Blog #47 Try this on Your Chartist

In my previous blog, I showed that investors tend to chase trends but don’t do a good job relative to a passive buy-and-hold investment strategy. Since you may not be convinced by research based on aggregate data, here’s a simple test of your prediction skill.  Below are eleven charts of daily “stock prices”. Only 5 of these charts are the prices of real stocks. The rest are fake prices, generated based on a random process much like flipping an unbiased coin. The test: identify the 5 real stocks (answers are at the bottom).Chartism.jpg

 

 

 

 

 

 

 

 

 

Answer: Series number 3, 5, 7, 9 ad 10 are real stocks.  Notice how the randomly generated ‘prices’ look awfully like real prices, enough to fool even experts, as the title of the Nassim Taleb bestseller, Fooled by Randomness suggests. 

 

 

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Blog #46 Fool’s Game

Most of us aren’t investment geniuses, so a passive “buy-and-hold” strategy will beat “active” investing hands down most of the time. But we are restless creatures; buy-and-hold sounds like a boring cop-out.

Active investment is about market timing and stock selection. It is the stuff that mutual fund and hedge fund managers do for the fat fees they charge. Legions of ordinary investors also try to pick stocks and predict markets on their own.  How these individual and professional investors actually perform has piqued the curiosity of academic researches for decades. Fortunately, we do have a trove of hard evidence on the usefulness or otherwise of active investing from studies of mutual funds, and to a lesser extent, of individual investors. What evidence there is shows one thing – it is hard to consistently beat a buy-and-hold strategy once you factor the costs. My focus today is on market timing.

Market timing is irresistible. Who can resist feeling bullish about the market after hearing the evening news that the STI has reached another record high? The term “trend chasers” describes investors who trade on momentum – buying after market rises and selling after market falls. Psychology research shows that our brains are ‘hardwired’ to see patterns even in randomness. Trend chasing is an example of behavior arising from our tendency to extrapolate patterns from the past.

The opposite of trend chasing is contrarian investing. Contrarians are more inclined to buy stocks after the market has fallen and sell them after the market has risen. Because contrarians go against the crowd, active contrarians feel often smug, believing their trading style be a cleverer strategy than following the trend. Of course, some investors are both momentum and contrarian traders, just at different times.

Regardless of whether you are momentum or contrarian, as long are trying to predict the market, sectors, or individual stocks to trade than invest for the long term, you are a market timer.  You are implicitly saying, “who gives a #$@% about the long term? I just want to make a quick buck.”

Is that a problem?  I would say YES if market timing forms the core of your investment strategy. The reason is simple. Markets aren’t that easy to conquer. Especially in the short run (days, weeks, even months), stock prices are pulled by a multitude of factors, including observable fundamentals (GDP, inflation, interest rates and the like) and market sentiments (much harder to measure). The interplay of these factors explain why stock prices zig and zig randomly from day to day, making consistent accurate predictions very hard. In that sense, market timing isn’t that different from betting in casinos where the odds are stacked in favor of the house. Similarly, market timing is a fool’s game because the odds of winning are stacked against you. While it is true that for every loser, there is a winner, randomness implies that nobody will be consistent winner. Randomness also means that occasional wins may be due to pure luck rather than prediction skills.

For more direct evidence on market timing, I will now share with you the findings of a detailed research study. The original study was done in the US some years back. The current extends this research using more recent data (to 2016).

The study looks for evidence on the effectiveness of market timing by comparing two concepts of average return over a sample period. The first average return is called buy-and-hold average return (BHR). The second average return is called dollar-weighted averaged return (DWR).

BHR is the return one earns on average by following a passive buy-and-hold strategy which does not attempt market timing. In the study, we assumed that investors buy a particular market index at the start of the sample period and hold this index until the end of the sample period. Details of sample periods are discussed below.

DWR gives more weight to periods when more money is held in the stock market and less weight in other periods. Like BHR, we compute DWR for the whole market comprising all types of investors. Some of these investors may be momentum investors, while others are contrarians. It doesn’t matter which group they belong to. DWR will incorporate the ‘net effect’ of these investment styles into the calculations. The important thing is that DWR will be different from BHR if investors as a whole deviate from a passive buy-and-hold strategy.

The key question we want to know is this: on the whole, is DWR higher or lower than BHR?  If DWR beats BHR, hats off to market timers!  However, if BHR beats DWR, then this result confirms that market timing is a bad idea. The phrase “on the whole” means we are talking about all investors in the aggregate and not singling out individual investors or even subsets of investors.

The data for the study consists of monthly values of market capitalisation and returns for the following markets:

  • NYSE-AMEX
  • NASDAQ
  • World
  • Europe
  • Asia
  • Japan
  • Singapore
  • Hong Kong

The sample period is 1926 to 2016 for NYSE-AMEX and 1974 to 2016 for NASDAQ. For the non-US markets, the data runs from 1970 to 2016. All data is monthly and are obtained from Thomson Reuters Datastream.

The table below shows the results where the average returns are presented as annual figures. The last column (p-values) are the results of a statistical test to check whether any difference between BHR and DWR are real or simply due to chance. A p-value of less than 0.05 indicates that the difference is real.

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The table is self explanatory. It shows that BHR is always greater than DWR (i.e. all markets and across different sample periods).  For the big NYSE-AMEX market, the difference between BHR and DWR is 1.24% a year. This may not seem much, but trading cost has not been factored into the calculations and such costs will significantly drag down DWR. Also, 1.24% compounded on a sum of $100,000 over 20 years gives a dollar return of almost $28,000, a non-trivial sum.

The table also shows that market timing is particularly damaging for certain markets like NASDAQ, HK, Japan and Europe where the annual difference between BHR and DWR is more than 2%. In all cases, p-values below 0.05, indicating that the return differences are not due to chance.

We can say more about investors from the data. The next figure shows the relationship between past returns and future money flowing into the US stock market. As shown by the straight line, this relationship is positive, meaning that on the whole, investors are trend chasers.

Dichev_2.jpg

So, if market timing is so bad, why do so many investors do it?

I believe a big part of the answer is that we are born ‘pattern seekers’. All of us are hardwired to predict. This instinct to seek patterns is something that nature has endowed us over the millennia for survival. Think of our cave men ancestors roaming the savannahs for food. For them, the ability to differentiate food from predators, to predict when and where each group gathers is clearly life critical. The mistake of modern humans is to assume that what is easy to predict in the natural world is also easy to do so in a world ruled by abstract concepts like stock markets, currencies, and economic fundamentals. In doing so, we underestimate the impact of uncertainty so central to financial markets, and confuse randomness for systematic patterns in the desperate search for superior returns. As Gandhi once said: “To those who are desperately hungry, God appears in the form of bread.” (paraphrased).

Blog #45 Are Index Trackers Created Equal?

Investors who don’t want to pick stocks have a simple and attractive investment option in the form of index trackers. An index tracker is a fund designed to investors exposure to a basket of stocks that comprises an index. This means investors get the benefits of diversification at (usually) relatively low cost.  For tracking the popular Straits Times Index (STI), the lowest cost options are two ETFs traded on the SGX – the SPDR Straits Times Index Fund (SPDR STI) and the Nikko AM Singapore STI Fund. The average total expense ratios (TER) of these two ETFs in the most recent 5 years (to Dec 2017) are 0.30% and 0.37% respectively.  All things equal, low TERs mean higher net returns.

A less well known STI tracker is the Singapore Index Fund (SIF). The SIF is not an ETF but a unit trust whose investment objective is to replicate the returns of the benchmark FTSE Straits Times Index. While this sounds a lot like what the SPDR STI and Nikko AM Singapore STI funds are doing, investors should bear in mind that unit trusts charge higher TERs than comparable ETFs. Another factor that may pull down a unit trust’s return is the ‘cash drag’ resulting from the need to hold a portion of the trust’s assets in cash to meet fund redemptions. On the flip side, being a unit trust gives the manager the flexibility to settle for less than perfect index replication either to reduce replication cost or as a deliberate strategy to profit by deviating from the index. In view of these complexities, I will let the data speak for itself by comparing the returns of the more established ETF, the SPDR STI against the SIF.

The sample period I use for this comparison is from May 2002 to Dec 2017. I start the data in May 2002 since the inception date of the SIF is April of that year. I exclude the Nikko AM STI as its inception date is later (February 2009). My data is downloaded from Datastream. The data is monthly and comprise total returns with dividends reinvested. All returns are net of costs.

The table below shows the mean and standard deviation of returns (annualized) for the two index trackers relative to the STI. I also show the ratio of mean return to volatility, which is roughly a fund’s ‘Sharpe ratio’ (SR) even no risk-free rate is actually subtracted from the mean. Finally, to see whether performance characteristics are robust over time, I report results for two sub-periods: 2002-2007 and 2008-2017 in addition to the full sample period.

SIF

The main results can be summarized quite easily:

  • The SIF is consistently more volatile than the STI.
  • Over the full sample period, this higher volatility did not translate to higher mean returns compared to the index. The SIF’s mean return (8.7%) is only marginally higher than that of the SPDR STI (8.4%) but in terms of the Sharp ratio, it is worse than either the index or the SPDR STI.
  • The SIF delivered a slightly higher mean return than the STI in the first sub-period. But it took too much risk to achieve this. Consequently, it continued to under-perform both the STI and the SPDR STI in terms of the Sharpe ratio.
  • The second sub-period saw the worst performance for the SIF. Despite having almost the same volatility as the STI (19.5% versus 19.2%), the SIF’s mean return was only 4% compared to 5.1% for the STI and 4.6% for the SPDR STI.
  • Relative to the index, the STI under-performed by an average of 1.1% per annum in the second sub-period. Its under-performance was twice that of the SPDR STI.
  • As all three ‘indices’ had very similar risk profile in the second sub-period, the stark performance gap of the SIF in this period is consistent with its higher expense ratio compared to the SPDR STI. This is what investors of unit trust index trackers should expect if the fund manager made no attempts to deviate from the index.

Conclusion: Clearly, not all index trackers are created equal!

 

 

 

 

Blog #44 Humpty Dumpty Markets

I want to revisit an aspect of global equity diversification that I haven’t covered in my previous posts. This is the fact that diversification will not protect you from losses if the global economy tanks. As the nursery rhyme says, “all the king’s horses and all the king’s men could not put humpty dumpty back together again.” Market risk is what investors bear when they hold a diversified portfolio. Just as a rising tide lifts all boats, a crashing tide sinks all boats!

That is the bad news.  The good news is that global diversification may still offer better protection against downside risk compared to investing locally. To see whether there is any truth in this, I will address two questions in this blog. Question #1: Does a globally diversified portfolio fall more or less than individual markets on average?  Question #2: when the dust settles and markets recover, does a globally diversified portfolio recover more or less than individual markets on average? Does it recover sooner or later?

To answer these questions, we will probe the data scientifically. In what follows, I will share with you the findings of an evidence-based study which examines downside risk across 16 stock markets over a long period of time. These findings come from supervised research done by senior NUS undergrads reading Personal Finance and Wealth Management, a course I taught in NUS Business School for many years.

Before I discuss the results, here are the background facts:

  • The study analyzed the total returns of 16 markets based on local MSCI indices.
  • Returns for individual markets are in local currency units (e.g., Australian dollars for the Australian market, Singapore dollar for the Singapore market and so on).
  • A global portfolio is formed by equally weighting international markets.
  • To study the effects of diversification, we take perspective of an investor based in each of the 16 countries (say, Singapore). We compare what this investor earns from investing only in Singapore stocks with what he could have earned by holding a globally diversified portfolio of 15 markets (all markets except Singapore). Let’s call this global portfolio, G.
  • To ensure that we are making an apples with apples comparison, the returns for G are converted to Singapore dollars (i.e., we assume that the Singapore investor does not hedge against currency risks). This is a realistic assumption for most people. We repeat this exercise for the other 15 markets.
  • We adjust all returns using the respective country’s CPI, so as to focus on real returns.
  • The sample period for the most recent study is December 1969 to June 2017, a period of 48 years.
  • The main question we ask is: how does G perform when a particular market is doing badly?  We define “badly” in a number of ways: first, as worst monthly return over the sample period, secondly as the 1% conditional value-at-risk or 1% CVAR and thirdly, as the 5% CVAR. In general, the x% CVAR is simply the average of the worst x% returns.

Here’s a quick pictorial recap of some of the above points:

#1 We study how G performs when a local market is doing badly.

Slide1.JPG

#2 We study a total of 16 markets.

Slide3

#3 We compare the average returns of each individual market with the returns of G, the globally diversified portfolio.

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#4 We want to see whether global diversification really protects against the downside risk of individual markets.

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RESULTS

I present five key results from the study/

Result #1. Over the short term (one-month time horizon), G outperforms individual markets in most cases. 

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Result #2. G has lower downside risk (smaller 5% CVAR) than any individual market

Slide7

How to read this graphic: The vertical axis plots the 5% CVAR for G; the horizontal axis plots the 5% CVAR for individual markets (shown as dots on the plot). Numbers on each axis are negative as expected since we are talking about bad months. For both axes, numbers are more negative nearer the origin. Therefore, if diversification protects against worse case scenarios of individual markets, all the dots should be on left of the diagonal line. This is what we find.

Result #3 G has smaller 1% CVAR than any individual market 

This result is qualitatively similar to result #2.

Slide8

Result #4. As time passes, G’s performance gap over individual markets widens

The graphic below shows that after 80 months, the 5% CVAR for G becomes positive, while the average CVAR for individual markets continue to be negative. Moral of the story: global diversification pays (eventually)!

Slide9

 

Result #5 The diversification story is the same when we look at worst returns instead of CVAR.

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In short, the evidence shows that diversification is good in the short run, and gets better in the long run as markets shake off the bad news and respond to the pull of the global economic engine.

You would think that investors are smart enough to know all this. WRONG! All over the world, investors are infected with a “home bias”, the tendency to believe that its best to own just local stocks (see next graphic). If this describes you, now is as good a time as any to kick the home bias habit.

Slide12

 

 

Blog #43 A Fresh Start for Investing

I’ve devoted quite a lot of space to the topic of long-term strategic investing (the discussion runs from Blog #35 and I’m not done yet!). The reason why I’m harping so much on this topic is because I’m wedded to the idea that investment should be simple (but not simplistic).  There is a huge difference between them.

Simplistic investing implies that one is naive, easily influenced by hearsay and sound bites, buying stocks for no better reason than everyone at the office water cooler is buying, flipping your stocks like burgers, chasing hot markets and so on. These are signs of sloppy investing, and I can tell you that it is rampant.

Good investors craft sensible asset allocation policies based on a correct understanding of how markets “get it right” over the long run even if they go off-tangent in the short run. For a start, good investors understand that risky assets like stocks provide a reliable positive equity risk premium when stocks are held over decades, not weeks. In other words, they understand the virtue of investing for long-term goals (see blogs 24, 25, 36). They also understand the wisdom of diversification and believe in earning a “diversification premium” by owning broadly diversified asset classes (blog 30). Third, they acknowledge the limits of predictions and choose buy-and-hold over market timing strategies (blog 29). Finally, they accept that risk tolerance has a shelf life, and thus take greater risks when young, lesser risks when old, the time-honored investment glide path approach to investment (blog 39).

If you have been a sloppy investor so far, make a fresh start and reinvent yourself! Revisit the above annotated blogs, read them thoughtfully and develop your own long-term investment strategy.

I have a “guest speaker” today who echoes most of what I have been saying about strategic investing. Antti Ilmanen holds a PhD from the University of Chicago Booth Business School and is currently a director of investment management firm, AQR Capital Management LLC. Below is a video clip of Dr. Ilmanen speaking on expected returns, market timing and strategic investment. 2.24 minutes into the clip, he talks about the futility of market timing. 6.23 minutes into the clip, he brings up the subject of strategic asset allocation. Enjoy!

 

 

Blog #42 Fresh Starts

It’s back to school for everyone.  And I mean everyone. Because there is such a thing as the School of Life – learned wisdom from personal and shared experiences to see us through the vicissitudes of life.

The first months of the year are times when many people come up with resolutions and lofty goals, with the good intention of sparking new beginnings in the way we manage our expenses, investments and sundry habits. Unfortunately, if you’re like most people, your January resolutions will likely vanish into thin air before the first quarter ends.

That’s okay, according to psychologists. Just give yourself a second chance.  This notion of second chance is incidentally the subject of an interesting 2014 paper by three researchers from the University of Pennsylvania’s Wharton School.  So, it isn’t just talk shop. The paper reports scientific evidence in what the researchers called, the “fresh start effect”.  The gist of this effect is that people are much more likely to set goals and plan for bigger things in their lives just after some temporal markers, which may be the start of a new month, the first day back at work, or after a vacation.

Perhaps you wanted to start saving for retirement but never really got down to it. Or you’ve been trying to take the first step to build an investment portfolio but got distracted by the baby, your job, or the Internet. That’s okay – find a convenient temporal marker and start from there. Think of this marker as the start of a whole new you. Indeed, the researchers also note that counselors who work with people who’ve gone through very serious changes in their lives, like a cancer diagnosis or an addiction recovery, found that after that turning point, these folks will often “describe their pre-change self as a distinct person.”

Cartoon

The change in drive and motivation that happens when one doesn’t give up but chooses to make fresh starts is what makes that effect so powerful.  You can read the full paper here and make a fresh start this year to manage your finances better. Indeed, this blog is designed to help you take concrete steps in managing your expenses, debts, investments, insurance, and retirement goals. But you and you alone have the take the first step. If you’re new to this blog, I encourage you to start reading from the very first blog and work your way forward step by step. There’s a reason why every blog post on this site is numbered. 

 

 

 

 

 

Blog #41 How Would You Like the Year to End?

It’s that time of the year again for reflections and resolutions. How did your year go? Okay, that’s too broad a question.  Let me rephrase. How did your investment plans do?  Does it look like you will end this year with regret over missed opportunities? Or much rejoicing over capital gains?  Or with equanimity and thankfulness? I chose equanimity and thankfulness trying my best to be calm, composed and thankful for whatever has transpired during 2017.

Did I have any regrets over missed opportunities? That’s actually a silly question to ask, even without looking about at the meteoric rise of bitcoin. At the same time, true investment is about sticking to a tried and tested philosophy, and despite never having dipped my toes into crypto-this or crypto-that, I am glad I stuck to my investment philosophy, which is to hold good stocks through thick and thin and not getting distracted by side-shows.

Do I have reasons to rejoice? Certainly. In fact, a couple of stocks in my portfolio deserve an oscar or two for giving total returns of well over 20% in 2017. At the same time, what’s there to rejoice over paper gains?  I love dividend payers, which includes practically every stock in my portfolio. If I sell, there will be no dividends. And if I don’t sell, all gains remain paper gains. The best course of action I reckon is no action at all. These stocks are welcome to stay in my portfolio as long as they are well run.

A belated Merry Christmas to you, dear readers and a purposeful new year ahead. This will be the last blog of this year. But, to quote Arnold Schwarzenegger (aka the Terminator), “I’ll be back.”

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