| Today I’m interviewing Leigh Drogen, a former quantitative analyst and fund manager who is now the founder and CEO of Estimize. Leigh started his career at Geller Capital where he ran a quantitative non systematic earnings acceleration and analyst estimate revision model which also relied heavily upon relative strength and trend following. He then went on to run Surfview Capital successfully employing a similar strategy before moving into the financial technology world. Prior to founding Estimize he was an early member of the product and business development teams at StockTwits, the largest social network of investors on the web. Estimize, Inc. was founded in 2011 to provide the financial community with a platform for the generation of estimate data sets from buy side and independent analysts, as opposed to current data sets such as IBES which only source from sell side analysts. Beginning with a focus on EPS and Revenue models for public companies, Estimize now has over 13,000 member analysts and coverage on over 1,000 companies. Their consensus estimate numbers are not only more accurate than comparable data sets from the sell side, but their open and transparent platform allows members to view the depth of estimates and identify the analysts with the highest accuracy metrics over time. You can now find the Estimize data set on Bloomberg and can register for the service here. So what’s in your library Leigh? Reminiscences of a Stock Operator by Edwin Lefevre - There’s no other text that captures as well what it feels like to operate in financial markets than Reminiscences of a Stock Operator. This book really was my indoctrination at the age of 15 and taught too many lessons to count. Above all though it drove home the fact that it’s far easier to make money positioned in the direction of the primary trend as opposed to attempting to outsmart everyone else by going against it. Play big a few times a year when the odds are heavily in your favor and go to the beach when it’s not your market. Six Days of War: June 1967 & The Making Of The Modern Middle East by Michael B. Oren - I studied war theory during my undergraduate education and was able to transfer many of the lessons into money management and building companies. Six Days of War was a detailed account of the calculations made by military and political leaders on both sides of the 1967 war between Israel and its neighbors as well as the global powers both before and during the conflict. The primary difference between Israel’s ability and Egypt’s inability to execute their strategies was the accurate, or in the case of Egypt, inaccurate flow of information from front line soldiers up to tactical and strategic decision makers. Information is power and you can not make quality decisions without it. Way of the Turtle: The Secret Methods That Turned Ordinary People Into Legendary Traders by Curtis Faith - Trading Places was a hilarious movie, so when someone told me a story about how some Chicago traders basically did it for real, I had to read Way of the Turtle. Putting aside the fact that it was an extremely entertaining story, the main lesson of developing and back testing a strategy that works, and then sticking to it over the course of time really resonated with me. It also deeply ingrained in me the belief that buying begets buying and selling begets selling. You want to scale up into longs and down into shorts, not the other way around, use momentum to your advantage don’t fight it, the big panic moves in both directions are where the money is made. The World Is Flat: A Brief History Of The 21st Century by Thomas Friedman - Tom Friedman is one of my favorite writers for his ability to see the forest through the trees. Tom writes how our minds should operate, he takes a lot of individual anecdotal stories along with hard data and builds a thesis with them. The World Is Flat taught me how to step back and think about the inevitability of certain global trends and how they impact the micro environment. Just being on the right macro curve and not fighting against the larger forces at work can set you up for success in many cases to a greater extent than even your execution in a micro sense. Technical Analysis of Stock Trends by John Magee - Technical analysis is scoffed at by many as using a crystal ball, largely because technicians have not positioned the skill set in the right way. Understanding patterns in the supply and demand for any asset is extremely important in order to manage risk, the study of technical analysis gives that to us. This book is basically the first text anyone who wishes to understand this practice should read, and in my opinion that is anyone who operates in financial markets where there is a bid and an ask. Thinking Fast and Slow by Daniel Kahneman - Daniel Kahneman is a hero of mine and a huge influence on how I think about, well, thinking. I believe heavily in behavioral finance and that markets are extremely inefficient due to the large gap between was Kahneman calls “Humans and Econs”, Econs being the fictional perfectly rational actors. His study of heuristics that influence our behavior and decision making is necessary reading for anyone who wants to understand their own decision making and how to take advantage of the flawed process that others use. Thanks Leigh. As always, you can buy the books Leigh talked about in the Sniper Book Bin Sign up for The Sure Shot Letter, my monthly newsletter. As an added bonus, I will throw in access to my blog, The Daily Kill Sheet. The Sure Shot Letter provides long-term investment ideas on a monthly basis, while The Daily Kill Sheet provides short-term trading ideas twice weekly. |
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Part II - Q&A With Samuel Eisenstadt...One Of The Founding Fathers Of Quantitative Analysis5/6/2013
Special Meeting of QWAFAFEW, New York April 16, 2012 Question: Asset allocation models used to include just stocks and bonds or stocks, bonds and cash. Now we see institutions and pundits talking about allocations to foreign developed markets, emerging markets, currencies, gold, commodities and other asset classes; What are the challenges in trying to model so many asset classes? Are we trying to make the world too complex? Answer: To start, lots more data are required as well as observations. With foreign markets, quality of the data becomes questionable and the consistency of the data from country to country raises doubts. I'm sure that powerful computer programs will be developed to manage these problems, however. Question: Amid all the discussion and debates about indexes and indexing, Value Line developed its own index of the market. Could you shed some light about how that came about and what things you learned after launching the index? Answer: Some time in the early 1950's Value Line developed a geometric index of the stocks in the survey at the time. Why a geometric index? Keep in mind that from Value Line's standpoint each company was a separate report, and each company was equally important. In getting a picture of the "typical" stock, we sought an index that treated each company equally. Subsequently, we found that a geometric index came close to passing through the middle of all of the price curves. In getting the relative price performance of stocks, we wanted a universe where as many stocks outperformed as underperformed. The geometric index appeared to achieve this. Thus it was used to measure the price performance of the rankings themselves - an improper usage in order to measure wealth performance. Here, of course, an arithmetic average would be the proper measurement. The question arose, "which was the correct measurement?" To get the typical chart of relative price performance, I would argue the geometric index is correct. To get the correct wealth growth performance of the groupings, the arithmetic index is correct. Thus, Value Line currently publishes both. One final thought: The University of Chicago at one time suggested that the average of the arithmetic index and the geometric index came closest to measuring the "typical". Question: Which people within the industry have earned the greatest amount of respect from you, including those you've merely observed through books and media, personal correspondence , or those you've met personally? Answer: Obviously the first person that impressed me most was Arnold Bernhard, the founder of Value Line and the person that hired me after my release from the service. I had come to Value Line with an undergraduate degree in statistics - not finance. Hence Value Line provided on the job training for me in finance. The idea for a "Value Line" and an attempt (albeit non-mathematical) at objectivity was his. Bernhard was a great writer and any improvement in my writing skills, I probably learned from him. He was a good analyst, but lacked statistical training, so our association was a good one. I most admired that he was open minded and always willing to try new things. Another person that helped me along in my beginning years was a gentleman named Daniel Embody - a name that I'm sure is unfamiliar to all of you. He joined Value Line a year or two after me. He came from the Bureau of Ships, U.S. Navy. I learned a lot about hand-run multiple regression analysis from him. He devised worksheets that enabled us to run by hand four- to five-variable analyses with ten or twenty years worth of observations. This was many years before electronic computers made their way into our office. While time consuming, running these studies manually gave us a better understanding of the process. Another person I must mention is Dr. Herbert Arkin, my stat professor at the City College of New York. He is the one who originally opened my eyes to this field, which was still in its infancy. So much so, that few at that time had any idea what a statistician did for a living! In Wall Street jargon at that time a statistician was an analyst that worked with numbers, but who had little training in statistical methods beyond measuring averages and medians. Professor Fabricant, a physics professor at Brooklyn Polytech who observed that when a Value Line analyst raised their annual earnings forecast on a company, there would be a favorable price response subsequently. This thought gave rise to the earnings surprise factor in the Ranking System sometime around 1969. Others have since started services that are primarily based on earnings surprise and claim its discovery. Victor Niederhoffer, a prominent trader and a strong believer in statistical testing of systems. Despite his University of Chicago training, he has devised strategies that have served him well, and sometimes not so well, but has remained stalwart in his belief that one must subject ideas to significant tests before acting upon them. He is the author of several books - The Education of a Speculator and Practical Speculation, both of which I would highly recommend to any budding speculators. Peter Bernstein, an eminent economist, financial theorist and original thinker, recently deceased and author of several excellent books on finance and economics. Mark Hulbert, financial reporter for the Wall Street Journal and Market Watch. Mark is one of the few financial writers who applies and respects statistical methods and testing in his writings. He keeps up with the learned economic articles and journals and keeps his readership informed on new developments in finance. He runs a service that evaluates many advisory services and keeps them honest on the basis of their advertising claims and results. Dr. Fischer Black (deceased) - A professor of Finance at the University of Chicago and MIT. Dr.Black wrote a now iconic paper,"Yes Virginia, There is Hope, Tests of the Value Line Ranking System". This paper, presented at the University of Chicago, propelled the Ranking System to the attention of academia and subsequently resulted in numerous research papers. Had Dr. Fischer lived, there is little doubt that he, too, would have received a Noble Prize in Financial Economics along with others. Question: Which industry practices and/or cliches irritate you the most? Are there any assertions that people continue to make even though the data simply do not back those assertions up? Answer: Discussions of price charts breaking moving averages of various lengths. Typically, these are made by chartists to justify resistance levels for stock prices. I think there are as many moving average rules as there are chartists. And you find them all over TV! I find these most irritating since no evidence is presented. Question: What are the dangers in trying to draw conclusions or derive investment strategies from historical data? Answer: Historical data can be tricky. Trends, auto correlations, and serial correlations can all impact the data. Transformations of the data may be necessary. Use of differences or absolute data, and seasonal adjustments may be called for. Data should be carefully examined before proceeding with the analysis. Also, trends can change. Keep in mind that we are not dealing with physical laws, like when the next eclipse will take place. Question: Have you ever tried using valuation relative to the stock's past trading multiples as a factor, and if so, what limitations kept you from adding it to your system? Answer: Annual earnings ranks and price ranks (non -parametric equivalents of price earnings ratios of preceding years) are components of the timeliness ranking system. At least they were when I left Value Line more than 3 years ago. I am not aware of what changes have been made since then. Also historical price /book, p/e and yield have been tried in the past but did not add to the explanatory power of the model. Keep in mind that the ranking forecast is for 6-12 months, a period that may be too short for valuation factors to assert themselves. Question: If you have found that the Timeliness Ranking System has stopped consistently outperforming because value oriented strategies have come to the fore since 2000, have you tried to add any value oriented factors to the system? If so, how has it turned out? Answer: .The Timeliness Ranking model was based upon many years - more than 30 when I was there. Predominance of value factors in recent years were not sufficient to overcome the "growth" of earlier periods, despite their out performance in recent years. Models based upon too few years can be susceptible to short-term cyclical behavior which would tend to make the model unstable. A model based upon the last 13 years would be essentially a "value" model and could be misleading when and if the market environment should change. Thank you Sam. Sign up for The Sure Shot Letter, my monthly newsletter. As an added bonus, I will throw in access to my blog, The Daily Kill Sheet. The Sure Shot Letter provides long-term investment ideas on a monthly basis, while The Daily Kill Sheet provides short-term trading ideas twice weekly. This week I am departing from my usual format to bring you excerpts from a talk given by Samuel Eisenstadt, the former Research Chairman of Value Line Inc. in front of a packed house of analysts and portfolio managers at a QWAFAFEW meeting in New York during April of last year. QWAFAFEW (The Quantitative Work Alliance For Applied Finance Economics And Wisdom) is a Quantitative Investment Society with chapters in New York, Princeton, Hartford, Boston, Washington DC, Chicago, Denver and San Francisco. The talk was given in a question and answer format. At the end of the presentation, I have added three questions that I asked Sam this week along with his answers. Sam started his financial career at Value Line in 1946 as a proof reader, but was quickly recognized for his acumen with statistics. "In 1965 Eisenstadt persuaded Bernhard to switch from time series regression analysis to "cross-sectional", a procedure that studies relationships at a point in time rather than across time. This was a major shift that greatly improved the performance of the ranking system in subsequent years. The new system ranked about 1700 stocks relative to one another, based largely on measures of momentum for both earnings and price. In effect the system is designed to ride winners and avoid losers. Sam became the Research Chairman for Value Line in 1987 and held that position through the remainder of his career at Value Line, which came to an end in 2009. Sam remains active in investment research and is often quoted by Mark Hulbert in his Market Watch articles. Special Meet of QWAFAFEW, New York April 16, 2012 Question: Please describe the circumstances that lead to the development of the Value Line Timeliness Ranking System. What needs were perceived to need addressing? When it started, did you have any idea how long it would take to complete? Answer: No, it was an open-ended research project for us. The purpose was to produce an improved system. We noticed that interrelationships between highly related variables frequently caused factors to drop out of regressions. An example of this was IBM, a consistently up-trending stock, where the lagged price became the most important factor. Question: The academic literature references relative earnings and price ranks, EPS growth, price momentum, and earnings surprise as being factors in the model. It has also been noted that Value Line was the first known system to use earnings surprise as a factor. How did this factor become a part of the system? Answer: A physics professor from Brooklyn Polytech, Professor Fabricant had detected that whenever an analyst's earnings projection (next 12 months) was raised, the relative price action of the stock subsequently improved. The question became: "How could we take advantage of this action in the Ranking System?" We believed that by examining the reason for the revision, we might improve the System. We found that, more often than not, the revision took place after an earnings release. Thus, by evaluating the earnings release, we could get a jump on the analyst revision itself. Hence the birth of the earnings surprise factor. It was tested, found significant, and introduced into the system prior to anyone else's use of this factor (to the best of our knowledge). Question: The first famous article about the Value Line ranking system as an "anomaly" to the Efficient Market Theory being trumpeted by academics was called "Yes, Virginia, There Is Hope; Tests Of The Timeliness Ranking System" by Dr Fischer Black. Could you tell us how the article came about; what assistance you provided in helping him perform his tests, and any other things you think we might find interesting? Answer: Dr. Black was invited by Arnold Bernhard to test our ranking system using any procedures and tests that he could think of. The result was "Yes Virginia, There Is Hope;". Value Line provided the computer power and Fischer was paid for his efforts. Several professors at the University of Chicago claimed that Dr. Black was "paid and quartered by Value Line", thus suggesting that his results were biased towards a favorable outcome for Value Line. Subsequent results of the ranking system for many years would appear to have validated Fischer Black's conclusions. Indeed, Value Line's ranking system results might have resulted in some modifications in the Capital Asset Pricing and Efficient Market discussion! Question: Why do you enjoy tinkering with data so much? Answer: I was always looking for numerical solutions to stock price forecasting. Discovery of statistical solutions and significance, particularly in stock price forecasting provided a thrill - even if only a momentary one, at times. It shed a bit of light on the darkness that enveloped the subject. I think the thrill would have been there, even if the subject under investigation were other than stock prices. Question: What other ranking systems and models have you been involved in testing and attempting to develop over the years? Answer: I've developed a technical ranking system. Most technicians do not use statistical methods to construct and test their systems. This is one field that requires such testing since much of their beliefs are not justified by mathematical verification. The technical system provides a small amount of explanatory power with mixed results, particularly in recent years. Relative strength is the primary tool, but applied using multiple regression techniques. Also, in the early years, a model was constructed in order to assign quality grades to companies based upon growth and price stability. Question: Getting back to some of the efficient market debates, one of the reasons for the persistence of that theory is the simple empirical fact that the majority of mutual fund managers under-perform their benchmark indexes even before fees. What are the biggest factors contributing to this prolonged phenomena? Answer: In a nutshell, success carries with it the risk of ultimate failure. This is also true to some extent of the Value Line timeliness rankings and other statistical approaches. Recent studies have indicated a shrinkage in the spread between good and bad companies making discrimination much more difficult. This may be due to the proliferation of ETFs which select companies on the basis of sector rather than individual company characteristics. These days, they buy the whole steel industry rather than only the "good" steel companies. Question: You once had a debate with Dr. Rex Sinquefeld who went on to become the founder of Dimensional Fund Advisors. What was the nature of this debate? Are there any specific exchanges that you can recall? Answer: The title of the debate was "The Efficiency of Capital Markets". The debate was held at the University of Chicago before a large group of MBA students. I was chosen, largely as a defender of the Value Line ranking system who argued that if our results at Value Line were accurate, then equity markets were not as efficient as claimed. This was largely a continuation of the old debate which Fischer Black had addressed in "Yes Virginia, There Is Hope". Either the Value Line results were misleading or the efficiency argument was faulty. After a lengthy discussion of Value Line results, Rex argued that if Value Line numbers were correct, in time the results would deteriorate as more and more followers followed the system. There was a large element of truth in this as later results would show. My response at the time, which drew considerable laughter from the MBA students, was that as long as the University of Chicago continued to teach their market efficiency argument, there was hope for us! Later papers on the market efficiency argument tended to soften the position of the university's academics. Question: Are there any areas of quantitative analysis that you would like to explore that just were not viable in the past due to lack of computing power or lack of data? Answer: I feel computer power and lack of data do not represent a problem today. Indeed, there may be too much data available - so much so that I sometimes think it serves to confuse the issue. Question: As more and more funds have turned towards quantitative analysis and rules-based investing have you seen any changes in the performance of your own models? Have factors that have suggested the potential for out performance in the past now stopped working? Also, have you seen the duration of the holding periods for the stocks that your model picks change? Answer: Factors that have worked in the past have not worked well in recent years. Earnings growth and price momentum which worked extremely well prior to 2000 were largely displaced by value factors since 2000. One does not hear of outstanding results from relative price strength in recent years. It appears that overall, the markets have become more efficient in recent years. The overall spread between outperforming stocks and underperformers has shrunk in the past 10-15 years. If good and bad stocks are performing more and more like the general market, it becomes more difficult to differentiate. Perhaps the influx of Phd's in mathematics and physicists from academia have accentuated this process. In addition, the phenomenal growth of ETF's, which tend to group stocks by industry and sector rather than by value or growth have contributed to the diminution of spread between attractive and unattractive. This may be carried too far and will probably reverse some day. Question: If you could impart one pearl of wisdom to someone just starting out in the investment field, what would it be? Answer: With respect to newcomers in the field, I would recommend - stick to it. I am optimistic. While answers may never be found, it's fun looking for them and potentially profitable even if one gets close. Thank you Sam. The above excerpts make up about half of the original Q&A session plus my three questions. The article is getting a bit long in the tooth now, though, so I will save the second half for some time in the future. If you'd like to sample my research, sign up for The Sure Shot Letter, my monthly newsletter. As an added bonus, I will throw in access to my blog, The Daily Kill Sheet. The Sure Shot Letter provides long-term investment ideas on a monthly basis, while The Daily Kill Sheet provides short-term trading ideas twice weekly.
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