Does sector momentum outperform stock momentum?

A momentum strategy may be implemented at an individual stock level or sector level.

Is there an advantage to partitioning stocks into sectors and owning the strongest sector versus buying the highest momentum stocks in the market?  Stock momentum funds have been available for a few years and recently sector rotation has been packaged into an ETF.

Ken French publishes a 10 industry portfolio and momentum portfolios for US equities which may be used to investigate:

Industry1

The blue curve is the top sector (ranked by 12 month return, monthly).  The red curve is top tercile stocks (ranked by 12 month return, monthly) in the largest 50% by market cap.

Sector rotation clearly outperforms stock momentum (by about 3% annually since 1950).  However, this result is for the highest ranked sector only.  It is difficult to determine the fraction of the market represented by the top sector versus top tercile large stocks but the sector slices are likely smaller.

Multiple sectors are compared below.  Top 2 and top 3 returns are comparable with the individual stock momentum strategy.

Industry3

Compare the “Top 1” curve with the simple rotation between value and momentum shown on this blog.  Similar performance (17% CAR) but with only 2 instruments and fewer trades.

Sector2.

Finally, combine sectors with value, momentum and risk-free (RF).  The top ranked portfolio by 12 month return is selected every month:

Sector+M+V

Returns are improved about 1% annually over the entire data-set but the 4% out-performance since 2000 (from avoiding the GFC correction) accounts for the majority.

Summary stats

Sector_stats

Conclusions

A rotational strategy with the top sector outperforms the large momentum FF stock portfolio.  However, this may be partially due to selecting a smaller section of the market.

These tests show that beating a simple value-momentum rotational model is tough.  Adding ten sectors to this model increases returns slightly, but at the expense of higher turnover.

This demonstrates what is possible with price-based ranking only.  Adding volatility and correlation to the scoring may give further improvement (e.g. Keller 2015 “Classical Asset Allocation”):

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2606884

Markowitz portfolio optimization with VBA code

Wouter, Butler and Kipnis [2015] recently demonstrated Classical Asset Allocation (CAA) for long only portfolios, based on Markowitz’ concepts. The method uses only two parameters thus minimizing the chances of curve-fitting and data snooping.  The parameters are lookback period (12 months) and target volatility.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2606884

Main results from the paper are as follows (from 1915 to 2015):

CAA8

CAA39

R annual return, V volatility, TV target volatility, D max. drawdown, EW equal weight

N=8 portfolio:

SP500, EAFE, EEM, US Tech, Japan Topix, T-Bills, US Gov10y, and US High Yield.

N=39 portfolio (N=8 +):

10 Fama/French US sectors, five US bonds, US Small Caps equities, GSCI, Gold, Foreign bonds, US TIPS, US Composite REITs, US Mortgage REITs, FTSE US 1000/US 1500/Global ex US/Developed/EM, JapanGov10y, Dow Util/Transport/Industry, FX-1x/2x, and Timber

Consistent results from all datasets gives further confidence in the method.

My main interest is in factor investing.  I applied the method to the momentum and value portfolios normally used in this blog plus Mkt factor and ‘risk-free’ (all from Ken French’s data library).  Dividends are continuously re-invested and trading frictions are neglected (this strategy only trades a few times per year).

Results from two target volatilities are shown below.  The lower volatility case exhibits a remarkable 65 year Sharpe Ratio of 1.3.  The t-statistic is 10.6!

Annualized returns are 9.7% and 12.6% respectively.

Marcowitz1

The next post will cover sector portfolios and real datasets.

I used Excel’s solver to maximize 12 month trailing return with a 12 month standard deviation target.  A constraint is applied of sum of weights = 1 (no leverage).

The spreadsheet is trivial to create.  The column layout to match the VBA code is as follows.  This is for 4 datasets but may be extended as necessary.  Columns F-L start at row 13 as they require 12 months of history.

Column    Description

A                Date

B-E            Monthly return data (4 datasets)

F-I             Portfolio weights (calculated by solver)

J                 Sum of weights (F:I)

K                Weights multiplied by 12 month returns, summed

L                 Weights multiplied by 12 month stdev of returns, summed

 

When the solver finishes, multiply monthly returns by weights from the previous row and sum to obtain the portfolio return.

VBA code for the solver is run as a macro in the sheet containing the data:

For i = 13 To 790 ‘ monthly return data in rows 2-790

SolverReset
SolverAdd CellRef:=”$J$” & i, Relation:=1, FormulaText:=”1″ ‘ sum of weights = 1
SolverAdd CellRef:=”$L$” & i, Relation:=1,   FormulaText:=”3″ ‘ target stdev = 3% (10% annualized)
SolverOptions AssumeNonNeg:=True
SolverOk SetCell:=”$K$” & i, MaxMinVal:=1, ValueOf:=”0″, ByChange:=”$F$” & i & “:$I$” & i
SolverSolve userfinish:=True
SolverFinish keepfinal:=1

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