forecasting asset returns in realistic environments
play

Forecasting Asset Returns in Realistic Environments David E. Rapach - PowerPoint PPT Presentation

Oct 01, 2023 •329 likes •1.45k views

Forecasting Asset Returns in Realistic Environments David E. Rapach Saint Louis University CFA Montreal Asset Management Forum October 8, 2015 David E. Rapach Forecasting Asset Returns Introduction Forecasting asset returns is fascinating

  1. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  2. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  3. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  4. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  5. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  6. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  7. Forecasting challenges & strategies Stating the obvious I We don’t know The Model of asset returns Actual DGP is highly complex & constantly evolving Substantial model uncertainty & instability Hedgehog approach inadvisable Need to incorporate info from many predictors But also need to avoid overfitting Effective strategies from recent literature Forecast combination & diffusion indices Stating the obvious II I’m a fox David E. Rapach Forecasting Asset Returns

  8. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  9. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  10. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  11. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  12. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  13. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  14. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  15. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  16. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  17. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  18. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  19. Stylized example: equity risk premium ERP: S&P 500 return minus risk-free return Huge literature on predicting ERP (Rapach & Zhou 2013) Predictive regression forecast: ˆ α t + ˆ R t + 1 = ˆ β t x t Incorporate info from x t to forecast R t + 1 Prevailing mean benchmark forecast: ¯ R t + 1 Assumes no return predictability (RW with drift) Stringent out-of-sample benchmark (Goyal & Welch 2008) Out-of-sample R 2 (Campbell & Thompson 2008) Proportional ↓ in MSFE for PR vis-à-vis PM Sample period: 1926:01–2014:12 Forecast evaluation period: 1960:01–2014:12 David E. Rapach Forecasting Asset Returns

  20. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  21. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  22. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  23. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  24. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  25. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  26. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  27. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  28. Highly plausible predictors Dividend yield (Fama & French 1988) T-bill yield (Ang & Bekaert 2007) Term spread (Campbell 1987) Default spread (Fama & French 1989) Inflation (Nelson 1976) Output gap (Cooper & Priestly 2009) Moving-average signal (Neely, Rapach, Tu, & Zhou 2014) Momentum signal (Neely, Rapach, Tu, & Zhou 2014) David E. Rapach Forecasting Asset Returns

  29. Individual monthly PR forecasts Dividend yield T-bill yield 2 2 1 1 0 0 -1 -1 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Term spread Default spread 2 2 1 1 0 0 -1 -1 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 David E. Rapach Forecasting Asset Returns

  30. Individual monthly PR forecasts Inflation Output gap 2 2 1 1 0 0 -1 -1 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 MA signal Momentum signal 2 2 1 1 0 0 -1 -1 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 David E. Rapach Forecasting Asset Returns

  31. R 2 OS stats (%) Semi- Predictor Monthly Quarterly annual Annual Dividend yield − 0.03 − 2.27 1 . 11 − 1.87 T-bill yield − 0.10 − 0.47 − 1.86 − 2.59 Term spread 0 . 25 0 . 45 0.40 2 . 24 Default spread 0.30 1 . 11 1 . 29 3 . 28 Inflation 0.03 0.63 1 . 07 1 . 76 Output gap 0.17 0.45 0 . 88 1.20 MA signal 0 . 41 − 0.16 − 1.05 − 1.66 Momentum signal − 0.09 − 0.46 − 1.12 − 0.30 David E. Rapach Forecasting Asset Returns

  32. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  33. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  34. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  35. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  36. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  37. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  38. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  39. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  40. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  41. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  42. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  43. Drawbacks to individual PR forecasts Individual PR forecasts perform inconsistently/unevenly We can’t identify best predictor a priori Relying on individual PR forecast is too risky Too ‘hedgehogy’ We need to get foxy Incorporate info from multiple plausible predictors Forecast diversification (Timmermann 2006) Bad strategy: include all predictors in one regression Kitchen sink PR: ˆ α t + ˆ β 1 , t x 1 , t + · · · + ˆ R t + 1 = ˆ β K , t x K , t R 2 OS stats: − 0 . 95 % , − 6 . 22 % , − 5 . 25 % , − 9 . 64 % In-sample overfitting (avoid overfitting like the plague) David E. Rapach Forecasting Asset Returns

  44. Monthly kitchen sink PR forecast 2 1 0 -1 1960 1970 1980 1990 2000 2010 David E. Rapach Forecasting Asset Returns

  45. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  46. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  47. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  48. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  49. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  50. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  51. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  52. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  53. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  54. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  55. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  56. Good foxy strategies Incorporate info from all predictors without overfitting Forecast combination (Rapach, Strauss, & Zhou 2010) Take simple average of individual PR forecasts Avoid overfitting by shrinking forecast to PM Shrink to ‘outside view’ R 2 OS stats: 0 . 77 % , 2 . 17 % , 2 . 52 % , 6 . 08 % Diffusion index (Ludvigson & Ng 2007) Extract first few principal components from all predictors PCs serve as predictors for PR forecast Filter noise in individual predictors ⇒ more reliable signal R 2 OS stats: 0 . 60 % , 1 . 62 % , 2 . 66 % , 6 . 97 % David E. Rapach Forecasting Asset Returns

  57. Monthly combination forecast 2 1 0 -1 1960 1970 1980 1990 2000 2010 David E. Rapach Forecasting Asset Returns

  58. Monthly diffusion index forecast 2 1 0 -1 1960 1970 1980 1990 2000 2010 David E. Rapach Forecasting Asset Returns

  59. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  60. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  61. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  62. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  63. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  64. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  65. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  66. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  67. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  68. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  69. Dynamic asset allocation MV investor allocates monthly across stocks & bills Allocation to risky stocks: w t = ( 1 /γ )(ˆ σ 2 R t + 1 / ˆ t + 1 ) Realistic portfolio constraint: − 0 . 5 ≤ w t ≤ 1 . 5 σ 2 Certainty equivalent return (CER): ˆ µ p − 0 . 5 γ ˆ p σ 2 µ p ( ˆ ˆ p ): portfolio mean (variance) over evaluation period Annualized CER gain vis-à-vis PM for γ = 5 Combination forecast: 1.79% Diffusion index forecast: 2.46% Return predictability is economically valuable NB: assuming ‘small’ investor David E. Rapach Forecasting Asset Returns

  70. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  71. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  72. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  73. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  74. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  75. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  76. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  77. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  78. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  79. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  80. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  81. Additional elements Combination weights (Rapach, Strauss, & Zhou 2010) Place more weight on particular forecasts NB: typically best to hew close to equal weighting Economic restrictions ⇒ ↓ estimation uncertainty Sign restrictions (Campbell & Thompson 2008) Valuation ratios (Ferreira & Santa-Clara 2011) Bayesian (Pettenuzzo, Timmermann, & Valkanov 2014) Useful even if not literally true (bias-efficiency tradeoff) Regime switching (Ang & Timmermann 2012) Markov switching (Henkel, Martin, & Nardari 2011) Time-varying parameters (Dangl & Halling 2012) David E. Rapach Forecasting Asset Returns

  82. Dynamic asset allocation: two recent studies MKT components (Kong, Rapach, Strauss, & Zhou 2011) Allocate across 25 Fama-French size/value portfolios Combination forecasts of component portfolio returns Based on 39 predictor variables Combination forecasts valuable inputs for DAA US stocks/bonds/bills (Almadi, Suri, & Rapach 2014) Active portfolio management vis-à-vis 60/35/5 benchmark Diffusion index forecasts of stock/bond/bill returns Extracted from fundamental, macro, and technical variables Diffusion index forecasts valuable inputs for DAA Largest gains typically realized during contractions/crises David E. Rapach Forecasting Asset Returns

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

1 How to Win a Forecasting Tournament? Philip E. Tetlock Wharton School CFA Asset Management

1 How to Win a Forecasting Tournament? Philip E. Tetlock Wharton School CFA Asset Management

1 How to Win a Forecasting Tournament? Philip E. Tetlock Wharton School CFA Asset Management Forum Montreal, October 8, 2015 WHAT ARE FORECASTING TOURNAMENTS? level-playing-field competitions to determine who knows what a disruptive

441 views • 25 slides
A multj-asset strategy: beyond a traditjonal approach for returns Tina Yao of BNP Paribas

A multj-asset strategy: beyond a traditjonal approach for returns Tina Yao of BNP Paribas

HIGH-IMPACT BRIEFING A multj-asset strategy: beyond a traditjonal approach for returns Tina Yao of BNP Paribas Investment Partners explains why a multj-asset class solutjon is appropriate for investors today, and reveals how to determine the

64 views • 3 slides
Causal Discovery and Forecasting in Nonstationary Environments with State - Space Models Biwei

Causal Discovery and Forecasting in Nonstationary Environments with State - Space Models Biwei

Causal Discovery and Forecasting in Nonstationary Environments with State - Space Models Biwei Huang 1 , Kun Zhang 1 , Mingming Gong 1,2 , Clark Glymour 1 1. Department of Philosophy, Carnegie Mellon University 2. Department of Biomedical

322 views • 8 slides
Returns Optimization 101 Episode 3: Streamlining Your Returns Workflow Returns Workflow

Returns Optimization 101 Episode 3: Streamlining Your Returns Workflow Returns Workflow

Returns Optimization 101 Episode 3: Streamlining Your Returns Workflow Returns Workflow Visibility into Returns Enables You to: Identify any hold-ups or bottlenecks Drive accountability across teams Improve your returns process over time

862 views • 10 slides
Returns Optimization 101 Episode 10: Optimizing your Returns What is Returns Optimization?

Returns Optimization 101 Episode 10: Optimizing your Returns What is Returns Optimization?

Returns Optimization 101 Episode 10: Optimizing your Returns What is Returns Optimization? Continuous improvement strategies that use Returns Data to enhance the shopper experience, improve operational efficiency, and grow profits Power of

492 views • 15 slides
Financial Intermediaries and the Cross-Section of Asset Returns Tobias Adrian - Federal Reserve

Financial Intermediaries and the Cross-Section of Asset Returns Tobias Adrian - Federal Reserve

Financial Intermediaries and the Cross-Section of Asset Returns Tobias Adrian - Federal Reserve Bank of New York 1 Erkko Etula - Goldman Sachs Tyler Muir - Kellogg School of Management May, 2012 1 The views expressed in this presentation are not

661 views • 27 slides
Welcome to Forecasting Using R Rob Hyndman Author, forecast Forecasting Using R What you will

Welcome to Forecasting Using R Rob Hyndman Author, forecast Forecasting Using R What you will

FORECASTING USING R Welcome to Forecasting Using R Rob Hyndman Author, forecast Forecasting Using R What you will learn Exploring and visualizing time series Simple benchmark methods for forecasting Exponential smoothing and

715 views • 22 slides
The conditional CAPM does not explain asset- pricing anomalies Jonathan Lewellen & Stefan

The conditional CAPM does not explain asset- pricing anomalies Jonathan Lewellen & Stefan

The conditional CAPM does not explain asset- pricing anomalies Jonathan Lewellen & Stefan Nagel HEC School of Management, March 17, 2005 Background Size, B/M, and momentum portfolios, 1964 2001 Monthly returns (%) Avg. returns CAPM

1.11k views • 78 slides
The conditional CAPM does not explain asset- pricing anomalies Jonathan Lewellen & Stefan

The conditional CAPM does not explain asset- pricing anomalies Jonathan Lewellen & Stefan

The conditional CAPM does not explain asset- pricing anomalies Jonathan Lewellen & Stefan Nagel HEC School of Management, March 17, 2005 Background Size, B/M, and momentum portfolios, 1964 2001 Monthly returns (%) Avg. returns CAPM

1.08k views • 73 slides
History Has Shown The Advantage Of True Diversifjcatjon Returns of asset classes Year Stocks

History Has Shown The Advantage Of True Diversifjcatjon Returns of asset classes Year Stocks

History Has Shown The Advantage Of True Diversifjcatjon Returns of asset classes Year Stocks Bonds Gold Average The best and worst performer changes 1995 -23% 3% 14% -2% 1996 -1% 13% -3% 3% year by year, but 2 out of 3 are

390 views • 20 slides
Exotic (Recursive) Preferences & Cyclical Properties of US Asset Returns David Backus

Exotic (Recursive) Preferences & Cyclical Properties of US Asset Returns David Backus

Exotic (Recursive) Preferences & Cyclical Properties of US Asset Returns David Backus (NYU), Bryan Routledge (CMU), and Stanley Zin (CMU) Birkbeck College | November 18, 2008 This version: December 10, 2008 Backus, Routledge, &

413 views • 39 slides
1 Tax efficient returns # The best of three asset classes HDFC Equity Savings Fund (An open

1 Tax efficient returns # The best of three asset classes HDFC Equity Savings Fund (An open

1 Tax efficient returns # The best of three asset classes HDFC Equity Savings Fund (An open ended scheme investing in equity, arbitrage and debt) Unhedged Debt Equity 10% to 35% 15% to 40% Arbitrage This product is suitable for investors

429 views • 21 slides
Elastic and Secure Energy Forecasting in Cloud Environments Andr Martin * , Andrey Brito # and

Elastic and Secure Energy Forecasting in Cloud Environments Andr Martin * , Andrey Brito # and

Elastic and Secure Energy Forecasting in Cloud Environments Andr Martin * , Andrey Brito # and Christof Fetzer * andre.martin@tu-dresden.de, andrey@dsc.ufcg.edu.br, christof.fetzer@tu-dresden.de * SE Group - Technische Universitt Dresden -

561 views • 11 slides
Nippon India Multi Asset Fund (An open ended scheme investing in Equity, Debt and Exchange Traded

Nippon India Multi Asset Fund (An open ended scheme investing in Equity, Debt and Exchange Traded

Nippon India Multi Asset Fund (An open ended scheme investing in Equity, Debt and Exchange Traded Commodity Derivatives and Gold ETF) Over the last decade we have seen divergent returns among Asset classes Winners keep changing among Asset

855 views • 37 slides
Forecasting 21 January 2013 1 FCAS Agenda Business Goals & Forecasting Approach

Forecasting 21 January 2013 1 FCAS Agenda Business Goals & Forecasting Approach

Forecasting 21 January 2013 1 FCAS Agenda Business Goals & Forecasting Approach Assumptions Data Visualization Forecasting Fresh Milk Sales ( SKU: Amul Taaza ) Forecasting Dahi and Yoghurt Sales (SKU: Saras

782 views • 11 slides
Lecture 10 Forecasting and Model Fitting Colin Rundel 02/20/2017 1 Forecasting 2 Forecasting

Lecture 10 Forecasting and Model Fitting Colin Rundel 02/20/2017 1 Forecasting 2 Forecasting

Lecture 10 Forecasting and Model Fitting Colin Rundel 02/20/2017 1 Forecasting 2 Forecasting ARMA Forecasts for stationary models necessarily revert to mean Differenced models revert to trend (usually a line) Why? AR gradually

476 views • 44 slides
Organizational Behavior and Leadership CASE STUDY North Carolina National Guard Support Services

Organizational Behavior and Leadership CASE STUDY North Carolina National Guard Support Services

PRT 506 Organizational Behavior and Leadership CASE STUDY North Carolina National Guard Support Services Division BOB MAY Support Services Division (SSD) Organizational Challenges Dwindling Resources Ability to Provide Services at Right

574 views • 15 slides
Synthesis of Challenges and Opportunities Daniel Z. Sui Department of Geography & Center for

Synthesis of Challenges and Opportunities Daniel Z. Sui Department of Geography & Center for

Synthesis of Challenges and Opportunities Daniel Z. Sui Department of Geography & Center for Urban & Regional Analysis (CURA) The Ohio State University December 12, 2011 Santa Barbara, CA Two Cultures of Ecology Holling, C.S. 1998.

496 views • 48 slides
Establishing & Maintaining Long-Term Customer Relationships Cindy Milrany CFO, CAO Freese

Establishing & Maintaining Long-Term Customer Relationships Cindy Milrany CFO, CAO Freese

Establishing & Maintaining Long-Term Customer Relationships Cindy Milrany CFO, CAO Freese and Nichols, Inc. Multi-service engineering, architecture and environmental science firm 118-year history means experience, strength and

390 views • 27 slides
Kingsbrook Why and How? Partnership between AVDC, BDW and RSPB Land had been put forward

Kingsbrook Why and How? Partnership between AVDC, BDW and RSPB Land had been put forward

Kingsbrook Why and How? Partnership between AVDC, BDW and RSPB Land had been put forward for development in the past but environmental sensitivities had prevented permission AVDC suggested Barratt team up with RSPB Exemplar

120 views • 9 slides
Vancouver Island north film commission 2018 / 2019 State of the Region Comox Valley Regional

Vancouver Island north film commission 2018 / 2019 State of the Region Comox Valley Regional

Vancouver Island north film commission 2018 / 2019 State of the Region Comox Valley Regional District 2018 Production information 99 Lead requests 31 projects filmed Feature Films - 3 ( Sonic Hedgehog , Come to Daddy,

349 views • 12 slides
Volume Materials Manufacturing -Polysilicon MARK LOBODA HEMLOCK SEMICONDUCTOR 1 Polysilicon

Volume Materials Manufacturing -Polysilicon MARK LOBODA HEMLOCK SEMICONDUCTOR 1 Polysilicon

Machine Learning Applications for High Volume Materials Manufacturing -Polysilicon MARK LOBODA HEMLOCK SEMICONDUCTOR 1 Polysilicon A Foundation for Solid State Microelectronics Parts per trillion impurity control 2 Polysilicon A

248 views • 13 slides
Whitehall Road lot Red pine and oak/ pine forest types Vernal pools Cellar hole 48

Whitehall Road lot Red pine and oak/ pine forest types Vernal pools Cellar hole 48

Whitehall Road lot Red pine and oak/ pine forest types Vernal pools Cellar hole 48 acres total 23 acres thinned red pine 2 aged, 20 acres white pine/oak Mortality present in red pine stand due to insects/disease Red

650 views • 5 slides
INVESTOR PRESENTATION February 2019 TSX:WEF Forward Looking Statements and Non-GAAP Financial

INVESTOR PRESENTATION February 2019 TSX:WEF Forward Looking Statements and Non-GAAP Financial

INVESTOR PRESENTATION February 2019 TSX:WEF Forward Looking Statements and Non-GAAP Financial Measures All financial references are expressed in C$ unless otherwise noted. This presentation contains statements and estimates that may constitute

530 views • 39 slides

More recommend