Understanding and Predicting Style Returns: … · London School of Economics e-mail: ... I Business model I How it affects the timing and risk of future ... February 2015 Understanding

IntroductionMomentum and Value Effects

How Excess Capacity Affects Momentum and Value ReturnsEmpirical Relation Between Excess Capacity, Momentum, and Value

Understanding and Predicting Style Returns:Macroeconomic and Firm-Level Fundamentals

Peter PopeLondon School of Economics

e-mail: [email protected]

Based on Co-Authored Research withKevin Aretz

Manchester Business School

February 2015 Understanding and Predicting Style Returns 1 / 35



Outline

I Valuation, fundamentals, and expected returnsI Macroeconomic risks in style factor returnsI Modelling momentum and long-term reversals: TheoryI Excess capacity in the cross-sectionI Aggregate excess capacity in predicting momentum and long-term

reversal returns




Concepts

I Returns = Expected Returns + Unexpected ReturnsI Expected Returns = f(Risk Factor Exposures × Risk Premia)I Unexpected Returns = f(Shocks to Risk Factors)

I Unpredictable unless investor forecasting skill

I Risk factors should be grounded in fundamentals that affect thevaluations of securities:

I Cash flow expectations and discount rates, e.g.:

Equity Value0 = BVE0 +∞

∑t=1

RIt(1 + re)t (RIVM)

I ultimately, risk factors must be macroeconomic in nature (Chan, Chen,and Hsiesh (1985), Chen, Roll, and Ross (1986), Cochrane (2001))




In John Cochrane’s (2005) Words

“The program of understanding the real, macroeconomic risksthat drive asset prices (or the proof that they do not do so at all)is not some weird branch of finance; it is the trunk of the tree.

As frustratingly slow as progress is, this is the only way toanswer the central questions of financial economics, and acrucial and unavoidable set of uncomfortable measurementsand predictions for macroeconomics.”




Factor Exposures

I Factor exposures should depend on firm fundamentalsI Capturing what goes on in a companyI Business modelI How it affects the timing and risk of future cash flowsI E.g., firms with long duration assets and short duration liabilities should

have high term structure exposures

I characteristic-based models (e.g., Fama-French) capture the jointeffects of macroeconomic risk premia and variation in factor exposures

I useful insight engines, but do not explain macroeconomic riskI macroeconomic exposures informative for investors with different

horizons and different liability exposures to macroeconomic factors




Macroeconomic Exposures of Style Portfolios(Aretz, Bartram, and Pope, JBF 2010)




Macroeconomic Exposures of Style Portfolios(Aretz, Bartram, and Pope, JBF 2010)




Macroeconomic Risk Factor Premia




But Is the World of Equity Risk Really Linear?

I Better theory can tell us more about how risk factor exposures originatein firm fundamentals

I A recent body of academic research has developed important newinsights into the origins of Value and Momentum Returns

I Cooper (2006), Sagi & Seasholes (2007), Hackbarth & Johnson (2014)

I We need to go back and link asset pricing to the economic decisionsmade by firms

I Expected returns depend on riskI Risk depends on corporate decisions . . .




Momentum and Long-term Reversals

I prior studies find certain anomalous relationships between firmcharacteristics and stock returns (“characteristic anomalies/effects”);

I some of these are by now well understood or have disappeared;I we turn to characteristic anomalies that remain challenging to explain;

I the momentum effect (Jegadeesh and Titman, 1993, 2001);I the long-term reversal effect (DeBondt and Thaler, 1985, 1987);

I there is also a short-term reversal effect (Jegadeesh, 1990; Lehman, 1990);I but likely driven by microstructure noise (Kaul and Nimalendran, 1989);

I the value and long-term reversal effects likely coincide (Zhang, 2005);I so our study is really about the drivers of value and momentum;




A Quick Look At Momentum




A Quick Look at Long-term Reversals




Our Story for Momentum and Long-term Reversals

I consider a producer of a single good in a world with a stochastic outputprice, irreversible investments, but an adjustable capacity utilization rate;

I past returns predict excess capacity (Sagi and Seasholes, 2007);I Momentum/long-term winners: high returns over the recent/long-term past

imply little to no excess capacity (“EC”), at least in expectation;I Momentum losers: low returns over the recent past imply mild EC;I Long-term losers: low returns over the long-term past imply great EC;

I the three types of firms react differently to news about the output price;I the winners will invest to increase output upon good news;I the momentum losers won’t invest or change their capacity utilization rate;I the long-term losers will (obviously) not invest, but they will continuously

adjust their capacity utilization rate upon good or bad news;

I the different behavior creates a very different systematic risk;




Local Profits of the Three Types of Firms




Our Main Result Under Complete Irreversibility




Intuition: No Vs. Mild Excess Capacity

I consider two exactly identical firms, except that one has an optimalcapacity level and the other has one production unit too many;

I considering the options indexed by K = 5, we find:1. the momentum loser owns a lower elasticity production unit instead of a higher

elasticity growth option, yielding a relatively lower expected return;2. the momentum loser owns a higher value production unit instead of a lower value

growth option, yielding a relatively higher expected return;

I when excess capacity is sufficiently low, the first effect dominates;




Intuition: No Vs. Substantial Excess Capacity

I consider two exactly identical firms, except that one has an optimalcapacity level and the other has five production units too many;

I considering the options indexed by K = 9:1. the long-term loser owns a high elasticity production unit instead of a high

elasticity growth option: nothing changes;2. the long-term loser owns a higher value production unit instead of a lower value

growth option, yielding a relatively higher expected return;

I if excess capacity is sufficiently high, the second effect dominates;




Our Main Result Under Different Degrees of Reversibility




Testable Implications

I we are first to conduct proper asset pricing tests of the above effects;

I we start with empirical tests not reliant on additional proxy variables;I we split the momentum/long-term reversal winners/losers into further portfolios

sorted based on how good or how bad their past returns were;I for example, long-term losers should generally plot to the right of the U-shape, but

some will have had significantly more negative past returns than others;I the latter should have higher future returns according to our theory;

I we then construct proxy variables for excess capacity and run standard tests;I Excess Assets: Stochastic Frontier Model (SFM) estimate of the difference

between a firm’s current and its ideal capacity-to-sales ratio (Cooper, 2006);I Prob. Zero Investment: LOGIT model estimate of the probability of future

investments into capacity;

I our initial tests assume that investment decisions are mostly irreversible;

I but we later also condition on investment reversibility proxies;I we use the Divestment Sensitivity and Land Availability as proxies;




Double-Sorted Portfolios (Fixed Past Returns)




Double-Sorted Momentum Portfolio Results




Double-Sorted Long-term Reversal Portfolio Results




Fama & MacBeth Regressions




Fama & MacBeth Regressions, Further Conditioned on theDegree of Investment Reversibility




Predicting Momentum Returns




Predicting Long-term Reversal Returns




Conclusion

I Expected returns depend on exposures to macroeconomic risk factorsI Theoretical and empirical support

I Evidence suggests that a small number of macrofactors account for therisk of equity portfolios;

I Growth, default, term structure

I Momentum and long-term reversals have defied coherent theoreticaland empirical explanations

I We show that excess capacity is an important explanatory factor inpredicting momentum and long-term reversal returns

I In both cross-section and time-series




Appendix: Table A.1




Appendix: How Does Our Model Differ from Others? I

I the three articles most strongly related to ours are Cooper (2006, JF), Sagi andSeasholes (2007, JFE), and Hackbarth and Johnson (2014, RES);

I SS (2007) consider a firm whose assets-in-place generate a profit equal to therealization of a stochastic variable minus a fixed cost;

I in the absence of other assets, the assets-in-place create a negative expectedreturn-profitability relationship (“operating leverage effect”);

I but when the firm can expand, the high risk of this option dominates the low riskfrom the operating leverage effect at high profit levels (“investment effect”);

I also, when the firm can abandon operations, the low risk of this option dominatesthe high risk from the operating leverage effect at low profit levels;

I ⇒ positive expected return-profitability relation, explaining momentum;

I in our work, the positive relation becomes U-shaped because the abandonmentoption is replaced by more realistic (in the short term) options to under-utilize;

I option to abandon: low risk vs. option to under-utilize capacity: high risk;




Appendix: How Does Our Model Differ from Others? II

I C (2006) and HJ (2014) model the assets-in-place’s profit as a non-linearfunction of the stochastic variable;

I the assets-in-place again create an operating leverage effect, but this effect is much“steeper” than in SS (2007), at least in relative terms;

I it dominates the lower risk of the option to abandon at low profit levels, and mostlydominates the higher risk of the option to expand at high profit levels;

I ⇒ “rotated J-shape” expected return-profitability relationship;

I in contrast to SS (2007), C (2006) and HJ (2014) can explain value/long-termreversal effects . . . but they cannot explain momentum effects;

I our U-shape explains momentum and long-term reversal effects jointly and in anintuitive way, assuming that investments are largely irreversible;

I when they become sufficiently reversible, our findings coincide with HJ (2014): theexpected return-profitability relationship turns positive almost everywhere;




Appendix: The Pindyck (1988) Model: Assumptions

I consider a monopolistic firm in an infinite horizon, continuous-time (indexed byt ∈ [0,∞)) model in which investments are completely irreversible;

I at start-up, it owns an infinite sequence of perpetual American growth options(indexed by K ∈ (0,∞)), each able to install a production unit for a unit cost of k ;

I each production unit is a perpetual American option (indexed by the sameK ), producing the increment of an output unit per time unit if “switched on”;

I if switched on, the unit cost of the Kth production unit is: c1 + c2K ;I the production unit can be costlessly switched on or off at each time;

I output is sold at a unit price determined by Geometric Brownian motion;

I at each time t (including t = 0), managers make two optimal decisions:I should the firm install new production units (exercise growth options)?

I exercised options immediately become production units (no time-to-build);

I which of the installed productions units should be switched on?




Appendix: Model Solution and Main Results

I Pindyck (1988) provides quasi-closed form solutions for the values of theproduction units (∆V(θ,K )) and growth options (∆F(θ,K ));

I we use these closed-form solutions to compute the options’ elasticities;

I an option’s expected excess return is its elasticity times the expectedexcess return of the underlying asset (we keep the latter constant!);

I we use the market values/elasticities to analytically prove the following:

I Proposition 1: Ceteris paribus, when excess capacity is sufficiently low,further increases in capacity lead expected returns to decline;

I Proposition 2: Ceteris paribus, when excess capacity is sufficiently high,further increases in capacity lead expected returns to rise;

I ergo, the excess capacity-expected return relationship is U-shaped;




Appendix: Model Limitations/Extensions

I we analyze how various extensions affect our main results;I Time-to-Build;

I growth options attract a lower value;I production units “under construction” have lower risk;I result: the U-shape becomes more pronounced;

I Cournot Competition Among Identical Firms;I price becomes more sensitive to output when there is more competition;I but more competition should also decrease the demand elasticity;I result: the above two effects likely cancel out: nothing happens;

I Mean Reversion in Demand (Output Price);I result: U-shape becomes more/less pronounced depending on whether

current demand is above/below its long-term average;I we have not yet determined why this is the case;

I Ability to Divest;I result: exactly like in Hackbarth and Johnson (2014);




Appendix: Measuring Excess Capacity

I excess capacity must increase with the ratio of capacity (as, e.g.,measured by total assets) to demand (as, e.g., measured by sales);

I the main problem with this idea is that the optimal capacity-to-sales ratio is(i) unobservable and (ii) very likely varies across firms;

I for example: high volatility firms should have a lower optimal ratio;

I we use a SFM to estimate the optimal capacity-to-sales ratio;I optimal capacity is an affine function of fundamentals plus an error term:

K ∗i,t = β′Di,t−1 + vi,t , with vi,t ∼ N[0,σ2v ].

I we relate optimal capacity to observed capacity via a second error term:

Ki,t = K ∗i,t + ui,t = β′Di,t−1 + vi,t + ui,t , with ui,t ∼ TN+[γ′Ei,t ,σ2u],

I Excess Assets: difference between actual and optimal ratio;I we use the LOGIT model Probability (of) Zero Investment in capacity in

the future as parsimonious alternative proxy;I intuition: the probability of future investments in capacity declines with

excess capacity according to our theory;




Appendix: Measuring the Degree of Investment Reversibility

I we use a new industry-level investment reversibility-proxy (DivestmentSensitivity), obtained from panel-data regression run by industry:

Divestmentsi,t = αi + αt + βMomentumi,t−1 + εi,t ,

where we only use industries producing more than 1,000 observations;

I the more negative the slope coefficient β, the more firms divest uponreceiving bad news and the more reversible their investments are;

I we also use a more parsimonious proxy (Land Availability), the ratio ofthe land held by a firm divided by its total assets (or PP&E);

I Campello and Giambiano (2014) and Kim and Kung (2015) argue that landis the ultimately reversible asset (it’s used in virtually all industries);

I firms holding land should have more reversible investments than others;


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Understanding and Predicting Style Returns: … · London School of Economics e-mail: ... I Business model I How it affects the timing and risk of future ... February 2015 Understanding