ALGN_Stock_Report
J.Korn
Forecasting a Stock’s Closing Price:
The report below details a exploratory analysis of the target stock performance over the past 5 years. The forecast is produced using R to fit three Bayesian structural time series models. The data consists of a .xts times series dataset available through “Yahoo Finance”.
Understand the SP500 Stocks & Extract Stock Symbol…
## 'data.frame': 505 obs. of 9 variables:
## $ Symbol : chr "MMM" "ABT" "ABBV" "ABMD" ...
## $ Security : chr "3M Company" "Abbott Laboratories" "AbbVie Inc." "ABIOMED Inc" ...
## $ SEC.filings : chr "reports" "reports" "reports" "reports" ...
## $ GICS.Sector : chr "Industrials" "Health Care" "Health Care" "Health Care" ...
## $ GICS.Sub.Industry : chr "Industrial Conglomerates" "Health Care Equipment" "Pharmaceuticals" "Health Care Equipment" ...
## $ Headquarters.Location : chr "St. Paul, Minnesota" "North Chicago, Illinois" "North Chicago, Illinois" "Danvers, Massachusetts" ...
## $ Date.first.added.3..4.: chr "" "1964-03-31" "2012-12-31" "2018-05-31" ...
## $ CIK : int 66740 1800 1551152 815094 1467373 718877 796343 2488 1158449 874761 ...
## $ Founded : chr "1902" "1888" "2013 (1888)" "1981" ...## [1] "ALGN"## [1] "Health Care"## [1] "Health Care Supplies"Import the Stock Dataset…
## [1] "ALGN"## An 'xts' object on 2014-01-17/2019-01-15 containing:
## Data: num [1:1257, 1:6] 63.9 65 62.4 63.1 62.8 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:6] "ALGN.Open" "ALGN.High" "ALGN.Low" "ALGN.Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2019-01-16 02:00:50"Exploratory Analysis of Stock Dataset…
Exploratory Analysis of stock dataset - Open Price: 
Exploratory Analysis of stock dataset - Close Price: 
Exploratory Analysis of stock dataset - High Price: 
Exploratory Analysis of stock dataset - Low Price: 
Exploratory Analysis of stock dataset - Volume: 
Exploratory Analysis of stock dataset - Candle Chart - SMA = 20: 
Exploratory Analysis of stock dataset - TA: Added Bollinger Bands, Volume, and Moving Average Convergence Divergence (MACD) plots: 
Exploratory Analysis of stock dataset - Calculate some predictor variables to add to the stock dataset:
## An 'xts' object on 2014-03-31/2019-01-15 containing:
## Data: num [1:1208, 1:18] 51.2 51.9 53.7 54.2 57.1 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:18] "Open" "High" "Low" "Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2019-01-16 02:00:50"## Open High Low Close Volume Adjusted daily.returns atr
## 2014-03-31 51.23 52.08 50.37 51.79 750600 51.79 0.02089495 1.814189
## 2014-04-01 51.88 53.73 51.46 53.59 1043500 53.59 0.03475572 1.846747
## 2014-04-02 53.66 54.75 52.18 54.24 744200 54.24 0.01212917 1.898408
## 2014-04-03 54.18 54.30 52.72 53.59 569300 53.59 -0.01198381 1.875664
## 2014-04-04 57.06 57.50 53.97 54.81 2606600 54.81 0.02276546 2.020974
## 2014-04-07 55.40 55.59 52.50 52.94 1504300 52.94 -0.03411790 2.097333
## SMI ADX oscillator pctB Delt.1.arithmetic
## 2014-03-31 -30.95785 36.36700 -75 0.0323759 0.69366672
## 2014-04-01 -28.22683 34.80366 -75 0.3697839 0.24595740
## 2014-04-02 -22.63524 32.48536 -75 0.5506890 0.19823924
## 2014-04-03 -17.98744 30.33265 -75 0.5238895 -0.09565211
## 2014-04-04 -13.29455 30.03287 20 0.9274704 0.39072060
## 2014-04-07 -13.04308 28.66418 20 0.5726071 0.11792595
## EMA maEMV signal mfi sar
## 2014-03-31 -0.1779297647 -0.0085033967 -1.593043 25.10341 54.14127
## 2014-04-01 0.0138123810 -0.0057103130 -1.588917 33.67032 53.80014
## 2014-04-02 0.1209582021 -0.0013686374 -1.518582 40.87221 50.73000
## 2014-04-03 0.1173777928 -0.0010247667 -1.426531 40.45136 50.73000
## 2014-04-04 0.0007492513 0.0051464866 -1.285921 50.88744 50.89080
## 2014-04-07 -0.1294253404 0.0002145164 -1.175251 42.69685 51.28735Exploratory Analysis of stock dataset - Volume: 
Exploratory Analysis of stock dataset - Scatterplot Matrix: 
Train a Bayesian Nowcasting Structural Time Series Model to Forecast Close Price…
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## =-=-=-=-=## $residual.sd
## [1] 2.025548
##
## $prediction.sd
## [1] 4.335814
##
## $rsquare
## [1] 0.9995701
##
## $relative.gof
## [1] -0.01388459Plot Model 
## [1] "sigma.obs" "trend.level.sd"
## [3] "trend.slope.mean" "trend.slope.ar.coefficient"
## [5] "trend.slope.sd" "sigma.seasonal.54"
## [7] "final.state" "state.contributions"
## [9] "one.step.prediction.errors" "log.likelihood"
## [11] "has.regression" "state.specification"
## [13] "prior" "timestamp.info"
## [15] "model.options" "family"
## [17] "niter" "original.series"Create Prediction of Close Price Using the Nowcasting Model…
## List of 5
## $ mean : num [1:20] 196 197 197 197 197 ...
## $ median : num [1:20] 196 196 195 197 196 ...
## $ interval : num [1:2, 1:20] 190 202 188 206 190 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "5%" "95%"
## .. ..$ : NULL
## $ distribution : num [1:16, 1:20] 196 194 194 201 188 ...
## $ original.series:'zoo' series from 2014-03-31 to 2019-01-15
## Data: num [1:1208] 51.8 53.6 54.2 53.6 54.8 ...
## Index: Date[1:1208], format: "2014-03-31" "2014-04-01" ...
## - attr(*, "class")= chr "bsts.prediction"Summary of Prediction:
summary(pred)## Length Class Mode
## mean 20 -none- numeric
## median 20 -none- numeric
## interval 40 -none- numeric
## distribution 320 -none- numeric
## original.series 1208 zoo numericPlot the Mean:
par(mfrow=c(2,2))
plot(pred$original.series)
plot(pred$mean)
plot(pred$median)
plot(pred$interval)
Plot Prediction:
plot(pred, plot.original = 20)
Nowcasting Prediction Table
tail(pred$original.series)## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-14 2019-01-15
## 192.95 200.32 202.64 198.25 193.94 195.05head(pred$mean)## [1] 196.0947 196.6138 197.1994 197.0591 196.8096 197.8121head(pred$median)## [1] 195.7412 196.1681 195.3619 196.8707 195.5823 195.3201head(pred$interval)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## 5% 190.0495 188.4840 190.0243 188.6279 186.8793 186.1630 184.1235
## 95% 202.2200 206.1656 208.9969 207.4257 214.5346 211.5079 213.9560
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 5% 182.5451 179.6935 174.9847 175.1728 173.9743 173.4521 171.6165
## 95% 214.6672 212.0593 212.7899 211.7149 216.8855 217.0218 216.3747
## [,15] [,16] [,17] [,18] [,19] [,20]
## 5% 171.9498 169.2952 176.2326 175.8482 175.7975 174.2338
## 95% 217.3313 221.0882 221.0799 223.9879 225.5532 227.6170Train a Bayesian Regression Structural Time Series Model to Forecast Close Price…
## =-=-=-=-= Iteration 0 Wed Jan 16 02:01:56 2019
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## =-=-=-=-= Iteration 10 Wed Jan 16 02:01:57 2019
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## =-=-=-=-= Iteration 50 Wed Jan 16 02:02:01 2019
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## =-=-=-=-= Iteration 90 Wed Jan 16 02:02:06 2019
## =-=-=-=-=## $residual.sd
## [1] 1.096192
##
## $prediction.sd
## [1] 3.078755
##
## $rsquare
## [1] 0.9998741
##
## $relative.gof
## [1] 0.4887925
##
## $size
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 1.822 2.000 3.000
##
## $coefficients
## mean sd mean.inc sd.inc
## daily.returns 7.935030e+01 3.2553088186 79.350296140 3.255309
## maEMV 1.265716e+01 8.7470123335 15.604715590 6.923909
## mfi 2.067146e-05 0.0001961067 0.001860432 0.000000
## sar 0.000000e+00 0.0000000000 0.000000000 0.000000
## signal 0.000000e+00 0.0000000000 0.000000000 0.000000
## EMA 0.000000e+00 0.0000000000 0.000000000 0.000000
## Delt.1.arithmetic 0.000000e+00 0.0000000000 0.000000000 0.000000
## pctB 0.000000e+00 0.0000000000 0.000000000 0.000000
## oscillator 0.000000e+00 0.0000000000 0.000000000 0.000000
## ADX 0.000000e+00 0.0000000000 0.000000000 0.000000
## SMI 0.000000e+00 0.0000000000 0.000000000 0.000000
## atr 0.000000e+00 0.0000000000 0.000000000 0.000000
## Adjusted 0.000000e+00 0.0000000000 0.000000000 0.000000
## Volume 0.000000e+00 0.0000000000 0.000000000 0.000000
## Low 0.000000e+00 0.0000000000 0.000000000 0.000000
## High 0.000000e+00 0.0000000000 0.000000000 0.000000
## Open 0.000000e+00 0.0000000000 0.000000000 0.000000
## (Intercept) 0.000000e+00 0.0000000000 0.000000000 0.000000
## inc.prob
## daily.returns 1.00000000
## maEMV 0.81111111
## mfi 0.01111111
## sar 0.00000000
## signal 0.00000000
## EMA 0.00000000
## Delt.1.arithmetic 0.00000000
## pctB 0.00000000
## oscillator 0.00000000
## ADX 0.00000000
## SMI 0.00000000
## atr 0.00000000
## Adjusted 0.00000000
## Volume 0.00000000
## Low 0.00000000
## High 0.00000000
## Open 0.00000000
## (Intercept) 0.00000000Parameter Estimates: (Sample mean of the Sampled beta value)
## (Intercept) Open High Low
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Volume Adjusted daily.returns atr
## 0.000000e+00 0.000000e+00 7.902265e+01 0.000000e+00
## SMI ADX oscillator pctB
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## Delt.1.arithmetic EMA maEMV signal
## 0.000000e+00 0.000000e+00 1.139144e+01 0.000000e+00
## mfi sar
## 1.860432e-05 0.000000e+00Plot Model 
## [1] "coefficients" "sigma.obs"
## [3] "trend.level.sd" "trend.slope.mean"
## [5] "trend.slope.ar.coefficient" "trend.slope.sd"
## [7] "sigma.seasonal.54" "final.state"
## [9] "state.contributions" "one.step.prediction.errors"
## [11] "log.likelihood" "has.regression"
## [13] "state.specification" "prior"
## [15] "timestamp.info" "model.options"
## [17] "family" "niter"
## [19] "original.series" "xlevels"
## [21] "terms" "predictors"Create Prediction of Close Price Using the Regression Model…
## List of 5
## $ mean : num [1:91] 199 197 195 195 195 ...
## $ median : num [1:91] 199 198 197 195 198 ...
## $ interval : num [1:2, 1:91] 196 201 190 202 186 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "5%" "95%"
## .. ..$ : NULL
## $ distribution : num [1:14, 1:91] 197 201 199 199 196 ...
## $ original.series:'zoo' series from 2014-03-31 to 2019-01-15
## Data: num [1:1208] 51.8 53.6 54.2 53.6 54.8 ...
## Index: Date[1:1208], format: "2014-03-31" "2014-04-01" ...
## - attr(*, "class")= chr "bsts.prediction"Summary of Prediction:
summary(pred)## Length Class Mode
## mean 91 -none- numeric
## median 91 -none- numeric
## interval 182 -none- numeric
## distribution 1274 -none- numeric
## original.series 1208 zoo numericPlot the Mean:
par(mfrow=c(2,2))
plot(pred$original.series)
plot(pred$mean)
plot(pred$median)
plot(pred$interval)
Plot Prediction:
plot(pred, plot.original = 90)
Regression Model Prediction Table
tail(pred$original.series)## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-14 2019-01-15
## 192.95 200.32 202.64 198.25 193.94 195.05head(pred$mean)## [1] 198.8495 196.9429 194.9769 194.5098 195.3795 195.3639head(pred$median)## [1] 198.9986 198.0707 196.7525 195.3671 198.3657 197.6144head(pred$interval)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## 5% 195.8427 189.8379 186.3295 182.5703 177.6630 176.7181 175.8349
## 95% 201.2147 201.9655 202.3557 203.9247 207.1557 208.9632 211.3245
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 5% 176.5442 173.5321 180.5698 178.1479 182.7386 180.4997 185.5488
## 95% 214.2572 210.8775 218.6091 218.2243 221.2218 222.4269 224.2141
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22]
## 5% 186.7466 181.3124 178.4630 180.283 182.7198 173.0101 175.7388 165.9549
## 95% 228.0970 222.3458 223.0734 225.958 233.9739 225.5325 230.1357 226.1698
## [,23] [,24] [,25] [,26] [,27] [,28] [,29]
## 5% 159.9893 151.6410 155.2222 145.5786 147.4992 154.1013 151.9166
## 95% 223.8873 215.4619 223.5388 212.5469 211.3298 215.9468 215.4518
## [,30] [,31] [,32] [,33] [,34] [,35] [,36]
## 5% 161.6488 162.0330 161.5921 162.5930 163.7265 164.6608 159.8939
## 95% 225.5605 231.6662 231.2677 233.2972 235.4342 236.2938 233.4590
## [,37] [,38] [,39] [,40] [,41] [,42] [,43]
## 5% 140.4305 153.0468 148.7702 154.8414 156.2117 161.6451 159.5937
## 95% 216.5147 235.0987 227.3509 230.5749 232.2998 238.9127 237.7080
## [,44] [,45] [,46] [,47] [,48] [,49] [,50]
## 5% 161.6236 164.2098 178.3320 170.4592 164.8849 157.0252 163.0743
## 95% 239.4909 237.7411 252.4195 246.8799 246.2472 241.9475 248.3442
## [,51] [,52] [,53] [,54] [,55] [,56] [,57]
## 5% 166.4100 168.6187 170.6581 160.4952 163.2028 165.0537 172.0915
## 95% 247.2961 245.0700 248.5679 237.4276 241.6545 245.5526 249.0922
## [,58] [,59] [,60] [,61] [,62] [,63] [,64]
## 5% 176.4060 172.5472 179.7072 171.3283 171.1759 175.0620 168.9885
## 95% 255.0929 257.3304 265.8487 260.9196 263.5965 266.0541 260.9330
## [,65] [,66] [,67] [,68] [,69] [,70] [,71]
## 5% 170.0103 169.8325 177.5715 179.2144 174.4409 172.1112 171.0021
## 95% 263.5003 260.5325 264.3532 267.2902 264.2874 261.6130 262.3544
## [,72] [,73] [,74] [,75] [,76] [,77] [,78]
## 5% 164.1381 170.5884 169.5199 169.0765 171.6739 168.3211 176.1369
## 95% 257.0621 263.6432 257.6256 261.8388 264.6973 257.8342 269.7964
## [,79] [,80] [,81] [,82] [,83] [,84] [,85]
## 5% 169.4417 170.2784 174.2605 171.1436 166.2769 176.5694 179.0829
## 95% 266.5231 269.2050 273.5391 267.6374 262.5342 273.8052 273.4130
## [,86] [,87] [,88] [,89] [,90] [,91]
## 5% 185.5635 182.0061 176.3259 169.4560 170.9481 171.4957
## 95% 271.6683 272.6105 270.1062 268.1681 266.0645 270.0151Train a Modified Bayesian Regression Structural Time Series Model to Forecast Close Price…
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## =-=-=-=-= Iteration 90 Wed Jan 16 02:02:19 2019
## =-=-=-=-=## $residual.sd
## [1] 1.051887
##
## $prediction.sd
## [1] 3.350503
##
## $rsquare
## [1] 0.9998841
##
## $relative.gof
## [1] 0.3945556
##
## $size
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 1.033 1.000 2.000
##
## $coefficients
## mean sd mean.inc sd.inc
## daily.returns 8.412071e+01 3.012543e+00 8.412071e+01 3.012543e+00
## Volume -2.660009e-09 1.585453e-08 -7.980026e-08 4.418233e-08
## Adjusted 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## (Intercept) 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## inc.prob
## daily.returns 1.00000000
## Volume 0.03333333
## Adjusted 0.00000000
## (Intercept) 0.00000000Parameter Estimates: (Sample mean of the Sampled beta value)
## (Intercept) Volume Adjusted daily.returns
## 0.000000e+00 -2.394008e-09 0.000000e+00 8.290968e+01Plot Model 
## [1] "coefficients" "sigma.obs"
## [3] "trend.level.sd" "trend.slope.mean"
## [5] "trend.slope.ar.coefficient" "trend.slope.sd"
## [7] "sigma.seasonal.54" "final.state"
## [9] "state.contributions" "one.step.prediction.errors"
## [11] "log.likelihood" "has.regression"
## [13] "state.specification" "prior"
## [15] "timestamp.info" "model.options"
## [17] "family" "niter"
## [19] "original.series" "xlevels"
## [21] "terms" "predictors"Create Prediction of Close Price Using the Modified Regression Model…
## List of 5
## $ mean : num [1:91] 192 194 194 195 195 ...
## $ median : num [1:91] 192 193 193 195 196 ...
## $ interval : num [1:2, 1:91] 187 196 188 203 185 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "5%" "95%"
## .. ..$ : NULL
## $ distribution : num [1:84, 1:91] 191 193 192 190 190 ...
## $ original.series:'zoo' series from 2014-03-31 to 2019-01-15
## Data: num [1:1208] 51.8 53.6 54.2 53.6 54.8 ...
## Index: Date[1:1208], format: "2014-03-31" "2014-04-01" ...
## - attr(*, "class")= chr "bsts.prediction"Summary of Prediction:
summary(pred)## Length Class Mode
## mean 91 -none- numeric
## median 91 -none- numeric
## interval 182 -none- numeric
## distribution 7644 -none- numeric
## original.series 1208 zoo numericPlot the Mean:
par(mfrow=c(2,2))
plot(pred$original.series)
plot(pred$mean)
plot(pred$median)
plot(pred$interval)
Plot Prediction:
plot(pred, plot.original = 90)
Regression Model Prediction Table
tail(pred$original.series)## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-14 2019-01-15
## 192.95 200.32 202.64 198.25 193.94 195.05head(pred$mean)## [1] 191.8285 193.9632 193.7384 195.3448 195.2987 192.6857head(pred$median)## [1] 191.8657 193.4272 193.1745 194.5816 195.9227 192.4790head(pred$interval)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## 5% 186.7359 187.6821 185.0559 185.5517 183.5621 178.9383 175.2599
## 95% 196.4425 202.7538 204.0565 205.6630 210.1852 209.8270 208.0228
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 5% 178.0774 170.4598 174.6646 172.0154 170.1631 172.0635 171.8353
## 95% 212.8196 208.6432 213.8198 210.1681 214.4725 216.3719 220.1788
## [,15] [,16] [,17] [,18] [,19] [,20] [,21]
## 5% 171.9405 165.6925 166.7130 167.7967 167.8754 161.8748 160.4803
## 95% 218.8016 217.6552 216.2999 217.6724 220.0839 218.1236 223.4669
## [,22] [,23] [,24] [,25] [,26] [,27] [,28]
## 5% 158.4471 159.0931 159.4170 158.0743 149.8735 154.8564 163.9889
## 95% 219.4753 221.1295 222.4541 227.4153 221.6837 229.2334 233.6785
## [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 5% 156.5563 164.4832 159.4787 155.6958 156.2335 159.6074 159.4234
## 95% 226.4420 237.0686 236.5071 231.6743 230.2689 233.0842 230.6116
## [,36] [,37] [,38] [,39] [,40] [,41] [,42]
## 5% 154.5245 145.3882 162.1517 152.7861 158.8758 152.6807 158.3889
## 95% 224.8413 215.6040 232.7299 232.2668 240.9074 238.6274 243.9063
## [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## 5% 152.8455 155.5113 158.5461 162.8342 154.4218 147.7902 143.7282
## 95% 239.9488 240.3088 244.9992 248.0589 238.6348 237.4902 239.5159
## [,50] [,51] [,52] [,53] [,54] [,55] [,56]
## 5% 153.2983 153.2929 148.5112 152.6803 145.8733 156.3975 153.1375
## 95% 247.9469 244.2584 246.1877 247.8779 237.1019 250.2813 249.6086
## [,57] [,58] [,59] [,60] [,61] [,62] [,63]
## 5% 152.7042 149.5503 145.1557 148.4093 142.7479 145.7405 148.7503
## 95% 246.5241 246.8214 240.1207 246.5694 240.0896 244.7363 247.4029
## [,64] [,65] [,66] [,67] [,68] [,69] [,70]
## 5% 140.3334 146.6643 144.4696 147.4884 144.9195 141.9802 136.9319
## 95% 238.6279 244.7010 241.4578 244.9022 248.4513 247.4281 248.1244
## [,71] [,72] [,73] [,74] [,75] [,76] [,77]
## 5% 140.0753 137.0386 137.7366 134.5236 136.4916 141.2606 136.0731
## 95% 246.2729 244.5314 250.5305 246.9829 249.4874 250.1482 245.9604
## [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85]
## 5% 144.6243 141.496 138.2127 140.8766 130.2767 128.0814 129.7992 126.935
## 95% 255.7401 250.208 248.5434 249.5410 242.9723 240.7063 246.2801 245.923
## [,86] [,87] [,88] [,89] [,90] [,91]
## 5% 128.6829 132.6882 129.2086 122.0651 124.2678 124.6200
## 95% 243.1573 247.5282 243.2342 237.9221 242.8898 250.4382