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Stock Bond Model from Don't Fear the Bear

13 Dec 2014

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Another interesing post from Don’t Fear the Bear : A stock bond model based on the average investor allocation to equities that is based on the The Single Greatest Predictor of Future Stock Market Returns

The main indicator:

Investor Allocation to Stocks (Average) = Market Value of All Stocks / (Market Value of All Stocks + Total Liabilities of All Real Economic Borrowers)

is constructed using FRED data in the here

It is based on the following data:

  • (a) Nonfinancial corporate business; corporate equities; liability, Level, Millions of Dollars, Not Seasonally Adjusted (NCBEILQ027S)
  • (b) Nonfinancial Corporate Business; Credit Market Instruments; Liability, Billions of Dollars, Seasonally Adjusted (BCNSDODNS)
  • (c) Households and Nonprofit Organizations; Credit Market Instruments; Liability, Level, Billions of Dollars, Seasonally Adjusted (CMDEBT)
  • (d) Federal Government; Credit Market Instruments; Liability, Level, Billions of Dollars, Seasonally Adjusted (FGSDODNS)
  • (e) State and Local Governments, Excluding Employee Retirement Funds; Credit Market Instruments; Liability, Level, Billions of Dollars, Seasonally Adjusted (SLGSDODNS)
  • (f) Financial business; corporate equities; liability, Level, Millions of Dollars, Not Seasonally Adjusted (FBCELLQ027S)
  • (g) Rest of the World; Credit Market Instruments; Liability, Level, Billions of Dollars, Seasonally Adjusted (DODFFSWCMI)

Please not the that (a) and (f) are Millions, rest in Billions; hence there is division by 1000 in the formula below:

Investor Allocation to Stocks (Average) = ((a+f)/1000)/(((a+f)/1000)+b+c+d+e+g)

Load historical data from FRED:

#*****************************************************************
# Load historical data
#*****************************************************************
library(SIT)
load.packages('quantmod')
tickers = spl('A=NCBEILQ027S,B=BCNSDODNS,C=CMDEBT,D=FGSDODNS,E=SLGSDODNS,F=FBCELLQ027S,G=DODFFSWCMI')

metric.data <- new.env()
getSymbols.extra(tickers, src = 'FRED', from = '1900-01-01', env = metric.data, auto.assign = T, getSymbols.fn=quantmod:::getSymbols)

Next let’s compute the Stock Allocation:

metric.data$StockAllocation = with(metric.data, ((A+F)/1000)/(((A+F)/1000)+B+C+D+E+G))

metric.data$AverageRatio  = SMA(ifna.prev(metric.data$StockAllocation),4)

metric.data$RelativeAverageRatio = 1 - (metric.data$AverageRatio - 0.25) / 0.20
  metric.data$RelativeAverageRatio = iif(metric.data$RelativeAverageRatio > 1, 1, metric.data$RelativeAverageRatio)
  metric.data$RelativeAverageRatio = iif(metric.data$RelativeAverageRatio < 0, 0, metric.data$RelativeAverageRatio)
  colnames(metric.data$RelativeAverageRatio) = 'RelativeAverageRatio'

plota(metric.data$RelativeAverageRatio, type='l')

plot of chunk plot-3

print(last(metric.data$RelativeAverageRatio, 20))
  RelativeAverageRatio
2009-10-01 0.7386075
2010-01-01 0.6448204
2010-04-01 0.6192706
2010-07-01 0.6073752
2010-10-01 0.5777071
2011-01-01 0.5408631
2011-04-01 0.4747850
2011-07-01 0.4852374
2011-10-01 0.4993816
2012-01-01 0.5045022
2012-04-01 0.5189942
2012-07-01 0.4680151
2012-10-01 0.4474356
2013-01-01 0.4258632
2013-04-01 0.3850559
2013-07-01 0.3436522
2013-10-01 0.2763624
2014-01-01 0.2348901
2014-04-01 0.1889742
2014-07-01 0.1637294

Now let’s test the strategy with Vanguard funds:

#*****************************************************************
# Test Strategy
#*****************************************************************
tickers='STOCK=VFINX,BOND=VUSTX'

data <- new.env()
getSymbols.extra(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)
for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)

print(bt.start.dates(data))
  Start
STOCK 1987-03-27
BOND 1989-12-14 
data$StockAllocation = make.stock.xts(metric.data$RelativeAverageRatio)

bt.prep(data, align='keep.all')

prices = data$prices

prices$StockAllocation = ifna.prev(prices$StockAllocation)

models = list()


#*****************************************************************
# Code Strategies
#*****************************************************************

data$weight[] = NA
	data$weight$BOND = 1
models$BOND = bt.run.share(data, clean.signal=F, silent=T)

data$weight[] = NA
	data$weight$STOCK = 1
models$STOCK = bt.run.share(data, clean.signal=F, silent=T)

data$weight[] = NA
	data$weight$STOCK = prices$StockAllocation
  data$weight$BOND  = 1 - prices$StockAllocation
models$strategy = bt.run.share(data, clean.signal=F, silent=T)


models = bt.trim(models, dates = '1990::')

                    
#*****************************************************************
# Create Report
#*****************************************************************
plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)
mtext('Cumulative Performance', side = 2, line = 1)

plot of chunk plot-4

print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T))
  BOND STOCK strategy
Period Jan1990 - Dec2014 Jan1990 - Dec2014 Jan1990 - Dec2014
Cagr 8 9.49 10
Sharpe 0.78 0.59 1.02
DVR 0.74 0.48 0.94
Volatility 10.45 18.06 9.71
MaxDD -18.78 -55.25 -25.07
AvgDD -2.16 -2.22 -1.38
VaR -1.05 -1.73 -0.9
CVaR -1.45 -2.67 -1.41
Exposure 100 100 100

There is more to investigate. The fun part is that i was able to replicate this strategy in about 2 hours.

(this report was produced on: 2014-12-25)

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