The combination of subjective views within a broadly accepted risk model is one of the main challenges in quantitative portfolio management. Indeed, any risk model, be it based on historical scenarios, parametric fits, or Monte Carlo scenarios generated according to a given distribution, is subject to estimation risk and thus it is inherently flawed. Therefore, it is important to provide a framework that allows practitioners to overlay their judgement to any risk model in a statistically sound way.
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These days, it is hard to find investments with the potential to generate attractive returns without taking on too much risk. At the conservative end of the spectrum, bond yields are well below historical norms. The average bond now yields just 2.5%/year, below the typical rate of inflation. If interest rates should recover to more normal levels, today’s bond investors would have locked in at paltry yields and may also suffer big losses in the value of their investments (particularly if they have invested in bond mutual funds as opposed to individual bonds). But, on the other hand, the stock market appears very risky after losing more than 50% from the top in 2007 to the bottom in March 2009 and after falling 16% from April-July in 2010 in response to the European debtcrisis.
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Scenario analysis allows the practitioner to explore the implications on a given portfolio of a set of subjective views on possible market realizations, see e.g. Mina and Xiao (2001). The pathbreaking approach pioneered by Black and Litterman (1990) (BL in the sequel) generalizes scenario analysis, by adding uncertainty on the views and on the reference risk model. Further generalizations have been proposed in recent years. Qian and Gorman (2001) provide a framework to stress-test volatilities and correlations in addition to expectations. Pezier (2007) processes partial views on expectations and covariances based on least discrimination. Meucci (2009) extends the above models to act on risk factors instead of returns, and thus covers highly non-linear derivative markets and views on external factors that influence the p&l only statistically.
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The purpose of this essay is to improve your ability to trade iron condor option spreads. Iron condors have become a very popular spread type among active options traders, but there is little detailed or quantitative information available about the best way to employ these spreads. As participants in the crash of 2008 and the intense bull market of 2010 can attest, the static “set-it-and-forget-it” method with which some novice traders approach iron condors does not produce ideal results. In the following, I will consider the circumstances in which it is appropriate to enter a condor spread, key techniques and considerations when structuring spreads, and will present some backtested historical returns for a few strategy variations.
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In August 2010, Cisco stock (ticker: CSCO) hovered just a few cents below $25. Several analysts identified the stock as a strong buy. They pointed to the rising demand for network infrastructure that, among other things, was being driven by explosive growth in online video gaming and Internet television. Cisco, they believed, would continue to dominate the consumer market while benefiting from a weak dollar and low manufacturing costs. They must have been wrong because the stock fell 15% when earnings were released on August 11. The price continued to decline until August 31, when it bottomed out at $19—24% below its previous high. About the time that everyone had given up and turned bearish, the stock began to rally. On November 10 the price was, once again, back up to $24.50. Then came another earnings report and another sharp decline. The price immediately fell 16% and continued plunging until, on December 3, it once again bottomed out at $19. These bizarre dynamics played out a third time, with the stock rallying steadily to $22 on February 9, 2011, before falling back to $18.92 the very next day after earnings were released—another 14% decline. Figure P.1 displays Cisco closing prices from June 1, 2010, to February 11, 2011.
Continue reading "Microsoft Excel for Stock and Options Traders: Build Your Own Analytical Tools for Higher Returns" »
In today’s volatile financial markets, many investors are having a hard time deciding whether or not to be invested in equities and/or bonds. After the stock market turbulence and two severe bear markets of the past decade, as well as the uncertainty about the future, you are probably more cautious than ever about your investments. You might have moved to bonds, relied on mutual funds, followed the broker-friendly “buy and hold” strategy, or deposited your funds in a money market fund (“cash”). You might have made your decisions based on emotion, rather than discipline and fact. You are not happy with the results. So, how can you do better?
Continue reading "Profiting from ETF Rotation Strategies in Turbulent Markets" »
A May 2009 report from Research and Markets of Dublin notes that “structured products are among the fastest growing investment classes in world financial markets.” Although not really an asset class, structured investment products represent an array of investment tools for retail and institutional investors. They can enhance the returns of traditional asset classes, provide exposure to hard-to-reach sectors and markets, and often mitigate investors’ risk of losing some or all of their principal.
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"People don’t perceive that they are going to be the one in a crash,” laments Russ Rader, media director at the IIHS (Insurance Institute for Highway Safety). “They believe that they are in control when they’re behind the wheel. They don’t sense how high the risk actually is.” The IIHS, a Virginia-based, national nonprofit that has helped significantly increase seat belt usage in the last twenty years, has a simple objective: lessen the risk taken in everyday driving behavior. The risk-measurement approach it employs has the potential to revolutionize how the investment community evaluates manager performance.
Continue reading "A New Approach to Calculating Risk-Adjusted Returns" »
The traditional MVO (mean-variance optimization) of Harry Markowitz (1952) and the versions that followed constitute an analytical approach to identifying optimal asset allocations from the universe of investable securities. The data needed for the traditional MVO are the expected rates of return, the risks of individual securities, and the covariance among securities. The outcome of this approach is usually illustrated as a curve on which optimal portfolios lie. Each portfolio on this curve, known as the efficient frontier, has the smallest degree of risk for its level of expected return. Markowitz’s traditional MVO was extended and simplified by William Sharpe (1963), in Sharpe’s well-known CAPM (capital asset pricing model). For an intuitive explanation of CAPM and portfolio optimization, refer to Markowitz’s “Crisis Mode” in the Spring 2009 issue of the Investment Professional.
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Indexes are probably the best instruments to trade condors, and the SPX, RUT, and NDX are the best of this group. The two main reasons are liquidity and their use of European-style options. The greater the liquidity, the easier it is to execute the trade, and European-style options increase your safety by eliminating the risk of early exercise.
The buying and selling of contracts takes place through an auction process. The bid represents what the buyer wants to pay, and the ask is the price the seller is seeking. There is no right way or wrong way to approach the bid-ask spread, but how you negotiate the price will result in different outcomes in your trading.
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“People don’t perceive that they are going to be the one in a crash,” laments Russ Rader, media director at the IIHS (Insurance Institute for Highway Safety). “They believe that they are in control when they’re behind the wheel. They don’t sense how high the risk actually is.” The IIHS, a Virginia-based, national nonprofit that has helped significantly increase seat belt usage in the last twenty years, has a simple objective: lessen the risk taken in everyday driving behavior. The risk-measurement approach it employs has the potential to revolutionize how the investment community evaluates manager performance.
In our industry any credible performance comparison is risk adjusted. It makes no sense to equate the returns of two funds that take different amounts of risk. The challenge has always been how to measure the risk taken by managers—mathematically speaking, what to stick in the denominator.
Continue reading "The Seat Belt Problem: A New Approach to Calculating Risk-Adjusted Returns " »
All markets are valued by a simple function between supply and demand; or at least that is how it seems throughout the typical economics 101 course. However, in reality, the components of supply and demand are highly complex and, some will argue, might be too overwhelming to effectively use as a speculative tool. Nonetheless, even those looking to trade commodities via technical analysis should have an overall idea of what might be impacting the data displayed on a price chart. It would be impossible to delve into market fundamentals in great depth within the scope of this writing, but I hope that you gain an understanding of price determinants and the ability to prioritize available information.
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There are basically two categories of dividend paying stocks: preferred shares, all of which pay dividends, and dividend paying common shares.
Preferred shares are a sort of cross between shares of common stock and regular corporate bonds—generally providing higher income than either, but with certain disadvantages compared to both.
Continue reading "Preferred Stock Versus Common Stock: Relatively Safe and High Yielding Versus Potential Growth in Income and Share Value" »
Since its inception, the 401(k) retirement plan has presented plan sponsors and participants with a significant challenge, requiring both groups to act as financial planners and portfolio managers for retirement investments. Participants are expected to forecast their retirement income needs, arrive at an appropriate asset allocation, and choose among contribution and investment options accordingly. Plan sponsors must create the universe of options from which participants choose and provide advice to participants, but only to the extent that such advice does not give rise to fiduciary responsibility under ERISA (Employee Retirement Income Security Act of 1974). This system has allowed a large number of plan participants to remain uninformed with respect to a very complex set of retirement plan decisions.
Continue reading "Target-Date Funds: Not a Set-It-And-Forget-It Option" »
Options, those high-risk, short-lived, and speculative instruments, can be used as a valuable alternative in swing trading strategies. The flexibility of options allows you to use either calls or puts, to go long or short, vary the number of contracts, or combine different approaches based on market conditions. The “swing trader” moves in and out of stock positions based on very short-term price movement (swings). The strategy relies on the tendency of prices to overreact to both good and bad news, creating exaggerated short-term price movement that will correct quickly, normally within a few trading sessions. In other words, short-term price movement is erratic and chaotic and cannot be relied upon for long-term timing. For the short term, however, this chaotic tendency is a great advantage for swing traders.
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There are three courses of action a trader can take to eliminate a margin call once it occurs: 1) add funds, 2) adjust positions, or 3) liquidate some or all of the trades in the account. Most will agree that depositing funds on short notice is the least desirable action. After all, if a margin call is triggered, it is probable that the account is suffering a draw down and throwing “good money after bad” isn’t a logical solution. On the other hand, if you did your homework prior to entering the positions in your account you likely have good reasons to believe that the market will eventually trade in your favor. One of the most mentally challenging aspects of trading is watching the market move in the manner that you had expected after trades have been liquidated. Failure to not benefit in an anticipated move is nearly as emotionally painful as being on the wrong side of a market and actually realizing losses.
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PDAs (personal digital assistants) have capabilities way beyond scheduling, e-mail, Internet access, and phone calls. These essential pieces of business equipment can now perform scaled-down spreadsheet functions that can be greatly enhanced with templates to perform analysis quickly when a few inputs are entered. Although Microsoft Office Excel’s functions are limited in the PDA environment, the template described below only requires the PV() function.
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Value at risk, or VaR, is viewed by some as a massively important measure. It is unique in how it characterizes risk. Most measures show risk either as a percentage (as standard deviation and tracking error do) or in units (as the Sharpe and Treynor risk-adjusted measures do). VaR shows risk in terms of money—that is, the money that might be lost.
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It is always better to use a forward-looking value that reflects the current market conditions. But the standard methods for calculating equity risk premiums rely on historical estimates—and therefore are backward looking. These approaches produce a type of average that may not reflect expectations of future returns at a particular moment in time (e.g., when volatility is high). This article describes a method of estimating a forward-looking equity risk premium based on prospective market multiples—in particular, the ratio of enterprise value to EBIT (earnings before interest and taxes). The example below is only an illustration, because forecasts are outdated by the time they make it to press. The numbers are based loosely on estimates from around mid-2009.
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Edward I. Altman’s z-score for predicting bankruptcy, introduced in 1968, was the precursor of credit-scoring models. Its application has since expanded to measure more than the publicly traded manufacturing companies investigated in Altman’s original paper. Using the following template—based on a paper by Arnold and Earl (2006)—you will be able to create a z-score calculator for your PDA. (In this case, we used an HP iPAQ PDA with Windows Mobile). While PDAs cannot take advantage of some of the Excel capabilities available on a PC, the Windows Mobile environment is functional enough to create a z-score template.
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