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.

 


A year in the life of a trending value portfolio

June 4, 2014

It’s kind of hard to believe but almost a year ago I introduced the trending value quant portfolio. Since I’m coming up on a rebalancing and I’ll start work on the new portfolio this weekend I wanted to take a look back and see how last year’s portfolio performed. Also, I’ll point out some highlights and lowlights in the portfolio.

Lets start with performance. Below is a snapshot as of this morning of the performance of the trending value portfolio from June 14,2013.

Trending Value 1 year performance from June 14 2013updated june 4 2014

I use a $100K portfolio starting value because FINVIZ has a feature where it automatically calculates the individual share counts needed to make an equal weighted $100K portfolio for you. Makes life easier then I can scale the values as needed for my real portfolio in my brokerage account.

The Gain% column in the table shows the price only gain since June 14, 2013. The price only change is 20.8%. The portfolio had an initial dividend yield of 1.5% which brings the total return for the trending value portfolio to 22.3% with 10 days to go to complete the year. The closest benchmark to the trending value portfolios is the IWM ETF. In the same time frame IWM has a total return of 14.96%. And since everyone always likes to compare to the SP500, the SPY ETF is up about 19% in the same time frame. The performance of the trending value portfolio is right in line with its historical average.

Managing this portfolio throughout the year was relatively easy. If you compare the stock list above to that in the original post you will find a few differences. During the year, Arkansas Best Corp (ABFS) changed its name to Arc Best Corp (ACRB). You wouldn’t have needed to anything in your brokerage account. The change was automatic. You would just have changed your tracking portfolio like I did in FINVIZ. Next, DEG had a 4:1 stock split during the year. Again, nothing for you to do in your brokerage account. You would just need to update your initial share count and buy price or you would get an inaccurate gain calculation. Lastly, and always the most tricky are mergers. One of the stocks, First Financial Holdings (FFCH) experienced a merger during the year. SCBT merged with FFCH at the end of July 2013. FFCH shareholder received 0.4237 shares of SCBT for every share held of FFCH. All of this is handled automatically by your brokerage as usual, you just update your tracking portfolio to the ownership in the new company. Now, according to the quant portfolio rules, see here, if prior to the merger FFCH’s share price had come within 95% of the offer price we would have sold the shares and replaced the stock in the portfolio. Well, I totally missed this at the time and just stuck with SCBT in the portfolio.

Now, take a step back and look at the performance of some of the individual names. There are a couple of real dogs. HGG is down 46% over the year. It started out great, going from 16.88 to just over 20 and then cratered. It’s come back a bit, off 27% from the lows, but still not pretty. MX was another early star that has not done well since its initial run. And TA was a dog pretty much from the beginning as well. Of course, we have some real beauties on the other side of the ledger. GPRE lead the way with a 115% gain, followed by ArcBest with a gain of 113%., then GNW with a 61% gain. These three were pretty much straight up since the beginning. Then there are the stocks which pretty much no investor would touch on their own. There was a Greek shipping stock in the portfolio, two Argentinian stocks, and two hard drive companies. Do you remember the headlines surrounding some of these themes last year? Would you have been able to hold these names? Do you think you could have beat the portfolio by stock picking within this portfolio? Maybe, but history says no. Most investors can’t even make the initial investment in such a portfolio.

In short, a very good performance for the trending value portfolio from June of last year. Now on to next year.


The value of financial advice

May 31, 2014

I am a huge believer in do-it yourself investing. I believe that any one can learn to manage their own investments effectively given the time and the inclination. And it has never been easier or cheaper to manage your own investments. But there are many investors who do not have the time, nor the inclination to take on this task on their own. Or for whom the learning curve is too steep too quick. Or, maybe more importantly, need help managing their emotions during trying times. This post is for them. Technology and competition is making it easier and cheaper to get investment help.

Have you ever heard of a robo-advisor? This is the hot thing in the portfolio management world right now. Basically, a computer/robot does the grunt work of investment selection for you. After all, managing a buy and hold portfolio of diversified ETFs is an easy thing to do for computers. The two biggest companies in this space are Betterment and WealthFront. You create an account, answer a series of question to determine your investment personality, then are recommended an allocation with a portfolio of ETFs. And, of course, you don’t pay too much for this. Below is a comparison from a recent post on Mebane Faber’s site.

robo adivsor vs custodian fees may 2014

As the table shows the new robo advisors are really shaking up the landscape. Also, investors pay way too much money to have buy and hold portfolios managed for them. The industry average fee for managed portfolios is 1.32%, according to Vanguard. For buy and hold. On top of the underlying ETF fees. Seriously! Fidelity seems particularly egregious. Buy and hold should be cheap. If you have a great investment advisor who is doing advanced things like tax harvesting, active investing, risk management, etc, great they should be payed more for this added value but not anything more than 0.5% to 1.0% in my opinion. Anything above this and I would go somewhere else.

But there is more value in personal financial advice than just the mechanics of investing. To me the biggest value in financial advice is helping to manage an investor’s emotions. That is most investor’s biggest failing. Its why investors under perform even the mediocre returns of mutual funds. I all has to do with our behavioral biases.  And here is where a good advisor can really pay off. This image depicts the value of a financial advisor better than any words I can write.

Value of a Financial Advisor

**Image by Carl Richards, Buck’s Blog at the NYT. Check out the blog for more simple yet powerful investment messages.

And you know what? A lot of that great investment advice coming from a great advisor will be ‘I recommend we do nothing and stick to our plan’. Yeah, do nothing is often the best investment advice you will receive. So, there is a ton of value in financial advice you just shouldn’t pay a lot for it. Vanguard has really taken the lead here with their 0.3% annual fee. Combined with their industry leading low costs ETFs it makes for a powerful combination. If anything you should use them as a benchmark to the advice you are getting today.

In short, there can be a great deal of value in financial advice. And many investors need it. There is just no reason to pay lot for it.

 


The impact of realistic spending on your retirement

May 28, 2014

In a post last week I showed that for the majority of retirees the standard recommended spending model in retirement does not match reality. Spending  in retirement actually grows by less than inflation over time. In this post I want to show you how using a more realistic sending model impacts how much you can withdraw from your portfolio in retirement.

If we go back and use the same model we used to calculate SWRs, we can adjust annual spending to see the impact of using a more realistic spending model on SWRs. For this post I simply changed the annual spending adjustment in retirement to grow by 1% less than inflation during the 30 year retirement periods (-1% per year real spending). This is actually pretty conservative compared to the actual retiree data.

Second, I took at look at combining a realistic spending model with a flexible spending model I’ve discussed in the past. The Floor Ceiling method basically allows spending adjustments in retirement based on the performance of the portfolio. For this post I used a floor ceiling model with unlimited upside and a max floor of -10% to inflation adjusted spending.

The results are shown in the table below for 30 year retirement periods, a 70-30 SP500 10yr UST bond stock portfolio with data going back to 1929.

SWRs for base vs realistic vs fcm model may 2014

The standard retirement model says your SWR is 4.39% for this portfolio. By simply using a more realistic spending model the SWR for the portfolio goes up to 4.90% an increase of almost 12%. The FCM model with its flexible spending says your SWR is 4.84% for this portfolio. By combining both approaches the SWR for the portfolio goes up to 5.39%, an increase of over 22%! That’s a raise of 22% per year in retirement simply by being more realistic and flexible. Very powerful stuff.

For those still building for retirement the implications are perhaps even more impactful. The percentage increases in SWR are equivalent to a reduction in the amount you need to fund your retirement. If you can spend 22% more with these models in retirement it also means you would need 22% less to retire. That could translate into several years of work or no work in this case.

 


Quant investing: building a better index

May 25, 2014

Today I wanted to take a look at how you can use quant investing to build a better stock market index. For my previous posts on quant investing see this series of posts.

In a way stock market indices are quantitative models. And due to history and other factors they are not very well constructed. For example, the Dow Jones index is comprised of 30 stocks weighted by the price of the stocks! Kind of silly in today’s day and age no? How about the grand daddy of the indices, the SP500? It’s much better than the Dow but still has some odd construction. Here’s is some of what it takes for a company to be in the SP500 (you can find all the requirements here):

  • Market capitalization greater than $4.6B
  • US companies only
  • Sum of 4 quarter earnings > 0
  • No BDCs, partnerships, LPs, MLPs, ADRs
  • Weighted by market capitalization
  • Addition to index decided by SP500 index committee

Basically, the SP500 is a quant index with some discretionary input from the committee. A few of its big limitations are the exclusion of foreign companies trading in the US, the exclusion of certain classes of companies all together, and the weighting of the index by market cap. We can do better by correcting some of these limitations and adding a value factor to the mix. As always, my reference for all this is O’Shaughnessy’s What Works On Wall Street which I’ll refer to as WWOWS.

WWOWS creates an all stock and a  large stock index, in the spirit of the SP500, by broadening the definition of an index to include small cap stocks, and the large cap index a bit looking for companies with a market cap greater than the database mean. It also allows for ADRs (foreign companies), and all company types. It then equal weights the companies instead of market cap weighting. Equal weighting has been shown to outperform market cap weighting consistently. See here. Then we’ll take it further by adding a value factor screen to the various stock universes. Now for the value tilt.

Dividends have been a reliable value factor in the stock market over time as I’ve discussed on this blog many times. I’m still a huge dividend fan but I’ve updated my views somewhat. Shareholder Yield, which adds stock buybacks to dividend yield, is a better value metric that dividend yield alone. If you want to totally geek on on Share Holder Yield see this recent study. So, we’ll sort the stocks in our new index by Shareholder Yield. These new ‘indexes’ are re-balanced to equal weight and sorted by value once a year. Lets see how all this works out in the table below.

Shareholder Yield Quant Strategy Performance May 2014

The table is sorted by risk adjusted returns, aka sharpe ratio. As you can see, the better index construction beats the SP500 and the addition of Share Holder Yield outperforms the old tried and true value indicator of dividend yield. Pretty impressive for such simple changes that basically broadened our universe of companies, sorted by value, and equal weighted the stocks in a portfolio.

This is about the simplest quant investing you can do on your own. For individual portfolios you would limit yourself to the top 25 or top 50 stocks to keep transaction costs down. The broader portfolios and dividend portfolios can be created for free with the screeners such as FINVIZ. The Share Holder Yield portfolios require fee based screeners such as SI Pro or Portfolio123.


Spending Realities in Retirement

May 20, 2014

It’s usually worthwhile to question conventional wisdom. At the minimum you usually learn where that wisdom came from. Often you learn the conventional wisdom is not the best approach. This happens to apply rather well to the topic of retirement spending. Lets take a look at the conventional wisdom regarding retirement spending and see why it may not be the best approach.

All the standard retirement models tell retirees to adjust spending every year for inflation right? This how the SWR (Safe Withdrawal Rate) model works. You pick a percentage of the portfolio to withdraw in year 1 and then adjust that spending every year for inflation. Simple enough. The theory being that at the minimum we want to maintain the same ‘standard of living’, i.e. real spending, from year to year. The biggest question to ask is this model actually how retiree spending evolves over time?

Anectodally, this model has always felt a bit off to me. For one, I meet a lot of retirees and speak to them quite a bit about finances and spending. Many I’ve met have held spending constant for years while maintaining their quality of life. In my personal situation, on average spending has either stayed the same or declined every year. What gives?

Fortunately, some recent research has shed some light on this topic. Michael Kitces has a great post covering this research which I highly recommend. I’ll just highlight a few points from the post. Lets first look at how retirees actually spend. Here is the data:

Annual Real Change in Consumption for Retirees May 20 2014

The orange line in the graph above is spending adjusted for inflation, real spending. This is the line all retirees fall on according to the recommended retirement models. The reality is somewhat different. For the majority of retirees real, inflation adjusted spending declines in retirement. Even towards the end of life. The declines are on the order of 1% a year, growing to 2% a year, then back to 1% a year in the later years but still never positive. Definitely a different reality than the models portray. An important point: this is real spending. Actual spending may still actually increase. For example, inflation is 3%, spending goes up 2%, real spending is then down 1%. Now lets look at how you can use this in your retirement.

I think the best way to use this research is to take a look at your retirement in stages. Michael also talks about this in his post but basically the idea is to divide retirement into three spending stages. Like pictured here;

three stage retirement model may 2014

 

Each of the three stages can be modeled with a different change in retirement spending. For first stage, say 60 to 70, -1% per year in real spending could be used (or conservatively the standard model’s o% per year), for the second stage, 70 to 85, -2% per year, and for the third stage back to -1% per year. This can be tailored to be fit your specific circumstances which is also much better than the standard retirement model.

OK, so far all we’ve done is match the retirement model to a better representation of retirees’ actual experiences. Now for the punchline. Being that realistic scenario is less spending than the model means one of several things. One, the standard SWRs are too conservative. Two, retirees need less money to retire to being with. Three, worst case, the SWRs from the standard retirement model plus a more realistic spending outlook means a much higher probability of success in retirement. I’ll take a look at some of these implications in later posts.


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