ࡱ> 02-./` =bjbj /5f:::8;;Tw^<nA(AAAQ2TUxvvvvvvv$xh9{vWcO QWWvAAo vZZZWAAvZWvZZ.p6rAR< p <:WXpvv0wp8{FXx{06r6r{sV";VZSVgVVVVvvYXVVVwWWWW>%>%  Long/Short Sector-based Trading Strategy Emergent Asset Management, LLC Presented by: Konstantin Savov Scott Smith Pin-Yew Wong Vaswar Mitra Vinaya Jain Table of Contents  TOC \o "1-3" \h \z \u  HYPERLINK \l "_Toc128757863" Project summary  PAGEREF _Toc128757863 \h 3  HYPERLINK \l "_Toc128757864" Project Methodology  PAGEREF _Toc128757864 \h 4  HYPERLINK \l "_Toc128757865" Objective and strategy outline  PAGEREF _Toc128757865 \h 4  HYPERLINK \l "_Toc128757866" Description of factors  PAGEREF _Toc128757866 \h 4  HYPERLINK \l "_Toc128757867" Scoring methodology  PAGEREF _Toc128757867 \h 5  HYPERLINK \l "_Toc128757868" Considerations in choosing sectors and final choice  PAGEREF _Toc128757868 \h 6  HYPERLINK \l "_Toc128757869" Sector-wise detailed results  PAGEREF _Toc128757869 \h 7  HYPERLINK \l "_Toc128757870" Financials  PAGEREF _Toc128757870 \h 7  HYPERLINK \l "_Toc128757871" Average annual factor returns for top and bottom quintiles  PAGEREF _Toc128757871 \h 7  HYPERLINK \l "_Toc128757872" Factors selected and calculated scores  PAGEREF _Toc128757872 \h 7  HYPERLINK \l "_Toc128757873" Factor correlations  PAGEREF _Toc128757873 \h 8  HYPERLINK \l "_Toc128757874" In sample analysis  PAGEREF _Toc128757874 \h 8  HYPERLINK \l "_Toc128757875" Other Results  PAGEREF _Toc128757875 \h 9  HYPERLINK \l "_Toc128757876" Out of sample analysis  PAGEREF _Toc128757876 \h 9  HYPERLINK \l "_Toc128757877" Healthcare  PAGEREF _Toc128757877 \h 10  HYPERLINK \l "_Toc128757878" Average annual factor returns for top and bottom quintiles  PAGEREF _Toc128757878 \h 10  HYPERLINK \l "_Toc128757879" Factors selected and calculated scores  PAGEREF _Toc128757879 \h 10  HYPERLINK \l "_Toc128757880" Factor correlations  PAGEREF _Toc128757880 \h 11  HYPERLINK \l "_Toc128757881" In sample analysis  PAGEREF _Toc128757881 \h 11  HYPERLINK \l "_Toc128757882" Other results  PAGEREF _Toc128757882 \h 12  HYPERLINK \l "_Toc128757883" Out of sample analysis  PAGEREF _Toc128757883 \h 12  HYPERLINK \l "_Toc128757884" Industrials  PAGEREF _Toc128757884 \h 13  HYPERLINK \l "_Toc128757885" Average annual factor returns for top and bottom quintiles  PAGEREF _Toc128757885 \h 13  HYPERLINK \l "_Toc128757886" Factors selected and calculated scores  PAGEREF _Toc128757886 \h 13  HYPERLINK \l "_Toc128757887" Factor correlations  PAGEREF _Toc128757887 \h 14  HYPERLINK \l "_Toc128757888" In sample analysis  PAGEREF _Toc128757888 \h 14  HYPERLINK \l "_Toc128757889" Other results  PAGEREF _Toc128757889 \h 15  HYPERLINK \l "_Toc128757890" Out of sample analysis  PAGEREF _Toc128757890 \h 15  HYPERLINK \l "_Toc128757891" Model imperfections and further research  PAGEREF _Toc128757891 \h 16  Project summary Our project aims to identify factors that are successful at predicting future returns for stocks within a particular industry sector. We aim to develop a quantitative stock screening model that can be used to rank stocks within a sector universe into different fractiles. The principal objectives of our analysis are: To identify factors (accounting, expectational, momentum or market sentiment based), which have predictive power of sector returns using a top down screening process. Use these factors to build a combined scoring system that can be used to rank the individual equities within a particular sector. This scoring system would be generated by looking at sector returns within an in sample period of data. To implement quantitative long/short market neutral equity strategy based on this scoring system. After we use the combined score to rank the stocks into fractiles, we would go long the first fractile of equities and short the last fractile. We would also evaluate whether such a strategy delivers any outperformance over the benchmark or alpha by testing it over a period of out of sample market data. Project Methodology Objective and strategy outline Our objective was to develop a quantitative long/short sector-based stock selection model that generates positive and consistent results. This project tested the hypothesis that it is possible to generate positive returns using a top down approach even in arguably efficient markets. We took this analysis a step further and analyzed how different factors apply for different sectors and whether diverse sectors can generate similar alphas. We defined our universe using the S&P1500 index and chose stocks with a market capitalization greater than five hundred million. The S&P 1500 index includes large, mid and small cap stocks in the US and combines the S&P 500, S&P 400 (Mid Cap Index) and the S&P 600 index of small cap stocks. Our sector choices were based on S&Ps GICS industry classification system. Due to data availability problems in the years prior to 1996, we limited our sample period from 1996 to 2003. The portfolio was rebalanced monthly. We selected an initial list of thirteen factors and performed a sector wise analysis to establish the most suitable factors for each sector. We applied our own objective scoring methodology to determine the optimal weights for each of these factors in the final combined score screen in the final combined scoring screen. We then reran the alpha tester to check whether our choice of factors and weights showed a trend in returns. The out of sample period for our analysis was 2004 and 2005. We went long the first quintile and short the fifth quintile and checked again whether our out of sample period showed the same promising results as the in sample period. We employed an equal weight strategy, both in the number of stocks 'per bucket' and in the amount invested or shorted per stock. Although our benchmark is value-weighted, we thought equal weight was appropriate for simplicity and to limit the impact from outlier performers. Description of factors We defined factors from each of the three categories: Fundamental, Expectational and Momentum. Our alpha tester tested the predictability of each of these factors and we chose the most suitable ones for a given sector. Fundamental Book to Price: book value per share / price per share Dividend Yield: dividends per share / price per share CFO Yield: cash flow from operations / price per share Earnings Yield: LTM earnings / price per share Return on Assets: annual net earnings / total assets % Change in ROA: % change in ROA over previous month Return on Equity: annual net earnings / total shareholder equity % Change in ROE: % change in ROE over previous month Expectational Revision Ratio: (Upward revisions downward revisions) / total revisions SUE Score: standard unexpected earnings (Earnings surprise / std deviation) Mean FY1 to Actual % Change: (FY1 Estimate Current Actual) Current Actual LT Projected Growth Historical Growth: Next 5 year annualized estimate previous 5 year annualized actual Momentum 12-Month Lagged Monthly Price Growth: Eg: (Jan06 Price Jan05 Price) / Jan05 Price Scoring methodology We created an objective scoring methodology of our own that is explained here using an example.  SHAPE \* MERGEFORMAT  In each month the quintiles are ranked based on returns. In the example above, on 5/1/2003, quintile 1 (F1) had the highest return and quintile 5 (F5) had the second lowest return. Points are assigned from highest to lowest rank (+2, +1, 0, -1, -2). Pursuing the same example, F1 gets +2 which F5 gets -1, based on their respective ranks. The total points for the in sample period (05/2003 to 12/2003) are summed for the two quintiles. Steps 1 through 3 are repeated for each factor. The raw point scores are scaled across factors from +10 to -10. However not all the scored parameters (the ten scores shown above) are considered for the scoring screen. This scoring methodology objectively rewards consistency of results. Based on this objective method and on our subjective evaluation of return trends as well as factor correlations, we chose only some of these factors for our final scoring screen. Considerations in choosing sectors and final choice We limited our universe based on the S&P1500 index and only considered stocks that had a market cap higher than five hundred million. We felt that this was a good balance between having a sufficiently large number of companies and avoiding some of the unnecessary volatility that comes with small cap stocks. This range would also give us sufficient liquidity necessary to execute the monthly rebalancing strategy. Furthermore, we applied the sector restriction using S&Ps GICS classification system. We started with five sectors: Financials, Healthcare, Industrials, Energy and Information Technology (IT) and later pared the list down to just the first three. The energy sector did not have sufficient number of companies in our defined universe. The IT sector has grown rapidly during the last few years and has been excessively volatile. This instability manifested itself in a lack of any predictability of returns with respect to our factors. We therefore chose the first three sectors which had sufficiently large number of companies in the defined universe and showed some predictability with respect to the chosen factors. Sector-wise detailed results Financials Average annual factor returns for top and bottom quintiles  SHAPE \* MERGEFORMAT  Factors selected and calculated scores  SHAPE \* MERGEFORMAT  Factor correlations  In sample analysis Heat map of multivariate factor returns  SHAPE \* MERGEFORMAT  Average multivariate factor returns across quintiles  SHAPE \* MERGEFORMAT  Other Results  Out of sample analysis Heat map of multivariate factor returns  SHAPE \* MERGEFORMAT  Average multivariate factor returns across quintiles  SHAPE \* MERGEFORMAT  Healthcare Average annual factor returns for top and bottom quintiles  SHAPE \* MERGEFORMAT  Factors selected and calculated scores  SHAPE \* MERGEFORMAT  Factor correlations  In sample analysis Heat map of multivariate factor returns  SHAPE \* MERGEFORMAT  Average multivariate factor returns across quintiles  SHAPE \* MERGEFORMAT  Other results  Out of sample analysis Heat map of multivariate factor returns  SHAPE \* MERGEFORMAT  Average multivariate factor returns across quintiles  SHAPE \* MERGEFORMAT  Industrials Average annual factor returns for top and bottom quintiles  SHAPE \* MERGEFORMAT  Factors selected and calculated scores  SHAPE \* MERGEFORMAT  Factor correlations  In sample analysis Heat map of multivariate factor returns  SHAPE \* MERGEFORMAT  Average multivariate factor returns across quintiles  SHAPE \* MERGEFORMAT  Other results  Out of sample analysis Heat map of multivariate factor returns  Average multivariate factor returns across quintiles  SHAPE \* MERGEFORMAT  Model imperfections and further research Given the time constraints, there are several imperfections in our analysis and represent areas for further investigation and potential improvement of results. Firstly, our portfolios were rebalanced monthly without any consideration of the trading costs involved. We did not consider potential restrictions or difficulties of executing short sales, particularly in the small cap universe. There are several options to improve this analysis. Firstly we could evaluate the trade off between the cost of turnover and the benefit of rebalancing frequently. This analysis might reveal that rebalancing quarterly or semi-annually is a better strategy than monthly rebalancing. Another way might be to combine this top-down approach with a bottom-up approach and ensure that fundamentally solid companies are retained in the long portfolio even if they temporarily fall off the top quintile. The same goes for fundamentally poor companies which should be retained in the short portfolio, even though they may show short term positive interfractile migration. Another approach to evaluate individual companies is through interfractile migration analysis. This analysis would tell us to include stocks that have remained in the top quintile through most of their history but have fallen off the list temporarily and to exclude unstable stocks that have a history of drifting across quintiles and therefore lead to excessive portfolio turnover. The second limitation of this model is the subjectivity of the scores even though we tried to incorporate a certain level of objectivity into this process. A potential area for improvement is to allow the optimizer to select long and short weights for a portfolio of quintile 1 and quintile 5 factor returns. The optimizer would however ignore the impact of a shift in trends over time. The problem is that the weights are static and we can potentially resolve this problem through dynamic weights. One way to do this might be to shift the in-sample time horizon with the passage of time. For example the in-sample time horizon in Jan 2006 would be Jan 1994 to Dec 2003. In Jan 2007 the horizon would shift a year and would be Jan 1995 to Dec 2004. Another approach for dynamic weighting would be through interaction variables. For example an interaction variable for the sign of yield curve slope would yield different factor weights depending on the positive or negative sign of the yield curve slope. Several of the approaches mentioned above can be used in conjunction to develop a more sophisticated model.     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