Tag Archives: transactionname

Debt Ratios And Pension Ratios: Connecting The Dots Between Them

A company with too much debt is like a tall vase with a narrow base. Everything looks fine until you kick the table, at which point it falls over and explodes into thousands of pieces. In an attempt to avoid companies that might explode, I always look for those with “prudent” amounts of debt. At the same time, I try not to be so restrictive that I can’t find anything to invest in. Avoiding companies with too much debt Over the years, I’ve defined “prudent” in several different ways, but my current rules of thumb for interest-bearing debts are: Only invest in a defensive sector company if its Debt Ratio is less than 5 Only invest in a cyclical sector company if its Debt Ratio is less than 4 (doesn’t apply to banks) The Debt Ratio , for reference, is the ratio between the company’s current total borrowings and its 5-year average post-tax profit (preferably normalised or adjusted profits). There is no magic to this; it’s just an approach which I’ve found to be reasonably good at differentiating between companies that are going to run into trouble because of their debts, and those that aren’t (I think I first read it in “Buffettology”, a book I highly recommend). Another financial obligation I’ve kept an eye on for the last few years is defined benefit pension schemes. Many companies don’t have one, but if a company does have one then in most cases (possibly all) it is legally obliged to ensure the pension scheme is well funded. If the pension scheme’s assets do not cover its long-term liabilities, the scheme will have a pension deficit and the company will have a legal obligation to close that deficit at some point. That will often mean shovelling cash into the fund, which means less cash for dividends or other things that create shareholder value. Avoiding companies with excessively large pension obligations I have a rule of thumb for pension obligations as well, just because I like to systemise my decision-making as much as possible. The rule is: Only invest in a company if its Pension Ratio is less than 10 The Pension Ratio is more or less the same as the Debt Ratio, only this time the company’s total defined benefit pension obligations are compared to its 5-year average post-tax profit. So far I’ve always looked at the Debt and Pension Ratios separately; I guess because I never thought about joining the dots, and because I haven’t seen anyone else look at debt and pension liabilities as two sides of the same coin. But last week, when I was reviewing my latest sell decision (June was a “sell” month for me), I noticed that the company in question ( Serco ( OTCPK:SECCF ) ( OTCPK:SECCY ), which I’ll write about soon) had both high levels of debt and a relatively large pension obligation. Specifically, Serco had a Debt Ratio of 5.2, which is too high according to my rules of thumb (Serco is in a cyclical sector and should have, by my rules, a Debt Ratio below 4). That alone would be enough to make me avoid buying the company, although not enough to make me sell it. It also had a Pension Ratio of 8.2, which is okay according to my Pension Ratio rule of thumb, but it’s pretty close to the limit of 10. That got me thinking. What if Serco had a slightly lower Debt Ratio? What if its Debt Ratio was 3.8 and its Pension Ratio was 8.2? That would be “okay” according to my rules of thumb, but is that a sensible outcome? Treating debt and pension obligations as interdependent risks If a company carries interest-bearing debts, then it will need to use cash to pay the interest on those debts. On the other hand, if a company has a large pension obligation then it either has, or could potentially have, a large pension deficit, and that would also require large amounts of cash to clear. Since there is only so much cash to go around, I think it’s sensible to look at these two financial obligations together, rather than in isolation. I don’t have some magical answer that will tell me exactly what a prudent amount of debt is for a company with a particular pension liability, or vice versa. However, I can take a reasonable guess, and refine it from there with experience. So my first stab at a rule of thumb which treats debt and pension obligations as if they impacted one another (because they do) is this: Only invest in a company if the sum of its Debt and Pension Ratios is less than 10 It isn’t rocket science, but I think it’s a good place to start. If, for example, a company has “medium” levels of debt, with a Debt Ratio of perhaps 3, then I would buy it (after a detailed analysis , of course) if its Pension Ratio is below 7. Or, if a company has a pension ratio of 8 then I’ll only buy it if the Debt Ratio is below 2. I think that should give me a fair balance between ruling out companies with excessive obligations, without being so restrictive as to rule out companies that are perfectly capable of handling their current situation. Of course, you may or may not use the same ratios as I do, but even if you don’t, I think treating debt and pension obligations as interdependent risks is still a sensible thing to do.

Momentum And Stop Losses

Stop losses are a form of trend following in which you switch from risky assets, such as stocks, to a risk-free or fixed income asset after there are pre-determined cumulative losses. The random walk hypothesis (RWH) was widely accepted in the 1960s and 1970s. It was synonymous with market efficiency. It effectively eliminated any academic interest in stop loss rules. Under RWH, with stock returns being independent, stop losses would decrease a strategy’s expected return. There remains a cultural affinity to RWH despite strong contrary evidence now. This may explain why there is still considerable indifference to stop loss policies and trend following in general among institutional investors, who were schooled in old academic ideas. In their paper, ” When Do Stop-Loss Rules Stop Losses? “, Kaminski and Lo (2013) show both theoretically and empirically that if stock returns have positive serial correlation (there is overwhelming evidence that they do), then stops can add value. Over monthly intervals of daily stock futures data from 1993 through 2011, the authors found that volatility-based stop loss rules could increase monthly returns 1.5% while substantially decreasing volatility. When stopped out of stocks, long-term bond futures are used as a safe harbor asset. In ” Taming Momentum Crashes: A Simple Stop-Loss Strategy “, Han, Zhou, and Zhu (2014) showed the effectiveness of a stop loss overlay applied to a momentum-based strategy. The authors examined the top decile of U.S. stocks from 1926 through 2011 based on relative strength momentum over the preceding 6 months (the authors showed similar results using 12-month momentum). They sold any stock if it dropped 10% below the beginning price of the month. They followed the same procedure for short positions. Portfolios were rebalanced monthly. This stop loss strategy raised the average monthly return from 1.01% to 1.73% (buy and hold was 0.62%) and reduced the monthly standard deviation from 6.07% to 4.67%. [1] Momentum crash risk (from short positions) was completely eliminated. The worst monthly return for buy and hold was -28.98%, while the worst monthly return for an equally weighted momentum strategy was -49.79%, showing the increased risk of applying momentum to individual stocks. The stop loss overlay improved the worst monthly return to only -11.34%. For value weighted portfolios, the maximum monthly loss for momentum and stop loss portfolios were greater at -65.34% and -23.69%, respectively. Average returns and Sharpe ratios doubled by using stops. This stop loss strategy had a positive skewness of 1.86, versus a negative skewness of -1.18 for the original momentum strategy, indicating a dramatic reduction in left tail risk when using stops. Both these papers show theoretically and empirically that risk control overlays, such as stop loss rules, can have dramatically positive effects on momentum-based strategies. This applies also to other trend following forms of risk control, such as moving average filters and absolute momentum, that may work as well or better than stops (the subject of a future post). Stop losses and other trend following methods are a way to head off some of the usual pitfalls of human judgement, such as the disposition effect, loss aversion, ambiguity aversion, and flight to safety. There is no reason why they should not be used by all momentum investors. [1] Stop loss strategies increased trading activity by 40%, but increases in return of about 70% helped overcome these high transaction costs.

Avoiding The Pitfalls Of Factor-Based Investing

By DailyAlts Staff The proliferation of smart beta ETFs may be a relatively recent phenomenon, but the risk factors used to construct smart-beta indexes – most notably value, momentum, low beta, quality, illiquidity, and size – have been a popular topic for financial researchers for nearly three decades. Building off the early handful of factors, factor-based investing has since been expanded to as many as 250 distinct factors that have allegedly generated historical outperformance, but Research Affiliates’ Jason Hsu, Vitali Kalesnik, and Vivek Viswanathan argue that the supposed outperformance of most (if not all) of these new factors is illusory, based on cherry-picking by researchers and “artifacts” of the data. In fact, Mr. Hsu and his colleagues believe at least one of the traditional factors may be unlikely to generate superior risk-adjusted returns going forward. The researchers make their case in the Summer 2015 edition of The Journal of Index Investing , in an article titled “A Framework for Assessing Factors and Implementing Smart Beta Strategies.” Factor Robustness Hsu, et al. allege that economists, financial researchers, and other quantitative analysts are constantly trying to determine new factors, and that only their positive results are likely to get published. New research undermining an existing and semi-popular factor is unlikely to make it to the stage of peer review, according to Research Affiliates. This means that investors, advisors, and other decision-makers must test would-be factors for robustness themselves. Behind the quantitative data, Hsu, et al. insist that factors must be based on economic intuition and make sense within a theoretical framework – otherwise, they’re likely to be statistical noise. Factor premiums can be based on risk or behavioral issues, but in either case, they should span across geographic markets. If back-testing reveals a factor premium for U.S. stocks, that same premium should be evident in Japan and elsewhere. But when analyzed across geographic regions, only the value and low-beta factors consistently hold up; while momentum, quality, and illiquidity are mixed; and size shows no consistency whatsoever. (click to enlarge) Factor Perturbations Since legitimate factors must make intuitive sense, it stands to reason that they should hold up under “perturbations” of their definitions. For example, the value factor is typically defined with book-to-price ratio, but dividend yield and earnings yield (earnings-to-price) also make sense. Therefore, if the value premium were only evident when measured according to book-to-price, the theoretical framework would crumble. Fortunately for value investors, Research Affiliates’ research indicates that value holds up well under a variety of definitions – as do the momentum, low-beta, and illiquidity factors – but quality and size do not. (click to enlarge) Size Doesn’t Matter? According to Hsu, et al., the small-size factor premium is based on back-testing that includes several months of major small-cap outperformance back in the 1930s, and the factor has not generated alpha since its discovery in the early 1980s. Of course, the 1930s were a time of deflation (strengthening dollar) and the 1980s kicked off a 30-year bull market in bonds (weakening the dollar), which could play a significant role in the data. Today, it is generally assumed that small-cap stocks – with a higher degree of U.S. dollar exposure – benefit from a strong currency. Implementation and Allocation Hsu, et al.’s paper looks into implementation and allocation issues, as well, and notes that transaction costs are rarely taken into account by factor-based investors – and this is a mistake. To maximize risk-adjusted returns, factor-based investors should rotate their portfolios only as often as is necessary to capture the factor premium, and no more. The authors say that factor allocation faces many of the same challenges as asset allocation, and that smart-beta solutions should be customized to meet individual investors’ unique risk tolerances. For more information, visit researchaffiliates.com to download a pdf copy of the paper .