Tag Archives: function

Pick A Valid Strategy, Stick With It

I’m not going to argue for any particular strategy here. My main point is this: every valid strategy is going to have some periods of underperformance. Don’t give up on your strategy because of that; you are likely to give up near the point of maximum pain, and miss the great returns in the bull phase of the strategy. Here are three simple bits of advice that I hand out to average people regarding asset allocation: Figure out what the maximum loss is that you are willing to take in a year, and then size your allocation to risky assets such that the likelihood of exceeding that loss level is remote. If you have any doubts on bit of advice #1, reduce the amount of risky assets a bit more. You’d be surprised how little you give up in performance from doing so. The loss from not allocating to risky assets that return better on average is partly mitigated by a bigger payoff from rebalancing from risky assets to safe, and back again. Use additional money slated for investing to rebalance the portfolio. Feed your losers. The first rule is most important, because the most important thing here is avoiding panic, leading to selling risky assets when prices are depressed. That is the number one cause of underperformance for average investors. The second rule is important, because it is better to earn less and be able to avoid panic than to risk losing your nerve. Rule three just makes it easier to maintain your portfolio; it may not be applicable if you follow a momentum strategy. Now, about momentum strategies – if you’re going to pursue strategies where you are always buying the assets that are presently behaving strong, well, keep doing it. Don’t give up during the periods where it doesn’t seem to work, or when it occasionally blows up. The best time for any strategy typically come after a lot of marginal players give up because losses exceed their pain point. That brings me back to rule #1 above – even for a momentum strategy, maybe it would be nice to have some safe assets on the side to turn down the total level of risk. It would also give you some money to toss into the strategy after the bad times. If you want to try a new strategy, consider doing it when your present strategy has been doing well for a while, and you see new players entering the strategy who think it is magic. No strategy is magic; none work all the time. But if you “harvest” your strategy when it is mature, that would be the time to do it. It would be similar to a bond manager reducing exposure to risky bonds when the additional yield over safe bonds is thin, and waiting for a better opportunity to take risk. But if you do things like that, be disciplined in how you do it. I’ve seen people violate their strategies, and reinvest in the hot asset when the bull phase lasts too long, just in time for the cycle to turn. Greed got the better of them. Markets are perverse. They deliver surprises to all, and you can be prepared to react to volatility by having some safe assets to tone things down, or, you can roll with the volatility fully invested and hopefully not panic. When too many unprepared people are fully invested in risky assets, there’s a nasty tendency for the market to have a significant decline. Similarly, when people swear off investing in risky assets, markets tend to perform really well. It all looks like a conspiracy, and so you get a variety of wags in comment streams alleging that the markets are rigged. The markets aren’t rigged. If you are a soldier heading off for war, you have to mentally prepare for it. The same applies to investors, because investing isn’t perfectly easy, but a lot of players say that it is easy. We can make investing easier by restricting the choices that you have to make to a few key ones. Index funds. Allocation funds that use index funds that give people a single fund to buy that are continually rebalanced. But you would still have to exercise discipline to avoid fear and greed – and thus my three example rules above. If you need more confirmation on this, re-read my articles on dollar-weighted returns versus time-weighted returns . Most trading that average people do loses money versus buying and holding. As a result, the best thing to do with any strategy is to structure it so that you never take actions out of a sense of regret for past performance. That’s easy to say, but hard to do. I’m subject to the same difficulties that everyone else is, but I worked to create rules to limit my behavior during times of investment pain. Your personality, your strategy may differ from mine, but the successful meta-strategy is that you should be disciplined in your investing, and not give into greed or panic. Pursue that, whether you invest like me or not. Disclosure: None

Using ETFs To Short The Market

Summary The structure and pricing of inverse and leveraged ETPs is complicated. The principal investments of this fund are money market instruments and derivatives. Daily re-balancing can be a concern. Reading through recent Seeking Alpha articles regarding the broader market, lengthy and often heated discussions tend to develop on the direction of the market. That being said, it seems as if opinions on the eventual breakthrough of the current sideways trading range are about half and half, with maybe slightly more bulls. I also recently read comments on an article about leveraged and inverse exchange-traded products, where many readers did not seem to have a clear understanding of how (particularly) inverse ETPs are priced. In light of these two observations, I thought it pertinent to analyze how an inverse ETF is structured, for the benefit of investors who are considering investing in one in order to profit from potential downside movement. Due to the referenced uncertainty and volatility present in the overall market, I decided to use the ProShares Short S&P 500 ETF (NYSEARCA: SH ) as the subject matter for my analysis. SH is an inverse ETF that attempts to return -1x the return of the S&P 500 on a daily basis. How does it achieve inverse returns? SH achieves returns that are inversely correlated to the S&P 500 by investing in assets and derivatives that perform (or historically perform) well when the market is not performing well. There are four main investments used by ProShares in its inverse index ETFs, and these are: Swaps (derivative market) Futures (derivative market) U.S. Treasury Bills (money market) Repurchase Agreements (money market) Derivatives : The sale of swaps will benefit in a falling market, because the buyer of the swaps is required to pay the seller the amount that the underlying has fallen in price. Inverse exposure through futures is likely most often achieved by short-selling index futures. Money Market Instruments : The use of short-term Treasuries and other money market instruments relates to the fact that short-term debt historically performs inversely to the market. This is due to there being a “flight to safety” when the equity markets are falling. Daily Re-balancing: For periods longer than a single day, the Fund will lose money when the level of the Index is flat, and it is possible that the Fund will lose money even if the level of the Index falls. – SH Prospectus The effect of daily rebalancing is one of the primary misunderstandings regarding inverse or leveraged ETFs that I see on Seeking Alpha. People discuss how they will “invest” in a leveraged ETF and hold it for several weeks, months, or even years in some instances. It is important to recognize that this is not the intended purpose of this type of ETF. These are intended to be traded for short time periods. In order to maintain the proper leverage ratio, inverse returns, and index exposure, SH is rebalanced each day. What this means for an investor is simple to illustrate: Suppose that at the end of the trading day on Monday, you invest $1,000 in a -1x inverse ETF @ $100 per share. The ETF tracks an underlying index with a value of $5,000. At market close on Tuesday, the index has decreased 10% to $4,500. In turn, the ETF has risen 10% to $110. By the close on Wednesday, the index has recovered to the original $5,000 – a roughly 11.11% gain. In turn, the ETF now loses 11.11%, which brings the value of your position to $97.78. Even though the index is exactly the same value as it was when you initiated the position, your position has lost money. This effect is also known as beta slippage. Note: This could theoretically work to your advantage, should the opposite situation occur. Conclusion: Simply by looking at a chart of SH, you will see that if you had held it for the duration of the 2008 collapse, you would have indeed profited: ND data by YCharts However, the return ratio was not accurate, with SH gaining approximately 26.07% from 1st January, 2007 to the first peak and SPX losing approximately 43.25% in the same time frame. In short, an inverse ETF like SH can be a great way to hedge short-term volatility or for intra-day trading, but if an investor is looking to actually short an asset (in this case, the S&P 500) for a long-term position, then it is not the most effective way to do so. Hopefully, with a clearer understanding of how this ETF is structured, prospective investors can make better use of it as a tool for his or her portfolio. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: This article is not intended to offer a recommendation to buy or sell any particular asset, and does not reflect the author’s opinions on the direction of the market. It is simply intended to provide an overview of how a complicated but useful financial instrument works.

The Low Volatility Anomaly: Overconfidence Bias

This series offers an expansive look at the Low Volatility Anomaly, or why lower risk stocks have historically produced stronger risk-adjusted returns than higher risk stocks or the broader market. This article hypothesizes that cognitive biases like the overconfidence bias contribute to the Low Volatility Anomaly. Like previous pieces in this series, this article covers a deviation between model and market that may contribute to the outperformance of low volatility strategies. A cognitive bias is a deviation in judgment where people draw inferences in an illogical fashion. As discussed in the last piece in this ongoing series on lottery preferences , cognitive biases can impact investment decision-making and likely contribute to the Low Volatility Anomaly. This article will discuss an additional cognitive biases that could contribute to this phenomenon – the overconfidence bias. The overconfidence bias suggests that a person’s subjective confidence in their own judgment is reliability greater than the objective accuracy of those judgments. The most oft cited example of the overconfidence bias is the 1980 finding by Ola Svensson that ninety-three percent of American drivers rate themselves better than the median. Traveling back from the highway to the Capital Asset Pricing Model (CAPM) assumptions, the model calls for homogeneous investor expectations, including expected values, standard deviations, and correlation coefficients. Valuing investments necessarily involves forecasting as a means to assessing tradeoffs between risk and return. Empirical evidence suggests that most people form confidence intervals that are too narrow. Borrowing from studies by Fischoff, Slovic, and Lichtenstein (1977) and Alpert and Raiffa (1982) , I conducted a similar study during a lecture to a fixed income class at my undergraduate alma mater in the fall of 2014. The students were asked a set of ten questions with numeric answers and asked to bound their answer by a confidence interval such that there was a ninety percent chance that their numeric answer would fall within the range. The ten questions were as follows: What is the population of the state? What is the seating capacity of the football stadium? What is number of different undergraduate majors at the University? What were the revenues of the athletic program in the previous year? What was the number of degrees conferred in the most recent academic year? What is the current yield level of the 30-year Treasury? What is the size of the U.S. Gross Domestic Product? What was the total number of jobs created in the U.S. in 2014? What was the total number of automobiles sold in the U.S? What is the U.S. Median Household Income? At a ninety percent confidence interval, half the class should have had nine or more results inside their confidence interval. Of the roughly thirty-five students, none had nine. Or eight. Or seven. Or six. Two students had five of their answers inside their bounds, but most of the students had between two and four. The class was overconfident. The first five questions were on the world around them at college, and the second five questions were on basic economic statistics. These topics should have yielded far better forecasts than the multi-year prognostications of market or security variables inherent in investment selection. The students did poorly – as poorly as the author when he first completed a similar exercise. The point of the exercise (aside from breaking up the monotony of my lecture) was to illustrate the overconfidence bias to the class. Overconfidence can drastically damage investment returns. Given the geographic proximity of the university to some of the nation’s leading onshore oil and gas resources, the rapid and unexpected drawdown in oil prices at the time of the lecture and the related implications on energy-related assets proved salient to the audience. Additional examples of the overconfidence bias given in the lecture included persistent overestimates of economic growth from the International Monetary Fund and Federal Reserve post-crisis, and the poor job of private and public sector economists at forecasting long-term interest rates, which were at the time rallying sharply in the face of consensus estimates for rising rates. Like the students surveyed, professional investors have proved similarly overconfident. Active managers implicitly assume that they are capable of beating their benchmark despite long-run evidence demonstrating that the average active manager fails to accomplish this feat on average over time ( Fama, French 2009 ). The collective overconfidence by the cadre of active managers violates that CAPM assumption of rationality and could be a factor that contributes to the Low Volatility Anomaly. If a manager is truly as skilled as they believe, then participation in higher volatility segments of the market offer the largest return proposition to capitalize on their perceived skill. If that same manager believed that the market was likely to fall, then they would not choose to invest in low volatility assets, which would outperform on a relative basis, but choose to exit the market entirely to outperform on an absolute basis. This overconfidence bias then likely contributes to the outperformance of low volatility stocks (referenced by SPLV ) relative to high beta stocks (referenced by SPHB ) depicted in the introductory article to this series . Further connecting the overconfidence bias to investment returns, we see more activity from market optimists than pessimists. Perhaps married to the market frictions inherent in the Leverage Aversion Hypothesis , the market in general is far less likely to short high volatility assets than it is to buy them. With skeptics more often sidelined than short, high beta assets with a more diffuse set of opinions on forward returns will then have more optimists among their holders, potentially pushing prices higher and future returns lower. In coming articles, I will highlight additional empirical evidence on the Low Volatility Anomaly, including utilization by a great investment mind, examples in fixed income, and examples crossing over between the equity and fixed income markets. I will then feature some ways in which Seeking Alpha readers can look to exploit the Low Volatility Anomaly in their portfolios. Disclaimer My articles may contain statements and projections that are forward-looking in nature, and therefore, inherently subject to numerous risks, uncertainties and assumptions. While my articles focus on generating long-term risk-adjusted returns, investment decisions necessarily involve the risk of loss of principal. Individual investor circumstances vary significantly, and information gleaned from my articles should be applied to your own unique investment situation, objectives, risk tolerance, and investment horizon. Disclosure: I am/we are long SPLV. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.