Introduction

In this weeks discussion, I attempt to forecast portfolio preformance, using the top down, bottom up, and middle out coupling methods. My chosen stocks are TSLA, GOOG, BA, and LMT, meaning my two industries of focus are tech and weapons manufacturing.

Graphs

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## Please cite as:
##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer


From the graphs above we can see that while tech stocks behave very similarly, Boeing and LMT are very different. A part of this is likely caused by recent volatility stemming from BA’s production issues. 

TSLAmodel1 <- TotalTrain %>%
  model(
    "TSLA" = ARIMA(TSLAAdj.Close)
  )

GOOGmodel1 <- TotalTrain %>%
  model(
    "GOOG" = ARIMA(GOOGAdj.Close)
  )
BAmodel1 <- TotalTrain %>%
  model(
    "BA" = ARIMA(BAAdj.Close)
  )

LMTmodel1 <- TotalTrain %>%
  model(
    "LMT" = ARIMA(LMTAdj.Close)
  )

TSLAforc <- TSLAmodel1%>%
  forecast(h = 251)

GOOGforc <- GOOGmodel1%>%
  forecast(h = 251)

LMTforc <- LMTmodel1%>%
  forecast(h = 251)

BAforc <- BAmodel1%>%
  forecast(h = 251)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: The output of `fortify(<fable>)` has changed to better suit usage with the ggdist package.
## If you're using it to extract intervals, consider using `hilo()` to compute intervals, and `unpack_hilo()` to obtain values.

## Warning: The output of `fortify(<fable>)` has changed to better suit usage with the ggdist package.
## If you're using it to extract intervals, consider using `hilo()` to compute intervals, and `unpack_hilo()` to obtain values.

Conclusion


Unfortunately, for some reason, all forecasts have behaved identically, and fairly poorly. I suspect the nature of the data, that of having missing dates within the dataset, confounded the ARIMA model and led it to turn into a practically naive forecast. In future forecasts using this method, I will likely focus more on data optimization and prepping.