituent stocks.The cumulative impulse response of abnormal returns to banks and to metals and mining, as aresult of a 100 basis point shock to abnormal returns for metals and mining, is shown.nnn, nn and n denote statistical significance at the 1%, 5% and 10% levels of significance, respectively,where the p-values calculated are for the cumulative response of banks being significantly negative andthe cumulative response of metals and mining being significantly positive.



liquid stocks of the banking and metals and mining industries and may notinvolve the less liquid stocks of those industries. In particular, using equallyweighted returns for the metals and mining industry may be inappropriate asmany of the constituent stocks are relatively small and possibly relativelyilliquid so that their returns are influenced by bid-ask bounce.

The analysis in this section suggests that abnormal profits can be earned as aresult of switching away from metals and mining into banks particularly if youcan forecast the abnormal shock to banks. The abnormal profits so earned aresignificantly positive with respect to the Fama and French (1993) three-factormodel. The same is true if you switch away from banks and into metals andmining, particularly if you can forecast the abnormal shock to the latter, butonly if trading is restricted to the largest most liquid stocks.

These strategies may be used by active fund managers in particular to lock inabnormal returns over a period of several days. If the shock was predicted by thefund managers the abnormal profit would be significantly higher thandescribed here. In practice, fund managers may only trade the most liquidstocks in a particular category to minimize their transaction costs.

B. Limitations of the present study

There are a number of limitations in our empirical analysis. First, we have notallowed for transaction costs, which will dilute the profitability of the switchingstrategies we describe. A second limitation is that we have not consideredwhether the stocks in particular industries have sufficient liquidity to be boughtor sold or short-sold, as required. This will impact on the set of stocks that canbe involved in switching strategies. Third, the variability of profits from theswitching strategies we describe has not been documented. Each of these issuescan be analyzed separately and are left for further studies.

We have also not discussed the possibility of correlated simultaneous shocksto categories, focusing instead on a shock to a single category. This importantarea is left for future research.

VI. CONCLUSIONS

This study, based on the theoretical model of switching by Barberis and Shleifer(2003), has shown that switching between sectors in the equity market is notnecessarily restricted to the value and growth or small-cap and large-capcategories. It has shown that switching in the Australian equity market doesoccur between the banking and metals and mining industries and this isprobably a result of the importance of these two industries to the Australianstock market as combined they account for nearly 40% of the marketcapitalization of the ASX 200 index.9

9 This is true as at February 28, 2005.



We used risk-adjusted daily returns in our analysis arguing that the use ofrisk-adjusted monthly and quarterly returns in prior studies could have beeninconsistent with the behavioral or trend chasing element of switchingemphasized in Barberis and Shleifer (2003). We found evidence of rapid trendchasing switches that had not been documented previously.

There are many areas for future research arising from this study. Amongthem, one would be how investors use derivatives markets to implementswitching strategies. For example, fund managers may gain rapid exposure to aparticular industry through appropriate transactions in the options market andthen enjoy strong price increases in these options before locking in these profitsby selling these options. The strong liquidity of certain options contracts maymake this an attractive strategy.

Another area for future research could involve investigating whetherswitching occurs in the Australian market between the industrials and resourcessectors. The allocation to industrials (mainly comprising banks) and resources(mainly comprising metals and mining stocks) is widely recognized as being animportant determinant of the performance of Australian fund managers.However, there are other industries within these sectors and switching mayoccur between these industries as a result of relative investor sentiment towardthe resources and industrials sectors.

Finally while the Barberis and Shleifer (2003) model assumes that investorsregard stocks in a category as being equivalent to each other, in practice stocksare not equivalent to each other. In particular, larger stocks are generally moreliquid than smaller stocks and therefore less expensive to trade. They maytherefore be more likely to be involved in switching activities and therefore therelative performance of the largest stocks of categories rather may driveswitching activities rather than overall category performance. These importantareas are all left for future research.

Tariq HaqueAdelaide Business SchoolThe University of Adelaide10 Pulteney StAdelaide, SA [email protected]

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APPENDIX AList of companies analyzed

Table A1 Constituent stocks of the industries in the analysis and the marketcapitalization of each stock as at 28.02.2005#

Stock Size (Australian dollars)

Industry: BanksCommonwealth Bank of Australia 46,099,868,842National Australia Bank Limited 44,857,669,507Australia and New Zealand Banking Group Limited 38,989,778,700Westpac Banking Corporation 34,179,534,576St George Bank Limited 12,635,213,500Bendigo Bank Limited 1,326,597,756Adelaide Bank Limited 1,009,252,441Bank of Queensland Limited 854,711,169Commonwealth Bank of Australia 46,099,868,842National Australia Bank Limited 44,857,669,507

Industry: Metals and MiningBHP Billiton Limited 68,487,322,200RIO Tinto Limited 14,608,995,616WMC Resources Limited 8,851,609,800Bluescope Steel Limited 7,241,337,000Alumina Limited 6,991,724,700Newcrest Mining Limited 5,629,710,000Zinifex Limited 1,670,000,000Sims Group Limited 1,629,580,880Onesteel Limited 1,518,418,882Iluka Resources Limited 1,322,304,000Lihir Gold Limited 1,169,488,804Oxiana Limited 1,073,053,500Centennial Coal Company Limited 788,233,619Smorgon Steel Group Limited 703,783,080Portman Limited 668,980,332Excel Coal Limited 622,909,000Consolidated Minerals Limited 538,278,820Jubilee Mines NL 490,554,306Minara Resources Limited 424,582,064GRD Limited 302,064,444Aquarius Platinum Limited 210,764,250Kimberley Diamond Company NL 201,465,000Lion Selection Group Limited 184,340,538Kingsgate Consolidated Limited 168,755,975Croesus Mining NL 159,279,375Capral Aluminium Limited 128,929,536

#The stocks listed belong to the stated industries and the ASX200 as at February 28, 2005.



Documents

Switching Between the Banking and Metals and Mining Sectors of Australia