Midterm Project for Statistical Methods in Finance LiulingDu and ld2742 New York,

Transcription

1 Midterm Project for Statistical Methods in Finance LiulingDu and ld2742 New York, Contents 0.1 Load the APPL and calculate the percentage log-returns Read the tickers for the data Example of a plot using dygraph and xts object. It plots the close price Preliminaries Load the required packages. library(zoo) Attaching package: 'zoo' The following objects are masked from 'package:base': as.date, as.date.numeric library("xts") #for time series library("nloptr") #for optimization library("dygraphs") #for plots library(plotly) # for 3D plots Loading required package: ggplot2 Attaching package: 'plotly' The following object is masked from 'package:ggplot2': last_plot The following object is masked from 'package:stats': filter The following object is masked from 'package:graphics': layout library("magrittr") # pipes library("webshot") library(fbasics) Loading required package: timedate Loading required package: timeseries ld2742@columbia.edu 1

2 Attaching package: 'timeseries' The following object is masked from 'package:zoo': time<- Rmetrics Package fbasics Analysing Markets and calculating Basic Statistics Copyright (C) Rmetrics Association Zurich Educational Software for Financial Engineering and Computational Science Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY Mail to: library('performanceanalytics') Attaching package: 'PerformanceAnalytics' The following objects are masked from 'package:timedate': kurtosis, skewness The following object is masked from 'package:graphics': legend setwd('/users/liulingdu/downloads') getwd() [1] "/Users/liulingdu/Downloads" Question Load the APPL and calculate the percentage log-returns AAPL <- read.csv(paste(getwd(),"/data/aapl.txt",sep='')) D <- AAPL[,1] D <- as.date(tostring(aapl[1,1]),'%y%m%d') for (t in 2:dim(AAPL)[1]) D <- c(d,as.date(tostring(aapl[t,1]),'%y%m%d')) AAPL_CLOSE_xts <- xts(aapl[,5], order.by = D) head(aapl_close_xts) [,1]

3 summary(aapl_close_xts) Index AAPL_CLOSE_xts Min. : Min. : st Qu.: st Qu.: Median : Median : Mean : Mean : rd Qu.: rd Qu.: Max. : Max. : r_close_aapl <- 100*diff(log(AAPL_CLOSE_xts[,1])) basicstats(r_close_aapl) r_close_aapl nobs NAs Minimum Maximum Quartile Quartile Mean Median Sum SE Mean LCL Mean UCL Mean Variance Stdev Skewness Kurtosis Read the tickers for the data ticker <- read.table(paste(getwd(),"/data/tickers.txt",sep=''), header=false) ticker V1 1 CASH 2 AAPL 3 ABBV 4 ABT 5 ACN 6 AEP 7 AIG 8 ALL 9 AMGN 10 AMZN 11 APA 12 APC 13 AXP 14 BA 15 BAC 16 BAX 17 BK 3

4 18 BMY 19 BRKB 20 C 21 CAT 22 CL 23 CMCSA 24 COF 25 COP 26 COST 27 CSCO 28 CVS 29 CVX 30 DD 31 DIS 32 DOW 33 DVN 34 EBAY 35 EMC 36 EMR 37 EXC 38 F 39 FB 40 FCX 41 FDX 42 FOXA 43 GD 44 GE 45 GILD 46 GM 47 GOOGL 48 GS 49 HAL 50 HD 51 HON 52 HPQ 53 IBM 54 INTC 55 JNJ 56 JPM 57 KO 58 LLY 59 LMT 60 LOW 61 MA 62 MCD 63 MDLZ 64 MDT 65 MET 66 MMM 67 MO 68 MON 69 MRK 70 MS 71 MSFT 4

5 72 NKE 73 NOV 74 NSC 75 ORCL 76 OXY 77 PEP 78 PFE 79 PG 80 PM 81 QCOM 82 RTN 83 SBUX 84 SLB 85 SO 86 SPG 87 T 88 TGT 89 TWX 90 TXN 91 UNH 92 UNP 93 UPS 94 USB 95 UTX 96 V 97 VZ 98 WAG 99 WFC 100 WMT 101 XOM paste(getwd(),"/data/", ticker$v1[4],".txt",sep ='') [1] "/Users/liulingdu/Downloads/data/ABT.txt" data_xts<-aapl_close_xts rets_xts<-r_close_aapl for (k in 3:dim(ticker)[1]) { prices <- read.csv(paste(getwd(),"/data/", ticker$v1[k],".txt",sep ='')) dates <- NULL; dates <- as.date(tostring(prices[1,1]),'%y%m%d') for (t in 2:dim(prices)[1]) dates <- c(dates,as.date(tostring(prices[t,1]),'%y%m%d')) prices_xts <- xts(prices[,5],order.by = dates) logret_xts <- 100*diff(log(prices_xts[,1])) rets_xts<-merge(rets_xts,logret_xts) data_xts<-merge(data_xts,prices_xts) } dim(data_xts) [1] return.table<-matrix(ncol=dim(rets_xts)[2],nrow=5) for (t in 1:dim(rets_xts)[2]){ miu<-mean(rets_xts[,t],na.rm=t) 5

6 variance<-var(rets_xts[,t],na.rm=t) sd<-sd(rets_xts[,t],na.rm=t) return.table[1,t]<-miu return.table[2,t]<-variance return.table[3,t]<-sd } return.table[4,]<-return.table[1,]*252 return.table[5,]<-return.table[3,]*(252^0.5) SharpeRatio<-return.table[4,]/return.table[5,] return.table<- rbind(return.table,sharperatio) rownames(return.table) <- c("sample_mean", "Variance","Standard Deviation","annualize_mean","annualize_s 0.3 Example of a plot using dygraph and xts object. It plots the close price CLOSE_log<- return.table[6,] plot(close_log) CLOSE_log ticker[which.max(close_log)+1,1] Index [1] MA 101 Levels: AAPL ABBV ABT ACN AEP AIG ALL AMGN AMZN APA APC AXP BA... XOM #Since the positive sharpe ratio is considered good for investors, the largest sharpe ratio is MA stock 6