GOOG_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] "GOOG"## [1] "Communication Services"## [1] "Interactive Media & Services"Import the Stock Dataset…
## [1] "GOOG"## An 'xts' object on 2014-01-17/2019-01-15 containing:
## Data: num [1:1257, 1:6] 575 577 580 576 572 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:6] "GOOG.Open" "GOOG.High" "GOOG.Low" "GOOG.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 02:08:16"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] 564 556 597 567 572 ...
## - 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 02:08:16"## Open High Low Close Volume Adjusted
## 2014-03-31 563.7900 563.8994 553.8845 553.9243 10800 553.9243
## 2014-04-01 555.6547 565.3414 555.6547 564.0585 7900 564.0585
## 2014-04-02 596.7090 601.5225 559.1157 563.8994 147500 563.8994
## 2014-04-03 566.7338 584.0685 561.0450 566.6244 5113100 566.6244
## 2014-04-04 571.5076 574.6105 540.0306 540.1699 6386800 540.1699
## 2014-04-07 537.7830 545.4807 524.2673 535.2072 4413700 535.2072
## daily.returns atr SMI ADX oscillator
## 2014-03-31 -0.0053929421 10.82747 -33.53977 23.15336 -70
## 2014-04-01 0.0182954220 10.86959 -36.29836 25.08085 -70
## 2014-04-02 -0.0002822048 13.12225 -37.82165 23.82583 -70
## 2014-04-03 0.0048324883 13.82948 -38.12098 22.66045 -70
## 2014-04-04 -0.0466879461 15.31165 -43.19195 22.08344 -100
## 2014-04-07 -0.0091873101 15.73320 -47.30844 22.41628 -100
## pctB Delt.1.arithmetic EMA maEMV signal
## 2014-03-31 0.02191260 -0.16519216 -0.5199409 -18.88975 -0.3883305
## 2014-04-01 0.13616488 -0.15804341 -0.2917477 -16.70284 -0.5884175
## 2014-04-02 0.35081510 2.20258964 -0.3795010 -10.37021 -0.7508086
## 2014-04-03 0.30677904 0.21422460 -0.4041978 -10.40161 -0.8718126
## 2014-04-04 0.06392328 0.09890972 -0.5110614 -10.43979 -1.0328525
## 2014-04-07 -0.07085107 -0.28911978 -0.4124297 -10.55061 -1.2229632
## mfi sar
## 2014-03-31 16.0881151 582.4584
## 2014-04-01 17.4870427 577.8929
## 2014-04-02 19.7588157 553.9243
## 2014-04-03 19.5890639 553.9243
## 2014-04-04 8.9083640 601.5225
## 2014-04-07 0.4320798 601.5225Exploratory 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] 4.653731
##
## $prediction.sd
## [1] 12.45421
##
## $rsquare
## [1] 0.9995363
##
## $relative.gof
## [1] -0.02596728Plot 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] 1076 1075 1076 1073 1076 ...
## $ median : num [1:20] 1077 1074 1076 1074 1075 ...
## $ interval : num [1:2, 1:20] 1053 1091 1050 1097 1046 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "5%" "95%"
## .. ..$ : NULL
## $ distribution : num [1:90, 1:20] 1080 1082 1084 1099 1090 ...
## $ original.series:'zoo' series from 2014-03-31 to 2019-01-15
## Data: num [1:1208] 554 564 564 567 540 ...
## 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 1800 -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$original.series)## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-14 2019-01-15
## 1076.28 1074.66 1070.33 1057.19 1044.69 1077.15head(pred$mean)## [1] 1076.096 1074.938 1075.674 1073.444 1076.323 1078.456head(pred$median)## [1] 1077.000 1073.501 1075.783 1073.786 1075.277 1079.716head(pred$interval)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## 5% 1053.291 1050.253 1045.771 1040.191 1042.209 1040.844 1037.484
## 95% 1090.687 1097.167 1098.795 1103.192 1111.731 1116.211 1120.822
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 5% 1037.422 1038.771 1038.805 1026.418 1024.441 1020.323 1020.174
## 95% 1120.207 1134.213 1128.781 1136.509 1137.636 1148.233 1158.923
## [,15] [,16] [,17] [,18] [,19] [,20]
## 5% 1009.938 1013.055 1009.121 1010.848 1006.712 1011.248
## 95% 1155.656 1155.984 1159.226 1158.208 1159.771 1159.111Train a Bayesian Regression Structural Time Series Model to Forecast Close Price…
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## =-=-=-=-= Iteration 90 Wed Jan 16 02:09:33 2019
## =-=-=-=-=## $residual.sd
## [1] 2.347485
##
## $prediction.sd
## [1] 7.695103
##
## $rsquare
## [1] 0.999882
##
## $relative.gof
## [1] 0.6083241
##
## $size
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 1.0 4.0 2.9 4.0 6.0
##
## $coefficients
## mean sd mean.inc sd.inc
## daily.returns 3.658029e+02 5.077317e+01 365.802864827 50.77317322
## EMA 5.744693e+00 4.861029e+00 9.232542779 2.33447809
## sar -6.248684e-02 6.632812e-02 -0.108150306 0.05140105
## Adjusted 5.981886e-02 6.630046e-02 0.119637730 0.03965051
## Low 1.502880e-03 6.369351e-03 0.016907400 0.01476968
## High 9.374259e-04 5.313597e-03 0.016873667 0.01709049
## Open -5.790669e-05 3.594042e-03 -0.001302900 0.01952039
## SMI -5.994437e-05 5.686822e-04 -0.005394993 0.00000000
## mfi 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## signal 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## maEMV 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## Delt.1.arithmetic 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## pctB 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## oscillator 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## ADX 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## atr 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## Volume 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## (Intercept) 0.000000e+00 0.000000e+00 0.000000000 0.00000000
## inc.prob
## daily.returns 1.00000000
## EMA 0.62222222
## sar 0.57777778
## Adjusted 0.50000000
## Low 0.08888889
## High 0.05555556
## Open 0.04444444
## SMI 0.01111111
## mfi 0.00000000
## signal 0.00000000
## maEMV 0.00000000
## Delt.1.arithmetic 0.00000000
## pctB 0.00000000
## oscillator 0.00000000
## ADX 0.00000000
## atr 0.00000000
## Volume 0.00000000
## (Intercept) 0.00000000Parameter Estimates: (Sample mean of the Sampled beta value)
## (Intercept) Open High Low
## 0.000000e+00 -5.211602e-05 8.436833e-04 1.352592e-03
## Volume Adjusted daily.returns atr
## 0.000000e+00 5.383698e-02 3.451865e+02 0.000000e+00
## SMI ADX oscillator pctB
## -5.394993e-05 0.000000e+00 0.000000e+00 0.000000e+00
## Delt.1.arithmetic EMA maEMV signal
## 0.000000e+00 5.170224e+00 0.000000e+00 0.000000e+00
## mfi sar
## 0.000000e+00 -5.623816e-02Plot 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] 1048 1046 1049 1051 1062 ...
## $ median : num [1:91] 1048 1046 1049 1051 1062 ...
## $ interval : num [1:2, 1:91] 1044 1053 1039 1052 1040 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "5%" "95%"
## .. ..$ : NULL
## $ distribution : num [1:2, 1:91] 1043 1054 1038 1053 1039 ...
## $ original.series:'zoo' series from 2014-03-31 to 2019-01-15
## Data: num [1:1208] 554 564 564 567 540 ...
## 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 182 -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$original.series)## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-14 2019-01-15
## 1076.28 1074.66 1070.33 1057.19 1044.69 1077.15head(pred$mean)## [1] 1048.420 1045.581 1048.682 1051.084 1061.686 1050.594head(pred$median)## [1] 1048.420 1045.581 1048.682 1051.084 1061.686 1050.594head(pred$interval)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## 5% 1043.832 1039.047 1039.921 1043.459 1048.729 1035.796 1049.782
## 95% 1053.008 1052.114 1057.442 1058.710 1074.644 1065.392 1079.228
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 5% 1048.606 1036.892 1045.919 1045.920 1046.745 1045.646 1059.720
## 95% 1077.441 1068.804 1069.930 1078.355 1079.988 1069.143 1078.993
## [,15] [,16] [,17] [,18] [,19] [,20] [,21]
## 5% 1068.039 1054.509 1058.169 1048.628 1037.033 1039.961 1041.822
## 95% 1084.141 1082.167 1098.429 1087.218 1078.623 1085.162 1080.901
## [,22] [,23] [,24] [,25] [,26] [,27] [,28]
## 5% 1013.305 1020.731 1019.097 1015.285 990.2831 1010.021 1025.74
## 95% 1056.399 1066.886 1069.258 1066.151 1033.0874 1046.329 1057.09
## [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 5% 1000.653 1018.558 1008.169 1002.163 1010.358 1017.584 1014.798
## 95% 1045.648 1067.002 1062.176 1057.414 1063.300 1057.799 1066.705
## [,36] [,37] [,38] [,39] [,40] [,41] [,42]
## 5% 989.220 1026.141 1004.289 984.9669 1006.081 1021.519 1008.487
## 95% 1043.665 1074.030 1052.818 1039.1861 1060.469 1081.946 1061.600
## [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## 5% 998.7286 991.469 1007.817 1035.897 1015.619 1010.941 997.8328
## 95% 1051.8848 1050.509 1065.096 1080.088 1065.272 1066.584 1062.0544
## [,50] [,51] [,52] [,53] [,54] [,55] [,56]
## 5% 1000.343 1005.083 1017.226 1012.868 986.999 1004.985 998.3561
## 95% 1073.413 1078.649 1089.339 1081.803 1044.145 1068.332 1071.6561
## [,57] [,58] [,59] [,60] [,61] [,62] [,63]
## 5% 1002.858 1023.148 1018.096 1065.996 1061.449 1067.587 1069.782
## 95% 1061.921 1078.057 1074.620 1105.164 1096.473 1089.216 1090.632
## [,64] [,65] [,66] [,67] [,68] [,69] [,70]
## 5% 1042.418 1068.409 1036.404 1049.386 1045.386 1052.371 1049.695
## 95% 1059.538 1085.284 1039.933 1051.924 1056.832 1058.467 1054.676
## [,71] [,72] [,73] [,74] [,75] [,76] [,77]
## 5% 1045.056 1041.988 1060.224 1057.084 1058.827 1046.342 1055.386
## 95% 1048.321 1046.440 1061.461 1068.836 1060.657 1047.501 1057.295
## [,78] [,79] [,80] [,81] [,82] [,83] [,84]
## 5% 1084.040 1069.520 1084.150 1087.356 1091.586 1067.557 1097.785
## 95% 1096.761 1089.229 1099.953 1091.617 1094.384 1075.860 1115.091
## [,85] [,86] [,87] [,88] [,89] [,90] [,91]
## 5% 1068.231 1068.291 1052.044 1045.899 1034.178 1023.857 1034.247
## 95% 1091.963 1089.569 1079.653 1074.211 1062.142 1061.985 1083.679Train a Modified Bayesian Regression Structural Time Series Model to Forecast Close Price…
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## =-=-=-=-= Iteration 90 Wed Jan 16 02:09:46 2019
## =-=-=-=-=## $residual.sd
## [1] 2.210037
##
## $prediction.sd
## [1] 8.495394
##
## $rsquare
## [1] 0.9998954
##
## $relative.gof
## [1] 0.5226127
##
## $size
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1
##
## $coefficients
## mean sd mean.inc sd.inc inc.prob
## daily.returns 402.4452 42.16706 402.4452 42.16706 1
## Adjusted 0.0000 0.00000 0.0000 0.00000 0
## Volume 0.0000 0.00000 0.0000 0.00000 0
## (Intercept) 0.0000 0.00000 0.0000 0.00000 0Parameter Estimates: (Sample mean of the Sampled beta value)
## (Intercept) Volume Adjusted daily.returns
## 0.0000 0.0000 0.0000 377.4992Plot 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] 1062 1062 1064 1068 1074 ...
## $ median : num [1:91] 1063 1058 1061 1064 1075 ...
## $ interval : num [1:2, 1:91] 1051 1072 1053 1077 1055 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "5%" "95%"
## .. ..$ : NULL
## $ distribution : num [1:8, 1:91] 1065 1072 1070 1068 1050 ...
## $ original.series:'zoo' series from 2014-03-31 to 2019-01-15
## Data: num [1:1208] 554 564 564 567 540 ...
## 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 728 -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$original.series)## 2019-01-08 2019-01-09 2019-01-10 2019-01-11 2019-01-14 2019-01-15
## 1076.28 1074.66 1070.33 1057.19 1044.69 1077.15head(pred$mean)## [1] 1062.446 1061.602 1064.334 1067.696 1073.702 1065.159head(pred$median)## [1] 1062.615 1057.625 1061.360 1063.950 1074.650 1069.765head(pred$interval)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## 5% 1051.380 1053.300 1054.635 1049.082 1048.93 1032.127 1033.725 1027.314
## 95% 1071.543 1077.167 1079.814 1095.090 1104.49 1098.268 1103.762 1094.067
## [,9] [,10] [,11] [,12] [,13] [,14] [,15]
## 5% 1016.641 1023.927 1035.987 1043.815 1039.693 1054.909 1057.112
## 95% 1091.293 1105.445 1115.940 1127.597 1128.393 1144.255 1155.915
## [,16] [,17] [,18] [,19] [,20] [,21] [,22]
## 5% 1048.684 1041.628 1030.721 1038.614 1027.547 1026.063 1007.716
## 95% 1151.664 1173.004 1177.533 1190.668 1199.975 1190.130 1181.664
## [,23] [,24] [,25] [,26] [,27] [,28] [,29]
## 5% 1014.446 1016.269 1014.453 995.2356 1021.287 1038.713 1006.384
## 95% 1194.792 1196.395 1197.477 1186.3856 1204.735 1215.579 1190.332
## [,30] [,31] [,32] [,33] [,34] [,35] [,36]
## 5% 1022.174 1004.283 991.2864 1008.218 1022.563 1017.959 993.7759
## 95% 1209.606 1204.852 1194.3871 1211.210 1203.682 1197.313 1175.1390
## [,37] [,38] [,39] [,40] [,41] [,42] [,43]
## 5% 1033.429 1003.831 996.0879 1025.693 1041.217 1027.280 1030.897
## 95% 1209.790 1174.691 1162.3389 1188.525 1195.942 1173.352 1166.130
## [,44] [,45] [,46] [,47] [,48] [,49] [,50] [,51]
## 5% 1027.259 1034.554 1042.205 1023.42 1009.514 1001.231 1005.923 1008.275
## 95% 1166.905 1190.663 1200.970 1186.34 1189.961 1184.040 1194.462 1208.008
## [,52] [,53] [,54] [,55] [,56] [,57] [,58]
## 5% 1019.169 1005.279 991.9454 1009.070 1012.508 1006.726 1017.727
## 95% 1214.100 1197.431 1173.2262 1197.951 1201.570 1205.017 1219.051
## [,59] [,60] [,61] [,62] [,63] [,64] [,65]
## 5% 1001.851 1028.302 1008.192 1005.821 1012.828 986.8021 1016.015
## 95% 1208.229 1225.390 1212.849 1217.482 1227.843 1198.0510 1227.483
## [,66] [,67] [,68] [,69] [,70] [,71] [,72]
## 5% 994.868 1007.808 1016.157 1011.660 1002.829 998.5024 987.4353
## 95% 1206.006 1221.633 1226.908 1232.077 1220.897 1215.5176 1213.0597
## [,73] [,74] [,75] [,76] [,77] [,78] [,79]
## 5% 1004.758 998.7331 994.9902 990.6137 1000.453 1033.949 1016.453
## 95% 1228.998 1229.4686 1228.6987 1226.7837 1246.541 1282.764 1251.030
## [,80] [,81] [,82] [,83] [,84] [,85] [,86]
## 5% 1017.466 1022.558 1030.164 1013.417 1043.832 1016.863 1025.917
## 95% 1253.910 1263.386 1263.914 1238.732 1279.189 1253.000 1259.551
## [,87] [,88] [,89] [,90] [,91]
## 5% 1021.655 1019.743 1020.860 1027.302 1043.697
## 95% 1259.648 1259.046 1258.115 1262.605 1284.576