RSS Feed
Twitter
Today I am blatantly plagiarizing from a post to The Option Club, a Yahoo group, by an experienced option trader, Michael Catolico. He’s an expert on adjusting option positions.
He called the group’s attention to the first chapter of a book by Mark Houston and Kathy Cosley, Alpine Climbing: Techniques to Take You Higher (The Mountaineers Books, 2004) that he believes teaches traders more than most books written by financial experts. Since I have trouble even climbing a step ladder (yes, I’m acutely acrophobic), I had no need for the book. The excerpts I provide here come from the Google Books preview.
“To climb mountains is to make decisions.... Good decisions are contextual, based on actual circumstances, and cannot be reduced to a set of rules…. In fact rules, guidelines, and codes, although useful for introducing concepts, ultimately become counterproductive when it comes to actually making choices… The simplest climb involves circumstances far too complex to be adequately addressed by rules. The mountain environment itself forces you to rely on your own skills of observation, your understanding of what you observe, and an accurate assessment of risks and of your own abilities.”
The authors continue: “Rules must be replaced by that mysterious quality called judgment. The acquisition of judgment begins with a mountaineer’s very first climb and continues throughout the climber’s entire career. It is a process that cannot be bypassed nor ever be considered complete.”
Principles that help guide decision making are:
Anticipate changes. “Continually look forward. Every change in terrain, route difficulty, or hazard may require a new strategy, mode of movement, or protective system to deal with new circumstances.” (p. 15)
Keep options open. “Any given decision can either maximize or limit other possible options in the future."
Analyze benefits and costs. “Addressing one risk or solving one problem often entails introducing other risks or aggravating other problems.”
Maintain momentum. “Staying focused on forward movement means always being a little bit stressed, but in such a potentially dangerous environment, some level of stress is, arguably, appropriate.”
Gather information. “Preparing ahead of time will give you a head start…. Above all, remember what you see. Every glimpse is a new piece of the puzzle.”
Recognize and correct errors. “Rather than expecting perfection, strive to recognize errors as early as possible, and take steps to correct the situation. Do not carry on blindly, hoping that everything will work out. Denial causes delay, piling error upon error until only good luck can prevent things from spiraling out of control.” (p. 16)
Assess your own skill and knowledge. “An honest and dispassionate self-critique is indispensable. For example, the capacity to observe, predict, and respond to cues improves over time, just as movement skills and climbing ability improve with practice; but on the other hand, competence can be degraded temporarily by states such as fear or fatigue or by inadequate information and inaccurate perception.” (p. 17)
Alpine climbers take on considerably more risk than traders. After all, traders lose only money; climbers can lose their lives. But the way to the top demands similar decision-making processes.
Robert Engle’s FT lectures on volatility, part 3: estimating risk
The basic measure of risk is VaR. For instance, on this graph the area to the left of the red line is the value at risk. This gives you some number that you’re 99% sure is worse than what is going to happen.
By using the GARCH model on historical data you can figure out what the variance is at this point in time. If it’s large, the portfolio curve gets pulled out; if it’s small, the portfolio curve gets pushed together. And the smaller it is, the less the value at risk.
We can see that between 2003 and 2006 volatility was quite low. It’s just as low as it was in the middle 90s. So, despite perceived risk, volatility isn’t showing it.
The red line is the S&P volatility; the green line is the VIX.
Melin, High-Performance Managed Futures
Mark H. Melin’s High-Performance Managed Futures: The New Way to Diversity Your Portfolio (Wiley, 2010) goes far beyond the standard asset allocation book. It not only makes the case for managed futures but does so from the perspective of risk. It is thorough and well documented, yet at the same time eminently practical. Both high net worth individual investors and financial advisors could profit from it.
The most compelling reason to invest in managed futures is that they are not correlated to the equity market. For instance, managed futures had positive returns in nine of the past ten stock market declines. One caveat: these returns are based on the performance of indexes such as the Barclay CTA index and the CASAM CISDM index in which no one can invest. Instead, the investor’s return depends on the performance of individual commodity trading advisors (CTAs) who pursue a variety of strategies. There is no generic CTA, just as there is no generic hedge fund.
Investors often think that they can mimic managed futures with ETFs. They have access to a broad spectrum of commodity ETFs, from agriculture to metals to energy. Although Melin doesn’t take on ETFs directly, he nonetheless defends his turf, explaining that managed futures generally show little correlation not only to stocks or hedge funds but “even the markets in which the CTAs trade.” (p. 148) CTAs can spread trade with different delivery months or slightly different products (such as feeder cattle and live cattle), they can hedge commodity futures with single stock futures, and they can pursue various option strategies.
Since non-correlation is the key selling point of managed futures, Melin spends some time discussing proper correlation analysis. The traditional price correlation matrix, he contends, is faulty. As it relates to managed futures, “price correlation does not consider the CTA strategy or markets traded, which can be critical points of correlation in managed futures that are often not visible in traditional returns correlation.” As an example he cites the naked S&P option-selling strategy, which before the fall of 2008 had a 0.05 correlation to the S&P 500. It looked “almost perfectly uncorrelated to the S&P but in fact the strategy was correlated and still is correlated to a catastrophic stock market collapse.” (p. 161) Melin maintains that the best correlation methodology includes five key ingredients: returns/price, strategy, market, time frame, and volatility.
What would a well-diversified managed futures portfolio look like? It would include allocations to various strategies—for instance, 30% trend following, 10% countertrend, 20% volatility, 20% discretionary, 20% spread. Melin provides filters for choosing CTAs, starting with an average drawdown recovery time filter.
I’ve just scratched the surface of this compelling book. Readers can also go to a website that has an assortment of data, reports, and academic studies as well as information on how to invest and financial advisor/introducing broker recommendations.
A grammatical footnote: “managed futures” normally takes a singular verb, as in “What is managed futures?". I couldn’t bring myself to follow this convention.
The most compelling reason to invest in managed futures is that they are not correlated to the equity market. For instance, managed futures had positive returns in nine of the past ten stock market declines. One caveat: these returns are based on the performance of indexes such as the Barclay CTA index and the CASAM CISDM index in which no one can invest. Instead, the investor’s return depends on the performance of individual commodity trading advisors (CTAs) who pursue a variety of strategies. There is no generic CTA, just as there is no generic hedge fund.
Investors often think that they can mimic managed futures with ETFs. They have access to a broad spectrum of commodity ETFs, from agriculture to metals to energy. Although Melin doesn’t take on ETFs directly, he nonetheless defends his turf, explaining that managed futures generally show little correlation not only to stocks or hedge funds but “even the markets in which the CTAs trade.” (p. 148) CTAs can spread trade with different delivery months or slightly different products (such as feeder cattle and live cattle), they can hedge commodity futures with single stock futures, and they can pursue various option strategies.
Since non-correlation is the key selling point of managed futures, Melin spends some time discussing proper correlation analysis. The traditional price correlation matrix, he contends, is faulty. As it relates to managed futures, “price correlation does not consider the CTA strategy or markets traded, which can be critical points of correlation in managed futures that are often not visible in traditional returns correlation.” As an example he cites the naked S&P option-selling strategy, which before the fall of 2008 had a 0.05 correlation to the S&P 500. It looked “almost perfectly uncorrelated to the S&P but in fact the strategy was correlated and still is correlated to a catastrophic stock market collapse.” (p. 161) Melin maintains that the best correlation methodology includes five key ingredients: returns/price, strategy, market, time frame, and volatility.
What would a well-diversified managed futures portfolio look like? It would include allocations to various strategies—for instance, 30% trend following, 10% countertrend, 20% volatility, 20% discretionary, 20% spread. Melin provides filters for choosing CTAs, starting with an average drawdown recovery time filter.
I’ve just scratched the surface of this compelling book. Readers can also go to a website that has an assortment of data, reports, and academic studies as well as information on how to invest and financial advisor/introducing broker recommendations.
A grammatical footnote: “managed futures” normally takes a singular verb, as in “What is managed futures?". I couldn’t bring myself to follow this convention.
Lo and Hasanhodzic, The Evolution of Technical Analysis
Long before I had ever heard of technical analysis I was involved in the production of a book that I considered the most obscure in a field of pretty obscure books. The exact title escapes me, but it was about Babylonian accounting. Little did I think that I’d ever revisit Babylonian financial recordkeeping. Then came the new book by Andrew W. Lo and Jasmina Hasanhodzic, The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals (Bloomberg Press, 2010) where I learned that over the course of four centuries Babylonian diaries recorded the market prices of the same six commodities—barley, dates, mustard/cuscuta, cress/cardamom, sesame, and wool. Fascinating! It all goes to show how my own interests have evolved.
The Evolution of Technical Analysis takes the reader on a whirlwind trip through history and across continents. Starting in the ancient world, progressing through the Middle Ages and the Renaissance, moving to Asia and then to the new world, the book proceeds to the “new age for technical analysis” exemplified by such luminaries as Dow and Gann, and finally looks at technical analysis today. It then offers a brief account of randomness and efficient markets and summarizes some academic approaches to technical analysis.
The history is largely reliant on secondary sources, but the authors have combed through the material to trace links between technical analysis and astrology,* highlight parallels between the writings of the eighteenth-century Japanese rice trader Munehisa Homma and Charles Dow, explore people’s penchant for manipulating markets, and showcase principles of technical analysis that have withstood the test of time. Their conclusion? “Technical analysis has had to undergo so little change precisely because it is so robust and so deeply relevant to how markets operate.” (p. 105)
Nonetheless, markets have evolved and continue to evolve. Indeed, “during the last three decades the market has been evolving so rapidly that the very intuition on which developing technical or quantitative trading strategies is based—the stronger the historical backtest results, the more promising the strategy—got turned on its head. The rate of change has been such that historical viability has become hardly indicative of present success, challenging today’s portfolio managers to develop adaptive strategies capable of detecting shifts in the market environment.” (p. 119)
The authors contend that pattern recognition may be the key to dealing with changing environments. The results of an online study that I referenced earlier on this blog “provided overwhelming statistical evidence (less than 1 percent probability of the findings being generated by pure chance) that humans can quickly learn to distinguish actual price series from randomly generated ones.” (p. 146) Since human pattern recognition skills outstrip those of any known algorithm, it may be possible, they argue, with the proper interface to “translate the hand-eye coordination of highly skilled video-gamers to completely unrelated pattern-recognition and prediction problems such as weather forecasting or financial trading.” (p. 129) I wonder what industry-standard terminals will look like in 2050!
________
*The authors find “nothing in favor of astrology’s inherent predictive value.” But, they continue, “For our predecessors of precomputer eras it may have seemed justifiable to use astrology as an input in their price-based forecasting models. In fact, astrological inputs may have been genuinely useful to prediction insofar as they played the same role that random-number generators play in today’s forecasting models.” (p. 104)
The Evolution of Technical Analysis takes the reader on a whirlwind trip through history and across continents. Starting in the ancient world, progressing through the Middle Ages and the Renaissance, moving to Asia and then to the new world, the book proceeds to the “new age for technical analysis” exemplified by such luminaries as Dow and Gann, and finally looks at technical analysis today. It then offers a brief account of randomness and efficient markets and summarizes some academic approaches to technical analysis.
The history is largely reliant on secondary sources, but the authors have combed through the material to trace links between technical analysis and astrology,* highlight parallels between the writings of the eighteenth-century Japanese rice trader Munehisa Homma and Charles Dow, explore people’s penchant for manipulating markets, and showcase principles of technical analysis that have withstood the test of time. Their conclusion? “Technical analysis has had to undergo so little change precisely because it is so robust and so deeply relevant to how markets operate.” (p. 105)
Nonetheless, markets have evolved and continue to evolve. Indeed, “during the last three decades the market has been evolving so rapidly that the very intuition on which developing technical or quantitative trading strategies is based—the stronger the historical backtest results, the more promising the strategy—got turned on its head. The rate of change has been such that historical viability has become hardly indicative of present success, challenging today’s portfolio managers to develop adaptive strategies capable of detecting shifts in the market environment.” (p. 119)
The authors contend that pattern recognition may be the key to dealing with changing environments. The results of an online study that I referenced earlier on this blog “provided overwhelming statistical evidence (less than 1 percent probability of the findings being generated by pure chance) that humans can quickly learn to distinguish actual price series from randomly generated ones.” (p. 146) Since human pattern recognition skills outstrip those of any known algorithm, it may be possible, they argue, with the proper interface to “translate the hand-eye coordination of highly skilled video-gamers to completely unrelated pattern-recognition and prediction problems such as weather forecasting or financial trading.” (p. 129) I wonder what industry-standard terminals will look like in 2050!
________
*The authors find “nothing in favor of astrology’s inherent predictive value.” But, they continue, “For our predecessors of precomputer eras it may have seemed justifiable to use astrology as an input in their price-based forecasting models. In fact, astrological inputs may have been genuinely useful to prediction insofar as they played the same role that random-number generators play in today’s forecasting models.” (p. 104)
Robert Engle’s FT lectures on volatility, part 2
The most common way to measure volatility is to measure standard deviation over smaller periods of time—a week or a month. These measures of volatility are known as historical volatility. The problem is that if you use a short series you get a very noisy measure, and if you use a long series it’s so smooth that it doesn’t respond well to new information. So, as an alternative to historical volatility you can use ARCH: autoregressive conditional heteroskedasticity. ARCH is a very simple concept, but Engle got the Nobel Prize in 2003 for inventing this model.
ARCH uses a big window but weighted averages. That is, we’ll give more weight to recent information, less to those that happened a long time ago. You can estimate these weights using an econometric model.
In yellow is the 5-day moving average of the standard deviations. In red is the one-year standard deviation, and the green is a five-year standard deviation. If you give the same information to a GARCH program (generalized autoregressive conditional heteroskedasticity) such as MatLab it will calculate the best set of weights.
* * *
For the transcript of the original presentation go to the FT Business School - NYU Stern School of Business site.
ARCH uses a big window but weighted averages. That is, we’ll give more weight to recent information, less to those that happened a long time ago. You can estimate these weights using an econometric model.
In yellow is the 5-day moving average of the standard deviations. In red is the one-year standard deviation, and the green is a five-year standard deviation. If you give the same information to a GARCH program (generalized autoregressive conditional heteroskedasticity) such as MatLab it will calculate the best set of weights.
Another way to look at the same output is in terms of a confidence interval. Each day you can ask “How high or low do I expect the market to go?”
The GARCH bands are time-varying confidence intervals. For instance, at any one point in time you can say with confidence that the market isn’t going to be higher than the blue band or lower than the green band.
* * *
For the transcript of the original presentation go to the FT Business School - NYU Stern School of Business site.
Hirschhorn, 8 Ways to Great
Doug Hirschhorn is well known as a trading coach. In his latest book 8 Ways to Great: Peak Performance on the Job and in Your Life (Penguin Books, 2010) he presents eight principles employed by all top performers. They are: find your “why?”, get to know yourself, learn to love the process, sharpen your edge, be all that you can be, keep your cool, get comfortable with being uncomfortable, and make yourself accountable.
The book is short (128 pp.), but I think its brevity helps drive home its messages. And they are, it should be noted, messages directed first and foremost at traders. So the reader who is a trader doesn’t have to extrapolate from advice given to business leaders or athletes.
There’s an abundance of sound advice in this book. One that should resonate with traders is that “two sharp edges are better than one.” As Hirschhorn writes, “Gaining a competitive advantage is like having a two-edged sword, and you need to keep both of them sharp. One edge is internal—knowing what unique skills you bring to the table. The other is external and comes from gathering knowledge that makes it more likely you’ll succeed.” (p. 45)
For those who suffer from analysis paralysis: “Words without action are just philosophy. And, as my old college baseball coach used to tell us, ‘You can’t sit there with the bat on your shoulder and “look” one out of the park. Sometimes you actually have to swing the bat.’” (p. 93)
All in all, a quick, worthwhile read. As always, the challenge is to act. Just as a person doesn’t lose weight by reading a diet book, so he doesn’t achieve peak performance by reading a book on becoming great. It’s comfortable to read a book, uncomfortable to stretch beyond one’s sometimes self-imposed limits. Yet we can all profit from being reminded now and again of what it takes to get beyond the middling.
The book is short (128 pp.), but I think its brevity helps drive home its messages. And they are, it should be noted, messages directed first and foremost at traders. So the reader who is a trader doesn’t have to extrapolate from advice given to business leaders or athletes.
There’s an abundance of sound advice in this book. One that should resonate with traders is that “two sharp edges are better than one.” As Hirschhorn writes, “Gaining a competitive advantage is like having a two-edged sword, and you need to keep both of them sharp. One edge is internal—knowing what unique skills you bring to the table. The other is external and comes from gathering knowledge that makes it more likely you’ll succeed.” (p. 45)
For those who suffer from analysis paralysis: “Words without action are just philosophy. And, as my old college baseball coach used to tell us, ‘You can’t sit there with the bat on your shoulder and “look” one out of the park. Sometimes you actually have to swing the bat.’” (p. 93)
All in all, a quick, worthwhile read. As always, the challenge is to act. Just as a person doesn’t lose weight by reading a diet book, so he doesn’t achieve peak performance by reading a book on becoming great. It’s comfortable to read a book, uncomfortable to stretch beyond one’s sometimes self-imposed limits. Yet we can all profit from being reminded now and again of what it takes to get beyond the middling.
Robert Engle’s FT lectures on volatility, part 1
Some time ago (well, I guess the pre-crisis days of 2007 qualify as “some time ago”) Robert Engle, professor of finance and director of the Center for Financial Econometrics at NYU’s Stern School of Business as well as a Nobel Prize winner for his work on ARCH, gave a five-part “mini-course” on volatility on FT.com. The videos are, as far as I can ascertain, no longer available, but the transcripts are. However, the transcripts don’t include the graphics to which Engle refers.
As is my wonkish wont, I took notes on this series; more important, I saved the graphics. Their quality was poor in the original, and capturing them with HyperSnap, pasting them into a Word document, and then extricating them so I could paste them into this blog certainly didn’t improve matters. But fuzzy is better than nothing.
While I read the books I have here for review (including a 654-page tome on probability and statistics for finance which I am determined to implant in my brain) I plan to fill in the blanks by publishing my notes on these videos. Of course, for anyone who cares about this subject I highly recommend going to the original transcripts; they’re very brief and not at all technical, probably about two typed pages each.
And, although I think I gave the link to NYU Stern’s Volatility Laboratory earlier, here it is again. It “provides real time measurement, modeling and forecasting of financial volatility, correlations and risk for a wide spectrum of assets.” It’s decidedly beta, and I suspect always will be. Interesting nonetheless.
So, here are my notes on and the accompanying graphics from the first video.
The red graphic indicates the amplitude of volatility.
As is my wonkish wont, I took notes on this series; more important, I saved the graphics. Their quality was poor in the original, and capturing them with HyperSnap, pasting them into a Word document, and then extricating them so I could paste them into this blog certainly didn’t improve matters. But fuzzy is better than nothing.
While I read the books I have here for review (including a 654-page tome on probability and statistics for finance which I am determined to implant in my brain) I plan to fill in the blanks by publishing my notes on these videos. Of course, for anyone who cares about this subject I highly recommend going to the original transcripts; they’re very brief and not at all technical, probably about two typed pages each.
And, although I think I gave the link to NYU Stern’s Volatility Laboratory earlier, here it is again. It “provides real time measurement, modeling and forecasting of financial volatility, correlations and risk for a wide spectrum of assets.” It’s decidedly beta, and I suspect always will be. Interesting nonetheless.
So, here are my notes on and the accompanying graphics from the first video.
The periods when volatility is high are those when the market is in decline. When the market is going up, as in the middle 1990s, volatility is low. Actually, in the mid-1990s volatility was at a record low.
Volatility tends to mean revert. Indeed, in the late 90s volatility was very high, meaning that risk was high. And subsequently, of course, the market turned down. And we had high volatility as the market declined.
What we are calculating in this chart is the standard deviation of the returns on an annualized basis from 1997 to 2003. Since the broad index has a lower volatility than its components you see the benefits of diversification. The highest volatility on this chart is small caps. Every piece of information that tells us whether this is an up and coming company or one that’s not going to make it is critical. This is the news theory.
This chart shows the median annual volatility of about 50 countries during the same period—1997 to 2003. The low vol countries are Chile, the UK, Australia, New Zealand, Austria, Canada, etc. The high vol countries are Turkey, Korea, Brazil, Finland, Russia, etc.
Maxwell & Shenkman, Leveraged Financial Markets
I will never be a direct participant in a market where the standard round lot is $1 million, but I nonetheless profited from reading Leveraged Financial Markets: A Comprehensive Guide to High-Yield Bonds, Loans, and Other Instruments by William F. Maxwell and Mark R. Shenkman (McGraw-Hill, 2010). Contributors analyze the major high-yield products, examine alternative credit risk metrics, and suggest ways to trade and manage high-yield assets. They also take the reader into the fascinating worlds of debtor-in-possession financing and distressed investing.
Today I’m going to look at a single chapter and a single credit model, Moody’s KMV EDF model, “the most widely used quantitative credit model around the world.” (p. 197) The financial crisis highlighted and called into question the competence of the credit rating agencies. But few of us know how the agencies go about rating the creditworthiness of individual companies. Leveraged Financial Markets provides us with some answers.
A full-blown credit review involves both fundamental analysis and credit models. Fundamental analysis can be slow and is by its very nature subjective. Since most investors demand an analysis that is both timely and accurate, “quantitative risk models have gained widespread appeal and are now used to assess credit risk across a broad range of borrowers from large Fortune 500 companies to small businesses and consumers.” (p. 199)
Credit risk models are designed to measure the probability of default. They fall into two categories: accounting-based, such as the Altman Z-score model, and market-based, such as Moody’s KMV (a company Moody’s acquired in 2002) EDF (expected default frequency) model.
The KMV model is grounded in the principle that “a borrower will default if the market value of its assets falls below the value of its debt obligations.” (People who walk away from under-water mortgages exemplify this principle.) The math that informs the model traces its roots to Robert C. Merton’s option pricing model according to which “a borrower’s equity has the same payoff as a call option, where the strike price of the option is equal to the face value of the borrower’s debt.” (p. 201) So the equity will either be worthless or it will be worth the difference between the value of the assets and the debt.
The three main drivers of default are asset value, asset volatility, and leverage. The first two drivers are fairly straightforward, but leverage is something of a catch-all. It includes the maturity profile of a borrower’s liabilities where, as homeowners in the old days knew, “a borrower funded with liquid assets and long-term liabilities can avoid default.” (p. 202) The KMV model incorporates this maturity profile in a basic “default point” equation. Default point = short-term liabilities + 50% of long-term liabilities.
The KMV EDF model then calculates the distance to default, which equals (Market Value of Assets – Default Point) / (Market Value of Assets x Asset Volatility). From there it uses an extensive database of more than 250,000 companies and over 4,700 incidents of default or bankruptcy to map the distance to default to EDFs, “thus generating a cardinal measure of credit risk.” (p. 204)
I suspect that, were we to see the math used in this proprietary model, it would be a lot more sophisticated than the arithmetical description provided here. But at least we now have some idea of how credit rating agencies work.
Leveraged Financial Markets is written for investors and managers who want an overview of the high-yield markets. It is broad in scope, yet there is also “meat on the bones.” I think it would be invaluable for anyone thinking about adding leveraged finance to their portfolio or their clients’ portfolios.
Today I’m going to look at a single chapter and a single credit model, Moody’s KMV EDF model, “the most widely used quantitative credit model around the world.” (p. 197) The financial crisis highlighted and called into question the competence of the credit rating agencies. But few of us know how the agencies go about rating the creditworthiness of individual companies. Leveraged Financial Markets provides us with some answers.
A full-blown credit review involves both fundamental analysis and credit models. Fundamental analysis can be slow and is by its very nature subjective. Since most investors demand an analysis that is both timely and accurate, “quantitative risk models have gained widespread appeal and are now used to assess credit risk across a broad range of borrowers from large Fortune 500 companies to small businesses and consumers.” (p. 199)
Credit risk models are designed to measure the probability of default. They fall into two categories: accounting-based, such as the Altman Z-score model, and market-based, such as Moody’s KMV (a company Moody’s acquired in 2002) EDF (expected default frequency) model.
The KMV model is grounded in the principle that “a borrower will default if the market value of its assets falls below the value of its debt obligations.” (People who walk away from under-water mortgages exemplify this principle.) The math that informs the model traces its roots to Robert C. Merton’s option pricing model according to which “a borrower’s equity has the same payoff as a call option, where the strike price of the option is equal to the face value of the borrower’s debt.” (p. 201) So the equity will either be worthless or it will be worth the difference between the value of the assets and the debt.
The three main drivers of default are asset value, asset volatility, and leverage. The first two drivers are fairly straightforward, but leverage is something of a catch-all. It includes the maturity profile of a borrower’s liabilities where, as homeowners in the old days knew, “a borrower funded with liquid assets and long-term liabilities can avoid default.” (p. 202) The KMV model incorporates this maturity profile in a basic “default point” equation. Default point = short-term liabilities + 50% of long-term liabilities.
The KMV EDF model then calculates the distance to default, which equals (Market Value of Assets – Default Point) / (Market Value of Assets x Asset Volatility). From there it uses an extensive database of more than 250,000 companies and over 4,700 incidents of default or bankruptcy to map the distance to default to EDFs, “thus generating a cardinal measure of credit risk.” (p. 204)
I suspect that, were we to see the math used in this proprietary model, it would be a lot more sophisticated than the arithmetical description provided here. But at least we now have some idea of how credit rating agencies work.
Leveraged Financial Markets is written for investors and managers who want an overview of the high-yield markets. It is broad in scope, yet there is also “meat on the bones.” I think it would be invaluable for anyone thinking about adding leveraged finance to their portfolio or their clients’ portfolios.
Bellafiore, One Good Trade
Mike Bellafiore’s One Good Trade: Inside the Highly Competitive World of Proprietary Trading (Wiley, 2010) outlines what it takes to win in intraday equity trading. His vantage point is prop trading, but most of the book applies equally well to the individual who is trading his own account from home.
One Good Trade is not a book of strategies, although it covers tape reading in some detail. Rather, it focuses on preparation, mindset, process, and performance review. Some of the material should be familiar to readers of Brett Steenbarger’s books and his still extant but no longer updated Traderfeed blog. But Bellafiore personalizes his discussions by introducing us to traders at SMB Capital, both successful and failed. Moreover, he provides enough information about SMB’s training program to jump start the education of the person who’s going it alone.
I’m going to limit this post to three takeaways from the book.
First, the virtues of tape reading over relying exclusively on charts. “I just don’t get it why people fall in love with their charts. There is so much more information from the prints, inside market, than the charts for short-term traders. And for long-term traders, reading the tape could dramatically improve their entry prices.” (p. 219) “[N]ot learning [how to read the tape] is like a basketball player not working on his free throws. This baller just decides to keep clanging his free throws and give up some easy points.” (p. 196)
Second, the importance of “if-then” thinking before entering a trade. For each specific trading setup there should be an accompanying if-then scenario. Bellafiore offers the following example of successive MOS trades: “Forty-eight was support. I got long. If 48 dropped the bid, then I would exit. It did, so I exited. If there were a held bid just below 48, then I would get long again. At 47.95, there was a held bid, so I re-entered. And I made a chop on this trade as MOS exploded higher. But what is most important is that I had predetermined plans of action with a handful of if-then statements. And then I just did what I told myself to do.” (p. 240)
Third, using video to review trades. SMB Capital records traders’ screens for later review. The independent trader using this technique wouldn’t have the benefit of mentor commentary, nor could he compare how he trades a setup to how his peers trade the same setup, so critical feedback would be much more difficult. But if the independent trader knows the kinds of things that tend to trip people up (position sizing, order execution skills, not exiting stocks that trade against them) he can probably improve his bottom line by identifying his own weaknesses.
The trader who faithfully follows SMB’s seven fundamentals (proper preparation, hard work, patience, a detailed plan before every trade, discipline, communication, and replaying important trades) will have made one good trade. “And this,” Bellafiore writes, “is what we do. Or certainly try to do. And we do it over and over again. We make One Good Trade, and then One Good Trade, and then One Good Trade.” (p. 35)
Bellafiore’s book is an inspiration, a kick in the pants, and a learning manual. The reader who puts its tenets into practice may just make one good trade again and again. Ka-ching.
One Good Trade is not a book of strategies, although it covers tape reading in some detail. Rather, it focuses on preparation, mindset, process, and performance review. Some of the material should be familiar to readers of Brett Steenbarger’s books and his still extant but no longer updated Traderfeed blog. But Bellafiore personalizes his discussions by introducing us to traders at SMB Capital, both successful and failed. Moreover, he provides enough information about SMB’s training program to jump start the education of the person who’s going it alone.
I’m going to limit this post to three takeaways from the book.
First, the virtues of tape reading over relying exclusively on charts. “I just don’t get it why people fall in love with their charts. There is so much more information from the prints, inside market, than the charts for short-term traders. And for long-term traders, reading the tape could dramatically improve their entry prices.” (p. 219) “[N]ot learning [how to read the tape] is like a basketball player not working on his free throws. This baller just decides to keep clanging his free throws and give up some easy points.” (p. 196)
Second, the importance of “if-then” thinking before entering a trade. For each specific trading setup there should be an accompanying if-then scenario. Bellafiore offers the following example of successive MOS trades: “Forty-eight was support. I got long. If 48 dropped the bid, then I would exit. It did, so I exited. If there were a held bid just below 48, then I would get long again. At 47.95, there was a held bid, so I re-entered. And I made a chop on this trade as MOS exploded higher. But what is most important is that I had predetermined plans of action with a handful of if-then statements. And then I just did what I told myself to do.” (p. 240)
Third, using video to review trades. SMB Capital records traders’ screens for later review. The independent trader using this technique wouldn’t have the benefit of mentor commentary, nor could he compare how he trades a setup to how his peers trade the same setup, so critical feedback would be much more difficult. But if the independent trader knows the kinds of things that tend to trip people up (position sizing, order execution skills, not exiting stocks that trade against them) he can probably improve his bottom line by identifying his own weaknesses.
The trader who faithfully follows SMB’s seven fundamentals (proper preparation, hard work, patience, a detailed plan before every trade, discipline, communication, and replaying important trades) will have made one good trade. “And this,” Bellafiore writes, “is what we do. Or certainly try to do. And we do it over and over again. We make One Good Trade, and then One Good Trade, and then One Good Trade.” (p. 35)
Bellafiore’s book is an inspiration, a kick in the pants, and a learning manual. The reader who puts its tenets into practice may just make one good trade again and again. Ka-ching.
Subscribe to:
Posts (Atom)