Case Closed: 8/10
strongInvestment Potential Rating: 8/10 (1 worst, 10 best)/strongbr /strong/strongbr /strong============/strongbr /strong/strongbr /strongCase Closed/strongbr /br /a title=”View other papers by this author” href=”http://www.quantitativeinvestment.com/Bob_Haugen.aspx” target=”_blank”span style=”font-size:78%;”Bob Haugen /span/abr /span style=”font-size:78%;”http://www.quantitativeinvestment.com//spanbr /a title=”View other papers by this author” href=”http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=117233″ target=”_blank”span style=”font-size:78%;”Nardin Baker/span/abr /span style=”font-size:78%;”UC-Irvine/spanbr /br /span style=”font-size:78%;”THE HANDBOOK OF PORTFOLIO CONSTRUCTION: CONTEMPORARY APPLICATIONS OF MARKOWITZ TECHNIQUES, John B. Guerard Jr., ed., Forthcoming/spanpa href=”http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1306523″span style=”font-size:78%;”http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1306523/span/abr /br /strongAbstract:/strongbr /This article provides conclusive evidence that the U.S. stock market is highly inefficient. Our results, spanning a 45 year period, indicate dramatic, consistent, and negative payoffs to measures of risk, positive payoffs to measures of current profitability, positive payoffs to measures of cheapness, positive payoffs to momentum in stock return, and negative payoffs to recent stock performance. Our comprehensive expected return factor model successfully predicts future return, out of sample, in each of the forty-five years covered by our study save one. Stunningly, the ten percent of stocks with highest expected return, in aggregate, are low risk and highly profitable, with positive trends in profitability. They are cheap relative to current earnings, cash flow, sales, and dividends. They have relatively large market capitalization and positive price momentum over the previous year. The ten percent with lowest expected return (decile 1) have exactly the opposite profile, and we find a smooth transition in the profiles as we go from 1 through 10. We split the whole 45-year time period into five sub-periods, and find that the relative profiles hold over all periods. Undeniably, the highest expected return stocks are, collectively, highly attractive; the lowest expected return stocks are very scary - results fatal to the efficient market hypothesis. While this evidence is consistent with risk loving in the cross-section, we also present strong evidence consistent with risk aversion in the market aggregate’s longitudinal behavior. These behaviors cannot simultaneously exist in an efficient market.br /br /strongData Source:/strongbr /The authors do not specify in this paper exactly where they get their data. We assume it comes from the CRSP/COMPUSTAT dataset.br /br /strongData Specification:/strongbr /”Good” scientists develop models based on logic and previous evidence on how the world works. Then the scientists use their model to develop hypothesis which they can test with data. With their results they can either accept or reject their hypothesis and contribute to the body of knowledge. Other scientists are usually a bit more practical — they data-mine available datasets and determine what the data is telling them and then develop a model to fit the data…we’ll call it “data reverse engineering.”br /br /Haugen and Baker are very practical (read: not very scientific in their approach). In a 1996 academic paper the two authors data-mine accounting and financial data to determine what elements have done the best explaining stock returns. It would be wise to be skeptical of their original results, however, in this paper, they come back and provide additional tests of the factors they determined to work best in their 1996 paper. The results are important for investment managers to understand…br /br /strongInvestment Strategy:/strongbr /br /After estimating an enormous regression with 70 factors the authors find that the following elements are the most significant in predicting the cross section of monthly returns (based on t-stats):br /br /•Residual Return is last month’s residual stock return unexplained by market.br /•Cash Flow-to-Price is the12-month trailing cash flow-per-share divided by the current price.br /•Earnings-to-Price is the 12-month trailing earnings-per-share divided by the current price.br /•Return On Assets is the12-month trailing total income divided by total assets.br /•Residual Risk is the trailing variance of residual stock return unexplained by market return).br /•12-month Return is the total return for the stock over past 12 months.br /•Return On Equity is the 12-month trailing earnings-per-share divided by the current book equity.br /•Variance is the 24-month trailing variance of total stock return.br /•Book-to-Price is the current book-to-price ratio.br /•Profit Margin is earnings before interest divided by sales.br /•3-month Return is the total return for the stock over the past 3 months.br /•Sales-to-Price is the12-month trailing sales-per-share divided by current price.br /br /None of these factors identified should be too surprising for readers of the blog. We have highlighted the value of many of these factors in previous posts.br /br /strongImplementation Issues and Remarks:/strongbr /br /Haugen and Baker essentially identify what HAS worked in investment management. Our job is to determine what will work in the future. There are many reasons to believe that many of the factors identified will continue to provide excess returns, but it’s important to understand why.br /br /Also, before analyzing research it is important to understand the author’s motives–Haugen has some baggage. Haugen runs a quantitative investment advice shop so it is in his interest to promote research which shows that quantitative models work. However, Haugen doesn’t hide the fact he is engaging in data-mining, nor does he try to portray the idea that he isn’t trying to “sell” his system.br /br /One critique we have of the author’s is that they are very dogmatic about their “the market is inefficient” hypothesis and we believe this may create huge biases in their data analysis (it may not, but we should take what they say with a grain of salt). We certainly believe in the market inefficiency hypothesis at some level, however, Fama and French point out that markets DO work in general and for every winner in the market there necessarily has to be a loser (a href=”http://www.dimensional.com/famafrench/2009/06/why-active-investing-is-a-negative-sum-gain.html”http://www.dimensional.com/famafrench/2009/06/why-active-investing-is-a-negative-sum-gain.html/a). Dismissing the powerful market efficiency theory can be dangerous to your investment health.br /Investment Potential Rating: 8/10/pdiv class=”blogger-post-footer”img width=’1′ height=’1′ src=’https://blogger.googleusercontent.com/tracker/4649005286890859363-7026794169375306317?l=empiricalfinanceresearch.blogspot.com’//divimg src=”http://feeds2.feedburner.com/~r/empiricalfinanceresearch/~4/boBLDQg0IRQ” height=”1″ width=”1″/
