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Predecitve model for Stock Return forecast (future prediction) for top technical firms in UK listed on London Stock Exchange

sincos

Predecitve model for Stock Return forecast (future prediction) for FTS100 Tech-Mark Series (top technical firms) in UK listed on London Stock Exchange

ARIMA Model

install.packages("quantmod") library(quantmod)

Input the Stock Variables for required firms predective analysis (SGE.L,SN.L,BA.L,CCC.L,GNS.L,QQ.L,FLTR.L,RWS.L,SPT.L,SXS.L) ( This is the Dataset, that will change accordingly when you input different Stock symbols or ticker symbols of Respective companies and you can also change the time set )

data<-getSymbols("SN.L", src = "yahoo",from=as.Date("2015-01-01"),to=as.Date("2022-12-31"),auto.assign = FALSE) #Daily Data data View(data) #Used For forecating Monthly data #data<-to.monthly(data) #Used For forecasting Weekly data #data<-to.weekly(data)

Construct only the closing price of stocks for forecasting

CLOSEPRICE<-data[,4] CLOSEPRICE<-na.omit(CLOSEPRICE) View(CLOSEPRICE)

summary(CLOSEPRICE) sd(CLOSEPRICE) skewness(CLOSEPRICE) kurtosis(CLOSEPRICE) sum(!complete.cases(CLOSEPRICE)) View(CLOSEPRICE) #Visulise the data chart_Series(CLOSEPRICE, col = "black") add_SMA(n = 100, on = 1, col = "red") add_SMA(n = 20, on = 1, col = "black") add_RSI(n = 14, maType = "SMA") add_BBands(n = 20, maType = "SMA", sd = 1, on = -1) add_MACD(fast = 12, slow = 25, signal = 9, maType = "SMA", histogram = TRUE) View(CLOSEPRICE)

Set the training (JAN 2015- DEC 2020) and testing (JAN 2021-DEC 2022) dataset

set.seed(123) train<-CLOSEPRICE[1:1518] testa<-CLOSEPRICE[1519:2021] plot(CLOSEPRICE,main="STOCK CLOSE PRICE,2015-2022",ylab="Price",xlab="Days") lines(train,col="blue") lines(testa,col="green") legend("bottomright",col=c("blue","green"),lty=1,legend=c("Training","Testing"))

ADF Test for Stationrity

library(tseries) adf<-adf.test(train) adf #ACF and PACF Plots acf(train) pacf(train) diff_train<-diff(train) diff_train<-diff_train[-1,] adf.test(diff_train) par(mfrow=c(1,1)) acf(diff_train) pacf(diff_train) library(caTools) library(forecast) #Fitting the ARIMA Model fitA<-arima(train,order=c(2,1,2)) fitA tsdisplay(residuals(fitA),lag.max = 40) checkresiduals(fitA) fitb<-arima(train,order = c(3,1,2)) fitb BIC(fitb) tsdisplay(residuals(fitb),lag.max = 40) checkresiduals(fitb) fitc<-arima(train,order = c(3,1,2)) fitc tsdisplay(residuals(fitc),lag.max = 40) checkresiduals(fitc) fitd<-arima(train,order=c(2,1,2)) fitd tsdisplay(residuals(fitd),lag.max = 40) checkresiduals(fitd) fite<-arima(train,order=c(2,1,2)) fite tsdisplay(residuals(fite),lag.max = 40) checkresiduals(fite) #Forecasting the future values for stock returns

"forecasta, forecastb, forecastc, forecastd,forecaste" is the predicted values of future stock prices of respective companies

forecasta<-forecast(fitA,h=503) plot(forecasta) forecasta forecastb<-forecast(fitb,h=503) plot(forecastb) forecastb forecastc<-forecast(fitc,h=503) plot(forecastc) forecastc forecastd<-forecast(fitd,h=503) plot(forecastd) forecaste<-forecast(fite,h=503) plot(forecaste)

Evaluate the best ARIMA model for forecasting on the basis of forecast accuracy measures

accuracy(forecasta) accuracy(forecastb) accuracy(forecastc) accuracy(forecasta) accuracy(forecasta) #Estimate the performance of the best ARIMA model with the available testing model accuracy(forecasta,testa)

#GARCH MODEL

Libraries

library(quantmod) library(xts) library(PerformanceAnalytics) library(rugarch) library(tseries)

# Input the Stock Variables for required firms predective analysis (SGE.L,SN.L,BA.L,CCC.L,GNS.L,QQ.L,FLTR.L,RNS.L,SPT.L,SXS.L) ( This is the Dataset, that will change accordingly when you input different Stock symbols or ticker symbols of Respective companies and you can also change the time set )

datag<-getSymbols("SN.L" ,src = "yahoo",from=as.Date("2015-01-01"),to=as.Date("2022-12-31"),auto.assign = FALSE) #For Daily Data View(datag) 0 #Used For Monthly data #datag<-to.monthly(datag) #Used For Weekly data #datag<-to.weekly(datag)

Construct only the closing price of stocks for forecasting

datag<-datag[,4] CLOSEPRICE<-datag CLOSEPRICET<-CLOSEPRICE[1:1518] CLOSEPRICEF<-CLOSEPRICE[1519:2021] chartSeries(SXS.L) View(CLOSEPRICE)

Calculate Daily returns

returnss <- CalculateReturns(CLOSEPRICE) View(returnss) returnss <- returnss[-1] #Contruct the training (JAN 2015- DEC 2020) and testing (JAN 2021- DEC 2022) dataset return<-returnss[1:1517] test<-returnss[1518:2020] class(test) hist(return) #Visuualise the data chart.Histogram(return, methods = c('add.density', 'add.normal'), colorset = c('blue', 'green', 'red')) chartSeries(CalculatedReturns)

Test for different Garch models

1. sGARCH model with contant mean

s <- ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(model = "sGARCH"), distribution.model = 'norm') m <- ugarchfit(data = return, spec = s) coef(m) plot(m)

9 0

2. GARCH with sstd

s <- ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(model = "sGARCH"), distribution.model = 'sstd') m <- ugarchfit(data = return, spec = s) m checkresiduals(m) s <-m@fit$fitted.values kurtosis(s) plot(m) 9 0

GARCH with General Error Distrubution

s <- ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(model = "sGARCH"), distribution.model = 'ged') m <- ugarchfit(data = return, spec = s) m plot(m) 9 0

3. GJR-GARCH

s <- ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(model = "gjrGARCH"), distribution.model = 'sstd') m <- ugarchfit(data = return, spec = s) coef(m) checkresiduals(m) s <-m@fit$fitted.values kurtosis(s) plot(m) 9 0

#4. GJR-GARCH in mean s <- ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(model = "sGARCH"), distribution.model = 'ged') m <- ugarchfit(data = return, spec = s) m plot(m) 9 0

Simulation with the best GARCH model evaluted on the bassis of providing the lowest Akakie

s <- ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(model = "gjrGARCH"), distribution.model = 'sstd') m <- ugarchfit(data = return, spec = s) m plot(m) 9 0 s sfinal <- s setfixed(sfinal)<-as.list(coef(m))

Forecast the futre returns of the stocks by the GJR-GARCH model( Values for forecasting changes according to the type of dataset choosed)

f <- ugarchforecast(m, n.ahead =503)

f plot(f) 4 0 par(mfrow = c(1,1)) #Showing the volatality of the stock plot(sigma(f)) predictedvalues<- ugarchpath(spec = sfinal, m.sim = 1, n.sim = 503, rseed = 123) plot.zoo(fitted(predictedvalues)) View(fitted(sim)) class(test) sigma(sim) plot.zoo(sigma(predictedvalues)) tail(CLOSEPRICET)

predicted values of stock return (Closing Price) of the firms on each day

#Substitute the last value of Stock close price from the trainiing set in the p variable to predict the future values from the GARCH model p <- 1510*apply(fitted(predictedvalues),1, 'cumsum') +1510

p is the predicted values of future stock prices of respective companies

p View(p) plot(p) table(p) class(p)

ForecastedValues plot(p, type = "l", lwd = 1, main = "Forecasted Values of SN.L by GJR-GARCH(1,1)") plot(p,closepricef, type = "l",lwd = 1) length(p) length(closepricef)

Evaluate the Performance of GJR-Garch Model ,

View(p) p<-as.numeric(p) closepricef<-as.numeric(CLOSEPRICEF$SN.L.Close) accuracy(p,closepricef) p-closepricef

rmse<-sqrt(mean((p)^2)) rmse abs<-abs((closepricef-p)/closepricef)*100

mean(abs) forecastedp<-ts(p,start=1) closepricets<-ts(CLOSEPRICEF$GNS.L.Close,start=1) accuracy(forecastedp) accuracy(forescastdp)

Evaluate the supreme model among ARIMA and GARCH model for being a better fit for the data depending on required time horizon

#ARIMA model accuracy(forecasta,testa) #GARCH model accuracy(forecastedp,closepricets)