My own principled approach

My own principled approach, based on my portfolio management career:

1. Accept reality

2. Don’t make an emotional decision - be objective

3. Make a right decision, rather than focusing on making a short-term profit / win

4. Play a game from a long-term perspective

5. Don’t have a big ego

6. Don’t stick to realized losses and/or profits

7. Analyze, improve, and then repeat this process over and over again

Hope you like this.

[Financial Analysts Journal] The Impact of Crowding in Alternative Risk Premia Investing

Nick Baltas (2019) The Impact of Crowding in Alternative Risk Premia Investing, Financial Analysts Journal, 75:3, 89-104, DOI: 10.1080/0015198X.2019.1600955

To link to this article: https://doi.org/10.1080/0015198X.2019.1600955 

The analysis shows that divergence premia, such as momentum, are more likely to underperform following crowded periods. Conversely, convergence premia, such as value, show signs of outperformance as they transition into phases of larger investor flows. 

[Financial Analysts Journal] Are Passive Funds Really Superior Investments? An Investor Perspective,

Edwin J. Elton, Martin J. Gruber & Andre de Souza (2019) Are Passive Funds Really Superior Investments? An Investor Perspective, Financial Analysts Journal, 75:3, 7-19, DOI: 10.1080/0015198X.2019.1618097

To link to this article: https://doi.org/10.1080/0015198X.2019.1618097 

In the last ve years, passive funds have increased from 16.4% of the assets under management to 26%.

An investor seeking to use passive portfolios to beat an active fund and attempting to use the Fama–French (market, small cap, value) or Carhart (these three plus momentum) methodology does not have an easily implementable strategy. 

(1) the authors searched for a parsimonious set of indexes that correctly price other indexes and (2) the authors show that exchange-traded funds (tradable assets)—rather than indexes—can be used to construct a set of portfolios that outperform active mutual funds. ETFs can be bought and shorted.

The authors found that a combination of five ETFs captures most of the variation in all available ETFs. Five ETFs consist of CRSP Market, Russell 1000 Growth, Russell 1000 Value, Russell 2000 Growth, and Russell Midcap Value 

Investors can outperform active funds by buying the lowest-cost ETF that matches each fund’s benchmark, but they can do significantly better by using the five-ETF model the authors developed in this study.

[Financial Analysts Journal] Choosing and Using Utility Functions in Forming Portfolios

My own summary for the paper "Choosing and Using Utility Functions in Forming Portfolios" on Financial Analyst Journal, Volume 75 Number 3, Third Quarter 2019:

- Utility functions and related analysis should be tailored (i.e., purposefully selected) to reflect the investor's circumstances. In this article, it is illustrated for four investor types (a private investor, an endowment fund, a defined-benefit fund, and a retired individual).

- Limitations of mean–variance analysis (essentially a single-period approach ): (1) portfolio return and risk over a discrete horizon (a problem for long-term investors under various economic and market conditions), (2) diversified investor objectives, e.g., delivering a real return, a required income stream, or sufficient assets to cover liabilities, and (3) return distributions are highly (negatively) skewed (and even with high kurtosis).

- Analyzing the mean and variance of his or her portfolio returns over some discrete time horizon is only vaguely relevant to the main concern—namely, the stream of income that can be drawn (from total assets) over time (and total liabilities).

- Choosing a utility function (with parameters) that is fit for a purpose seems more important than seeking validation from an unsettled literature (for functional forms and parameters).

- Advantages of utility functions: (1) available for return distributions of any shape, (2) considerable flexibility in available functions that can encapsulate a wide range of investor objectives and preferences (various time horizons, both up and down-sides of markets, combined strategies in investment and withdrawal in a dynamic framework)

- Utility functions: power utility and two variations of reference-dependent utility

Reference:

Geoffrey J. Warren (2019) Choosing and Using Utility Functions in Forming

Portfolios, Financial Analysts Journal, 75:3, 39-69, DOI: 10.1080/0015198X.2019.1618109

https://doi.org/10.1080/0015198X.2019.1618109

R: Stepwise Regression

EQ_z.csv

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EQ_readme.txt

# stard R Console on MacOS # get a working directory getwd() # change the working directory setwd("/Users/yoshi/Downloads/") dat <- read.csv('EQ_z.csv') head(dat) #         SR InstAUMNetFlow       Views PassedScreens #1  0.168106    -0.07055196 -0.33920655   -1.70145662 #2  0.155648    -0.09568507 -0.56343473   -1.66043722 #3 -0.335729    -0.13467625  1.28164263   -0.01438553 #4  1.249691    -0.14978894 -0.05731972   -0.77510918 #5  0.502996    -0.13648457 -0.49296294    0.27428721 #6  0.451817    -0.04294418  0.11565621   -0.21678547 # All the data are expressed in z-score. ########## 1.1 Regression Analysis (SR ~ InstAUMNetFlow) reg_InstAUMNetFlow <- lm(SR~InstAUMNetFlow,data=dat) summary(reg_InstAUMNetFlow) #Call: #lm(formula = SR ~ InstAUMNetFlow, data = dat) # #Residuals: #    Min      1Q  Median      3Q     Max #-3.2166 -0.6249 -0.0146  0.6306  2.9913 # #Coefficients: #               Estimate Std. Error t value Pr(>|t|) #(Intercept)     0.02248    0.04290   0.524   0.6006 #InstAUMNetFlow  0.51079    0.22452   2.275   0.0233 * #--- #Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # #Residual standard error: 0.9966 on 564 degrees of freedom #Multiple R-squared:  0.009094, Adjusted R-squared:  0.007337 #F-statistic: 5.176 on 1 and 564 DF,  p-value: 0.02328 #Multiple R-squared:  0.009094 # InstAUMNetFlow does not explain SR (Sharpe Ratio) very much. plot(dat$InstAUMNetFlow,dat$SR,xlab='Inst AUM Net Flow 1Y (%)',ylab='Sharpe Ratio (USD, 1Y)') abline(reg_InstAUMNetFlow) ########## 1.2 Regression Analysis (SR ~ Views) reg_Views <- lm(SR~Views,data=dat) summary(reg_Views) #Call: #lm(formula = SR ~ Views, data = dat) # #Residuals: #    Min      1Q  Median      3Q     Max #-3.2360 -0.5970 -0.0080  0.6647  2.9892 # #Coefficients: #            Estimate Std. Error t value Pr(>|t|)   #(Intercept) 0.001304   0.041664   0.031 0.975047   #Views       0.140832   0.041670   3.380 0.000776 *** #--- #Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # #Residual standard error: 0.9912 on 564 degrees of freedom #Multiple R-squared:  0.01985, Adjusted R-squared:  0.01811 #F-statistic: 11.42 on 1 and 564 DF,  p-value: 0.0007759 #Multiple R-squared:  0.01985 # Views do not explain SR (Sharpe Ratio) very much. plot(dat$Views,dat$SR,xlab='Views 1Y (%)',ylab='Sharpe Ratio (USD, 1Y)') abline(reg_Views) ########## 1.3 Regression Analysis (SR ~ PassedScreens) reg_PassedScreens <- lm(SR~PassedScreens,data=dat) summary(reg_PassedScreens) #Call: #lm(formula = SR ~ PassedScreens, data = dat) # #Residuals: #    Min      1Q  Median      3Q     Max #-3.2178 -0.5859 -0.0177  0.6604  2.9539 # #Coefficients: #              Estimate Std. Error t value Pr(>|t|) #(Intercept)   0.001163   0.041945   0.028   0.9779 #PassedScreens 0.081500   0.042048   1.938   0.0531 . #--- #Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # #Residual standard error: 0.9979 on 564 degrees of freedom #Multiple R-squared:  0.006617, Adjusted R-squared:  0.004856 #F-statistic: 3.757 on 1 and 564 DF,  p-value: 0.05309 #Multiple R-squared:  0.006617 # Passed Screens do not explain SR (Sharpe Ratio) very much. plot(dat$PassedScreens,dat$SR,xlab='PassedScreens 1Y (%)',ylab='Sharpe Ratio (USD, 1Y)') abline(reg_PassedScreens) ########## 2 Multiple Regression Analysis (SR ~ InstAUMNetFlow + Views + PassedScreens) ##### multiple regression (with all explanatory variables) reg_multiple <- lm(SR~InstAUMNetFlow+Views+PassedScreens,data=dat) summary(reg_multiple) #Call: #lm(formula = SR ~ InstAUMNetFlow + Views + PassedScreens, data = dat) # #Residuals: #    Min      1Q  Median      3Q     Max #-3.1985 -0.5797 -0.0276  0.6324  3.0271 # # #Coefficients: #               Estimate Std. Error t value Pr(>|t|)   #(Intercept)     0.02399    0.04253   0.564  0.57288   #InstAUMNetFlow  0.55353    0.22643   2.445  0.01481 * #Views           0.11937    0.04466   2.673  0.00773 ** #PassedScreens   0.05587    0.04542   1.230  0.21916   #--- #Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # #Residual standard error: 0.9872 on 562 degrees of freedom #Multiple R-squared:  0.03122, Adjusted R-squared:  0.02605 #F-statistic: 6.036 on 3 and 562 DF,  p-value: 0.0004748 #Multiple R-squared:  0.03122 # R2 is still very small. ##### stepwise regression reg0 <- lm(SR~1,dat) step(reg0,direction='both', scope=list(upper=~InstAUMNetFlow+Views+PassedScreens)) #Start:  AIC=1.37 #SR ~ 1 # #                 Df Sum of Sq    RSS     AIC #+ Views           1   11.2227 554.15 -7.9799 #+ InstAUMNetFlow  1    5.1412 560.23 -1.8022 #+ PassedScreens   1    3.7411 561.63 -0.3894 #<none>                        565.37  1.3683 # #Step:  AIC=-7.98 #SR ~ Views # #                 Df Sum of Sq    RSS      AIC #+ InstAUMNetFlow  1    4.9513 549.19 -11.0598 #<none>                        554.15  -7.9799 #+ PassedScreens   1    0.6017 553.54  -6.5948 #- Views           1   11.2227 565.37   1.3683 # #Step:  AIC=-11.06 #SR ~ Views + InstAUMNetFlow # #                 Df Sum of Sq    RSS      AIC #<none>                        549.19 -11.0598 #+ PassedScreens   1    1.4748 547.72 -10.5818 #- InstAUMNetFlow  1    4.9513 554.15  -7.9799 #- Views           1   11.0328 560.23  -1.8022 # #Call: #lm(formula = SR ~ Views + InstAUMNetFlow, data = dat) # #Coefficients: #   (Intercept)           Views  InstAUMNetFlow #       0.02199         0.13965         0.50131 #As a result of stepwise regression, Views is selected first, InstAUMNetFlow is selected second, and then PassedScreens is rejected. #If you look at a correlation matrix of data, PassedScreens is highly correlated to Views. # cor(dat) #                       SR InstAUMNetFlow      Views PassedScreens #SR             1.00000000     0.09536016 0.14089073    0.08134542 #InstAUMNetFlow 0.09536016     1.00000000 0.01267051   -0.17021810 #Views          0.14089073     0.01267051 1.00000000    0.36147622 #PassedScreens  0.08134542    -0.17021810 0.36147622    1.00000000 ##### multiple regression (after removing PassedScreens) reg_multiple2 <- lm(SR~InstAUMNetFlow+Views,data=dat) summary(reg_multiple2) #Call: #lm(formula = SR ~ InstAUMNetFlow + Views, data = dat) # #Residuals: #    Min      1Q  Median      3Q     Max #-3.1469 -0.6066  0.0004  0.6293  3.0273 # #Coefficients: #               Estimate Std. Error t value Pr(>|t|)   #(Intercept)     0.02199    0.04252   0.517 0.605257   #InstAUMNetFlow  0.50131    0.22251   2.253 0.024646 * #Views           0.13965    0.04152   3.363 0.000823 *** #--- #Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # #Residual standard error: 0.9877 on 563 degrees of freedom #Multiple R-squared:  0.02861, Adjusted R-squared:  0.02516 #F-statistic:  8.29 on 2 and 563 DF,  p-value: 0.0002829 ########## stepwise regression (explained variable: InstAUMNetFlow) reg0 <- lm(InstAUMNetFlow~1,dat) step(reg0,direction='both', scope=list(upper=~SR+Views+PassedScreens)) #Start:  AIC=-382.15 #InstAUMNetFlow ~ 1 # #                Df Sum of Sq    RSS     AIC #+ SR             1   1.30504 61.456 -385.74 #<none>                       62.761 -382.15 #+ Views          1   0.10122 62.659 -380.58 #+ PassedScreens  1   0.00000 62.761 -380.15 # #Step:  AIC=-385.74 #InstAUMNetFlow ~ SR # #                Df Sum of Sq    RSS     AIC #<none>                       61.456 -385.74 #+ Views          1   0.09613 61.359 -384.15 #+ PassedScreens  1   0.00373 61.452 -383.75 #- SR             1   1.30504 62.761 -382.15 # #Call: #lm(formula = InstAUMNetFlow ~ SR, data = dat) # #Coefficients: #(Intercept)           SR #   -0.05398      0.07050 reg_multiple3 <- lm(InstAUMNetFlow~SR,data=dat) summary(reg_multiple3) #Call: #lm(formula = InstAUMNetFlow ~ SR, data = dat) # #Residuals: #    Min      1Q  Median      3Q     Max #-0.7203 -0.1332 -0.0501  0.0531  5.9795 # #Coefficients: #            Estimate Std. Error t value Pr(>|t|) #(Intercept) -0.05398    0.02958  -1.825   0.0692 . #SR           0.07050    0.02977   2.368   0.0186 * #--- #Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # #Residual standard error: 0.4825 on 264 degrees of freedom #Multiple R-squared:  0.02079, Adjusted R-squared:  0.01708 #F-statistic: 5.606 on 1 and 264 DF,  p-value: 0.01862 plot(dat$SR,dat$InstAUMNetFlow,xlab='Sharpe Ratio (USD, 1Y)',ylab='Inst AUM Net Flow 1Y (%)') abline(reg_multiple3)