Van K. Tharp came up with a terrific title for his latest book—Trading Beyond the Matrix: The Red Pill for Traders and Investors (Wiley, 2013). The reference, of course, is to the film The Matrix. “We live in a world of illusion shaped by our programming. And at some level, we seem to know that, and we seem to know that there is something better. At this point, you have a choice. You can take the blue pill and go back into a comfortable sleep where nothing changes. … Or you take the red pill and, as Morpheus says in the movie, ‘see how deep the rabbit hole goes.’” (pp. xxiv-xxv)
Tharp takes the reader down a pretty deep rabbit hole in this book. Although the first part of the book (“Transformation of the Trading Game: Understanding the Basics”) touches on his work on position sizing and risk:reward ratios, the book’s main thrust is personal transformation (“Psychological Transformations to Help You Function at a Superior Level within the Matrix” and “Moving Beyond the Matrix by Transforming Your Level of Consciousness”). He called on students to write eleven of the book’s eighteen chapters describing their own transformations, some more dramatic than others, in the wake of Tharp’s courses or personal mentoring. Tharp also describes his own “personal journey of miracles.”
The goal, Tharp claims, is to move from trading from “low levels of consciousness such as fear and greed” to “trading from higher states of consciousness such as acceptance, peace, and enlightenment.” That is, you should aspire to “observe what the market is doing right now with no interference of any kind.” (pp. 337-38) You should rid yourself of or transform 5,000 (!) major beliefs and stop the internal chatter in your head. You should increase your level of consciousness (and hence your happiness) “until you disappear.” Tharp, by the way, is now a Oneness trainer and offers a Oneness Awakening course through the Van Tharp Institute.
There is no doubt that psychological hang-ups can impede trading. Whether traders need a major consciousness transformation or just some brief therapy (think back to Brett Steenbarger’s work) remains an open question, however. Personally, I lean toward the latter, which means that I would have preferred a (not the) purple pill.
Wednesday, February 27, 2013
Monday, February 25, 2013
Pham, The Big Trade
Peter Pham has written an engaging book. The Big Trade: Simple Strategies for Maximum Market Returns (Wiley, 2013) is not only an introduction to Excel-based, probabilistic trading strategies but a “blue jeans to BMW and back again” autobiography. (The BMW came from trading, the “back again” from a failed financial media business venture in Vietnam.)
Part of what makes Pham’s book a worthwhile read rests with his belief that markets demand humility on the part of the trader. He is not touting strategies that will consistently deliver huge returns. On the contrary, the big trade is, in Pham’s opinion, “the one you make that allows you the opportunity to make the next one.” (p. 143) The trader’s job is to read the state of the market, react decisively based on the probabilities, and keep repeating this cycle. Becoming a great trader requires, in addition to desire and dedication, “practice and repetition; reading, reacting, and ’rithmetic.” (p. 126)
The strategies that Pham describes are indeed simple, but they’re a great place for the beginning trader to start and perhaps even a decent fallback for the more experienced trader who may be overcomplicating his systems. Moreover, he shows how to build a spreadsheet to analyze a stock, starting with open, high, low, close, and volume data from a public source such as Yahoo and then adding such calculated columns as “Up or Down Day,” “Consecutive Up Days,” “Consecutive Down Days,” “High Break,” “Low Break,” “Total Breaks,” “Inside Day,” “High + Low Break,” “Range,” “High – Low,” “Open-to-High Move,” “Open = High,” “Open-to-Low Move,” “Open = Low,” and some averages or percentage probabilities: “Avg. High/Low Move,” “Break Prev High/Low,” “Total Breaks,” “Inside Day,” “High + Low Break,” and “Open is High/Low.” No higher-level math or advanced statistics required here, just a couple of basic Excel functions that Pham illustrates.
For those traders whose platforms include consolidated bid/ask volume statistics, Pham offers tips on using volume delta. For those focused on the opening range, he provides a handy cheat sheet to answer the question: “If this stock goes up X dollars, what is the probability that it will go up Y dollars?” (p. 64)
Pham urges traders to formulate falsifiable hypotheses. This approach, he argues, stands in stark contrast to traditional technical analysis. Technical analysis falls short because the “descriptive data sets (i.e., price and time charts) and the inductive statistics one builds from them are simply observations without any grounding in a hypothesis. … Data can tell you nothing in and of itself.” (pp. 15-16) “Wouldn’t you rather work from a set of numbers that you can verify and test than a set of rules and scribbles that are completely subjective?” (p. 28)
If you’d like to be a quant but have no quant skills, Pham offers a simple (but, as always, not easy) path. As a bonus, you get to know someone who sounds like a pretty nice guy.
Part of what makes Pham’s book a worthwhile read rests with his belief that markets demand humility on the part of the trader. He is not touting strategies that will consistently deliver huge returns. On the contrary, the big trade is, in Pham’s opinion, “the one you make that allows you the opportunity to make the next one.” (p. 143) The trader’s job is to read the state of the market, react decisively based on the probabilities, and keep repeating this cycle. Becoming a great trader requires, in addition to desire and dedication, “practice and repetition; reading, reacting, and ’rithmetic.” (p. 126)
The strategies that Pham describes are indeed simple, but they’re a great place for the beginning trader to start and perhaps even a decent fallback for the more experienced trader who may be overcomplicating his systems. Moreover, he shows how to build a spreadsheet to analyze a stock, starting with open, high, low, close, and volume data from a public source such as Yahoo and then adding such calculated columns as “Up or Down Day,” “Consecutive Up Days,” “Consecutive Down Days,” “High Break,” “Low Break,” “Total Breaks,” “Inside Day,” “High + Low Break,” “Range,” “High – Low,” “Open-to-High Move,” “Open = High,” “Open-to-Low Move,” “Open = Low,” and some averages or percentage probabilities: “Avg. High/Low Move,” “Break Prev High/Low,” “Total Breaks,” “Inside Day,” “High + Low Break,” and “Open is High/Low.” No higher-level math or advanced statistics required here, just a couple of basic Excel functions that Pham illustrates.
For those traders whose platforms include consolidated bid/ask volume statistics, Pham offers tips on using volume delta. For those focused on the opening range, he provides a handy cheat sheet to answer the question: “If this stock goes up X dollars, what is the probability that it will go up Y dollars?” (p. 64)
Pham urges traders to formulate falsifiable hypotheses. This approach, he argues, stands in stark contrast to traditional technical analysis. Technical analysis falls short because the “descriptive data sets (i.e., price and time charts) and the inductive statistics one builds from them are simply observations without any grounding in a hypothesis. … Data can tell you nothing in and of itself.” (pp. 15-16) “Wouldn’t you rather work from a set of numbers that you can verify and test than a set of rules and scribbles that are completely subjective?” (p. 28)
If you’d like to be a quant but have no quant skills, Pham offers a simple (but, as always, not easy) path. As a bonus, you get to know someone who sounds like a pretty nice guy.
Wednesday, February 13, 2013
Mirabile, Hedge Fund Investing
Kevin R. Mirabile’s Hedge Fund Investing: A Practical Approach to Understanding Investor Motivation, Manager Profits, and Fund Performance (Wiley, 2013) covers a lot of familiar territory, including the standard fund strategies. But it’s more sophisticated and provides more color than the run-of-the-mill hedge fund book, and therein lies its strength.
Mirabile is both a practitioner and an academic, and he tailors his book accordingly. It addresses potential hedge fund investors, professionals, and students. In its (dominant) textbook “personality” it includes discussion questions and problems as well as extensive bibliographies at the end of each chapter. It also deals with topics that I suspect most individual investors would find abstruse, such as economies of agglomeration or skills and staffing requirements that are unique to a particular investing style. The individual looking to invest in a hedge fund or a fund of funds would probably doze off part way through the book. His notion of due diligence would be severely strained. The student or professional, on the other hand, would keep turning pages and continue to be rewarded for his effort.
After an overview of hedge funds and a discussion of strategies, the author turns in the final part of the book to evaluating factors influencing individual fund risk and reward: measuring performance and performance persistence, the impact of fund characteristics and terms on performance, performing due diligence on specific managers and funds, and evaluating the roles of service providers.
On the first topic he contends that “investors evaluating a particular manager’s track record or a fund’s performance should prepare their analysis from four distinct perspectives. 1. What is the historical net return and volatility of the fund under review, both stand-alone and relative to peers or strategy indices? 2. Are the returns of the fund and the strategy normally distributed, and if not, what is the implication for future return expectations and potential fat tail risk? 3. What is the sensitivity of the current portfolio to future changes in specific market variables such as rates and credit spreads to the value of the S&P 500 market? 4. How reliable are the data being used in the performance or risk analysis, and are they biased?” (p. 234) Mirabile briefly explains the concepts necessary to answer these questions.
The book includes numerous tables, charts, and graphs that elaborate on the author’s points.
Although Hedge Fund Investing is not a must-have book for the individual investor, it would be useful for anyone managing a large portfolio or contemplating setting up shop.
Mirabile is both a practitioner and an academic, and he tailors his book accordingly. It addresses potential hedge fund investors, professionals, and students. In its (dominant) textbook “personality” it includes discussion questions and problems as well as extensive bibliographies at the end of each chapter. It also deals with topics that I suspect most individual investors would find abstruse, such as economies of agglomeration or skills and staffing requirements that are unique to a particular investing style. The individual looking to invest in a hedge fund or a fund of funds would probably doze off part way through the book. His notion of due diligence would be severely strained. The student or professional, on the other hand, would keep turning pages and continue to be rewarded for his effort.
After an overview of hedge funds and a discussion of strategies, the author turns in the final part of the book to evaluating factors influencing individual fund risk and reward: measuring performance and performance persistence, the impact of fund characteristics and terms on performance, performing due diligence on specific managers and funds, and evaluating the roles of service providers.
On the first topic he contends that “investors evaluating a particular manager’s track record or a fund’s performance should prepare their analysis from four distinct perspectives. 1. What is the historical net return and volatility of the fund under review, both stand-alone and relative to peers or strategy indices? 2. Are the returns of the fund and the strategy normally distributed, and if not, what is the implication for future return expectations and potential fat tail risk? 3. What is the sensitivity of the current portfolio to future changes in specific market variables such as rates and credit spreads to the value of the S&P 500 market? 4. How reliable are the data being used in the performance or risk analysis, and are they biased?” (p. 234) Mirabile briefly explains the concepts necessary to answer these questions.
The book includes numerous tables, charts, and graphs that elaborate on the author’s points.
Although Hedge Fund Investing is not a must-have book for the individual investor, it would be useful for anyone managing a large portfolio or contemplating setting up shop.
Monday, February 11, 2013
Sanderson and Forsythe, China’s Superbank
China’s Superbank: Debt, Oil and Influence—How China Development Bank Is Rewriting the Rules of Finance (Wiley/Bloomberg, 2013) by Henry Sanderson and Michael Forsythe, both Bloomberg reporters working in Beijing, is a fascinating book. Those of us who learn about the Chinese economy only through the standard media outlets have very little idea of how it is being fueled. This book follows the money trail, from the China Development Bank to local Chinese municipalities and to the far corners of the globe (if, that is, the globe actually had corners). The task is daunting since “CDB’s assets and lending are a large black hole in global finance. Few other banks have been as unwilling to answer questions from the public….” (p. 178)
CDB, an arm of the state, is financed almost exclusively by bond sales rather than deposits. “It sells bonds to commercial banks, which use people’s savings to buy the bonds and earn higher yields for absolutely zero risk on their balance sheets. To this day, the banking regulator has allowed these commercial banks, among the largest in the world, to assign a zero-risk weighting on the bonds, meaning they have to set no capital against them, even as CDB has ramped up lending to a host of commercial sectors and risky countries with long histories of defaults, such as Venezuela and Ecuador. The bonds are, in effect, sovereign bonds, but the state takes no overt responsibility for the lending. For the commercial banks they are free returns. The yields on CDB bonds have been for the most part higher than the benchmark deposit rate but lower than the lending rate—that is, the commercial banks can earn a return buying risk-free CDB bonds with depositors’ money rather than lending the money out and taking the risk of default.” (p. 69)
International banks that buy CDB bonds also believe that the state will stand behind them, so CDB bonds aren’t rated. Or, to be more precise, when CDB bonds sell overseas, the rating agencies rate them at the same level as sovereign bonds.
An underlying assumption of CDB’s lending to local infrastructure projects is that land prices will increase as a result of the projects. Local governments uproot farmers from their plots, give them little by way of compensation, and use this land (often at wildly inflated prices) as collateral for their loans and bond offerings. The town of Loudi is a case in point. There, according to a 2011 bond prospectus, eighteen tracts of land valued at $1.5 million an acre were used as collateral. “That’s the price recently offered for an acre of land adjoining a private golf course on Indian Hill Road in Winnetka, Illinois, one of the wealthiest towns anywhere in the world. Average family income in Winnetka: $250,000 a year. In Loudi, average yearly take-home pay is $2,323. And yet the bond was rated AA by Beijing-based Dagong Global Credit Rating Co., one level higher than the same company rated US sovereign debt in 2012. The company that gave the land appraisal was in the same office as the city government’s land bureau. ‘The income from selling land is a reliable guarantee for the timely payment of interest on this bond,’ the bond’s prospectus said.” (p. 16)
CDB backs a wide swath of Chinese government interests. For instance, in 2010 it lent $14.7 billion to clean energy and other energy-saving projects, about seven times the sum that the U.S. Federal Financing Bank lent that year. It actively pursues loans-for-energy deals. It has a private equity subsidiary that makes domestic yuan investments, a securities company that was the top corporate bond underwriter in China in 2012, and a leasing subsidiary.
Is this behemoth and its development model sustainable? That’s the XXXX-trillion-dollar question. China’s Superbank offers readers the necessary background to begin to formulate hypotheses.
CDB, an arm of the state, is financed almost exclusively by bond sales rather than deposits. “It sells bonds to commercial banks, which use people’s savings to buy the bonds and earn higher yields for absolutely zero risk on their balance sheets. To this day, the banking regulator has allowed these commercial banks, among the largest in the world, to assign a zero-risk weighting on the bonds, meaning they have to set no capital against them, even as CDB has ramped up lending to a host of commercial sectors and risky countries with long histories of defaults, such as Venezuela and Ecuador. The bonds are, in effect, sovereign bonds, but the state takes no overt responsibility for the lending. For the commercial banks they are free returns. The yields on CDB bonds have been for the most part higher than the benchmark deposit rate but lower than the lending rate—that is, the commercial banks can earn a return buying risk-free CDB bonds with depositors’ money rather than lending the money out and taking the risk of default.” (p. 69)
International banks that buy CDB bonds also believe that the state will stand behind them, so CDB bonds aren’t rated. Or, to be more precise, when CDB bonds sell overseas, the rating agencies rate them at the same level as sovereign bonds.
An underlying assumption of CDB’s lending to local infrastructure projects is that land prices will increase as a result of the projects. Local governments uproot farmers from their plots, give them little by way of compensation, and use this land (often at wildly inflated prices) as collateral for their loans and bond offerings. The town of Loudi is a case in point. There, according to a 2011 bond prospectus, eighteen tracts of land valued at $1.5 million an acre were used as collateral. “That’s the price recently offered for an acre of land adjoining a private golf course on Indian Hill Road in Winnetka, Illinois, one of the wealthiest towns anywhere in the world. Average family income in Winnetka: $250,000 a year. In Loudi, average yearly take-home pay is $2,323. And yet the bond was rated AA by Beijing-based Dagong Global Credit Rating Co., one level higher than the same company rated US sovereign debt in 2012. The company that gave the land appraisal was in the same office as the city government’s land bureau. ‘The income from selling land is a reliable guarantee for the timely payment of interest on this bond,’ the bond’s prospectus said.” (p. 16)
CDB backs a wide swath of Chinese government interests. For instance, in 2010 it lent $14.7 billion to clean energy and other energy-saving projects, about seven times the sum that the U.S. Federal Financing Bank lent that year. It actively pursues loans-for-energy deals. It has a private equity subsidiary that makes domestic yuan investments, a securities company that was the top corporate bond underwriter in China in 2012, and a leasing subsidiary.
Is this behemoth and its development model sustainable? That’s the XXXX-trillion-dollar question. China’s Superbank offers readers the necessary background to begin to formulate hypotheses.
Wednesday, February 6, 2013
Bulkowski,Swing and Day Trading
Swing and Day Trading is the final volume of Thomas N. Bulkowski’s trilogy, Evolution of a Trader (Wiley, 2013). In it he relies heavily on his chart pattern research to describe potentially profitable trades. He also offers the beginning day trader tips on such diverse topics as building a home office, managing expectations, and constructing a pre-market checklist.
As usual, Bulkowski writes vivid prose. For instance, he found that the high and tight flag (HTF) was the best performing upward breakout pattern over a period of one, two, three, and six months. He writes: “Identifying an HTF is as easy as picking out a zebra from a herd of elephants.” And yet, “if identification is easy, swing trading the HTF is not. First, you have to find the courage to buy a stock after price has doubled. It reminds me of standing on the edge of a cliff. There is a little voice inside that says, ‘Jump!’ Buying a stock showing an HTF is like listening to that voice and jumping,” (p. 34) presumably with much happier results.
He provides swing traders not only with promising entries but with the eight best exit signs and his ten favorite sell signals. For those who like trading events, he introduces the reader to his all-time favorite pattern, the inverted dead-cat bounce. “The dead-cat bounce is to a nuclear meltdown as the inverted variety is to a winning lottery ticket.” (p. 106)
Day traders are treated to his opening gap setup, which rates a full chapter, and thus cannot be summarized briefly. They are also cautioned repeatedly. “Imagine,” he writes, “that you have a job that pays you $50,000 annually. A survey of 1,000 traders’ tax returns says that just 4 percent of them made more than that.” (p. 175) If the trader remains convinced that he belongs to that top 4 percent, Bulkowski delivers another blow, concluding the book with “ten horror stories.”
Pattern traders, and those who aspire to be pattern traders, would do well to add this book to their library. It’s well researched, written with humor, and devoid of empty hype.
As usual, Bulkowski writes vivid prose. For instance, he found that the high and tight flag (HTF) was the best performing upward breakout pattern over a period of one, two, three, and six months. He writes: “Identifying an HTF is as easy as picking out a zebra from a herd of elephants.” And yet, “if identification is easy, swing trading the HTF is not. First, you have to find the courage to buy a stock after price has doubled. It reminds me of standing on the edge of a cliff. There is a little voice inside that says, ‘Jump!’ Buying a stock showing an HTF is like listening to that voice and jumping,” (p. 34) presumably with much happier results.
He provides swing traders not only with promising entries but with the eight best exit signs and his ten favorite sell signals. For those who like trading events, he introduces the reader to his all-time favorite pattern, the inverted dead-cat bounce. “The dead-cat bounce is to a nuclear meltdown as the inverted variety is to a winning lottery ticket.” (p. 106)
Day traders are treated to his opening gap setup, which rates a full chapter, and thus cannot be summarized briefly. They are also cautioned repeatedly. “Imagine,” he writes, “that you have a job that pays you $50,000 annually. A survey of 1,000 traders’ tax returns says that just 4 percent of them made more than that.” (p. 175) If the trader remains convinced that he belongs to that top 4 percent, Bulkowski delivers another blow, concluding the book with “ten horror stories.”
Pattern traders, and those who aspire to be pattern traders, would do well to add this book to their library. It’s well researched, written with humor, and devoid of empty hype.
Monday, February 4, 2013
Gray and Carlisle,Quantitative Value
I often despair about the inordinate number of hours I spend on this blog. Moreover, reading and writing about books that don’t engage me is a tedious project—and I really hate tedium. I don’t mind wasting time; I’m good at that. But I want to waste my time in a pleasurable way. I bitch and moan and frequently consider ditching the whole project when along comes a book that makes everything worthwhile. Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray and Tobias E. Carlisle (Wiley, 2013) is just such a book.
I guess serendipity must have been at work because Quantitative Value was an unlikely book to capture my imagination. I don’t consider myself a value investor and I’ve admitted more than once that I’m no quant. But this book is a brilliant synthesis not only of the philosophies of Warren Buffett and Ed Thorp but of recent literature on such themes as behavioral biases and checklists. The authors discuss these topics in a way that makes them seem fresh even to a seasoned reader.
Gray and Carlisle take the reader step by step through the process of building a quantitative value model, a process that I can’t possibly outline here—not because it’s overly mathematical (it isn’t) but because it can’t meaningfully be reduced to a few sentences. It is probably sufficient to say that they “don’t generate [their] ideas through statistical analysis and curve fitting.” No Bangladeshi butter production predicting the S&P 500 close for them. As they write, “We rely on tried-and-true security analysis techniques, and we supplement these metrics with academic research and common sense. We also tend toward simplicity in our measures where possible.” (p. 208)
Once the model is tested and shows outstanding past results, the investor must stick with the model even though “it often feels like one or more of the current crop of stocks selected by the model are particularly weak and should be avoided.” “Fiddling with the model’s output is an error that leads inexorably to underperformance.” They quote Jim Simons of Renaissance Technologies: “Did you like what the model said or did you not like what the model said? That is a hard thing to backtest. If you are going to trade using models, you just slavishly use the models; you do whatever the hell it says no matter how smart or dumb you think it might be at that moment.” (p. 249) Simons certainly has the track record to prove that his fund’s models were pretty darned smart.
And what about the authors’ model? How has it performed? Between 1974 and 2011 it generated a compound annual growth rate of 17.68% as opposed to the S&P 500 Total Return Index’s return of 10.46%, and it experienced lower volatility and a smaller worst drawdown.
Quantitative Value is to my mind a must-read book. It doesn’t matter whether you are a short-term trader, a trend follower, a technical analysis junkie, or an investor in search of a strategy. You will find something of value (pun intended) here.
I guess serendipity must have been at work because Quantitative Value was an unlikely book to capture my imagination. I don’t consider myself a value investor and I’ve admitted more than once that I’m no quant. But this book is a brilliant synthesis not only of the philosophies of Warren Buffett and Ed Thorp but of recent literature on such themes as behavioral biases and checklists. The authors discuss these topics in a way that makes them seem fresh even to a seasoned reader.
Gray and Carlisle take the reader step by step through the process of building a quantitative value model, a process that I can’t possibly outline here—not because it’s overly mathematical (it isn’t) but because it can’t meaningfully be reduced to a few sentences. It is probably sufficient to say that they “don’t generate [their] ideas through statistical analysis and curve fitting.” No Bangladeshi butter production predicting the S&P 500 close for them. As they write, “We rely on tried-and-true security analysis techniques, and we supplement these metrics with academic research and common sense. We also tend toward simplicity in our measures where possible.” (p. 208)
Once the model is tested and shows outstanding past results, the investor must stick with the model even though “it often feels like one or more of the current crop of stocks selected by the model are particularly weak and should be avoided.” “Fiddling with the model’s output is an error that leads inexorably to underperformance.” They quote Jim Simons of Renaissance Technologies: “Did you like what the model said or did you not like what the model said? That is a hard thing to backtest. If you are going to trade using models, you just slavishly use the models; you do whatever the hell it says no matter how smart or dumb you think it might be at that moment.” (p. 249) Simons certainly has the track record to prove that his fund’s models were pretty darned smart.
And what about the authors’ model? How has it performed? Between 1974 and 2011 it generated a compound annual growth rate of 17.68% as opposed to the S&P 500 Total Return Index’s return of 10.46%, and it experienced lower volatility and a smaller worst drawdown.
Quantitative Value is to my mind a must-read book. It doesn’t matter whether you are a short-term trader, a trend follower, a technical analysis junkie, or an investor in search of a strategy. You will find something of value (pun intended) here.
Friday, February 1, 2013
Standard & Poor’s 500 Guide, 2013 Edition
It’s commonplace for authors to write revised editions of their books. But book reviewers are not supposed to serve up revised editions of their reviews. The former are billed as new and improved; the latter seem nothing more than warmed-up fare. The problem is that sometimes it’s difficult to start from scratch when reviewing a book that, while completely new, is also identical in structure. Such is the case with the 2013 edition of the Standard & Poor’s 500 Guide. So, with apologies, herewith a revised edition of last year’s review.
This is a very big paperback—8 ½” x 11”, more than 1000 pages, and weighing in at about 4.5 lbs. With so much information available online, why would anyone need this book? I can think of several compelling reasons.
First, a personal preference: I enjoy flipping through pages, making serendipitous discoveries. (The one downside this year: thanks to UPS, the bottom of the book got wet, so the pages don’t exactly flip.) I don’t have the same kind of experience online since I normally am looking for something specific, not just seeing what comes my way.
Second, the two pages devoted to each company in the S&P 500 are jam-packed with data, including ten years of company financials (per share data, income statement analysis, and balance sheet and other financial data), six years of revenue and earnings per share, and the four most recent dividend payments. The summary of the company’s business is also more analytical than the run-of-the-mill online fare.
Third, and taking up almost half of the space allocated to each company, is proprietary S&P information, ranging from analysts’ reports to the famous five-star system of investment recommendations. The analysts’ reports are not always timely; a fair number are from August, although the stock reports are from November. Other data include S&P’s qualitative risk assessment and quantitative evaluations, including each company’s relative strength rank. For each stock there is also a price chart from June 2009 through November 2012 overlaid with S&P proprietary metrics.
For the reader who cannot live without stock screens, the book provides lists of companies with five consecutive years of earnings increases, stocks with A+ rankings, rapid growth stocks, and fast-rising dividends.
The book is somewhat unwieldy to handle (it’s definitely best read on a desk, which I personally find awkward), but this is a small price to pay for the amount of information available.
This is a very big paperback—8 ½” x 11”, more than 1000 pages, and weighing in at about 4.5 lbs. With so much information available online, why would anyone need this book? I can think of several compelling reasons.
First, a personal preference: I enjoy flipping through pages, making serendipitous discoveries. (The one downside this year: thanks to UPS, the bottom of the book got wet, so the pages don’t exactly flip.) I don’t have the same kind of experience online since I normally am looking for something specific, not just seeing what comes my way.
Second, the two pages devoted to each company in the S&P 500 are jam-packed with data, including ten years of company financials (per share data, income statement analysis, and balance sheet and other financial data), six years of revenue and earnings per share, and the four most recent dividend payments. The summary of the company’s business is also more analytical than the run-of-the-mill online fare.
Third, and taking up almost half of the space allocated to each company, is proprietary S&P information, ranging from analysts’ reports to the famous five-star system of investment recommendations. The analysts’ reports are not always timely; a fair number are from August, although the stock reports are from November. Other data include S&P’s qualitative risk assessment and quantitative evaluations, including each company’s relative strength rank. For each stock there is also a price chart from June 2009 through November 2012 overlaid with S&P proprietary metrics.
For the reader who cannot live without stock screens, the book provides lists of companies with five consecutive years of earnings increases, stocks with A+ rankings, rapid growth stocks, and fast-rising dividends.
The book is somewhat unwieldy to handle (it’s definitely best read on a desk, which I personally find awkward), but this is a small price to pay for the amount of information available.
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