Week 15: Code along 13
CHAPTER 11 Monte Carlo Simulation
Jackson Curtis
2023
Goal
Simulate future portfolio returns
five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”
market: “SPY”
from 2012-12-31 to 2017-12-31
1 Import stock prices
2 Convert prices to returns
3 Assign a weight to each asset
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"## [1] 0.25 0.25 0.20 0.20 0.10## # A tibble: 5 × 2
## symbols weights
## <chr> <dbl>
## 1 AGG 0.25
## 2 EEM 0.25
## 3 EFA 0.2
## 4 IJS 0.2
## 5 SPY 0.14 Build a portfolio
## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0204
## 2 2013-02-28 -0.00239
## 3 2013-03-28 0.0121
## 4 2013-04-30 0.0174
## 5 2013-05-31 -0.0128
## 6 2013-06-28 -0.0247
## 7 2013-07-31 0.0321
## 8 2013-08-30 -0.0224
## 9 2013-09-30 0.0511
## 10 2013-10-31 0.0301
## # ℹ 50 more rows5 Simulating growth of a dollar
## [1] 0.005899133## [1] 0.02347492## [1] 1.796280e-02 1.770971e-02 -1.836576e-02 3.150785e-02 -2.828874e-02
## [6] -7.742381e-03 4.154660e-03 4.201184e-02 -5.240419e-04 7.020332e-03
## [11] -6.477951e-03 -1.349972e-03 -8.791242e-03 -7.957506e-03 -2.084662e-02
## [16] 2.142265e-02 -2.185133e-02 -7.577245e-03 6.830754e-03 -1.608584e-02
## [21] -1.295048e-03 -2.123194e-02 -1.157223e-02 -2.621031e-02 -4.597178e-03
## [26] 1.535860e-03 1.078317e-02 -1.536779e-02 6.139537e-03 1.434121e-02
## [31] 2.057349e-03 -1.450976e-02 6.528629e-03 -1.556314e-02 -4.435468e-02
## [36] 5.605628e-03 4.067641e-02 -1.316802e-02 2.316397e-02 -2.914211e-02
## [41] 1.680483e-02 2.196814e-02 8.959433e-03 1.292516e-02 -1.271757e-03
## [46] 4.424042e-02 7.933548e-03 3.445974e-02 -9.545859e-03 7.008307e-03
## [51] 2.408388e-02 -1.022125e-02 -6.571631e-03 1.302089e-02 2.602339e-02
## [56] -1.781358e-02 3.822972e-02 2.964508e-02 3.960641e-04 3.210430e-02
## [61] -6.860796e-03 3.683711e-02 -2.957210e-02 3.007677e-03 2.340698e-02
## [66] 5.078404e-03 -1.370519e-02 -1.490582e-02 1.829093e-02 -3.827953e-02
## [71] 3.145058e-02 1.465802e-02 -1.406893e-02 -8.418543e-03 3.392716e-02
## [76] -4.739901e-03 -3.092025e-02 1.018527e-03 5.307102e-03 -1.724829e-03
## [81] -1.538037e-03 -7.197898e-03 2.066451e-02 4.072290e-02 7.016841e-02
## [86] 1.990194e-02 4.150660e-02 4.370418e-02 -1.472707e-02 -9.746480e-03
## [91] -7.700122e-03 5.942837e-04 3.855307e-03 4.156732e-03 2.011593e-02
## [96] -5.718786e-02 -6.660392e-03 6.681213e-02 -1.367422e-05 3.686283e-02
## [101] 1.473433e-02 -1.023049e-02 -3.189591e-03 -1.502908e-02 -6.325350e-04
## [106] -3.769110e-03 -3.302310e-02 1.824293e-02 -3.537872e-02 -1.531755e-02
## [111] 1.500933e-03 -1.059760e-02 -1.409365e-02 -5.811393e-03 7.685699e-04
## [116] -1.076433e-02 1.578144e-03 -4.543568e-03 4.201280e-03 4.337425e-02## # A tibble: 121 × 1
## returns
## <dbl>
## 1 1
## 2 1.02
## 3 1.02
## 4 0.982
## 5 1.03
## 6 0.972
## 7 0.992
## 8 1.00
## 9 1.04
## 10 0.999
## # ℹ 111 more rows## # A tibble: 121 × 1
## growth
## <dbl>
## 1 1
## 2 1.02
## 3 1.04
## 4 1.02
## 5 1.05
## 6 1.02
## 7 1.01
## 8 1.02
## 9 1.06
## 10 1.06
## # ℹ 111 more rows## [1] 3.6207986 Simulation function
## # A tibble: 121 × 1
## growth
## <dbl>
## 1 1
## 2 1.02
## 3 0.997
## 4 0.994
## 5 1.02
## 6 1.03
## 7 1.02
## 8 1.03
## 9 1.07
## 10 1.11
## # ℹ 111 more rows7 Running multiple simulations
## sim1 sim2 sim3 sim4 sim5 sim6 sim7 sim8 sim9 sim10 sim11 sim12 sim13
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## sim14 sim15 sim16 sim17 sim18 sim19 sim20 sim21 sim22 sim23 sim24 sim25 sim26
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## sim27 sim28 sim29 sim30 sim31 sim32 sim33 sim34 sim35 sim36 sim37 sim38 sim39
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## sim40 sim41 sim42 sim43 sim44 sim45 sim46 sim47 sim48 sim49 sim50 sim51
## 1 1 1 1 1 1 1 1 1 1 1 1## # A tibble: 6,171 × 3
## month sim growth
## <int> <chr> <dbl>
## 1 1 sim1 1
## 2 1 sim2 1
## 3 1 sim3 1
## 4 1 sim4 1
## 5 1 sim5 1
## 6 1 sim6 1
## 7 1 sim7 1
## 8 1 sim8 1
## 9 1 sim9 1
## 10 1 sim10 1
## # ℹ 6,161 more rows## 0.5% 2.5% 25% 50% 75% 97.5% 99.5%
## 1.02 1.22 1.64 1.87 2.30 3.13 3.168 Visualizing simulations with ggplot
## # A tibble: 1 × 3
## max median min
## <dbl> <dbl> <dbl>
## 1 3.16 1.87 0.969