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Regression Analysis and Multicollinearity: Two Case Studies

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Abstract

In this chapter, we explore two applications of regression modeling: the question of regression-weighting of GNP forecasts and the issue of estimating models associated with security totals returns. We examine the forecasting of GNP by major econometric firms and the modeling of security returns as a function of well-known investment variables and strategies. We illustrate regression analysis and problems with highly correlated independent variables. We will refer to the correlation among independent variables as multicollinearity.

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Notes

  1. 1.

    The reader will see a variation of (4.1) and (4.2) in Chap. 5 when we discuss optimal security weights in a portfolio. The Bates and Granger optimal forecast weighting is a variation of the optimal Markowitz (1959) two-asset security calculation.

  2. 2.

    Granger (1989) additionally pointed out that if the optimum value for k is 0.3, one may still obtain poor combined forecast if k takes two values only, being 0 on 60% of occasions and 1.0 on the remaining 40%.

  3. 3.

    This research was supported in part by the National Science Foundation under Grant IST 8600788. We thank George Jaszi of the BEA and Donald Straszheim of Wharton, who graciously provided the forecasts from their respective econometric models. The authors are indebted to Professors S. Sharma and W.L. James for providing access to their LRR procedure as described in Sharma and James (1981). The original data for this analysis is not available from either John Guerard or Bob Clemen.

  4. 4.

    Savita Subramanian (2011), “US Quantitative Primer,” Bank of America Merrill Lynch, May.

  5. 5.

    There are many approaches to security valuation and the creation of expected returns. The first approaches to security analysis and stock selection involved the use of valuation techniques using reported earnings and other financial data. Graham and Dodd (1934) recommended that stocks be purchased on the basis of the price-earnings (P/E) ratio and Basu (1977) reported evidence supporting the low P/E model. James (Jim) Miller, Chief Investment Officer, CIO, of Continental Bank commissioned the project with Drexel, Burnham, Lambert, in 1989. Miller and Guerard (1991) presented a stock selection model at The Berkeley Program in Finance that used earnings, book value, cash flow, sales, relative variables, and earnings per share forecast revisions. Miller and Guerard experimented with a price momentum variable, the Columbine Alpha, described in Brush (2001). Jack Brush’s Columbine Alpha “pushed out” the eight-factor EP, BP, CP, SP, and relative variables’ Efficient Frontier. Guerard delivered paper sat Columbine Equity Research conferences in 1989 and 1994. See Guerard (1990).

  6. 6.

    Guerard (2006) reestimated the GPRD model using PACAP data at The Wharton School from Wharton Research Data Services (WRDS). The WRDS/PACAP data is as close to the GPRD data as was possible in academia. The average cross-sectional quarterly WLRR model F-statistic in the GPRD analysis was 16 during the 1974–1990 period whereas the corresponding F-statistic reported in the Guerard (2006) was 11 for the post-publication, 1993–2001 period. Both sets of models were highly statistically significant and could be effectively used as stock selection models.

  7. 7.

    Haugen and Baker (2010) extended their 1996 study in a recent volume to honor Harry Markowitz. Haugen and Baker estimated their model using weighted least squares. In a given month they estimated the payoffs to a variety of firm and stock characteristics using a weighted least squares multiple regression in each month in the period 1963 through 2007. Haugen and Baker found the most significant factors were; residual Return is last month’s residual stock return unexplained by the market.

    • Cash Flow-to-Price is the 12-month trailing cash flow-per-share divided by the current price.

    • Earnings-to-Price is the 12-month trailing earnings-per-share divided by the current price.

    • Return on Assets is the 12-month trailing total income divided by the most recently reported total assets.

    • Residual Risk is the trailing variance of residual stock return unexplained by market return.

    • 12-month Return is the total return for the stock over the trailing 12 months.

    • Return on Equity is the 12-month trailing eps divided by the most recently reported book equity.

    • Volatility is the 24-month trailing volatility of total stock return.

    • Book-to-Price is the most recently reported book value of equity divided by the current market price.

    • Profit Margin is 12-month trailing earnings before interest divided by 12-month trailing sales.

    • 3-month Return is the total return for the stock over the trailing 3 months.

    • Sales-to-Price is 12-month trailing sales-per-share divided by the market price.

    The four measures of cheapness are found in the USER model: cash-to-price, earnings-to-price, book-to-price, and sales-to-price, all have significant positive payoffs. Haugen and Baker (2010) found statistically significant results for the four fundamental factors as did the previously studies we reviewed. The Haugen and Baker (2010) analysis and results are consistent with the Bloch et al. (1993) model.

References

  • Arnott, R. 1979. “Relative Strength Revisited”. Journal of Portfolio Management, 19–23.

    Google Scholar 

  • Arnott, R.1985. “The Use and Misuse of Consensus Earnings.” Journal of Portfolio Management, 18–27.

    Google Scholar 

  • Armstrong, J.S., Long Range Forecasting: From Crystal Ball to Computer, 2nd edition New York: Wiley, 1985.

    Google Scholar 

  • Ashton, A.H. and Ashton, R.H. 1985. “Aggregating Subjective Forecasts: Some Empirical Results”. Management Science 31, 1499–1508.

    Article  Google Scholar 

  • Basu, S. 1977. “Investment Performance of Common Stocks in Relations to their Price Earnings Ratios: A Test of Market Efficiency.” Journal of Finance 32, 663–682.

    Article  Google Scholar 

  • Bates, J.M. and Granger, C.W.J.1969. “The Combination of Forecasts”. Operations Research Quarterly 20, 451–468.

    Article  Google Scholar 

  • Beaton, A.E. and J. W. Tukey. 1974. “The Fitting of Power Series, Meaning Polynomials, Illustrated on Bank-Spectroscopic Data,” Technometrics 16, 147–185.

    Article  Google Scholar 

  • Belsey, D.A., 1984. “Collinearity and Forecasting”. Journal of Forecasting, 3, 183–96.

    Article  Google Scholar 

  • Belsey, D.A., Kuh, E. and Welsch, R.E. 1980. Regression Diagnostics, New York: Wiley.

    Book  Google Scholar 

  • Belsley, D.A. 1982. “Assessing the Presence of Harrmful Collinearity and other Forms of Weak Data through a Test for Signal-to-Noise”. Journal of Econometrics 20, 211–53.

    Article  Google Scholar 

  • Blin, J.M. S. Bender, and J. B.Guerard, Jr. 1995. “Why Your Next Japanese Portfolio Should be Market-Neutral- And How”. Journal of Investing 4, 33–40.

    Google Scholar 

  • Blin, J.M. S. Bender, and J. B.Guerard, Jr. 1997. “Earnings Forecasts, Revisions and Momentum in the Estimation of Efficient Market-Neutral Japanese and U.S. Portfolios.” In Research in Finance 15, edited by A. Chen.

    Google Scholar 

  • Bloch, M., J.B. Guerard, Jr, H. M. Markowitz, P. Todd, and G-L. Xu. 1993. “A Comparison of Some Aspects of the U.S. and Japanese Equity Markets.” Japan & the World Economy 5, 3–26.

    Google Scholar 

  • Bloch, M., J.B. Guerard, Jr, H. M. Markowitz, P. Todd, and G-L. Xu. 1993. “A Comparison of Some Aspects of the U.S. and Japanese Equity Markets.” Japan &. the World Economy 5, 3–26.

    Google Scholar 

  • Brown, L.D. 1999 and 2008. Annotated I/B/E/S Bibliography.

    Google Scholar 

  • Brown, L.D. 1999 and 2008. Annotated I7B/E/S Bibliography.

    Google Scholar 

  • Bruce, B. and C.B. Epstein. 1995. The Handbook of Corporate Earnings Analysis. Chicago: Probus.

    Google Scholar 

  • Brush, J. S. 2007. “A Flexible Theory of Price Momentum”. Journal of Investing 16.

    Google Scholar 

  • Brush, J. S. 2007. “A Flexible Theory of Price Momentum”. Journal of Investing 16.

    Google Scholar 

  • Brush, J. S. and K.E. Boles. 1983. “The Predictive Power in Relative Strength and CAPM”. Journal of Portfolio Management, 20–23.

    Google Scholar 

  • Brush, J. S. and K.E. Boles. 1983. “The Predictive Power in Relative Strength and CAPM”. Journal of Portfolio Management, 20–23.

    Google Scholar 

  • Brush, J.S. 2001.”Price Momentum: A Twenty-Year Research Effort”. Columbine Newsletter Special Issue.

    Google Scholar 

  • Brush, J.S. 2001.”Price Momentum: A Twenty-Year Research Effort”. Columbine Newsletter Special Issue.

    Google Scholar 

  • Bunn, D. W. 1989. “Forecasting with more than One Model”. Journal of Forecasting 8, 161–166.

    Google Scholar 

  • Bunn, D.W. 1975. “A Bayesian Approach to the Linear Combination of Forecasts”, Operational Research Quarterly 26, 325–329.

    Article  Google Scholar 

  • Chan, L.K.C., J. Lakonishok, and Y. Hamao 1993. "Can Fundamentals Predict Japanese Stock Returns" Financial Analysts Journal.

    Google Scholar 

  • Chan, L.K.C., N. Jegadeesh and J. Lakonishok. 1996. “Momentum Strategies.” Journal of Finance 51, 1681–1713.

    Article  Google Scholar 

  • Chan, L.K.C., Y. Hamao, and J. Lakonishok. 1991. “Fundamentals and Stock Returns in Japan”. Journal of Finance 46, 1739–1764.

    Article  Google Scholar 

  • Chan, L.K.C., Y. Hamao, and J. Lakonishok. 1991. “Fundamentals and Stock Returns in Japan”. Journal of Finance 46, 1739–1764.

    Article  Google Scholar 

  • Chen, N-F., R. Roll, and S. Ross. 1986. “Economic Forces and the Stock Market.” Journal of Business 59, 383–403.

    Article  Google Scholar 

  • Clemen, R. T. and J.B. Guerard, Jr. 1989. “Econometric GNP Forecasts: Incremental Information Relative to Naïve Extraploation.” International Journal of Forecasting 5, 417–426.

    Article  Google Scholar 

  • Clemen, R.T. 1989. “Combining Forecasts: A Review and Annotated Bibliography”. International Journal of Forecasting 5, 559–583.

    Article  Google Scholar 

  • Clemen, R.T. and Winkler, R.L. 1986, “Combining Economic Forecasts”. Journal of Business and Economic Statistics 4, 39–46.

    Google Scholar 

  • Clemen, R.T., 1986. ”Linear Constraints and the Efficiency of Combined Forecasts”. Journal of Forecasting 5, 31–38.

    Article  Google Scholar 

  • Cragg, J.G. and B.G. Malkiel.1968. “The Consensus and Accuracy of Some Predictions of the Growth of Corporate Earnings”. Journal of Finance 23, 67–84.

    Article  Google Scholar 

  • Dimson, E. 1988. Stock Market Anomalies. Cambridge: Cambridge University Press.

    Google Scholar 

  • Doornik, J. A. and D. F. Hendry. 2009. Empirical Econometric Modelling. Timberlake Consultants, Ltd.

    Google Scholar 

  • Doornik, J. A. and D. F. Hendry. 2009. Modelling Dynamic Systems. Timberlake Consultants, Ltd.

    Google Scholar 

  • Dremen, D. 1998. Contrarian Investment Strategies: The Next Generation. New York: Simon &. Schuster.

    Google Scholar 

  • Elton, E. J. and M.J. Gruber. 1972. “Earnings Estimates and the Accuracy of Expectational Data.” Management Science 18, B409–B424.

    Article  Google Scholar 

  • Elton, E. J., M.J. Gruber, and M. Gultekin. 1981. “Expectations and Share Prices.” Management Science 27, 975–987.

    Article  Google Scholar 

  • Elton, E. J., M.J. Gruber, and M. Gultekin. 1981. “Expectations and Share Prices.” Management Science 27, 975–987.

    Article  Google Scholar 

  • Elton, E.J., M.J. Gruber, S.J. Brown, and W.N. Goetzman. 2007. Modern Portfolio Theory and Investment Analysis. John Wiley &. Sons, Inc., Seventh Edition.

    Google Scholar 

  • Fama, E.F. and J.D. MacBeth. 1973. “Risk, Return, and Equilibrium: Empirical Tests.” Journal of Political Economy 81, 607–636.

    Article  Google Scholar 

  • Fama, E.F. and K. R. French. 1992. “Cross-Sectional Variation in Expected Stock Returns.” Journal of Finance 47, 427–465.

    Article  Google Scholar 

  • Fama, E.F. and K. R. French. 2008. “Dissecting Anomalies”. Journal of Finance 63, 1653–1678.

    Article  Google Scholar 

  • Fama, E.F. and K.R. French. 1995. “Size and the Book-to-Market Factors in Earnings and Returns. Journal of Finance 50, 131–155.

    Article  Google Scholar 

  • Fama, E.F., and K.R. French. 1996. “Multifactor Explanations of Asset Pricing Anomalies”. Journal of Finance 51, 55–84.

    Article  Google Scholar 

  • Farrell, J.L. Jr. 1974. “Analyzing Covariance of Returns to Determine Homogeneous Stock Groupings.” Journal of Business 47, 186–207.

    Article  Google Scholar 

  • Farrell, J.L. Jr. 1997. Portfolio Management: Theory and Applications. New York McGraw-Hill/Irwin.

    Google Scholar 

  • Ferson, W.E. and C.R. Harvey. 1998. “Fundamental Determinants of National Equity Market Returns: A Perspective on Conditional Asset Pricing.” Journal of Banking &. Finance 21, 1625–1665.

    Article  Google Scholar 

  • Figlewski, S. and Urich, T. 1983. ”Optimal Aggregation of Money Supply Forecasts: Accuracy, Profitability and Market Efficiency”. Journal of Finance 28, 695–710.

    Article  Google Scholar 

  • Fisher, L. 1966a. “An Algorithm for Finding Exact Rates of Return.” Journal of Business 39, 111–118.

    Article  Google Scholar 

  • Fisher, L. 1966b. “Some New Stock-Market Indexes.” Journal of Business 39, 191–255.

    Article  Google Scholar 

  • Fisher, L. and J. H. Lorie. 1964. “Rates of Return on Investments in Common Stocks.” Journal of Business 7, 1–21.

    Google Scholar 

  • Fisher, L. and J. H. Lorie. 1970. “Some Studies of Variability of Returns on Investments in Common Stocks.” Journal of Business 43, 39–143.

    Google Scholar 

  • Graham, B., D. Dodd, and S. Cottle. 1934, 1962. Security Analysis: Principles and Technique. New York: McGraw-Hill Book Company. Editions one and four.

    Google Scholar 

  • Granger, C.W.J. 1989. “Invited Review: Combining Forecasts-Twenty Years Later”. Journal of Forecasting 8, 167–173.

    Article  Google Scholar 

  • Granger, C.W.J. and Newbold, P. 1977. Forecasting Economic Time Series, New York: Academic Press.

    Google Scholar 

  • Granger, C.W.J. and Ramanathan, R., 1984. “Improved Methods of Combining Forecasts”, Journal of Forecasting, 3 (1984), 197–204.

    Article  Google Scholar 

  • Grinold, R and R. Kahn. 1999. Active Portfolio Management New York: McGraw-Hill/Irwin.

    Google Scholar 

  • Guerard, J.B., Jr. 2012. “Global Earnings Forecast Efficiency” in J. Kensinger, Ed., Research in Finance 28, 19–47.

    Google Scholar 

  • Guerard, J.B. 1997. “Is There a Cost to be Socially Responsible in Investing?” Journal of Forecasting 16, 475–490.

    Article  Google Scholar 

  • Guerard, J.B. 2012. Global Earnings Forecast Efficiency (2012), Research in Finance 28, 19–47.

    Article  Google Scholar 

  • Guerard, J.B., Jr. 1990. “Optimal Portfolio Analysis using Composite Model-Tilting”. Drexel Burnham, Lambert. Unpublished working paper.

    Google Scholar 

  • Guerard, J.B., Jr. 2006. “Quantitative Stock Selection in Japan and the US: Some Past and Current Issues.” Journal of Investing, 15.

    Google Scholar 

  • Guerard, J.B., Jr. and M. Takano. 1991. “The Development of Mean-Variance Portfolios in Japan and the U.S.”. Journal of Investing 1.

    Google Scholar 

  • Guerard, J.B., Jr. and R.T. Clemen. 1989. “Collinearity and the Use of Latent Root Regression for Combining GNP Forecasts”. Journal of Forecasting 8, 231–238.

    Article  Google Scholar 

  • Guerard, J.B., Jr., M. Gultekin, and B.K. Stone. 1997. “The Role of Fundamental Data and Analysts’ Earnings Breadth, Forecasts, and Revisions in the Creation of Efficient Portfolios.” In Research in Finance 15, edited by A. Chen.

    Google Scholar 

  • Guerard, J.B., Jr., M.N. Gultekin, and G. Xu. 2012. “Investing with Momentum: The Past, Present, and Future”, Journal of Investing 21, 68–80.

    Article  Google Scholar 

  • Guerard, J.B., Jr., M.N. Gultekin, and G. Xu. 2012. “Investing with Momentum: The Past, Present, and Future”, Journal of Investing 21, 68–80.

    Article  Google Scholar 

  • Guerard, J.B., Jr., S. C. Chettiappan, and G-L, Xu. 2010. “Stock-Selection Modeling and Data Mining Corrections: Long-Only versus 130/30 Models”. The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques. New York: Springer.

    Google Scholar 

  • Gunst, R.F., J.T. Webster, and R.L. Mason. 1976. “A Comparison of Least Squares and Latent Root Regression Estimators.” Technometrics 18, 75–83.

    Article  Google Scholar 

  • Gunst, R.F., Webster, J.T. and Mason, R.L. 1976. “A Comparison of Least Squares and Latent Root Regression Estimators”. Technometrics, 18, 75–83.

    Article  Google Scholar 

  • Gupta, S. and Wilton, P.C., “Combination of Forecasts: An Extension”, Management Science, 33 (1987), 356–72.

    Article  Google Scholar 

  • Haugen, R. and N. Baker. 2010. “Case Closed”. The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques. New York: Springer.

    Google Scholar 

  • Haugen, R. and N. Baker. 2010. “Case Closed”. The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques. New York: Springer.

    Google Scholar 

  • Hawawini, G. and D.B. Keim. 1995. “On the Predictability of Common Stock Returns: World-Wide Evidence”, in R.A. Jarrow, V. Maksimovic, and W.T. Ziemba, Editors, Handbooks in Operations Research and Management Science: Finance 9, 497–537.

    Google Scholar 

  • Hocking, R.R. 1976. “The Analysis and Selection of Variables in Linear Regression”. Biometrics 32, 1–49.

    Article  Google Scholar 

  • Kang, H. 1986. “Unstable Weights in the Combination of forecasts”. Management Science 32, 683–95.

    Article  Google Scholar 

  • Keane, M.P. and D.E. Runkle. 1998. “Are Financial Analysts’ Forecasts of Corporate Profits Rational?” The Journal of Political Economy 106, 768–805.

    Article  Google Scholar 

  • Korajczyk, R.A. and R. Sadka. 2004. “Are Momentum Profits Robust to Trading Costs?” Journal of Finance 59 (2004), 1039–1082.

    Article  Google Scholar 

  • Lakonishok, J., A. Shleifer, and R.W. Vishny. 1994. “Contrarian Investment, Extrapolation and Risk.” Journal of Finance 49, 1541–1578.

    Article  Google Scholar 

  • Latane, H. A., D. L. Tuttle, and CP. Jones. 1975. Security Analysis and Portfolio Management. New York: The Roland Press. 2nd Edition.

    Google Scholar 

  • Latane, H.A. 1959.”Criteria for Choice Among Risky Ventures”. Journal of Political Economy 67, 144–155.

    Google Scholar 

  • Lesmond, D.A., M.J. Schill, and C. Zhou. 2004. “The Elusory Nature of Trading Profits”. Journal of Financial Economics 71, 349–380.

    Article  Google Scholar 

  • Levy, H. 1999. Introduction to Investments. Cincinnati: South-Western College Publishing. Second Edition.

    Google Scholar 

  • Lim, T. 2001. “Rationality and Analysts’ Forecast Bias”. Journal of Finance 56, 369–385.

    Article  Google Scholar 

  • Lindley, D.V. and Smith, A.F.M. 1972. “Bayes estimates for the linear model”. Journal of the Royal Statistical Society, Series B, 34, 1–41.

    Google Scholar 

  • Lintner, J. 1965a. “Security Prices, Risk, and the Maximum Gain from Diversification.” Journal of Finance 30.

    Google Scholar 

  • Lintner, J. 1965b. “The Valuation of Risk Assets on the Selection of Risky Investments in Stock Portfolios and Capital Investments.” The Review of Economics and Statistics, 13–37.

    Google Scholar 

  • Makridakis, S. and Winkler, R.L. 1983. “Averages of Forecasts: Some Empirical Results”. Management Science 29, 987–996.

    Article  Google Scholar 

  • Markowitz, H. M. 1956. The Optimization of a Quadratic Function Subject to Linear Constraints. Naval Research Logistics Quarterly 3, 111–133.

    Article  Google Scholar 

  • Markowitz, H.M. 1952. “Portfolio Selection.” Journal of Finance 7, 77–91.

    Google Scholar 

  • Markowitz, H.M. 1952. “Portfolio Selection.” Journal of Finance 7, 77–91.

    Google Scholar 

  • Markowitz, H.M. 1959. Portfolio Selection: Efficient Diversification of Investment. Cowles Foundation Monograph No. 16. New York, John Wiley &. Sons.

    Google Scholar 

  • Markowitz, H.M. 1959. Portfolio Selection: Efficient Diversification of Investment. Cowles Foundation Monograph No.16. New York, John Wiley & Sons.

    Google Scholar 

  • Markowitz, H.M. 1976. “Investment in the Long Run: New Evidence for an Old Rule,” Journal of Finance 31, 1273–1286.

    Article  Google Scholar 

  • Markowitz, H.M. 2000. Mean-Variance Analysis in Portfolio Choice and Capital Markets. New Hope, PA: Frank J. Fabozzi Associates.

    Google Scholar 

  • Markowitz, H.M. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Oxford: Basil Blackwell, 1987.

    Google Scholar 

  • Markowitz, H.M. and G.L. Xu. 1994. “Data Mining Corrections”. Journal of Portfolio Management. Fall.

    Google Scholar 

  • Markowitz, H.M., and G. Xu. 1994. “Data Mining Corrections.” Journal of Portfolio Management 21, 60–69.

    Article  Google Scholar 

  • Miller, J. D., J.B. Guerard, Jr. and M. Takano. 1991. “Bridging the Gap Between Theory and Practice in Equity Selection Modeling: Case Studies of U.S. and Japanese Models. An earlier version was presented at the Berkeley Program in Finance. September 1990. Santa Barbara, CA.

    Google Scholar 

  • Mincer, J. and V. Zarnowitz. (1969). “The Evaluation of Economic Forecasts,” J. Mincer editor, Economic Forecasts and Expectations, New York, Columbia University Press.

    Google Scholar 

  • Montgomery, D.C. and Peck, E.A. 1982. Introduction to Linear Regression Analysis, New York: Wiley.

    Google Scholar 

  • Mossin, J. 1966. “Equilibrium in a Capital Asset Market.” Econometrica 34, 768–783.

    Article  Google Scholar 

  • Nerlove, M. 1968. “Factors Affecting Differences Among Rates of Return on Investments in Individual Common Stocks”. Review of Economics and Statistics 50, 312–331.

    Article  Google Scholar 

  • Newbold, P. and Granger, C.W.J. 1974. “Experience with Forecasting Univarate Time Series and the Combination of Forecasts”. Journal of the Royal Statistical Society, Series A., 137, 131–164.

    Google Scholar 

  • Rachev, S., B. Rachev-Itova, S Stoyanov, and F. Fabozzi. 2010. “Risk Management and Portfolio Optimization for Volatile Markets”, in Guerard, J.B., editor, The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques, New York: Springer.

    Google Scholar 

  • Ramnath, S., S. Rock, and P. Shane. 2008. “The Financial Analyst Forecasting Literature: A Taxonomy with Suggestions for Further Research. International Journal of Forecasting 24, 34–75.

    Article  Google Scholar 

  • Rudd, A. and B. Rosenberg. 1980. “The ’Market Model’ in Investment Management.” Journal of Finance 5, 597–607.

    Google Scholar 

  • Sharma, S. and James, W.L. 1981. “Latent Root Regression: An Alternative Procedure for Estimating Parameters in the Presence of Multicollinearity”. Journal of Market Research 18, 154–161.

    Article  Google Scholar 

  • Stone, B. K. and J.B. Guerard, Jr. 2010a. Methodologies for Isolating and Assessing the Portfolio Performance Potential of Stock Return Forecast Models with an Illustration, in J.B. Guerard, Jr., editor, The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques. New York: Springer.

    Google Scholar 

  • Stone, B. K. and J.B. Guerard, Jr. 2010b. “Quantitative Stock Selection in Japan and the United States: Some Past and Current Issues.” Proceedings, 50th Annual CRSP Forum (Chicago, November 2010)

    Google Scholar 

  • Subramanian, S. 2011. “US Quantitative Primer”, Bank of America Merrill Lynch, May.

    Google Scholar 

  • Vander Weide, J. H. 2010. “Principles for Lifetime Portfolio Selection: Lessons from Portfolio Theory”, in J. Guerard, Ed. Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques. New York: Springer.

    Google Scholar 

  • Webster, J.T., Gunst, R.F. and Mason, R.L. 1974. “Latent Root Regression Analysis”. Technometrics 16, 513–22.

    Article  Google Scholar 

  • Wheeler, L.B. 1994. “Changes in Consensus Earnings Estimates and Their Impact on Stock Returns.” In The Handbook of Corporate Earnings Analysis. Edited by B. Bruce and C. B. Epstein.

    Google Scholar 

  • Winkler, R.L. and Makridakis, S. 1983. “The Combination of Forecasts”. Journal of the Royal Statistical Society, Series A., 146, 150–157.

    Google Scholar 

  • Winkler, R.L., 1981. “Combining Probability Distributions from Dependent Information Sources”. Management Science 27, 479–88.

    Article  Google Scholar 

  • Ziemba, W. T. 1990. “Fundamental Factors in US and Japanese Stock Returns.” Berkeley Program in Finance.

    Google Scholar 

  • Ziemba, W. T. 1992. Invest Japan. Chicago: Probus Publishing Co.

    Google Scholar 

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    John B. Guerard Jr.

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Guerard, J.B. (2013). Regression Analysis and Multicollinearity: Two Case Studies. In: Introduction to Financial Forecasting in Investment Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5239-3_4

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