### Logistic Regression on Stock Data using Google and SPY (SPDR S&P 500)

As part of a Pluralsight training presentation, Understanding and Applying Logistic Regression, students worked through various exercises, one of which was predicting stock price changes, up or down, on Google, using Google and Spyder closing prices.

As an ordered list of actions:

Source data is here.

As an ordered list of actions:

- Load data - Yahoo financials for each day for 5 years, taking only date and closing price for this analysis
- Transform sources: Merge sources, change column headings, cast the Date column as DATE type, sort descending
- Perform logistic regression
- Create a frame of actual versus predicted changes, and add a column for the correct/incorrect prediction result
- Find percent correct, on whether the price moved correctly up or down

Source data is here.

```
# Clear memory
rm(list = ls())
# Set working directory
setwd("../Data")
getwd()
# load data
# Data is Yahoo financial price for each day for 5 years,
# taking only date and closing price for this analysis
GOOG.data <- read.csv("GOOG.csv", header = TRUE, sep = ",")[, c("Date", "Adj.Close")]
SPY.data <- read.csv("SPY.csv", header = TRUE, sep = ",")[, c("Date", "Adj.Close")]
# merge sources
GOOG.merged <- merge(GOOG.data, SPY.data, by = "Date")
# change column headings for Google and for SPY
names(GOOG.merged)[2] <- "GOOG.Close"
names(GOOG.merged)[3] <- "SPY.Close"
# cast as date
# in some data sets, the date is formatted as MM/DD/YYYY
GOOG.merged$Date <- as.Date(GOOG.merged$Date)
# sort descending
GOOG.merged <- GOOG.merged[order(GOOG.merged$Date, decreasing = TRUE),]
# calculate returns from previous row
GOOG.returns <- GOOG.merged
GOOG.returns[-nrow(GOOG.merged), -1] <- GOOG.merged[-nrow(GOOG.merged), -1] / GOOG.merged[-1, -1] - 1
# change column headings for returns
names(GOOG.returns)[2] <- "GOOG.Returns"
names(GOOG.returns)[3] <- "SPY.Returns"
# remove last row
GOOG.returns <- GOOG.returns[-nrow(GOOG.returns),]
# correlation of prices
(corr.Prices <- cor.test(GOOG.merged$GOOG.Close, GOOG.merged$SPY.Close))
# correlation of returns
(corr.Return <- cor.test(GOOG.returns$GOOG.Returns, GOOG.returns$SPY.Returns))
# combine prices and returns using lagged data
GOOG.lagging <- data.frame(GOOG.returns[-nrow(GOOG.returns),], GOOG.returns[-1, -1])
# rename the lagged columns
names(GOOG.lagging)[4:5] <- c("GOOG.Returns.Lagged", "SPY.Returns.Lagged")
# create a column for logistic regression
GOOG.lagging$Up = GOOG.lagging$GOOG.Returns >= 0
# perform logistic regression
GOOG.logRegression <- glm(GOOG.lagging$Up ~ GOOG.lagging$GOOG.Returns.Lagged + GOOG.lagging$SPY.Returns.Lagged, fam = binomial)
summary(GOOG.logRegression)
# create a frame of actual versus predicted
GOOG.fitted <- data.frame(GOOG.lagging$Up, fitted(GOOG.logRegression) >= 0.5)
names(GOOG.fitted) <- c("Actual", "Predicted")
# create column for correct/incorrect prediction
GOOG.fitted$CorrectForecast = GOOG.fitted$Actual == GOOG.fitted$Predicted
# find percent correct
length(GOOG.fitted$CorrectForecast[GOOG.fitted$CorrectForecast == TRUE]) / length(GOOG.fitted$CorrectForecast)
```