Learning objectives

Datacamp

• Explore on your own
• No specific required class this week

Macro data sources

• For Singapore: Data.gov.sg
  • Covers: Economy, education, environment, finance, health, infrastructure, society, technology, transport
• For real estate in Singapore: URA’s REALIS system
  • Access through the library
• WRDS has some as well
• For US: data.gov, as well as many agency websites
  • Like BLS or the Federal Reserve

Loading macro data

• Singapore business expectations data (from data.gov.sg)

# extract out Q1, finance only
expectations_avg <- expectations %>%
  filter(quarter == 1,                               # Keep only the first quarter
         level_2 == "Financial & Insurance") %>%     # Keep only financial responses
  group_by(year) %>%                                 # Group data by year
  mutate(fin_sentiment=mean(value, na.rm=TRUE)) %>%  # Calculate average
  slice(1)                                           # Take only 1 row per group

• At this point, we can merge with our accounting data

What was in the macro data?

expectations %>%
  arrange(level_2, level_3, desc(year)) %>%  # sort the data
  select(year, quarter, level_2, level_3, value) %>%  # keep only these variables
  datatable(options = list(pageLength = 5), rownames=FALSE)  # display using DT

dplyr makes merging easy

• For merging, use dplyr’s *_join() commands
  • left_join() for merging a dataset into another
  • inner_join() for keeping only matched observations
  • outer_join() for making all possible combinations
• For sorting, dplyr’s arrange() command is easy to use
  • For sorting in reverse, combine arrange() with desc()

Merging example

Merge in the finance sentiment data to our accounting data

# subset out our Singaporean data, since our macro data is Singapore-specific
df_SG <- df_clean %>% filter(fic == "SGP")

# Create year in df_SG (date is given by datadate as YYYYMMDD)
df_SG$year = round(df_SG$datadate / 10000, digits=0)

# Combine datasets
# Notice how it automatically figures out to join by "year"
df_SG_macro <- left_join(df_SG, expectations_avg[,c("year","fin_sentiment")])

## Joining, by = "year"

Building in macro data

• First try: Just add it in

macro1 <- lm(revt_lead ~ revt + act + che + lct + dp + ebit + fin_sentiment,
             data=df_SG_macro)
library(broom)
tidy(macro1)

## # A tibble: 8 x 5
##   term          estimate std.error statistic       p.value
##   <chr>            <dbl>     <dbl>     <dbl>         <dbl>
## 1 (Intercept)     24.0     15.9        1.50  0.134        
## 2 revt             0.497    0.0798     6.22  0.00000000162
## 3 act             -0.102    0.0569    -1.79  0.0739       
## 4 che              0.495    0.167      2.96  0.00329      
## 5 lct              0.403    0.0903     4.46  0.0000114    
## 6 dp               4.54     1.63       2.79  0.00559      
## 7 ebit            -0.930    0.284     -3.28  0.00117      
## 8 fin_sentiment    0.122    0.472      0.259 0.796

It isn’t significant. Why is this?

Scaling matters

• All of our firm data is on the same terms as revenue: dollars within a given firm
• But fin_sentiment is a constant scale…
  • Need to scale this to fit the problem
    • The current scale would work for revenue growth

df_SG_macro %>%
  ggplot(aes(y=revt_lead,
             x=fin_sentiment)) + 
  geom_point()

df_SG_macro %>%
  ggplot(aes(y=revt_lead,
    x=scale(fin_sentiment) * revt)) + 
  geom_point()

Scaled macro data

• Normalize and scale by revenue

# Scale creates z-scores, but returns a matrix by default.  [,1] gives a vector
df_SG_macro$fin_sent_scaled <- scale(df_SG_macro$fin_sentiment)[,1]
macro3 <-
  lm(revt_lead ~ revt + act + che + lct + dp + ebit + fin_sent_scaled:revt,
     data=df_SG_macro)
tidy(macro3)

## # A tibble: 8 x 5
##   term                 estimate std.error statistic       p.value
##   <chr>                   <dbl>     <dbl>     <dbl>         <dbl>
## 1 (Intercept)           25.5      13.8         1.84 0.0663       
## 2 revt                   0.490     0.0789      6.21 0.00000000170
## 3 act                   -0.0677    0.0576     -1.18 0.241        
## 4 che                    0.439     0.166       2.64 0.00875      
## 5 lct                    0.373     0.0898      4.15 0.0000428    
## 6 dp                     4.10      1.61        2.54 0.0116       
## 7 ebit                  -0.793     0.285      -2.78 0.00576      
## 8 revt:fin_sent_scaled   0.0897    0.0332      2.70 0.00726

glance(macro3)

## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic   p.value    df logLik   AIC
##       <dbl>         <dbl> <dbl>     <dbl>     <dbl> <int>  <dbl> <dbl>
## 1     0.847         0.844  215.      240. 1.48e-119     8 -2107. 4232.
## # … with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

Model comparisons

baseline <-
  lm(revt_lead ~ revt + act + che + lct + dp + ebit,
     data=df_SG_macro[!is.na(df_SG_macro$fin_sentiment),])
glance(baseline)

## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic   p.value    df logLik   AIC
##       <dbl>         <dbl> <dbl>     <dbl>     <dbl> <int>  <dbl> <dbl>
## 1     0.843         0.840  217.      273. 3.13e-119     7 -2111. 4237.
## # … with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

glance(macro3)

## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic   p.value    df logLik   AIC
##       <dbl>         <dbl> <dbl>     <dbl>     <dbl> <int>  <dbl> <dbl>
## 1     0.847         0.844  215.      240. 1.48e-119     8 -2107. 4232.
## # … with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

Adjusted \(R^2\) and AIC are slightly better with macro data

Model comparisons

anova(baseline, macro3, test="Chisq")

## Analysis of Variance Table
## 
## Model 1: revt_lead ~ revt + act + che + lct + dp + ebit
## Model 2: revt_lead ~ revt + act + che + lct + dp + ebit + fin_sent_scaled:revt
##   Res.Df      RSS Df Sum of Sq Pr(>Chi)   
## 1    304 14285622                         
## 2    303 13949301  1    336321 0.006875 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Macro model definitely fits better than the baseline model!

Takeaway

1. Adding macro data can help explain some exogenous variation in a model
   • Exogenous meaning outside of the firms, in this case
2. Scaling is very important
   • Not scaling properly can suppress some effects from being visible

Scaling up our model, again

Building a model requires careful thought!

• What macroeconomic data makes sense to add to our model?

This is where having accounting and business knowledge comes in!

Validation

• Ideal:
  • Withhold the last year (or a few) of data when building the model
  • Check performance on hold out sample
• Sometimes acceptable:
  • Withhold a random sample of data when building the model
  • Check performance on hold out sample

Estimation

• As we never constructed a hold out sample, let’s end by estimating UOL’s 2018 year revenue
  • Announced in 2019

p_uol <- predict(forecast2, uol[uol$fyear==2017,])
p_base <- predict(baseline,
  df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear==2017,])
p_macro <- predict(macro3,
  df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear==2017,])
p_world <- predict(forecast4,
  df_clean[df_clean$isin=="SG1S83002349" & df_clean$fyear==2017,])
preds <- c(p_uol, p_base, p_macro, p_world)
names(preds) <- c("UOL 2018 UOL", "UOL 2018 Base", "UOL 2018 Macro",
                  "UOL 2018 World")
preds

##   UOL 2018 UOL  UOL 2018 Base UOL 2018 Macro UOL 2018 World 
##       3177.073       2086.437       2024.842       2589.636

Visualizing our prediction

In Sample Accuracy

# series vectors calculated here -- See appendix
rmse <- function(v1, v2) {
  sqrt(mean((v1 - v2)^2, na.rm=T))
}

rmse <- c(rmse(actual_series, uol_series), rmse(actual_series, base_series),
          rmse(actual_series, macro_series), rmse(actual_series, world_series))
names(rmse) <- c("UOL 2018 UOL", "UOL 2018 Base", "UOL 2018 Macro", "UOL 2018 World")
rmse

##   UOL 2018 UOL  UOL 2018 Base UOL 2018 Macro UOL 2018 World 
##       175.5609       301.3161       344.9681       332.8101

Why is UOL the best for in sample?

Actual Accuracy

UOL posted a $2.40B in revenue in 2018.

preds

##   UOL 2018 UOL  UOL 2018 Base UOL 2018 Macro UOL 2018 World 
##       3177.073       2086.437       2024.842       2589.636

Why is the global model better? Consider UOL’s business model (2018 annual report)

The question

How can we predict quarterly revenue for retail companies, leveraging our knowledge of such companies?

• In aggregate
• By Store
• By department

More specifically…

• Consider time dimensions
  • What matters:
    • Last quarter?
    • Last year?
    • Other time frames?
  • Cyclicality

Time matters a lot for retail

How to capture time effects?

The data

• From quarterly reports
• Two sets of firms:
  • US “Hypermarkets & Super Centers” (GICS: 30101040)
  • US “Multiline Retail” (GICS: 255030)
• Data from Compustat - Capital IQ > North America - Daily > Fundamentals Quarterly

Formalization

1. Question
   • How can we predict quarterly revenue for large retail companies?
2. Hypothesis (just the alternative ones)
   1. Current quarter revenue helps predict next quarter revenue
   2. 3 quarters ago revenue helps predict next quarter revenue (Year-over-year)
   3. Different quarters exhibit different patterns (seasonality)
   4. A long-run autoregressive model helps predict next quarter revenue
3. Prediction
   • Use OLS for all the above – \(t\)-tests for coefficients
   • Hold out sample: 2015-2017

Variable generation

library(tidyverse)  # As always
library(plotly)  # interactive graphs
library(lubridate)  # import some sensible date functions

# Generate quarter over quarter growth "revtq_gr"
df <- df %>% group_by(gvkey) %>% mutate(revtq_gr=revtq / lag(revtq) - 1) %>% ungroup()

# Generate year-over-year growth "revtq_yoy"
df <- df %>% group_by(gvkey) %>% mutate(revtq_yoy=revtq / lag(revtq, 4) - 1) %>% ungroup()

# Generate first difference "revtq_d"
df <- df %>% group_by(gvkey) %>% mutate(revtq_d=revtq - lag(revtq)) %>% ungroup()

# Generate a proper date
# Date was YYMMDDs10: YYYY/MM/DD, can be converted from text to date easily
df$date <- as.Date(df$datadate)  # Built in to R

• Use mutate for variables using lags
• as.Date() can take a date formatted as “YYYY/MM/DD” and convert to a proper date value
  • You can convert other date types using the format= argument
    • i.e., “DD.MM.YYYY” is format code “%d.%m.%Y”
    • Full list of date codes

Example output

## # A tibble: 6 x 3
##   conm          date       datadate  
##   <chr>         <date>     <chr>     
## 1 ALLIED STORES 1962-04-30 1962/04/30
## 2 ALLIED STORES 1962-07-31 1962/07/31
## 3 ALLIED STORES 1962-10-31 1962/10/31
## 4 ALLIED STORES 1963-01-31 1963/01/31
## 5 ALLIED STORES 1963-04-30 1963/04/30
## 6 ALLIED STORES 1963-07-31 1963/07/31

Create 8 quarters (2 years) of lags

# Equivalent brute force code for this is in the appendix
# Custom function to generate a series of lags
library(rlang)
multi_lag <- function(df, lags, var, postfix="") {
  var <- enquo(var)
  quosures <- map(lags, ~quo(lag(!!var, !!.x))) %>%
    set_names(paste0(quo_text(var), postfix, lags))
  return(ungroup(mutate(group_by(df, gvkey), !!!quosures)))
}
# Generate lags "revtq_l#"
df <- multi_lag(df, 1:8, revtq, "_l")

# Generate changes "revtq_gr#"
df <- multi_lag(df, 1:8, revtq_gr)

# Generate year-over-year changes "revtq_yoy#"
df <- multi_lag(df, 1:8, revtq_yoy)

# Generate first differences "revtq_d#"
df <- multi_lag(df, 1:8, revtq_d)

• paste0(): creates a string vector by concatenating all inputs
• paste(): same as paste0(), but with spaces added in between
• setNames(): allows for storing a value and name simultaneously
• mutate_at(): is like mutate but with a list of functions

Example output

Clean and split into training and testing

# Clean the data: Replace NaN, Inf, and -Inf with NA
df <- df %>%
  mutate_if(is.numeric, list(~replace(., !is.finite(.), NA)))

# Split into training and testing data
# Training data: We'll use data released before 2015
train <- filter(df, year(date) < 2015)

# Testing data: We'll use data released 2015 through 2018
test <- filter(df, year(date) >= 2015)

• Same cleaning function as last week:
  • Replaces all NaN, Inf, and -Inf with NA
• year() comes from lubridate

Univariate stats

• To get a better grasp on the problem, looking at univariate stats can help
  • Summary stats (using summary())
  • Correlations using cor()
  • Plots using your preferred package such as ggplot2

summary(df[,c("revtq","revtq_gr","revtq_yoy", "revtq_d","fqtr")])

##      revtq              revtq_gr         revtq_yoy      
##  Min.   :     0.00   Min.   :-1.0000   Min.   :-1.0000  
##  1st Qu.:    64.46   1st Qu.:-0.1112   1st Qu.: 0.0077  
##  Median :   273.95   Median : 0.0505   Median : 0.0740  
##  Mean   :  2439.38   Mean   : 0.0650   Mean   : 0.1273  
##  3rd Qu.:  1254.21   3rd Qu.: 0.2054   3rd Qu.: 0.1534  
##  Max.   :136267.00   Max.   :14.3333   Max.   :47.6600  
##  NA's   :367         NA's   :690       NA's   :940      
##     revtq_d               fqtr      
##  Min.   :-24325.21   Min.   :1.000  
##  1st Qu.:   -19.33   1st Qu.:1.000  
##  Median :     4.30   Median :2.000  
##  Mean   :    22.66   Mean   :2.478  
##  3rd Qu.:    55.02   3rd Qu.:3.000  
##  Max.   : 15495.00   Max.   :4.000  
##  NA's   :663

ggplot2 for visualization

• The next slides will use some custom functions using ggplot2
• ggplot2 has an odd syntax:
  • It doesn’t use pipes (%>%), but instead adds everything together (+)

library(ggplot2)  # or tidyverse -- it's part of tidyverse
df %>%
  ggplot(aes(y=var_for_y_axis, x=var_for_x_axis)) +
  geom_point()  # scatterplot

• aes() is for aesthetics – how the chart is set up
• Other useful aesthetics:
  • group= to set groups to list in the legend. Not needed if using the below though
  • color= to set color by some grouping variable. Put factor() around the variable if you want discrete groups, otherwise it will do a color scale (light to dark)
  • shape= to set shapes for points – see here for a list

ggplot2 for visualization

library(ggplot2)  # or tidyverse -- it's part of tidyverse
df %>%
  ggplot(aes(y=var_for_y_axis, x=var_for_x_axis)) +
  geom_point()  # scatterplot

• geom stands for geometry – any shapes, lines, etc. start with geom
• Other useful geoms:
  • geom_line(): makes a line chart
  • geom_bar(): makes a bar chart – y is the height, x is the category
  • geom_smooth(method="lm"): Adds a linear regression into the chart
  • geom_abline(slope=1): Adds a 45° line
• Add xlab("Label text here") to change the x-axis label
• Add ylab("Label text here") to change the y-axis label
• Add ggtitle("Title text here") to add a title
• Plenty more details in the ‘Data Visualization Cheat Sheet’ on eLearn

Plotting: Distribution of revenue

What do we learn from these graphs?

Plotting: Mean revenue by quarter

What do we learn from these graphs?

Plotting: Revenue vs lag by quarter

What do we learn from these graphs?

1. Revenue
   • Revenue is really linear! But each quarter has a distinct linear relation.
2. Quarterly growth
   • All over the place. Each quarter appears to have a different pattern though. Quarters will matter.
3. Year-over-year growth
   • Linear but noisy.
4. First difference
   • Again, all over the place. Each quarter appears to have a different pattern though. Quarters will matter.

Correlation matrices

cor(train[,c("revtq","revtq_l1","revtq_l2","revtq_l3", "revtq_l4")],
    use="complete.obs")

##              revtq  revtq_l1  revtq_l2  revtq_l3  revtq_l4
## revtq    1.0000000 0.9916167 0.9938489 0.9905522 0.9972735
## revtq_l1 0.9916167 1.0000000 0.9914767 0.9936977 0.9898184
## revtq_l2 0.9938489 0.9914767 1.0000000 0.9913489 0.9930152
## revtq_l3 0.9905522 0.9936977 0.9913489 1.0000000 0.9906006
## revtq_l4 0.9972735 0.9898184 0.9930152 0.9906006 1.0000000

cor(train[,c("revtq_gr","revtq_gr1","revtq_gr2","revtq_gr3", "revtq_gr4")],
    use="complete.obs")

##              revtq_gr   revtq_gr1   revtq_gr2   revtq_gr3   revtq_gr4
## revtq_gr   1.00000000 -0.32291329  0.06299605 -0.22769442  0.64570015
## revtq_gr1 -0.32291329  1.00000000 -0.31885020  0.06146805 -0.21923630
## revtq_gr2  0.06299605 -0.31885020  1.00000000 -0.32795121  0.06775742
## revtq_gr3 -0.22769442  0.06146805 -0.32795121  1.00000000 -0.31831023
## revtq_gr4  0.64570015 -0.21923630  0.06775742 -0.31831023  1.00000000

Retail revenue has really high autocorrelation! Concern for multicolinearity. Revenue growth is less autocorrelated and oscillates.

Correlation matrices

cor(train[,c("revtq_yoy","revtq_yoy1","revtq_yoy2","revtq_yoy3", "revtq_yoy4")],
    use="complete.obs")

##            revtq_yoy revtq_yoy1 revtq_yoy2 revtq_yoy3 revtq_yoy4
## revtq_yoy  1.0000000  0.6554179  0.4127263  0.4196003  0.1760055
## revtq_yoy1 0.6554179  1.0000000  0.5751128  0.3665961  0.3515105
## revtq_yoy2 0.4127263  0.5751128  1.0000000  0.5875643  0.3683539
## revtq_yoy3 0.4196003  0.3665961  0.5875643  1.0000000  0.5668211
## revtq_yoy4 0.1760055  0.3515105  0.3683539  0.5668211  1.0000000

cor(train[,c("revtq_d","revtq_d1","revtq_d2","revtq_d3", "revtq_d4")],
    use="complete.obs")

##             revtq_d   revtq_d1   revtq_d2   revtq_d3   revtq_d4
## revtq_d   1.0000000 -0.6181516  0.3309349 -0.6046998  0.9119911
## revtq_d1 -0.6181516  1.0000000 -0.6155259  0.3343317 -0.5849841
## revtq_d2  0.3309349 -0.6155259  1.0000000 -0.6191366  0.3165450
## revtq_d3 -0.6046998  0.3343317 -0.6191366  1.0000000 -0.5864285
## revtq_d4  0.9119911 -0.5849841  0.3165450 -0.5864285  1.0000000

Year over year change fixes the multicollinearity issue. First difference oscillates like quarter over quarter growth.

R Practice

• This practice will look at predicting Walmart’s quarterly revenue using:
  • 1 lag
  • Cyclicality
• Practice using:
  • lm()
  • ggplot2
• Do the exercises in today’s practice file
  • R Practice
  • Short link: rmc.link/420r3

1 period models

mod1 <- lm(revtq ~ revtq_l1, data=train)

mod2 <- lm(revtq ~ revtq_l1 + revtq_l4, data=train)

mod3 <- lm(revtq ~ revtq_l1 + revtq_l2 + revtq_l3 + revtq_l4 + 
             revtq_l5 + revtq_l6 + revtq_l7 + revtq_l8, data=train)

mod4 <- lm(revtq ~ (revtq_l1 + revtq_l2 + revtq_l3 + revtq_l4 +
             revtq_l5 + revtq_l6 + revtq_l7 + revtq_l8):factor(fqtr),
           data=train)

Quarter lag

summary(mod1)

## 
## Call:
## lm(formula = revtq ~ revtq_l1, data = train)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -24438.7    -34.0    -11.7     34.6  15200.5 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.639975  13.514877   1.157    0.247    
## revtq_l1     1.003038   0.001556 644.462   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1152 on 7676 degrees of freedom
##   (662 observations deleted due to missingness)
## Multiple R-squared:  0.9819, Adjusted R-squared:  0.9819 
## F-statistic: 4.153e+05 on 1 and 7676 DF,  p-value: < 2.2e-16

Quarter and year lag

summary(mod2)

## 
## Call:
## lm(formula = revtq ~ revtq_l1 + revtq_l4, data = train)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20245.7    -18.4     -4.4     19.1   9120.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.444986   7.145633   0.762    0.446    
## revtq_l1    0.231759   0.005610  41.312   <2e-16 ***
## revtq_l4    0.815570   0.005858 139.227   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 592.1 on 7274 degrees of freedom
##   (1063 observations deleted due to missingness)
## Multiple R-squared:  0.9954, Adjusted R-squared:  0.9954 
## F-statistic: 7.94e+05 on 2 and 7274 DF,  p-value: < 2.2e-16

2 years of lags

summary(mod3)

## 
## Call:
## lm(formula = revtq ~ revtq_l1 + revtq_l2 + revtq_l3 + revtq_l4 + 
##     revtq_l5 + revtq_l6 + revtq_l7 + revtq_l8, data = train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5005.6   -12.9    -3.7     9.3  5876.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.02478    4.37003   0.921   0.3571    
## revtq_l1     0.77379    0.01229  62.972  < 2e-16 ***
## revtq_l2     0.10497    0.01565   6.707 2.16e-11 ***
## revtq_l3    -0.03091    0.01538  -2.010   0.0445 *  
## revtq_l4     0.91982    0.01213  75.800  < 2e-16 ***
## revtq_l5    -0.76459    0.01324 -57.749  < 2e-16 ***
## revtq_l6    -0.08080    0.01634  -4.945 7.80e-07 ***
## revtq_l7     0.01146    0.01594   0.719   0.4721    
## revtq_l8     0.07924    0.01209   6.554 6.03e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 346 on 6666 degrees of freedom
##   (1665 observations deleted due to missingness)
## Multiple R-squared:  0.9986, Adjusted R-squared:  0.9986 
## F-statistic: 5.802e+05 on 8 and 6666 DF,  p-value: < 2.2e-16

2 years of lags, by observation quarter

summary(mod4)

## 
## Call:
## lm(formula = revtq ~ (revtq_l1 + revtq_l2 + revtq_l3 + revtq_l4 + 
##     revtq_l5 + revtq_l6 + revtq_l7 + revtq_l8):factor(fqtr), 
##     data = train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6066.6   -13.9     0.1    15.1  4941.1 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            -0.201107   4.004046  -0.050 0.959944    
## revtq_l1:factor(fqtr)1  0.488584   0.021734  22.480  < 2e-16 ***
## revtq_l1:factor(fqtr)2  1.130563   0.023017  49.120  < 2e-16 ***
## revtq_l1:factor(fqtr)3  0.774983   0.028727  26.977  < 2e-16 ***
## revtq_l1:factor(fqtr)4  0.977353   0.026888  36.349  < 2e-16 ***
## revtq_l2:factor(fqtr)1  0.258024   0.035136   7.344 2.33e-13 ***
## revtq_l2:factor(fqtr)2 -0.100284   0.024664  -4.066 4.84e-05 ***
## revtq_l2:factor(fqtr)3  0.212954   0.039698   5.364 8.40e-08 ***
## revtq_l2:factor(fqtr)4  0.266761   0.035226   7.573 4.14e-14 ***
## revtq_l3:factor(fqtr)1  0.124187   0.036695   3.384 0.000718 ***
## revtq_l3:factor(fqtr)2 -0.042214   0.035787  -1.180 0.238197    
## revtq_l3:factor(fqtr)3 -0.005758   0.024367  -0.236 0.813194    
## revtq_l3:factor(fqtr)4 -0.308661   0.038974  -7.920 2.77e-15 ***
## revtq_l4:factor(fqtr)1  0.459768   0.038266  12.015  < 2e-16 ***
## revtq_l4:factor(fqtr)2  0.684943   0.033366  20.528  < 2e-16 ***
## revtq_l4:factor(fqtr)3  0.252169   0.035708   7.062 1.81e-12 ***
## revtq_l4:factor(fqtr)4  0.817136   0.017927  45.582  < 2e-16 ***
## revtq_l5:factor(fqtr)1 -0.435406   0.023278 -18.704  < 2e-16 ***
## revtq_l5:factor(fqtr)2 -0.725000   0.035497 -20.424  < 2e-16 ***
## revtq_l5:factor(fqtr)3 -0.160408   0.036733  -4.367 1.28e-05 ***
## revtq_l5:factor(fqtr)4 -0.473030   0.033349 -14.184  < 2e-16 ***
## revtq_l6:factor(fqtr)1  0.059832   0.034672   1.726 0.084453 .  
## revtq_l6:factor(fqtr)2  0.154990   0.025368   6.110 1.05e-09 ***
## revtq_l6:factor(fqtr)3 -0.156840   0.041147  -3.812 0.000139 ***
## revtq_l6:factor(fqtr)4 -0.106082   0.037368  -2.839 0.004541 ** 
## revtq_l7:factor(fqtr)1  0.060031   0.038599   1.555 0.119936    
## revtq_l7:factor(fqtr)2  0.061381   0.034510   1.779 0.075344 .  
## revtq_l7:factor(fqtr)3  0.028149   0.025385   1.109 0.267524    
## revtq_l7:factor(fqtr)4 -0.277337   0.039380  -7.043 2.08e-12 ***
## revtq_l8:factor(fqtr)1 -0.016637   0.033568  -0.496 0.620177    
## revtq_l8:factor(fqtr)2 -0.152379   0.028014  -5.439 5.54e-08 ***
## revtq_l8:factor(fqtr)3  0.052208   0.027334   1.910 0.056179 .  
## revtq_l8:factor(fqtr)4  0.103495   0.015777   6.560 5.78e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 299.7 on 6642 degrees of freedom
##   (1665 observations deleted due to missingness)
## Multiple R-squared:  0.9989, Adjusted R-squared:  0.9989 
## F-statistic: 1.935e+05 on 32 and 6642 DF,  p-value: < 2.2e-16

Testing out of sample

• RMSE: Root mean square Error
• RMSE is very affected by outliers, and a bad choice for noisy data where you are OK with missing a few outliers here and there
  • Doubling error quadruples the penalty

rmse <- function(v1, v2) {
  sqrt(mean((v1 - v2)^2, na.rm=T))
}

• MAE: Mean absolute error
• MAE is measures average accuracy with no weighting
  • Doubling error doubles the penalty

mae <- function(v1, v2) {
  mean(abs(v1-v2), na.rm=T)
}

Both are commonly used for evaluating OLS out of sample

Testing out of sample

What about for revenue growth?

Backing out a revenue prediction, \(revt_t=(1+growth_t)\times revt_{t-1}\)

What about for YoY revenue growth?

Backing out a revenue prediction, \(revt_t=(1+yoy\_growth_t)\times revt_{t-4}\)

What about for first difference?

Backing out a revenue prediction, \(revt_t= change_t+ revt_{t-1}\)

Takeaways

1. The first difference model works about as well as the revenue model at predicting next quarter revenue
   • From earlier, it doesn’t suffer (as much) from multicollinearity either
     • This is why time series analysis is often done on first differences
       • Or second differences (difference in differences)
2. The other models perform pretty well as well
3. Extra lags generally seems helpful when accounting for cyclicality
4. Regressing by quarter helps a bit, particularly with revenue growth

What about for revenue growth?

Predicting quarter over quarter revenue growth itself

What about for YoY revenue growth?

What about for first difference?

Predicting first difference in revenue itself

RS Metrics’ approach

Read the press release: rmc.link/420class3

• How does RS Metrics approach revenue prediction?
• What other creative ways might there be?

For next week

• For next week:
  • First individual assignment
    • Finish by the end of Thursday
    • Submit on eLearn
  • Datacamp
    • Practice a bit more to keep up to date
      • Using R more will make it more natural