Publications by Sumaj Billin
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# Load packages # Core library(tidyverse) library(tidyquant) # Source function source("../Desktop/PSU_FIN3100_FinancialAnalytics/00_scripts/simulate_accumulation.R") 1 Import stock prices Revise the code below. Replace symbols with your stocks. Replace the from and the to arguments to date from 2012-12-31 to present. symbols <- c("SPY", ...
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# Load packages # Core library(tidyverse) library(tidyquant) # time series library(timetk) Goal Simulate future portfolio returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG” market: “SPY” from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG") prices <- tq_get(x = ...
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# Load packages # Core library(tidyverse) library(tidyquant) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. 1 Import stock prices Choose your stocks from 2012-12-31 to present. symbols <- c("TSLA", "MSFT", "GOOG") prices <- tq_get(x = symbol...
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# Load packages # Core library(tidyverse) library(tidyquant) library(readr) # Time series library(lubridate) library(tibbletime) # modeling library(broom) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. five stocks: “SPY”, “EFA”, �...
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Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_u...
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Ch19 Functions Introduction When should you write a function? # For reproducible work set.seed(1234) # Creat a data frame df <- tibble::tibble( a = rnorm(10), b = rnorm(10), c = rnorm(10), d = rnorm(10) ) # Rescale each column df$a <- (df$a - min(df$a, na.rm = TRUE)) / (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE)) df...
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library(tidyverse) library(tidyquant) Goal Calculate and visualize your portfolio’s beta. Choose your stocks and the baseline market. from 2012-12-31 to present 1 Import stock prices symbols <- c("TSLA", "MSFT", "GOOG") prices <- tq_get(x = symbols, get = "stock.prices", from = "2012-12-31") 2 Con...
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# Load packages # Core library(tidyverse) library(tidyquant) Goal Measure the portfolio’s beta coefficient, which can be thought of as the portfolio’s sensitivity to the market or its riskiness relative to the market. five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG” market: “SPY” from 2012-12-31 to 2017-12-31 1 Imp...
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# Load packages # Core library(tidyverse) library(tidyquant) Goal Visualize and examine changes in the underlying trend in the performance of your portfolio in terms of Sharpe Ratio. Choose your stocks. from 2012-12-31 to present 1 Import stock prices symbols <- c("AAPL", "MSFT", "GOOG") prices <- tq_get(x = symbols, get ...
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# Load packages # Core library(tidyverse) library(tidyquant) Goal Measure which portfolio is expected to yield a higher return adjusted for risk using the Sharpe Ratio. The Sharpe Ratio is the mean of the excess portfolio returns (returns - risk free rate) divided by the standard deviation of the excess returns. five stocks: “SPY”, “E...
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