* Corporates vs Treasuries Analysis on Thu Jun 10 14:12:23 2021
- input data directory: ./data
- results directory: ./results
- transcript to: ./results/corporates-vs-treasuries-transcript.txt
Script Archival:
----------------
* Archived analysis script(s) to ./results:
- ./corporates-vs-treasuries.r
Loading datasets:
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* Loading stock market, treasury, and corporate returns data
- Input file ./data/tsm-itt-itc-data.tsv
- Structure of data found:
'data.frame': 150 obs. of 7 variables:
$ Year : int 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 ...
$ TSM.Nominal: num 15.6 11.19 -2.51 4.67 5.31 ...
$ ITT.Nominal: num 2.75 1.79 4.24 13.7 6.47 7.18 3.63 6.42 2.93 5.26 ...
$ ITC.Nominal: num 3 1.4 3.39 12.72 6.99 ...
$ TSM.Real : num 13.86 8.74 1.99 12.46 11.78 ...
$ ITT.Real : num 1.2 -0.46 9.05 22.15 13.01 ...
$ ITC.Real : num 1.45 -0.84 8.16 21.11 13.56 ...
- Column names and types check out
- No missing data
- Year range check passed: 1871 - 2020
Exploratory correlations:
-------------------------
* Real correlations:
- Correlation chart of TSM.Real, ITT.Real, ITC.Real to ./results/correlation-chart-Real.png
- Significance of difference of correlations 0.16 and 0.28 (150 points):
Z = 1.09, p = 0.14
- Also, 3d scatterplot to ./results/scatterplot-3d-Real.gif
* Nominal correlations:
- Correlation chart of TSM.Nominal, ITT.Nominal, ITC.Nominal to ./results/correlation-chart-Nominal.png
- Significance of difference of correlations 0.03 and 0.22 (150 points):
Z = 1.64, p = 0.051
- Also, 3d scatterplot to ./results/scatterplot-3d-Nominal.gif
Call: cv.glmnet(x = as.matrix(df[, c(2, 3)]), y = df[, 4, drop = TRUE], type.measure = "mse", foldid = df$Fold, alpha = 1, family = "gaussian")
Measure: Mean-Squared Error
Lambda Measure SE Nonzero
min 0.0402 9.984 2.969 2
1se 1.1461 12.510 2.670 2
3 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) -0.14359923
TSM.Real 0.06392401
ITT.Real 0.94145839
Call:
lm(formula = as.formula(sprintf("%s ~ %s + %s", colnames(df)[[4]],
colnames(df)[[2]], colnames(df)[[3]])), data = df)
Residuals:
Min 1Q Median 3Q Max
-18.2266 -1.1336 -0.2595 0.8417 21.8937
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.17198 0.29205 -0.589 0.557
TSM.Real 0.06585 0.01437 4.584 9.68e-06 ***
ITT.Real 0.94562 0.03109 30.419 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.14 on 147 degrees of freedom
Multiple R-squared: 0.8736, Adjusted R-squared: 0.8719
F-statistic: 507.9 on 2 and 147 DF, p-value: < 2.2e-16
Just modernity, thanks...:
--------------------------
* Real correlations:
- Correlation chart of TSM.Real, ITT.Real, ITC.Real to ./results/correlation-chart-Real 1980-2020.png
- Significance of difference of correlations -0.01 and 0.39 (41 points):
Z = 1.83, p = 0.034
- Also, 3d scatterplot to ./results/scatterplot-3d-Real 1980-2020.gif
Call: cv.glmnet(x = as.matrix(df[, c(2, 3)]), y = df[, 4, drop = TRUE], type.measure = "mse", foldid = df$Fold, alpha = 1, family = "gaussian")
Measure: Mean-Squared Error
Lambda Measure SE Nonzero
min 0.0419 24.85 6.028 2
1se 1.1942 30.49 8.750 2
3 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) -0.0708163
TSM.Real 0.2091127
ITT.Real 0.7962118
Call:
lm(formula = as.formula(sprintf("%s ~ %s + %s", colnames(df)[[4]],
colnames(df)[[2]], colnames(df)[[3]])), data = df)
Residuals:
Min 1Q Median 3Q Max
-10.7725 -2.4481 0.1203 1.5592 17.3253
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.12055 0.94350 -0.128 0.899
TSM.Real 0.21179 0.04582 4.622 4.29e-05 ***
ITT.Real 0.80151 0.09057 8.850 9.09e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.636 on 38 degrees of freedom
Multiple R-squared: 0.7223, Adjusted R-squared: 0.7077
F-statistic: 49.41 on 2 and 38 DF, p-value: 2.682e-11
Start year sensitivity:
-----------------------
* Examining sensitivity of Treas/Corp real corr diff with start year:
- Start year: 1970 - 1990
- End year: 2020
- Significance of difference of correlations 0.12 and 0.48 (51 points):
Z = 1.97, p = 0.025
- Significance of difference of correlations 0.14 and 0.50 (50 points):
Z = 1.97, p = 0.024
- Significance of difference of correlations 0.13 and 0.50 (49 points):
Z = 1.96, p = 0.025
- Significance of difference of correlations 0.14 and 0.50 (48 points):
Z = 1.95, p = 0.026
- Significance of difference of correlations 0.10 and 0.48 (47 points):
Z = 1.95, p = 0.026
- Significance of difference of correlations 0.03 and 0.40 (46 points):
Z = 1.82, p = 0.034
- Significance of difference of correlations 0.05 and 0.40 (45 points):
Z = 1.70, p = 0.044
- Significance of difference of correlations 0.04 and 0.39 (44 points):
Z = 1.68, p = 0.046
- Significance of difference of correlations 0.01 and 0.37 (43 points):
Z = 1.71, p = 0.043
- Significance of difference of correlations -0.01 and 0.36 (42 points):
Z = 1.73, p = 0.042
- Significance of difference of correlations -0.01 and 0.39 (41 points):
Z = 1.83, p = 0.034
- Significance of difference of correlations 0.02 and 0.44 (40 points):
Z = 1.96, p = 0.025
- Significance of difference of correlations -0.01 and 0.41 (39 points):
Z = 1.92, p = 0.028
- Significance of difference of correlations -0.04 and 0.47 (38 points):
Z = 2.30, p = 0.011
- Significance of difference of correlations -0.03 and 0.47 (37 points):
Z = 2.25, p = 0.012
- Significance of difference of correlations -0.02 and 0.50 (36 points):
Z = 2.30, p = 0.011
- Significance of difference of correlations -0.10 and 0.47 (35 points):
Z = 2.45, p = 0.0072
- Significance of difference of correlations -0.13 and 0.47 (34 points):
Z = 2.53, p = 0.0057
- Significance of difference of correlations -0.16 and 0.46 (33 points):
Z = 2.55, p = 0.0053
- Significance of difference of correlations -0.15 and 0.46 (32 points):
Z = 2.51, p = 0.0061
- Significance of difference of correlations -0.19 and 0.46 (31 points):
Z = 2.54, p = 0.0055
- Results plotted to ./results/start-year-dependency.png
* Corporates vs Treasuries Analysis completed Thu Jun 10 14:12:42 2021 (19.2 sec elapsed).