Author Archives: Scalper1

Be A Proactive Investor

During volatile times in the market, like what we have been experiencing since May, it’s difficult to see through the disparaging news headlines (Oil is Collapsing! Bear Market in Stocks! US Is In A Recession!) and to not lose the forest for the trees. Investing is a long-term game with seemingly unlimited number of opportunities and it’s imperative as an investor to not get caught up in the day-to-day swings (and explanations) of the stock market. It’s times like this where a word like “casino” gets tossed around as a synonym for the stock market. And you know what, in the short run, the market is a lot like a casino. One day the market is up, the next day the market is down. Don’t believe me since it feels like the market has been down a whole lot more than it has been up lately? Well, would you be surprised to know that over the past 200-days developed world equities have been up 47% of the days and down 53% of the days. Pretty close to a 50-50 coin flip, right? Percentage Of Positive Performance Days For Stocks Proactive Investor But long-term investors know that the stock market isn’t really like a casino at all. The “payoff odds” in the stock market are not static like they are in a casino. Hitting the right number in roulette will always pay 35:1 but investing in the right stock could return 10% or it could return 10,000%. Therefore, it’s key to think of investing in terms of probabilities instead of binary outcomes. Investing is not about calling the top or bottom in the market exactly right. It’s about understanding if there are more positive investment opportunities in the market than there are negative opportunities (or vice versa). Put another way, it’s about properly identifying where the market currently falls on the risk/reward spectrum. This way, you as an investor can be proactive rather reactive to changes in the market. We have known for quite some time that this is the longest running cyclical bull market in a secular bear market , so a selloff like the one we are in now was bound to happen sometime. And in the long term, that is actually great news for investors because future returns have undoubtedly improved thanks to the opportunity to buy stocks on “sale.” But this is where investor psychology really comes into play. If your risk antennae was not tuned up to the fact that the probability of a selloff had increased (i.e. the opportunity set had shifted from more buying opportunities to more selling opportunities), then it’s really difficult to realize after a 15-20% decline that the opportunity set is ALREADY shifting again back into your favor. You are reacting to the declining market and when you are reacting, it’s hard to make the correct rational decision. To sell stocks into a declining market is always hard because in the back of your mind you know you missed out on the optimal time to get out and it’s so easy to tell yourself “I’ll sell out of stock XYZ just as soon as it rises 5-10%.” Of course, in a slide like we are in now, it’s very rare for the market to ever give you that 5-10% gain, and so you sit on the underperforming stock far longer than you would have liked. However, if an investor is proactive in identifying where we currently sit on the risk/reward spectrum, there is a very good chance that that investor had begun to shift his or her portfolio into more defensive sectors and perhaps into cash as well. While they would have been undoubtedly early and missed out on some of the gain back in May, they are already mentally prepared to begin to take advantage of some of the positive opportunities that are presenting themselves in this correction. This is why at Gavekal Capital, we focus so much on risk management. Yes, risk management is about protecting the downside. But more so, it’s about being proactive in your investment process so that when the risk/reward spectrum flips in your favor you are ready to take advantage of it and capture the gains in your portfolio. Disclosure: None.

HARKing Back: Lessons In Investing From Science

Confirm Ye Not Here’s what ought to be a really boring idea – we need scientists in general and psychologists and economists in particular to stop hypothesising after results are known (HARKing, geddit?). Instead, they need to state what they’re looking for before they conduct their experiments because otherwise they cherry pick the results they find to confirm hypotheses they never previously had. The underlying problem is our old foe, confirmation bias . And the solution for scientists and social scientists alike is known as pre-registration. It would be no bad thing for investors to demand a similar process for fund managers and financial experts. Or, for that matter, to apply some of the ideas to their own investing strategies. No No Negatives It’s been known for years that a lot of scientific research isn’t very reliable. There are numerous problems, chief amongst them being the non-publication of negative results: an issue known as publication bias . There’s no kudos in showing that your hypotheses were wrong, so researchers and corporations tend to bury the data, but it’s still valuable information that should be shared: scientists see further by standing on the shoulders of others, we shouldn’t be encouraging them to shrug them off because they’ve got bored. Worse still, though, is the fact that many studies turn out not to be replicable. The ability to re-run an experiment and produce the same result is an absolute cornerstone of the scientific method : science works because it’s not built on faith, it’s constructed out of evidence. If it turns out that the evidence is unreliable then what’s being done isn’t science, it’s more like religious studies with instruments. Or economics. Repeat, Again Once we move to the social sciences then the problems are even worse. Human beings are terrible things to experiment on , being inclined to change their minds, develop opinions about the experiments and to second-guess what the researchers would like them to do, just to be nice. All too many experiments in the social sciences turn out to be flawed because of social or situational factors that didn’t seem important at the time. Given this, you’d think that repeating experiments to make sure the results held would be even more important for psychologists than it is for researchers in the hard sciences. Well, guess again. According to research by Matthew Makel, Jonathan Plucker and Boyd Hegarty , only a little over 1% of psychology studies have ever been replicated. Everything else is simply a matter of faith in the integrity and lack of bias of the original researchers. Which is not science: in the words of John Tukey, quoted at the head of their paper: “Confirmation comes from repetition. Any attempt to avoid this statement leads to failure and more probably to destruction.” Pre-Register The best solution to this we’ve yet found is known as pre-registration: studies have to be registered in advance, and the hypotheses under investigation stated up front before the research is done. This prevents the experimenters from looking at their data after the event and picking out interesting positive correlations which they didn’t control for, but which are likely to get published. Where pre-registration has happened the proportion of studies giving positive results has fallen dramatically: analysis of studies into treatments for heart disease have shown a frightening drop in positive results since pre-registration was mandated: “17 out of 30 studies (57%) published prior to 2000 showed a significant benefit of intervention on the primary outcome in comparison to only 2 among the 25 (8%) trials published after 2000”. Some of this may be because the low-hanging fruit on the subject was picked earlier, but it’s a scary result all the same. It seems likely that because the researchers can no longer consciously or unconsciously pick the results, they prefer they remove the possibility of confirmation bias – and the fall is so dramatic it places the previous results in question. And, of course, it’s not clear how many of those have been replicated. Creative Scientists Pre-registration isn’t universally popular: there is much rending of white coats and grinding of molars over the issue. Opponents argue that it risks putting scientists in a creative straight-jacket. Although when respectable peer reviewed journals start publishing papers alleging the existence of extra-sensory perception based on … “Anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms” … then you have to wonder whether the creative juices maybe need a touch of reduction – oh, and the results of this experiment don’t seem to be replicable, bet they never saw that coming. So, what other group of people do we know who are given to making ad-hoc hypotheses, investing loads of money in them, and then ignoring the results while cherry picking specific successes in order to publicly claim that they were successful? OK, apart from politicians. Investing Feedback Investors have all of these faults, and a few more. If we truly wanted to become better investors, then we’d pre-register our hypotheses – including our expected timescales – and then measure our results against the results. Doubtless the outcome would frequently be embarrassing, but the evidence that we do have suggests that getting real feedback about our performance is the only way to improve predictive capability in complex systems like the stock market (see: Depressed Investors Don’t Need Feedback. Everyone Else Does ). The other thing this would do would be to force us to face up to the reality that we can be successful by luck and can fail through no fault of our own. In complex adaptive systems, we simply cannot predict every possible situation; we can only hope to be able to predict a little better than average. But a little better is enough to make a turn, so every percentage point improvement we can make is worth it. Commit and Document So I wonder if some enterprising developer out there fancies setting up a pre-registration website for investors keen to improve their returns, rather their personal status? Public commitment backed up by a positive rewards system has been shown to produce powerful results in a whole variety of situations. For example, in Tying Odysseus to the Mast: Evidence from a Commitment Savings Product in the Philippines , Nava Ashraf, Dean Karlan and Wesley Yin showed: “Commitment-treatment group participants have a 12.3 (9.6) percent higher probability of increasing their savings by more than 20 percent after six (twelve) months, relative to the control group participants, and an 11 (6.4) percent higher probability of increasing their savings by more than 20 percent, relative to the marketing group participants. The increase in savings over the twelve months suggests that the savings response to the commitment treatment is a lasting change, not merely a short-term response to the new product” I suspect that even a non-financial reward system based on peer support would facilitate uptake. HARK, hear… Avoiding HARKing is the future of the hard and the soft sciences. And, by analogy, as investors, if we don’t have hypotheses about what we’re investing in, then we’re simply the modern equivalents of astrologers. And, if we have hypotheses, we should write them down and test whether they’re right, not simply crow about the random successes and ignore the equally random failures. It’s worrying, of course, that this isn’t already the basic investing process. But to be honest it’s even more worrying that it doesn’t seem to be the basic scientific process. Genius and creativity has its place in all human activity – Kepler came up with his third law of planetary motion by mapping orbits to harmonic ratios , believing these to be a sign of heavenly perfection. But Kepler was a mad genius who happened to be correct, so here’s my hypothesis: relying on mad geniuses for humanity’s future and your family’s well-being is probably not prudent.

The Altman Z-Score After 50 Years: Use And Misuse

By Larry Cao, CFA This is the second installment in my interview series with Edward Altman in which we discuss the most advisable and problematic applications of the Altman Z-score. For additional details of our conversation, check out the first installment. Larry Cao, CFA: It’s been almost 50 years since the Z-score was first developed. Would you suggest doing anything differently today? Edward Altman: Over the years, the so-called cutoff scores in the model has been retained by the people who applied the model. But in my opinion, that is not the best thing to do. Over time, I began to observe that the average Z-score of American companies mainly, but even global companies, began to get lower and lower. [The bond market] became more available for both investment grade and non-investment grade companies and companies periodically took advantage of low interest rates to raise their leverage. As a result, the financial risk of companies began to increase. Also with global competition, companies’ profitability began to diminish. And so the average Z-score became lower and lower, which meant that more firms would have been classified as likely bankrupt using the Z model if we kept the original cutoff scores. In order to modernize the model, we needed bond-rating equivalence of the scores, which changes constantly and adds on an updated nature to the interpretations of the scores. We now think the most important attribute of the Z model is the probability of default (PD), not the zone classification – safe, grey, or distress. We do it in a two-step process. We get the PD from the score of the company, whether it be from Z, or Z prime, or Z double prime. And then we look at the bond rating equivalent as of that point in time. For example, 2015 – the average B-rate company has a Z-score [of] about 1.6. That would be in a distress zone back in 1968. But today, B is a very common bond rating for many companies. In fact, globally it’s probably [the] most dominant junk rating category. If you rated all companies in the world, the average would probably be about B if they had a rating. And so we ascribe a probability of default based on a bond rating equivalent by looking at the historic incidence of default given a B rating at birth. Cumulatively, I can tell you, from one to 10 years, what the likelihood of default is given a bond rating equivalent. So no longer do we only look at the cutoff scores for the three zones of credit worthiness. Okay, bond rating equivalent is in and cutoff scores are out. What mistakes do you see practitioners making in using the Z-score today? To this date, I would say the vast majority of people are misusing the Z-score because they are applying it across the board regardless of the sector, the industry. And what we found over the years is that non-manufacturers, especially in certain industries like services or retail, have on average higher Z-scores than manufacturing companies. My advice for users is if you are outside the United States, and particularly if you are not a manufacturer, you should look at Z” and its bond rating equivalence approach for ascertaining a PD. Would you say the value of the Z-score is more in its methodology or the score itself? That’s a great question, Larry. Yes, I’ve always argued it’s better to use a local model rather than the original US model. And I’ve done it myself. I’ve personally built models in Brazil, Australia, France, Italy, and Canada. And you will find references to models almost anywhere in the world in the literature. It’s a pretty easy methodology for Ph.D students and practitioners to adapt to a different environment. But then again, even if it isn’t the best model that could be built for service companies or energy companies in 2016, it’s still a good benchmark and has retained its accuracy. If I had the time, I would build the model for Malaysian companies or Indonesian companies or Hong Kong companies or Asia all together. I suppose that there are good researchers there who might just attempt that! Will there be a data issue? For a lot of these countries, the history may not be there. They don’t have bond rating equivalence. That’s exactly right. That’s a very good point. The bond rating equivalence in almost all cases has to be derived from data from the United States. We have lots of defaults, lots of bankruptcies in the US, so you can get probability distributions based on ratings that have a fairly large sample. You can’t do that in emerging markets or countries like Australia, where they haven’t had a recession since the early 1990s. So yes, people said I should have continually updated the Z model but that means you have to keep publishing the updates. People have to find it. People have to use it and test it. It’s much easier to just periodically test the model, and to even build new models that incorporate the lot of data from the relevant countries and industries and combine this firm data with market value measures and possibly even macroeconomic data. What advice do you have for practitioners who want to build their own version of the Z-score model? For example, what’s your secret sauce for putting together the sample? Although the methodology is pretty straightforward, there are subtleties to it. You need a sample of healthy companies and unhealthy companies. There are issues such as sample size. Should [there] be [an] equal number in the two groups or should there be more representatives of the population – 99% non-default, 1%, 2%, or 3% default, depending on the time period? Should they all be manufacturers? Should they be a cross section of industries? Disclaimer: Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.