Unbelievable and Amazing R Shiny–Web Parameter Test in 1.5 Hours

Life keeps getting better and better.  Yesterday, I discovered the absolutely unbelievable and amazing work RStudio has done with Shiny employing one of my favorite R packages websockets.  As proof of the ease and quality, within a couple of minutes, I was able to get it up and running.  This morning basically starting from scratch in less than 1.5 hours I was able to achieve a web-based interactive parameter test for a moving average system as my first example.

Below is a screencast of this very basic parameter testing web app.  I can only hope that this simple application can illustrate just how powerful Shiny is.  Just imagine pairing this with d3 or knitr.

R code for server.R and ui.R from GIST:

Cash–Opportunity Lost or Opportunity Gained

Tom Brakke from http://researchpuzzle.com/ wrote a great thought piece Cash as Trash, Cash as King, and Cash as a Weapon for the CFA Institute blog.  My favorite part comes in the last paragraph:

“That’s the kind of analysis that should be brought to the discussion of cash, not simple sayings that bounce back and forth in response to the mood of the market. Individual investors should not be afraid to hold cash, even when it’s earning little, if it’s available to them when needed most. And investment professionals should get away from misguided notions about how much cash is too much cash in a portfolio. Let the manager use the value and power of cash to execute a strategy. Then you can judge whether the strategy makes sense. Don’t remove cash as an effective weapon.”

Another way of looking at cash is does it represent the commonly accepted notion of opportunity lost (opportunity cost or “cash as trash”) or does it represent opportunity gained (Buffett’s cash as a “call option” as described in the solid Globe and Mail article).  I hope those who know me or read this blog know where I stand.  Cash is a refuge in the absence of opportunity, and I plan to spend significant time over the next couple months exploring how to mathematically price cash as a call option. If anyone has attempted this or read any research, please share it with me.

Interestingly enough as a byproduct of some other research, yesterday I was confronted with something that I should have already known.  If you compare the 1 year US Treasury (not really cash but close enough) with just the price return of the S&P 500 starting from 1960, the price only S&P 500 is extraordinarily unremarkable.

From TimelyPortfolio

From TimelyPortfolio

Also, cash does not look so bad when we consider the new Research Affiliates research "Glidepath Illusion".  Certainly commonly accepted “wisdom” does not seem so wise.

R code from GIST:

Resurrect Posts on Japan and the Yen

As the Yen and Japan continue to get more interesting in my mind, I just wanted to resurrect some posts that I have done on Japan and the Yen and sort them by my favorites.

Japan Trade by Geographic Region
Japanese Trade and the Yen
Japan Intentional or Accidental Pursuit of Deflation
Japan Trade More Specifically with Korea

Just to add a chart, here is one using data from the Federal Reserve Bank of St. Louis (FRED).  While the extreme correlation between the Yen and the S&P 500 has limited the opportunity available in the Yen, the correlation has recently weakened as Japanese deficits have worsened and the Yen stopped getting stronger.

From TimelyPortfolio

R code:

require(latticeExtra)
require(quantmod)

getSymbols("DEXJPUS",src="FRED")
getSymbols("SP500", src="FRED")

asTheEconomist(xyplot(DEXJPUS,main="US Dollars for Japanese Yen Since 1970\nSource: Federal Reserve Bank of St. Louis"))

#merge the weekly returns of Yen and SP500
ret <- na.omit(merge(weeklyReturn(DEXJPUS),weeklyReturn(SP500)))
#use the rolling correlation method from PerformanceAnalytics chart.RollingCorrelation
rollcor <- as.xts(rollapply(ret, width = 208, FUN = function(x) cor(x[,
                     1, drop = FALSE], x[, 2, drop = FALSE]), by = 1,
                     by.column = FALSE, na.pad = FALSE, align = "right"))
xyplot(na.omit(merge(SP500,rollcor,DEXJPUS)),col=brewer.pal("RdBu",n=9)[c(9,2,8)],
              lattice.options=theEconomist.opts(),
              par.settings=theEconomist.theme(box="transparent"),
              scale=list(y=list(rot=0)),
              xlab=NULL,
              strip=strip.custom(factor.levels=c("S&P 500","Correlation (Rolling 4 Year) S&P 500 and USD/Yen","USD/Japanese Yen")),
              main = "S&P 500 and USD/Yen Since 1970\nSource: Federal Reserve Bank of St. Louis")

Japanese Government Bond (JGB) Data Since 1974

The Ministry of Finance Japan very generously provides data on JGBs back to 1974.  Here is a quick example how to pull it into R and then graph it.

From TimelyPortfolio

R code in GIST (do raw for copy/paste):

Life on the Big International Frontier

Although I have used the Kenneth French data library extensively in various posts, I have not yet used the international data sets paired with the wonderful paper.

Eugene F. Fama and Kenneth R. French (2012) "Size, Value, and Momentum in International Stock Returns", Critical Finance Review

To rectify this home bias, let’s generate some efficient frontiers for the biggest cap stocks by geographic region to see how the frontiers have evolved over the last 20 years.

From TimelyPortfolio

Eventually, I would like to think through some other methods of comparing risk, return, and weights across multiple frontiers.

R code from GIST (do raw for copy/paste):

Not Much of a Grand Finale. What if We Go To 0?

When I ask the question “What if the US 10 year goes to 0?", most do not know the effect, the catalyst, or if 0 has ever happened before.  The math is fairly simple to do in Excel or with an old-school calculator, but let’s use RQuantLib to do the pricing and then use LatticeExtra with some slight adjustment in SVG.  RQuantLib spits out a total return of 17% if we go to 0 by the end of October, which seems like a decent amount until we look at on a chart.

From TimelyPortfolio

Mildly impressive, but the move is almost undetectable on a log scale.

From TimelyPortfolio

Throughout history, we have really only one good reference point in Japan whose 10 year went very briefly to 0.47%, but we need to remember that was in extended deflation in which stocks and real estate lost 90%.  That 17% return if we go to 0 (actually much less since 0.47% 0.43% was the stopping point) is not all that helpful in this devastating environment.

Even more strange is that the move we experienced over the last 15 months is greater than the potential move from here to 0.  On a six month change in yield chart, the 2% from April to 0% in October seems perfectly normal if we forget about the starting point.

From TimelyPortfolio

Similarly, a 12 month rolling total return chart does not reveal anything odd.

From TimelyPortfolio

However, starting point is critical.  Instead of subtracting ending from starting yield, ending yield/starting yield is more appropriate at this critical level.  Now we can see how unusual the move really is.

From TimelyPortfolio

If you are buying bonds to protect/benefit from a disastrous, deflationary “end of the world”, please be aware that best case you make a fairly measly 17%.  Just moving back to where we were Spring 2011 would mean a bigger loss than the absolute best case.

THIS IS NOT INVESTMENT ADVICE.  ALL OF THE ABOVE IS SIMPLY FOR ILLUSTRATION.

R code from GIST: (do raw for copy/paste)

Emerging as Low Vol

Extending the series begun with When Russell 2000 is Low Vol, I thought I should take a look at Emerging Market stocks during periods of low relative volatility to the S&P 500.  So you can replicate even without access to expensive data, let’s use the Vanguard Emerging Market Fund (VEIEX) and the Vanguard S&P 500 Fund (VFINX) as proxies.  In the 12 month rolling regression, we see the same fairly steadily increasing beta and correlation of the Emerging Market stocks to the S&P 500 that we saw in the Russell 2000.

From TimelyPortfolio

If I progress further on this research, I will have to work on an adaptive definition of “low vol”, but for the purpose of this post, I defined “low vol” as

Emerging 50 day std. dev – S&P 500 50 day sd > –0.075

For the Russell 2000, we used a more strict 0.0125.  Although the numeric definition is different, the chart shows a very similar profile.

From TimelyPortfolio

R code from GIST:

When Russell 2000 is Low Vol

Continuing in my exploration of the Russell 2000 (Russell 2000 Softail Fat Boy), I thought I would try to approach the topic with a low volatility paradox mindset.  Since 2005, beta of the Russell 2000 compared to the S&P 500 has exceeded 1.2 with a max of 1.6 for almost every rolling 1 year period.  This suggests that the Russell 2000 is anything but low vol.

From TimelyPortfolio

However, we can take a more simplistic view by comparing the rolling 50-day standard deviation of the Russell 2000 with the S&P 500.  Russell 2000 on an absolute and relative basis does very well when rolling 50-day standard deviation of the Russell 2000 minus the same standard deviation on the S&P 500 exceeds –1.25%, so the Russell 2000 performs best when volatility approaches the S&P 500.  In low relative volatility environments, it seems we should own the high beta Russell 2000.  You will see the largest down moves all occur in the non-shaded time periods.

From TimelyPortfolio

I intentionally wanted this post to be simple, so I hid a lot of the preliminary work and extra links.  Far more went into this than appears above.

R code from GIST:

Obviousness of REITs?

I very much enjoy papers such as

Antonacci, Gary, Risk Premia Harvesting Through Momentum (September 5, 2012). Available at SSRN: http://ssrn.com/abstract=2042750 or http://dx.doi.org/10.2139/ssrn.2042750

Faber, Mebane T., A Quantitative Approach to Tactical Asset Allocation (February 17, 2009). Journal of Wealth Management, Spring 2007. Available at SSRN: http://ssrn.com/abstract=962461

and clearly the finance community appreciates these with the first as a First place winner of the 2012 NAAIM Wagner Awards for Advancements in Active Investment Management and the second with a download rank of 2 on SSRN with 273,000 abstract views.

However, I struggle mightily with how obvious would these papers’ choice of assets been without the benefit of hindsight.  I already briefly touched on this flaw in Bonds Much Sharpe -r Than Buffett.  Of course, using what we now know is one of the best asset classes in the history of the world that has also experienced an anomalous and extremely negative correlation with equities during their distress will provide a very positive result.  Unfortunately, I have yet to find any research from the late 1970s or early 1980s that predicted such a glorious environment for bonds.

Similar but not quite as extreme, adding REITs (all my posts about REITs)  over the last 12 years in any way would almost guarantee a pleasant result.  However, REITs were not so stellar for the 15 year period 1984-1999.  Would REITs have been such an obvious choice in 1999?  Of course, if we know the future, but I’m not so sure when we only knew the past.

From TimelyPortfolio

From TimelyPortfolio

From TimelyPortfolio

Also, how obvious would gold have been in 1998 or how obvious would high yield have been in the early 1980s when they did not even exist? Can we expect the next 10 years to look like the last 10 years?

R code from GIST:

Big Issue with System Backtests

Almost always, when I see a system backtested, the backtest assumes a static portfolio with no contributions or withdrawals.  This assumption only covers an extremely limited subset of my clients.  Cash flows in and out of a portfolio or system can have a much larger impact on ending net worth than the geometrically linked performance of a system or money manager.  Most of the systems I have shown for demonstration purposes on this blog have suffered from this unrealistic assumption.  I thought I should show how contributions similar to those in a 401k can affect a simple moving average system.

In a roaring bull market, any momentum system will underperform so significantly that potential abandonment of the system due to lack of confidence is highly likely.  As shown below, there was very little discussion of moving average strategies 1990-2000 due to the simple fact that buy/hold absolutely clobbered them.  While normal adjustments like return/risk and a log scale can soften the impact on the screen, the psychological impact to a client can be very damaging as the focus moves entirely to $ value of the portfolio.  Below is a comparison of buy and hold versus a 200 day moving average system with no additions or withdrawals.  As discussed, 1990-2000 was not kind to the moving average.

From TimelyPortfolio

However, if we add a simple framework similar to a 401k investor starting with $100,000 and adding $10,000 per year ($2,500 per quarter), the results differ significantly.

From TimelyPortfolio

I intentionally played a little trick by changing the y axis to a log scale.  Clients don’t think in log scale when evaluating performance.  They simply look at $ value of the portfolio.  If we look at the results without a log scale, underperformance by 2000 is visible, but it is nowhere near as great as shown in the first chart of the post, and outperformance after the 2008-2009 collapse is very healthy.  I actually think a client could accept this profile much more readily than that shown with a static portfolio.

From TimelyPortfolio

Of course, this test was not perfect, and all sorts of assumptions and simulations can be added, but we can start to see how inflows and outflows can impact a portfolio whether it is buy/hold, discretionary, or systematic.

R code from GIST ( do raw for copy/paste ):