Learning objectives

Datacamp

• For next week:
  • Just 1 chapter on linear regression
• The full list of Datacamp materials for the course is up on eLearn

R Installation

• If you haven’t already, make sure to install R and R Studio!
  • Instructions are in Session 1’s slides
  • You will need it for this week’s individual
• Please install a few packages using the following code
  • These packages are also needed for the first assignment
  • You are welcome to explore other packages as well, but those will not be necessary for now

# Run this in the R Console inside RStudio
install.packages(c("tidyverse","plotly","tufte","reshape2"))

• The individual assignment will be provided as an R Markdown file

The format will generally all be filled out – you will just add to it, answer questions, analyze data, and explain your work. Instructions and hints are in the same file

R Markdown: A quick guide

• Headers and subheaders start with # and ##, respectively
• Code blocks starts with ```{r} and end with ```
  • By default, all code and figures will show up in the document
• Inline code goes in a block starting with `r ` and ending with `
• Italic font can be used by putting * or _ around text
• Bold font can be used by putting ** around text
  • E.g.: **bold text** becomes bold text
• To render the document, click
• Math can be placed between $ to use LaTeX notation
  • E.g. $\frac{revt}{at}$ becomes \frac{revt}{at}
• Full equations (on their own line) can be placed between $$
• A block quote is prefixed with >
• For a complete guide, see R Studio’s R Markdown::Cheat Sheet

The question

How can we predict revenue for a company, leveraging data about that company, related companies, and macro factors

• Specific application: Real estate companies

More specifically…

• Can we use a company’s own accounting data to predict it’s future revenue?
• Can we use other companies’ accounting data to better predict all of their future revenue?
• Can we augment this data with macro economic data to further improve prediction?
  • Singapore business sentiment data

What is a linear model?

\hat{y}=\alpha + \beta \hat{x} + \varepsilon

• The simplest model is trying to predict some outcome \hat{y} as a function of an input \hat{x}
  • \hat{y} in our case is a firm’s revenue in a given year
  • \hat{x} could be a firm’s assets in a given year
  • \alpha and \beta are solved for
  • \varepsilon is the error in the measurement

I will refer to this as an OLS model – Ordinary Least Square regression

Example

Let’s predict UOL’s revenue for 2016

# revt: Revenue, at: Assets
summary(uol[,c("revt", "at")])

##       revt               at       
##  Min.   :  94.78   Min.   : 1218  
##  1st Qu.: 193.41   1st Qu.: 3044  
##  Median : 427.44   Median : 3478  
##  Mean   : 666.38   Mean   : 5534  
##  3rd Qu.:1058.61   3rd Qu.: 7939  
##  Max.   :2103.15   Max.   :19623

Linear models in R

• To run a linear model, use lm()
  • The first argument is a formula for your model, where ~ is used in place of an equals sign
    • The left side is what you want to predict
    • The right side is inputs for prediction, separated by +
  • The second argument is the data to use
• Additional variations for the formula:
  • Functions transforming inputs (as vectors), such as log()
  • Fully interacting variables using *
    • I.e., A*Bincludes, A, B, and A times B in the model
  • Interactions using :
    • I.e., A:B just includes A times B in the model

# Example:
lm(revt ~ at, data = uol)

Example: UOL

mod1 <- lm(revt ~ at, data = uol)
summary(mod1)

## 
## Call:
## lm(formula = revt ~ at, data = uol)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -295.01 -101.29  -41.09   47.17  926.29 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -13.831399  67.491305  -0.205    0.839    
## at            0.122914   0.009678  12.701  6.7e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 221.2 on 27 degrees of freedom
## Multiple R-squared:  0.8566, Adjusted R-squared:  0.8513 
## F-statistic: 161.3 on 1 and 27 DF,  p-value: 6.699e-13

Why is it called Ordinary Least Squares?

Example: UOL

• This model wasn’t so interesting…
  • Bigger firms have more revenue – this is a given
• How about… revenue growth?
• And change in assets
  • i.e., Asset growth

\Delta x_t = \frac{x_t}{x_{t-1}} - 1

Calculating changes in R

• The easiest way is using tidyverse’s dplyr
  • lag() function along with mutate()
• The default way to do it is to create a vector manually

# tidyverse
uol <- uol %>%
  mutate(revt_growth1 = revt / lag(revt) - 1)

# R way
uol$revt_growth2 = uol$revt / c(NA, uol$revt[-length(uol$revt)]) - 1

identical(uol$revt_growth1, uol$revt_growth2)

## [1] TRUE

# faster with in place creation
library(magrittr)
uol %<>% mutate(revt_growth3 = revt / lag(revt) - 1)
identical(uol$revt_growth1, uol$revt_growth3)

## [1] TRUE

You can use whichever you are comfortable with

A note on mutate()

• mutate() adds variables to an existing data frame
  • Also mutate_all(), mutate_at(), mutate_if()
  • mutate_all() applies a transformation to all values in a data frame and adds these to the data frame
  • mutate_at() does this for a set of specified variables
  • mutate_if() transforms all variables matching a condition
    • Such as is.numeric
• Mutate can be very powerful when making more complex variables
  • For instance: Calculating growth within company in a multi-company data frame
  • It’s way more than needed for a simple ROA though.

Example: UOL with changes

# Make the other needed change
uol <- uol %>%
  mutate(at_growth = at / lag(at) - 1)  # From dplyr
# Rename our revenue growth variable
uol <- rename(uol, revt_growth = revt_growth1)  # From dplyr
# Run the OLS model
mod2 <- lm(revt_growth ~ at_growth, data = uol)
summary(mod2)

## 
## Call:
## lm(formula = revt_growth ~ at_growth, data = uol)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.57736 -0.10534 -0.00953  0.15132  0.42284 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.09024    0.05620   1.606   0.1204  
## at_growth    0.53821    0.27717   1.942   0.0631 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2444 on 26 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1267, Adjusted R-squared:  0.09307 
## F-statistic: 3.771 on 1 and 26 DF,  p-value: 0.06307

Example: UOL with changes

• \DeltaAssets doesn’t capture \DeltaRevenue so well
• Perhaps change in total assets is a bad choice?
• Or perhaps we need to expand our model?

Scaling up!

\hat{y}=\alpha + \beta_1 \hat{x}_1 + \beta_2 \hat{x}_2 + \ldots + \varepsilon

• OLS doesn’t need to be restricted to just 1 input!
  • Not unlimited though (yet)
    • Number of inputs must be less than the number of observations minus 1
• Each \hat{x}_i is an input in our model
• Each \beta_i is something we will solve for
• \hat{y}, \alpha, and \varepsilon are the same as before

Scaling up our model

We have… 464 variables from Compustat Global alone!

• Let’s just add them all?

Scaling up our model

Building a model requires careful thought!

• What makes sense to add to our model?

This is where having accounting and business knowledge comes in!

Why formalize?

• Our current approach has been ad hoc
  • What is our goal?
  • How will we know if we have achieved it?
• Formalization provides more rigor

Scientific method

1. Question
   • What are we trying to determine?
2. Hypothesis
   • What do we think will happen? Build a model
3. Prediction
   • What exactly will we test? Formalize model into a statistical approach
4. Testing
   • Test the model
5. Analysis
   • Did it work?

Hypotheses

• Null hypothesis, a.k.a. H_0
  • The status quo
  • Typically: The model doesn’t work
• Alternative hypothesis, a.k.a. H_1 or H_A
  • The model does work (and perhaps how it works)

We will use test statistics to test the hypotheses

Test statistics

• Testing a coefficient:
  • Use a t or z test
• Testing a model as a whole
  • F-test, check adjusted R squared as well
    • Adj R^2 tells us the amount of variation captured by the model (higher is better), after adjusting for the number of variables included
      • Otherwise, more variables (almost) always equals a higher amount of variation captured
• Testing across models
  • Chi squared (\chi^2) test
  • Vuong test (comparing R^2)
  • Akaike Information Criterion (AIC) (Comparing MLEs, lower is better)

Formalizing our last test

Is this model better?

anova(mod2, mod3, test="Chisq")

## Analysis of Variance Table
## 
## Model 1: revt_growth ~ at_growth
## Model 2: revt_growth ~ lct_growth + che_growth + ebit_growth
##   Res.Df    RSS Df Sum of Sq Pr(>Chi)  
## 1     26 1.5534                        
## 2     24 1.1918  2   0.36168   0.0262 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

A bit better at p<0.05

• This means our model with change in current liabilities, cash, and EBIT appears to be better than the model with change in assets.

Expanding our methodology

• Why should we limit ourselves to 1 firm’s data?
• The nature of data analysis is such:

Adding more data usually helps improve predictions

• Assuming:
  • The data isn’t of low quality (too noisy)
  • The data is relevant
  • Any differences can be reasonably controlled for

Expanding our question

• Previously: Can we predict revenue using a firm’s accounting information?
  • This is simultaneous, and thus is not forecasting
• Now: Can we predict future revenue using a firm’s accounting information?
  • By trying to predict ahead, we are now in the realm of forecasting
  • What do we need to change?
    • \hat{y} will need to be 1 year in the future

First things first

• When using a lot of data, it is important to make sure the data is clean
• In our case, we may want to remove any very small firms

# Ensure firms have at least $1M (local currency), and have revenue
# df contains all real estate companies excluding North America
df_clean <- filter(df, df$at>1, df$revt>0)

# We cleaned out 578 observations!
print(c(nrow(df), nrow(df_clean)))

## [1] 5161 4583

# Another useful cleaning funtion:
# Replaces NaN, Inf, and -Inf with NA for all numeric variables in the data!
df_clean <- df_clean %>%
  mutate_if(is.numeric, funs(replace(., !is.finite(.), NA)))

Looking back at the prior models

uol <- uol %>% mutate(revt_lead = lead(revt))  # From dplyr
forecast1 <-
  lm(revt_lead ~ lct + che + ebit, data=uol)
library(broom)  # Let's us view bigger regression outputs in a tidy fashion
tidy(forecast1)  # present regression output

## # A tibble: 4 x 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)   87.4     124.        0.707  0.486 
## 2 lct            0.213     0.291     0.731  0.472 
## 3 che            0.112     0.349     0.319  0.752 
## 4 ebit           2.49      1.03      2.42   0.0236

glance(forecast1)  # present regression statistics

## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
## *     <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.655         0.612  357.      15.2 9.39e-6     4  -202.  414.  421.
## # ... with 2 more variables: deviance <dbl>, df.residual <int>

This model is ok, but we can do better.

Expanding the prior model

forecast2 <- 
  lm(revt_lead ~ revt + act + che + lct + dp + ebit , data=uol)
tidy(forecast2)

## # A tibble: 7 x 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)  15.6       97.0       0.161 0.874  
## 2 revt          1.49       0.414     3.59  0.00174
## 3 act           0.324      0.165     1.96  0.0629 
## 4 che           0.0401     0.310     0.129 0.898  
## 5 lct          -0.198      0.179    -1.10  0.283  
## 6 dp            3.63       5.42      0.669 0.511  
## 7 ebit         -3.57       1.36     -2.62  0.0161

• Revenue to capture stickiness of revenue
• Current assest & Cash (and equivalents) to capture asset base
• Current liabilities to capture payments due
• Depreciation to capture decrease in real estate asset values
• EBIT to capture operational performance

Expanding the prior model

glance(forecast2)

## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
## *     <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.903         0.875  203.      32.5 1.41e-9     7  -184.  385.  396.
## # ... with 2 more variables: deviance <dbl>, df.residual <int>

anova(forecast1, forecast2, test="Chisq")

## Analysis of Variance Table
## 
## Model 1: revt_lead ~ lct + che + ebit
## Model 2: revt_lead ~ revt + act + che + lct + dp + ebit
##   Res.Df     RSS Df Sum of Sq  Pr(>Chi)    
## 1     24 3059182                           
## 2     21  863005  3   2196177 1.477e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This is better (Adj. R^2, \chi^2, AIC).

Panel data

• Panel data refers to data with the following characteristics:
  • There is a time dimension
  • There is at least 1 other dimension to the data (firm, country, etc.)
• Special cases:
  • A panel where all dimensions have the same number of observations is called balanced
    • Otherwise we call it unbalanced
  • A panel missing the time dimension is cross-sectional
  • A panel missing the other dimension(s) is a time series
• Format:
  • Long: Indexed by all dimensions
  • Wide: Indexed only by other dimensions

All Singapore real estate companies

# Note the group_by -- without it, lead() will pull from the subsequent firm!
# ungroup() tells R that we finished grouping
df_clean <- df_clean %>% 
  group_by(isin) %>% 
  mutate(revt_lead = lead(revt)) %>%
  ungroup()

All Singapore real estate companies

forecast3 <-
  lm(revt_lead ~ revt + act + che + lct + dp + ebit , data=df_clean[df_clean$fic=="SGP",])
tidy(forecast3)

## # A tibble: 7 x 5
##   term        estimate std.error statistic  p.value
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)  25.0      13.2         1.89 5.95e- 2
## 2 revt          0.505     0.0762      6.63 1.43e-10
## 3 act          -0.0999    0.0545     -1.83 6.78e- 2
## 4 che           0.494     0.155       3.18 1.62e- 3
## 5 lct           0.396     0.0860      4.60 5.95e- 6
## 6 dp            4.46      1.55        2.88 4.21e- 3
## 7 ebit         -0.951     0.271      -3.51 5.18e- 4

All Singapore real estate companies

glance(forecast3)

## # 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     0.844         0.841  210.      291. 2.63e-127     7 -2237. 4489.
## # ... with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

Lower adjusted R^2 – This is worse? Why?

• Note: \chi^2 can only be used for models on the same data
  • Same for AIC

Worldwide real estate companies

forecast4 <-
  lm(revt_lead ~ revt + act + che + lct + dp + ebit , data=df_clean)
tidy(forecast4)

## # A tibble: 7 x 5
##   term          estimate std.error statistic  p.value
##   <chr>            <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)  222.      585.          0.379 7.04e- 1
## 2 revt           0.997     0.00655   152.    0.      
## 3 act           -0.00221   0.00547    -0.403 6.87e- 1
## 4 che           -0.150     0.0299     -5.02  5.36e- 7
## 5 lct            0.0412    0.0113      3.64  2.75e- 4
## 6 dp             1.52      0.184       8.26  1.89e-16
## 7 ebit           0.308     0.0650      4.74  2.25e- 6

Worldwide real estate companies

glance(forecast4)

## # 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     0.944         0.944 36459.    11299.       0     7 -47819. 95654.
## # ... with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

Higher adjusted R^2 – better!

• Note: \chi^2 can only be used for models on the same data
  • Same for AIC

Model accuracy

Why is 1 model better while the other model is worse?

• Ranking:
  1. Worldwide real estate model
  2. UOL model
  3. Singapore real estate model

Noise

Statistical noise is random error in the data

• Many sources of noise:
  • Other factors not included in
  • Error in measurement
    • Accounting measurement!
  • Unexpected events / shocks

Noise is OK, but the more we remove, the better!

Removing noise: Singapore model

• Different companies may behave slightly differently
  • Control for this using a Fixed Effect
  • Note: ISIN uniquely identifies companies

forecast3.1 <-
  lm(revt_lead ~ revt + act + che + lct + dp + ebit + factor(isin),
     data=df_clean[df_clean$fic=="SGP",])
# n=7 to prevent outputting every fixed effect
print(tidy(forecast3.1), n=7)

## # A tibble: 27 x 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)   1.58     39.4       0.0401 0.968    
## 2 revt          0.392     0.0977    4.01   0.0000754
## 3 act          -0.0538    0.0602   -0.894  0.372    
## 4 che           0.304     0.177     1.72   0.0869   
## 5 lct           0.392     0.0921    4.26   0.0000276
## 6 dp            4.71      1.73      2.72   0.00687  
## 7 ebit         -0.851     0.327    -2.60   0.00974  
## # ... with 20 more rows

Removing noise: Singapore model

glance(forecast3.1)

## # 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     0.856         0.844  208.      69.4 1.15e-111    27 -2223. 4502.
## # ... with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

anova(forecast3, forecast3.1, 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 + factor(isin)
##   Res.Df      RSS Df Sum of Sq Pr(>Chi)
## 1    324 14331633                      
## 2    304 13215145 20   1116488   0.1765

This isn’t much different. Why? There is another source of noise within Singapore real estate companies

Another way to do fixed effects

• The library lfe has felm(): fixed effects linear model
  • Better for complex models

library(lfe)
forecast3.2 <-
  felm(revt_lead ~ revt + act + che + lct + dp + ebit | factor(isin),
       data=df_clean[df_clean$fic=="SGP",])
summary(forecast3.2)

## 
## Call:
##    felm(formula = revt_lead ~ revt + act + che + lct + dp + ebit |      factor(isin), data = df_clean[df_clean$fic == "SGP", ]) 
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1181.88   -23.25    -1.87    18.03  1968.86 
## 
## Coefficients:
##      Estimate Std. Error t value Pr(>|t|)    
## revt  0.39200    0.09767   4.013 7.54e-05 ***
## act  -0.05382    0.06017  -0.894  0.37181    
## che   0.30370    0.17682   1.718  0.08690 .  
## lct   0.39209    0.09210   4.257 2.76e-05 ***
## dp    4.71275    1.73168   2.721  0.00687 ** 
## ebit -0.85080    0.32704  -2.602  0.00974 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 208.5 on 304 degrees of freedom
##   (29 observations deleted due to missingness)
## Multiple R-squared(full model): 0.8558   Adjusted R-squared: 0.8435 
## Multiple R-squared(proj model): 0.7806   Adjusted R-squared: 0.7618 
## F-statistic(full model):69.41 on 26 and 304 DF, p-value: < 2.2e-16 
## F-statistic(proj model): 180.3 on 6 and 304 DF, p-value: < 2.2e-16

Why exactly would we use fixed effects?

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)

# Import the csv file
expectations <- read.csv("general-business-expectations-by-detailed-services-industry-quarterly.csv",
                         stringsAsFactors = FALSE)
# split the year and quarter
expectations$year <- as.numeric(substr(expectations$quarter, 1, 4))
expectations$quarter <- as.numeric(substr(expectations$quarter, 7, 7))
# cast value to numeric
expectations$value <- as.numeric(expectations$value)

# extract out Q1, finance only
expectations_avg <- filter(expectations, quarter == 1 & level_2 == "Financial & Insurance")

# build a finance-specific measure
expectations_avg <- expectations_avg %>%
  group_by(year) %>%
  mutate(value=mean(value, na.rm=TRUE)) %>%
  slice(1)

# rename the value column to something more meaningful
colnames(expectations_avg)[colnames(expectations_avg) == "value"] <- "fin_sentiment"

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

dplyr makes things 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[df_clean$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"

Sorting example

expectations %>%
  filter(quarter == 1) %>%  # using dplyr
  arrange(level_2, level_3, desc(year)) %>%  # using dplyr
  select(year, quarter, level_2, level_3, value) %>%  # using dplyr
  datatable(options = list(pageLength = 5), rownames=FALSE)  # using DT

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)
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

##   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?

For next week

• For next week:
  • 1 chapter of 1 course on Datacamp
  • First individual assignment
    • Do this one individually!
    • Turn in on eLearn by the end of next Thursday

Packages used for these slides

• broom
• DT
• knitr
• lfe
• magrittr
• plotly
• revealjs
• tidyverse

Custom code

# Graph showing squared error (slide 4.6)
uolg <- uol[,c("at","revt")]
uolg$resid <- mod1$residuals
uolg$xleft <- ifelse(uolg$resid < 0,uolg$at,uolg$at - uolg$resid)
uolg$xright <- ifelse(uolg$resid < 0,uolg$at - uolg$resid, uol$at)
uolg$ytop <- ifelse(uolg$resid < 0,uolg$revt - uolg$resid,uol$revt)
uolg$ybottom <- ifelse(uolg$resid < 0,uolg$revt, uolg$revt - uolg$resid)
uolg$point <- TRUE

uolg2 <- uolg
uolg2$point <- FALSE
uolg2$at <- ifelse(uolg$resid < 0,uolg2$xright,uolg2$xleft)
uolg2$revt <- ifelse(uolg$resid < 0,uolg2$ytop,uolg2$ybottom)

uolg <- rbind(uolg, uolg2)

uolg %>% ggplot(aes(y=revt, x=at, group=point)) + 
         geom_point(aes(shape=point)) + 
         scale_shape_manual(values=c(NA,18)) + 
         geom_smooth(method="lm", se=FALSE) +
         geom_errorbarh(aes(xmax=xright, xmin = xleft)) + 
         geom_errorbar(aes(ymax=ytop, ymin = ybottom)) + 
         theme(legend.position="none")

# Chart of mean revt_lead for Singaporean firms (slide 7.19)
df_clean %>%                                           # Our data frame
  filter(fic=="SGP") %>%                               # Select only Singaporean firms
  group_by(isin) %>%                                   # Group by firm
  mutate(mean_revt_lead=mean(revt_lead, na.rm=T)) %>%  # Determine each firm's mean revenue (lead)
  slice(1) %>%                                         # Take only the first observation for each group
  ungroup() %>%                                        # Ungroup (we don't need groups any more)
  ggplot(aes(x=mean_revt_lead)) +                      # Initialize plot and select data
  geom_histogram(aes(y = ..density..)) +               # Plots the histogram as a density so that geom_density is visible
  geom_density(alpha=.4, fill="#FF6666")               # Plots smoothed density 

Custom code

# Chart of predictions (slide 11.4)
library(plotly)
df_SG_macro$pred_base <- predict(baseline, df_SG_macro)
df_SG_macro$pred_macro <- predict(macro3, df_SG_macro)
df_clean$pred_world <- predict(forecast4, df_clean)
uol$pred_uol <- predict(forecast2, uol)
df_preds <- data.frame(preds=preds, fyear=c(2018,2018,2018,2018), model=c("UOL only", "Base", "Macro", "World"))
plot <- ggplot() + 
  geom_point(data=df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,], aes(y=revt_lead,x=fyear, color="Actual")) +
  geom_line(data=df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,], aes(y=revt_lead,x=fyear, color="Actual")) + 
  geom_point(data=uol[uol$fyear < 2017,], aes(y=pred_uol,x=fyear, color="UOL only")) +
  geom_line(data=uol[uol$fyear < 2017,], aes(y=pred_uol,x=fyear, color="UOL only")) +
  geom_point(data=df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,], aes(y=pred_base,x=fyear, color="Base")) +
  geom_line(data=df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,], aes(y=pred_base,x=fyear, color="Base")) +
  geom_point(data=df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,], aes(y=pred_macro,x=fyear, color="Macro")) +
  geom_line(data=df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,], aes(y=pred_macro,x=fyear, color="Macro")) + 
  geom_point(data=df_clean[df_clean$isin=="SG1S83002349" & df_clean$fyear < 2017,], aes(y=pred_world,x=fyear, color="World")) +
  geom_line(data=df_clean[df_clean$isin=="SG1S83002349" & df_clean$fyear < 2017,], aes(y=pred_world,x=fyear, color="World")) + 
  geom_point(data=df_preds, aes(y=preds, x=fyear, color=model), size=1.5, shape=18)
ggplotly(plot)

# Calculating Root Mean Squared Error (Slide 11.5)
actual_series <- df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,]$revt_lead
uol_series <- uol[uol$fyear < 2017,]$pred_uol
base_series <- df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,]$pred_base
macro_series <- df_SG_macro[df_SG_macro$isin=="SG1S83002349" & df_SG_macro$fyear < 2017,]$pred_macro
world_series <- df_clean[df_clean$isin=="SG1S83002349" & df_clean$fyear < 2017,]$pred_world

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 only", "UOL 2018 Baseline", "UOL 2018 w/ macro", "UOL 2018 w/ world")
rmse