A better retirement through quantitative investing

September 22, 2014

Today I finally get around to doing something I mentioned way back in my first post on quantitative investing. Quant portfolios can help an investor handily beat the market over time and often with even lower risk. If that is the case then how could they impact safe withdrawal rates (SWRs) in retirement? Lets find out.

I’ve sort of already shown the effect of quant or automatic investing systems on SWRs in my posts on the various flavor of the IVY portfolios. For example, you can more than double your SWR with certain versions of the IVY portfolios. Beyond the basic buy and hold versions, the IVY portfolios are just different versions of quant systems in which whole asset classes and ETFs are used instead of individual stocks. In the IVY systems the quant metrics just happen to be trend following and momentum for the whole asset class. But I warned that the attractive SWRs from the IVY portfolios were too good to be true because the historical data did not encompass the worst period to retire in history, 1966, which defines the worst case SWRs. I want to extend that analysis of SWRs to quantitative systems using individual stocks I’ve discussed here before, namely the trending value portfolio and the combined consumer staples/utilities portfolio. These portfolios exhibit very high risk adjusted returns. Also, maybe the best part, is that we have historical data that encompasses 1966, the worst case retirement period which allows a very valid comparison to traditional retirement portfolios. OK, lets get to it.

First thing to be done is to re-run the standard stock, bond retirement portfolio (SP500) data for the period studied which is 1964 to 2013. Then do the same for the trending value (TV) portfolio and the consumer staples/utilities portfolio (XLP/XLU). Diversified portfolios are constructed with the US 10 year note. Below is a comparison of the SWRs for different stock bond allocations for the three portfolios.

SWRs for SP500 TV XLPXLU Portfolios Sep 2014

Although it’s not my purpose here, note that when you remove the great depression from the data set that even for the SP500 portfolios SWRs rise along with allocation to stocks. This is not the case when you include the great depression where the SWR falls off after its peak at 70% stocks 30% bonds. Probably a whole other topic for another day where I’ll muse about institutional learning, the rise of fiat monetary systems and the like. But for now lets just say there is not much incremental benefit for SWRs beyond 60/40. At 60/40 look at the increase in SWRs from both the quantitative portfolios; 6.39% SWR for the XLP/XLU portfolio and 7% SWR for the TV portfolio. Pretty darn good. But what about the two other retirement portfolio metrics I like to look at; maximum drawdown and average end wealth? I present those two tables below.

Max DD and AEW for SP500 TV XLPXLU Portfolios Sep 2014

MaxDD in the top table is the maximum drawdown, peak portfolio value to trough portfolio value, on an annual basis. It is the same as the worst year if there is only one down year. In the case of multiple down years it represents the compounding of those bad years after retirement withdrawals which is what matters most. $AEW is the Average Ending Wealth, or ending portfolio value, of all the 30 year retirement periods in the analysis. This is the value of the portfolio you could potentially bequeath to your family and/or charities. The quant portfolios deliver on these metrics as well. Those high SWRs in the first table come with lower drawdowns and more wealth on average across the board. Again great news across the board.

In the MaxDD and $AEW table I highlighted the 30% stocks, 70% bond rows for a good reason. The more and more I talk to retired investors the more I think that SWR is not the parameter that most retirees should try to maximize. Losses or drawdowns are what gives retired investors the most fits and more importantly is what most often derails their investment plans which leads to very poor investment results. Keeping that in mind I highlighted in the MaxDD table a level that I think most investors can tolerate, less than 10% peak to trough loss on a yearly basis. That level is at a 30% stock 70% bond allocation for the various portfolios. With the quant portfolios even at that conservative allocation level you can support an SWR of over 5% and at the same time end your retirement with an average wealth that is almost as great as a 100% stock allocation if you were just using the SP500 for your equity investments. This is an example of what Larry Swedore calls ‘low-beta, high tilt’ portfolios where in you use the bond allocation to control beta or risk (i.e. drawdowns) to a tolerable level and use a ’tilt’ to factors that increase return such as small cap, value, and momentum. This is just one example. You can choose your own optimal allocation based on your preferences.

In summary, quantitative portfolios can significantly improve the important metrics of retirement portfolios; SWRs, maximum drawdown or loss, and average ending wealth. Or at a minimum they can vastly improve the odds of a successful retirement even at very conservative SWRs or equity allocations.


The flip side of a successful retirement: spending

September 17, 2014

Most of the ink spilled in talking about retirement is limited to the investment side of the equation. How much do you need for retirement? How much can you withdraw from your portfolio in retirement? How should I invest my retirement assets? And obviously, all these questions are critical. But just as important and not talked about as often is the amount of spending in retirement. I’ll touch on some of my thoughts on this topic and my personal experience.

A few weeks ago I was reminded of an old saying, ‘The easiest way to double your money is to take it out of your pocket, fold it in half, and put it back in your pocket’. Another one I like goes something like, ‘a dollar not spent is a 100% guaranteed return on your money’. These are somewhat old school thoughts. It reminds me of how my grandparents funded their retirement with basically a 100% cash allocation and tight control on spending. Just getting them into bank CDs was a major multi year effort. However, there is a ton of wisdom in some old school thoughts. And spending in retirement is just as important as investing in retirement. The three aspects of spending in retirement are the absolute level of spending, the yearly change in spending required to maintain the same lifestyle, and the amount of flexibility in the spending level. Here I want to focus on the absolute level of spending.

Your spending level in retirement can be a lot less than what most retirement resources tell you. The typical recommendation is to plan for 85% to 100% of your pre-retirement spending. Obviously, this is highly individual but I think its important to point out that you can have an amazing retirement by spending a lot less than you did pre-retirement. At least that has been my experience. Nina and I spend approximately half of what we did before we left our careers, almost nine years after the fact. Below is our level of spending by year as a percentage of our pre-retirement spending. 2005 was our last year of pre-retirement spending so that is the base year.

Change from pre retirement spending levels sep 2014

 

As you can see it fluctuates quite a bit but the general trend has been down. And our quality of life is infinitely higher. Not working in the traditional sense opens up a host of possibilities to spend less. For example, we spend less in “rent” (RV camping fees plus fuel) than we spent on association fees for our condo in San Francisco. And don’t even ask me about property taxes. Working has an implied cost that is commonly overlooked (clothes, commuting, etc…). When you remove the costs and constraints of working it can translate into a needing lot less to retire than if you planned for 100% of pre-retirement spending. Some times less is truly more. This is a huge topic and other people cover it a lot better than I can give it time. A good resource and community on spending less can be found at Mr Money Mustache. How would your life decisions change if you could spend half of what you spend now?

Once you have the absolute level of spending down then the task moves to managing the change in spending every year and the surprises that inevitably come down the line to throw your plans into the air. Anyone notice 2009 in the table above? That was not really a surprise but in late 2008 we decided to move back from Hong Kong to the US. The surprise was the sticker shock from the cost of the move. What better time to be flexible, right? I took a job in the US and negotiated a decent relocation allowance that paid for our move back. I stayed in the job for just less than year and during that time we lived the high life and started planning our RV adventure. The increase in spending from 2008 to 2009 was pretty much covered by my salary (I still withdrew from my retirement assets that year but it was a small percentage). In 2010 we moved into the RV and the rest is history so to speak. What I think is key here is that you have more control and flexibility than you think. If there is another big surprise down the road whether it be from a big spending increase or a big portfolio loss I have the flexibility to cut spending and/or increase income.

In summary, you don’t need to spend as much in retirement as the common wisdom suggests. This also means you don’t need as much to retire as well. Controlling spending is as big a part of successful retirement as the investment side of the equation. You have more control and flexibility than is commonly thought.


Understanding modern retirement calculators

August 31, 2014

In today’s post I want to explain and demonstrate how modern retirement calculators work. There are two basic ways to calculate how much you can safely withdraw from your portfolio in retirement; looking backwards using historical data for past retirees and looking forward using possible future investment returns. On this blog I’ve pretty much only described the historical safe withdrawal rate (SWR) approach. That’s where the 4% SWR number comes from that you hear talked about all the time. Modern retirement calculators do not use this approach. They use a forward looking approach called Monte-Carlo analysis to come up with their SWRs. Lets see how these calculators work.

What’s wrong with using historical data to come up with SWRs? The problem is history only represents one outcome of all the possible future paths from that previous point. If you were back in 1966 looking forward to a 30 year retirement the actual outcome of your 30 year retirement period, which happened to become the worst in history, was only one of many possibilities. Modern retirement calculators eliminate this drawback by looking at many many possible future retirement outcomes. That’s the fundamental difference. The rest is calculation details. How do they do this? They assume that future investment returns follow some probability distribution, since the future is fundamentally indeterminate and probabilistic. So, for example, they assume that future investment returns are normally distributed (they also use some really fancy distributions like multi-variate log normal but the results are similar), i.e. follow a bell curve. They then randomly generate a time series of investment returns and use those investment returns to calculate what the SWR would be in each of those random series of returns. Since they can’t do this for the infinite variety of possible returns they usually stop at a sufficiently large number that generates statistically robust results. The generally accepted large enough number for these retirement calculators is 10,000 trials.

Now, here is the catch with these retirement calculators. They still involve some kind of bias or human forecasting under this seemingly unbiased random robust scientific method. For those assumed normal distributions  you still need to pick a mean and a standard deviation to generate those random investment returns. And you still need to forecast inflation as well. You can’t model all possible potential future outcomes. Confused yet?

It’s really pretty simple, it’s just the calculations that get intense. I put together a simple retirement calculator in excel to illustrate how this is done. In one excel file I generated 1,000 random series of 30 year investment returns (note: every time you open, save, or make any changes to this file the random numbers change, unless you turn off automatic calc in options). I generated stock and bond returns independently. I then copied these results to another excel file where I did the work of calculating the SWR for those 1,000 random simulations. In this first pass I use the historical mean and standard deviations for US stocks and US 10 year government bonds from 1929 through 2013. I also used historical inflation. And this leads to the next important observation regarding retirement calculators. All of them define a successful SWR to one that has an 80% to 85% success rate. In a 10,000 trial simulation that means 1,500 to 2,000 of the possible time series of investment returns result in failure, i.e. running out of money. It’s just that those cases are lower probability events. Yet still possible. Using an 85% success rate my simple model yields and SWR of 3.92% for a 50/50 stock bond allocation. Pretty close to the historical SWRs for such a simple model.

But none of the modern retirement calculators I’ve seen use historical returns in their calculators. They all use a return forecast that is significantly lower than historical returns have been. I think the simplest and best retirement calculator is the one from Vanguard. I like it because there is no need for a ton of inputs on the user’s part. It just generates the SWR based on the retirement period and one of three asset allocations. All the gory details are behinds the scenes. Their future return assumptions are updated once a year and explained in the Vanguard Capital Markets Model. It’s well worth the read. Basically, they use diversified portfolio returns of 3-5% real going forward, less than returns have been in the past. For a 30 yr retirement period and a moderate asset allocation their model yields and SWR of 3.8%. I used these lower future expected returns in my simple model and that yielded an SWR of 3.30%. You can access those files here and here.

At the end of all this fancy analysis we have a tool that tells us that future SWRs will be lower than the past because we are forecasting future returns to be lower than historical returns. And specifically lower than the 30 year retirement period that started in 1966 which is what gave us the worst case historical SWR to begin with. Seems kind of obvious when you put it that way. So, basically this all relies on a forecast of the future which are notoriously inaccurate. But its the best tool we have. From today’s valuation levels and interest rates it is possible the SWRs will be slightly to a good deal lower than 4%. But they may not be.

That pretty much sums up how modern retirement calculators work. Now you can at least use these tools with an understanding of what’s under the hood. You can even generate your own models if you want.

Note: the T Rowe Price retirement calculator is another very popular one. I find the added inputs and complexity unnecessary.


SWRs from 100% US TIPs portfolio update

August 21, 2014

Contrary to the overwhelming consensus at the beginning of the year, bonds have done remarkably well year to date. With that in mind I wanted to update the SWRs for the 100% US TIPs retirement portfolio I presented last year. With rates down, SWRs will be down as well but lets see by how much.

The updated SWRs from a 100% US TIPs bond ladder are shown below. I also compare them to the SWRs from my previous analysis plus the SWRs from various risky portfolios.

TIPS Model Aug 2014

As the bottom table shows, the SWRs for a 100% US TIPs bond ladder went down from 4.37% to 4.20% for a 30 year retirement period. This still compares very well to the historical SWRs from the risky portfolios. Although you almost never see this option discussed as retirement portfolio option (no one can make a lot of fees off this model), the results could be very compelling for the most risk adverse retirees.

Note: I’ve uploaded my detailed spreadsheet model here for anyone that is interested. You need to be familiar with Excel’s solver function to run the analysis for yourself.


Trending value performance update (Aug 2013 port)

August 15, 2014

This is a simple house keeping post on a quant portfolio from last year. In the comments section of my post last year on the Trending Value portfolio I linked to a Trending Value portfolio I created as of Aug 9 2013 to help some readers compare holdings in their versions of trending value portfolios. The link to that portfolio in Google sheets is here. The portfolio I reference here is the TV2 FCF NA 50 tab. Below I update the one year performance for that portfolio.

First, the snapshot at the close on Aug 9 2014 from my FINVIZ tracking portfolio is below.

Trending Value Aug 9 2013 One Year Performance Aug 2014

First thing to note is that the portfolio lost 3 holdings during the year. It started with 25 stocks. There were 3 mergers/acquisitions during the year; SPRD, CGX, and ASI. Those need to be added back in to the portfolio at the merger or sell prices.

Second, and the most important portfolio change throughout the year was in ESI. On Feb 26, 2014 the US gov’t announced an investigation into ESI. That breaks rule #2 of the quant portfolio rules and the stock should have been sold from the portfolio. I waited a few days to do some investigating and sold ESI at $30.95, for a 4% gain. If the stock had been held  in the portfolio the loss would have been -71% as shown above.

Lastly, dividends need to be added to the capital gain returns shown above. The portfolio has a dividend of 1.24%.

I’ve added all these changes and return calculations to the original link for the portfolio.

All changes incorporated, the portfolio had a gain for the year of 17.7%, handily beating its closest benchmark the Russel 2000 which had a return over the same period of 9.8%. The SP500 returned 16% over the period.


Yes, you are trying too hard

July 7, 2014

“True wisdom comes to each of us when we realize how little we understand about life, ourselves, and the world around us.” Socrates

When I read the Socrates quote above I can’t imagine what he would have said about today’s world. We live in a highly complex non-linear world with access to way more information than even massive computers can process. And yet we try and get by with our relatively poor information processing brains that simply cant deal with the onslaught of data and are subject to slews of behavioral biases which affect our decision making. No where is this more true than investing where there is the added complexity of trying to predict the future under in such an environment. I was reminded of this recently by a white paper from one of my favorite quantitative investment researchers Wes Gray titled Are you trying too hard? In the paper he makes the case for systematic decision making, i.e. quant investing. It’s a compelling case that all investors should consider. I’ll highlight some of the gems in the paper but by all means read it all and try a take a few minutes from the information hose to digest what it could mean for you.

First, lets start with the basic summary of the white paper;

Because we recognize our frequent irrational urges, we often seek the judgment of experts, to avoid becoming our own worst enemy. We assume that experts, with years of experience in their particular fields, are better equipped and incentivized to make unbiased decisions. But is this assumption valid? A surprisingly robust, but neglected branch of academic literature, has studied the assumption that experts make unbiased decisions for over 60 years. The evidence tells a decidedly one-sided story: systematic decision-making, through the use of simple quantitative models with limited inputs, outperforms discretionary decisions made by experts.

The evidence is overwhelming. He understates his case. And its even worse than he makes out as investors even underperform the experts they are paying to underperform the market by chasing returns. The solution lies in relying on models and focusing our precious limited bandwidth on choosing said models and evaluating their performance.

Students of decision-making break the decision-making process into three components (see Figure 3):

  1. Research and development
  2. Implementation
  3. Assessment

I would argue that human experts are required for the first and third phases of a decision-making process, which are the research and development phase and the assessment phase, respectively. The crux of my argument is that human experts should not be involved in the second phase of decision-making, or the implementation phase.

Exactly. Focus on the model and how it performs. This need not be a complex quant model. Standard allocation and Buy and hold indexing is a model. A model that can work quite well for most investors over the long term. The IVY portfolios whether buy and hold or the trend following ones are models. So are all the quant portfolios I’ve discussed on the blog. Once you decide to go on a path to more systematic decision making, the question becomes which one is right for you. Buy and hold is just fine if you can handle the drawdowns in bad years. Many can’t. Then what? Choose a different model.

What’s even more enlightening is that even experts armed with proven  models under perform the model. How’s that for human fallibility. Turns out the models represent a ceiling on performance.

In follow-on tests, the researchers gave the experts the output of the model and disclosed that the model has “previously demonstrated high predictive validity in identifying the presence or absence of intellectual deterioration associated with brain damage.” Using the model, experienced clinicians significantly improved their accuracy ratio from 58.3% to 75% and the inexperienced clinicians moved from 62.5% to 66.5%. Nonetheless, the experts were still unable to outperform the stand-alone model, which had already established the gold standard 83.3% success rate. This study suggests that models don’t represent a floor on performance; rather, models reflect a ceiling on performance, from which the experts detract. The “secret sauce” of human judgment ruins the beautiful simplicity of a calculation

What this tells me is that I need to accept that even after I make the conscious decision to use models I shouldn’t expect my performance to equal the model. Inevitably I will get in the way of the model and mess things up. That’s OK. We are not robots nor can we expect to become one but we should strive to adhere to the models and/or find ones that better fit our individual behavioral biases. This is not an easy switch to make.

Relegating your decision-making processes to systems requires a massive dose of humble pie.
Most—if not all—are unable to consume this dish. But to be a better decision maker we must eat our humble pie. As I have shown in this essay, in order for decision making to be effective, it must be systematic. And the only systematic thing about humans is our flaws. Therefore, it is best to leave the stock picking to Warren Buffett, and for the rest of us, who suffer from behavioral biases, which result in flawed decision making, we should stare into the mirror, and ask ourselves: Are You Trying Too Hard?

Yes, I was. And probably still am. My personal journey to quant investing started in 2005 when I bought two books that began a fundamental shift in investment thinking. The first book was Joel Greenblatt’s The Little Book That Beats the Market and the second was O’Shaughnessy’s What Works on Wall Street (links are to newer editions of the books). Then in 2009 it was The IVY Portfolio by Meb Faber which introduced me to ETF systems and risk reduction with trend following. That began a slow transition to quant investing and benchmarking myself against models. I’m now down to one individual discretionary stock holding and still a bit too discretionary for my bond holdings but I’m almost to where I want to be. My conclusion after all of this; even if I assume I can beat the models, which btw can beat the market, with above-average stock picking ability or market timing it’s not worth the time, work, or anxiety. I have better things I can do with my time and energy. You have probably been trying too hard too.


Technology is shaking up the investing landscape

July 4, 2014

There’s been couple of recent announcements in the portfolio management industry which show the extent of technology’s penetration into the industry. The announcements further continue the rise of such automatic investing companies like Betterment and Wealthfront which are using modern technology to automate and drive down investment costs for individuals.

First, Covestor announced a series of Core ETF Portfolios with zero management fees. Basically, this means you can get professionally managed buy and hold ETF portfolios for no management fees. The only fees are the underlying ETF fees and ETF transaction costs of approximately $20 per year. This is probably less than what it wold cost you to do it yourself but without having to make any decisions during the year. You just pick a certain allocation model, say the Balanced Core Portfolio (60% stocks, 40% bonds) and the rest is taken care of for you. See details here.

Second, with Motif Investing you can build your own portfolios of individual stocks or ETFs for a single one-way commission of $9.95. You can then change any individual position within your portfolio for $4.95 per transaction. Overall, this is lower than you can do on your own in any of the discount brokers that I am aware of. See details here. For example, think about implementing any of the quant models or IVY portfolios I’ve discussed on the blog with this transaction structure. You can pretty much beat the costs any broker or individual stock ETF with this model.

All of these technological developments could have huge implications for professional management, either passive or active. I believe there is a role for professional financial advice, as I discussed here, the question is how much are you going to pay for it? You think Vanguard is not aware of this trend? And why would you pay any more than 0.3% to Vanguard for any buy and hold advice? These developments also lower the bar to any active management of your portfolio and that active management of your portfolio should be leading to well documented investment outperformance and/or significant risk reduction (drawdowns, down years, etc…). If it’s not call Vanguard or do it yourself.

 


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