## # A tibble: 548 × 5
## # Groups: symbol [4]
## symbol date price change text
## <chr> <date> <dbl> <dbl> <glue>
## 1 GDPC1 1947-01-01 2034. NA 1947.1,
## Growth: NA
## 2 GDPC1 1947-04-01 2029. -0.00267 1947.2,
## Growth: -0.3%
## 3 GDPC1 1947-07-01 2025. -0.00207 1947.3,
## Growth: -0.2%
## 4 GDPC1 1947-10-01 2057. 0.0156 1947.4,
## Growth: 1.6%
## 5 GDPC1 1948-01-01 2087. 0.0150 1948.1,
## Growth: 1.5%
## 6 GDPC1 1948-04-01 2122. 0.0165 1948.2,
## Growth: 1.7%
## 7 GDPC1 1948-07-01 2134. 0.00573 1948.3,
## Growth: 0.6%
## 8 GDPC1 1948-10-01 2136. 0.00112 1948.4,
## Growth: 0.1%
## 9 GDPC1 1949-01-01 2107. -0.0138 1949.1,
## Growth: -1.4%
## 10 GDPC1 1949-04-01 2100. -0.00341 1949.2,
## Growth: -0.3%
## # … with 538 more rowsAnalyze your company’s data. Consider the following:
timing depth *duration
Of downturns in sales.
Timing During the recession the economy and Comptus both rose in sales.
Depth For the economy in 2019 the growth was 0.5%, then it went down to -1.3% in 2020. Computus in 2019 was at .5%, but went down to -10.6% in 2020. So Computus relies heavely on the economy.
Duration Comptus mainly stays above the negative growth line. Also, it is mostly positive for the entirity of the companies life, other than around Covid.