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sent_vol.scripts.r
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sent_vol.scripts.r
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load.libraries <- function(){
# Baselina libraries
library(tidyverse)
library(L1pack)
library(latex2exp)
library(xtable)
## Libraries for Bykhovskaya.regression
#install.packages("nloptr")
library(nloptr)
library(matlib)
library(stats)
library(expm)
}
## Load and preprocess data
load.vol <- function(){
folder <- "C:/data/"
vol <- read.csv(paste0(folder,"rk_1m.txt"), header = FALSE)
colnames(vol) <- unlist(read.csv(paste0(folder,"ids.txt"), header = FALSE))
preserve <- colnames(read.csv("C:/conectedness_2net_rk.txt", header = TRUE))[-1]
vol <- vol[,preserve]
return(vol)
}
load.net <- function(){
net <- read.csv("C:/matlab/results/LF/conectedness_0network_rk.txt", header = FALSE)
net <- as.data.frame(t(net))
days <- read.csv("C:/matlab/data/daysplit.txt", header = FALSE)
rownames(net) <- as.Date(as.character(days[,1]), format = "%d-%m-%Y")
rm(days)
lbls <- as.character(read.csv("C:/results/LF/conectedness_0lbls.txt", header = FALSE))
cns <- rep("",length(lbls)*length(lbls))
for(i in 1:length(lbls))for(j in 1:length(lbls)) cns[(i-1)*10+j] <- paste0(lbls[j],",",lbls[i])
colnames(net) <- cns
return(net)
}
load.mkt <- function(net){
mkt <- read.csv("C:/data/MKT.csv", header = TRUE)
mkt <- mkt%>%mutate(MKT = Mkt.RF+RF,X=parse_date(as.character(X), format = "%Y%m%d"))%>%select(X,MKT)%>%mutate(nMKT=1*(MKT<0))
rownames(mkt) <- mkt$X
mkt <- mkt%>%select(MKT,nMKT)
mkt$MKT <- mkt$MKT / sd(mkt$MKT, na.rm = TRUE)
mkt <- mkt[rownames(mkt)%in%rownames(net),]
return(mkt)
}
load.sentiment <- function(net,market.wide = TRUE){
sentiment <- read.csv("C:/data/sentiment_ecsector_daily_ew.csv")
rownames(sentiment) <- as.Date(sentiment[,1], format = "%d.%m.%Y")
sentiment <- sentiment[,-1]
sentiment <- sentiment[rownames(sentiment)%in%rownames(net),]
#sentiment <- sentiment%>% mutate(across(where(is.numeric), scale))
#pSentiment <- nSentiment <- sentiment / sd(unlist(sentiment))
#pSentiment[pSentiment<0] <- 0
#nSentiment[pSentiment>0] <- 0
#nSentiment <- abs(nSentiment)
if(market.wide){
sentiment$TOT <- rowSums(sentiment)
sentiment$TOT <- sentiment$TOT / sd(sentiment$TOT)
return(sentiment%>%select(TOT))
}else{
sentiment <- sentiment / sd(unlist(sentiment))
return(sentiment)
}
}
construct.data <- function(net,mkt,sentiment){
# 2.1 Construct baseline data set: first two network lags
mdl.data <- data.frame(net[rownames(net)[-c(1,2)],colnames(net)[2]])
colnames(mdl.data)[1] <- paste0("X_",colnames(net)[2],"_1")
peers <- list()
for(lag in 1:2){
for(edge in colnames(net)){
cmpnents <- unlist(strsplit(edge, split=","))
peers <- c(peers,cmpnents[1],cmpnents[2])
if(length(cmpnents)==2&&cmpnents[2]!=cmpnents[1]){
if(lag==1){
mdl.data[,paste0("X_",edge,"_",lag)] <- data.frame(net[rownames(net)[-c(1,nrow(net))],edge])
}else if(lag==2){
mdl.data[,paste0("X_",edge,"_",lag)] <- data.frame(net[rownames(net)[-c(nrow(net)-1,nrow(net))],edge])
}
}
}
}
peers <- unique(unlist(peers))
n <- length(peers) # n <- (1+sqrt(1+4*ncol(mdl.data)/2))/2
# 2.2 Add (lag-1) triangular peer effects
col_list <- list()
for(colmn in colnames(mdl.data)) if(unlist(strsplit(colmn,"_"))[3]=="1") col_list <- c(col_list,colmn)
col_list <- unlist(col_list)
for(edge in col_list){
edge_info <- unlist(strsplit(edge,"_"))
cmponents <- unlist(strsplit(edge_info[2],","))
cn <- paste(edge_info[1],edge_info[2],"tr", sep = "_")
mdl.data[,cn] <- 0
for(intermediate_peer in peers) if(intermediate_peer != cmponents[1] && intermediate_peer != cmponents[2]){
c1 <- paste0("X_",cmponents[1],",",intermediate_peer,"_1")
c2 <- paste0("X_",intermediate_peer,",",cmponents[2],"_1")
mdl.data[,cn] <- mdl.data[,cn] + sqrt(mdl.data[,c1]*mdl.data[,c2]) / (n - 2)
#print(paste0(cn," += ",c1," * ",c2))
}
}
cutoffs <- (1:3) * (n*(n-1))
# 2.3 Add sentiment data
#mdl.data[,paste0("S_plus_",colnames(pSentiment))] <- pSentiment[-c(1,nrow(net)),]
#mdl.data[,paste0("S_minus_",colnames(nSentiment))] <- nSentiment[-c(1,nrow(net)),]
#cutoffs <- c(cutoffs,ncol(mdl.data))
# 2.4 Add quadratic sentiment data
#mdl.data[,paste0("S2_plus_",colnames(pSentiment))] <- pSentiment[-c(1,nrow(net)),]^2
#mdl.data[,paste0("S2_minus_",colnames(nSentiment))] <- nSentiment[-c(1,nrow(net)),]^2
#cutoffs <- c(cutoffs,ncol(mdl.data))
# 2.5 Add interactions with information (sign of MTK return) variable
#mdl.data[,"I"] <- mkt$nMKT[-c(1,nrow(net))]
#mdl.data[,paste0("IS_plus_",colnames(pSentiment))] <- mkt$nMKT[-c(1,nrow(net))]*pSentiment[-c(1,nrow(net)),]
#mdl.data[,paste0("IS_minus_",colnames(nSentiment))] <- mkt$nMKT[-c(1,nrow(net))]*nSentiment[-c(1,nrow(net)),]
#mdl.data[,paste0("IS2_plus_",colnames(pSentiment))] <- mkt$nMKT[-c(1,nrow(net))]*pSentiment[-c(1,nrow(net)),]^2
#mdl.data[,paste0("IS2_minus_",colnames(nSentiment))] <- mkt$nMKT[-c(1,nrow(net))]*nSentiment[-c(1,nrow(net)),]^2
# 2.3-2.5 Add market data, sentiment data, and interactions
mdl.data[,"MKT"] <- mkt$MKT[-c(1,nrow(net))]
mdl.data <- mdl.data%>%mutate(aMKT = abs(MKT), MKT2 = MKT^2)
mdl.data[,"S"] <- sentiment$TOT[-c(1,nrow(net))]
mdl.data <- mdl.data%>%mutate(aS = abs(S), S2 = S^2)
mdl.data <- mdl.data%>%mutate(MMT_S = MKT*S, MMT_aS = MKT*aS, MMT_S2 = MKT*S2)
# 2.6 Retain only non-null data
cutoffs <- c(cutoffs,ncol(mdl.data))
keep <- rowSums(is.na(mdl.data))==0
# Return results
return(list(mdl.data=mdl.data,cutoffs=cutoffs,keep=keep,peers=peers,n=n))
}
## Implementation of Anna Bykhovskaya (2023) Time Series Approach to the Evolution of Networks: Prediction and Estimation, Journal of Business & Economic Statistics, 41:1, 170-183, DOI: 10.1080/07350015.2021.2006669
Bykhovskaya.regression <- function(dta, maxiter = 2.5*10^4, fit.lower.bound = 0, fit.upper.bound = Inf){
# Define data
y <- dta[,"y"]
X <- dta
X[,"y"] <- 1
X <- X%>%rename("(Intercept)"="y")
n <- ncol(X)
T_obs <- nrow(X)
# Define fit function
fit.f <- function(X,b) return(as.numeric(rowSums(sweep(X, MARGIN=2,b, `*`))))
# Define objective function
eval_f <- function(b){
return(sum(abs(y-pmax(rep(0,length(y)),as.numeric(rowSums(sweep(X, MARGIN=2,b, `*`)))))))
}
# Define constraint function
eval_g_ineq <- function(b){
return(c(fit.lower.bound-min(as.numeric(rowSums(sweep(X, MARGIN=2,b, `*`)))), max(as.numeric(rowSums(sweep(X, MARGIN=2,b, `*`))))-fit.upper.bound))
#return(fit.lower.bound-min(as.numeric(rowSums(sweep(X, MARGIN=2,b, `*`)))))
}
# Initialize parameters based on standard LTD regression estimates
mdl2 <- lad(y~., data = dta)
coef <- as.numeric(mdl2$coefficients)
se <- sqrt(diag(vcov(mdl2)))
tstat <- coef / se
pval <- 2 * pt(abs(tstat), df = nrow(dta) - nrow(mdl2$R) - 2, lower.tail = FALSE)
res.b <- data.frame(coef, se, tstat, pval)
rownames(res.b) <- names(mdl2$coefficients)
coef.b <- coef
rm(coef,se,tstat,pval)
# Perform optimization
# See https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/
start <- Sys.time()
optmodel <- nloptr::nloptr(x0=coef.b,
eval_f = eval_f,
eval_g_ineq = eval_g_ineq,
lb = rep(-Inf,length(coef.b)),
ub = rep(Inf,length(coef.b)),
opts = list("algorithm"="NLOPT_LN_COBYLA", # "NLOPT_LN_BOBYQA"
"xtol_rel" = 1.0e-10,"ftol_abs" = 1.0e-10,
"maxeval" = maxiter))
end <- Sys.time()
# Coefficients and fit
coef <- as.numeric(optmodel$solution)
fit <- fit.f(X, coef)
# Objective and constraint function values
f <- eval_f(coef)
g <- eval_g_ineq(coef)
# Coef Cov matrix
X.adj <- X
X.adj[fit<0,] <- 0
M0_hat <- matrix(nrow = n, ncol = n)
for(i in 1:n) for(j in 1:n) M0_hat[i,j] <- mean(X.adj[,i]*X.adj[,j])
M0_sq_inv <- chol2inv(chol(sqrtm(M0_hat)))
if(min(mdl2$fitted.values)<fit.lower.bound||max(mdl2$fitted.values)>fit.upper.bound){
fu <- density(y-fit, kernel = "rectangular")
i <- sum(fu$x<=0)
fu0 <- fu$y[i] -fu$x[i]/(fu$x[i+1] - fu$x[i])*(fu$y[i+1] - fu$y[i])
scale <- 1/2/fu0
}else scale <- mdl2$scale
#vcov(mdl2)
#M0_sq_inv%*%M0_sq_inv * 2 * mdl2$scale^2 / T_obs
#se <- sqrt(diag(M0_sq_inv%*%M0_sq_inv * 2 * mdl2$scale^2 / T_obs))
#sqrt(diag(vcov(mdl2)))
# se, tstat, pvals
R <- M0_sq_inv%*%M0_sq_inv * 2 * scale^2 / T_obs
se <- sqrt(diag(R))
tstat <- coef / se
pval <- 2 * pt(abs(tstat), df = nrow(dta) - ncol(dta) - 2, lower.tail = FALSE)
coef.summary <- data.frame(coef,se,tstat,pval)
rownames(coef.summary) <- names(mdl2$coefficients)
# Return results
return(list(
opt.time=end-start,
converged=optmodel$iterations<maxiter&&sum(g>0)==0,
coeficients=list(opmod=coef.summary,benchmod=res.b),
Cov=R,
scale=scale,
fit=fit,
resid=y-fit,
SumAbsResid=f,
IneqCons=g))
}
run.Bykhovskaya.regression <- function(net,mdl.data,keep,n,X.cols = NA){
if(is.na(X.cols[1])) X.cols <- list(full=(1:ncol(mdl.data)))
filename <- "C:/results/coefficients.bench"
if(file.exists(filename)){
load("C:/results/actual.bench")
load("C:/results/fit.bench")
load(filename)
}else{
row <- 0
actual <- data.frame(matrix(nrow = sum(keep), ncol = n * (n-1)))
fit <- list()
fit[["base"]] <- data.frame(matrix(nrow = sum(keep), ncol = n * (n-1)))
for(mdl.name in names(X.cols)) fit[[mdl.name]] <- data.frame(matrix(nrow = sum(keep), ncol = n * (n-1)))
results <- list()
for(edge in colnames(net)){
cmpnents <- unlist(strsplit(edge, split=","))
if(length(cmpnents)==2&&cmpnents[2]!=cmpnents[1]){
row <- row + 1
print(paste0("[",format(Sys.time(),"%a %b %d %X %Y"),"] Estimating baseline LAD model and saving results for [",row,"/",n*(n-1),"] ",edge,".."))
actual[,row] <- as.numeric(data.frame(net[rownames(net)[-c(1,2)],edge])[keep,])
colnames(actual)[row] <- edge
# Baseline model
filename2 <- paste0("results/models/benchmark_",edge)
if(file.exists(filename2)) load(filename2) else{
dta <- cbind(actual[,row],mdl.data[keep,grep(edge, colnames(mdl.data))])
colnames(dta)[1] <- "y"
mdl <- Bykhovskaya.regression(dta, 10^5, fit.lower.bound = 0, fit.upper.bound = 100)
save(mdl, file = filename2)
}
fit[["base"]][,row] <- mdl$fit
colnames(fit[["base"]])[row] <- edge
results[["base"]][[edge]] <- mdl$coeficients$opmod
#plot(as.numeric(data.frame(net[rownames(net)[-c(1,2)],edge])[keep,]), type='l')
#lines(mdl$fit, col="blue")
# Other models
for(mdl.name in names(X.cols)){
print(paste0("[",format(Sys.time(),"%a %b %d %X %Y"),"] Estimating ",mdl.name," LAD model and saving results for [",row,"/",n*(n-1),"] ",edge,".."))
filename2 <- paste0("results/models/bench_",mdl.name,"_",edge)
if(file.exists(filename2)) load(filename2) else {
dta <- cbind(actual[,row],mdl.data[keep,c(grep(edge, colnames(mdl.data)),X.cols[[mdl.name]])])
colnames(dta)[1] <- "y"
mdl <- Bykhovskaya.regression(dta, 10^5, fit.lower.bound = 0, fit.upper.bound = 100)
save(mdl, file = filename2)
}
fit[[mdl.name]][,row] <- mdl$fit
colnames(fit[[mdl.name]])[row] <- edge
results[[mdl.name]][[edge]] <- mdl$coeficients$opmod
#lines(mdl$fit, col="red")
}
}
}
save(actual, file = "C:/results/actual.bench")
save(fit, file = "C:/results/fit.bench")
save(results, file = filename)
}
return(list(actual=actual,fit=fit,coef.summary=results))
}
results.out <- function(actual,fit,results,n){
TRBC.map <- list("X50"="EN",
"X51"="MT",
"X52"="ID",
"X53"="CS_C",
"X54"="CS_N",
"X55"="FN",
"X56"="HC",
"X57"="IT",
"X59"="UT",
"X60"="RE")
# 3.1 General model R^2 and F-stat for S_plus and S_minus coefficients
R2 <- 0
T_obs <- length(unlist(c(actual)))
cutoffs <- list()
nms <- names(fit)
for(ctf in nms){
nR2 <- cor(unlist(c(actual)),unlist(c(fit[[ctf]])))^2
cutoffs <- c(cutoffs,nrow(results[[ctf]][[1]]))
print(paste0("R2 for model ",ctf," is: ", sprintf("%.3f", nR2)))
print(paste0("Difference for model ",ctf," is: ", sprintf("%.3f", nR2-R2), " (",sprintf("%.4f", 1 - pnorm((atanh(sqrt(nR2))-atanh(sqrt(R2)))/sqrt(2/(T_obs - 3)))),")"))
print(paste0("Adjusted R2 for model ",ctf," is: ",sprintf("%.3f", 1-(1-nR2)*(T_obs - 1)/(T_obs - nrow(results[[ctf]][[1]]) - 1))))
R2 <- nR2
}
cutoffs <- as.numeric(unlist(cutoffs))
# 3.2 F-statistics for each individual equation
for(i in 2:length(nms)){
for(j in 1:(i-1)){
Fstat <- sd(unlist(c(actual - fit[[nms[i-j]]]))) / sd(unlist(c(actual - fit[[nms[i]]])))
print(paste0("F-stat for model ",i," vs. model ",i-j," is: ", sprintf("%.4f",Fstat), " (", sprintf("%.4f", 1-pf(Fstat, T_obs-1, T_obs-1)), ")"))
}
}
# 3.3 Number of significant coefficients at the 5% level in each model
for (mdl.name in nms) {
significant.coefs <- 0
total.coefs <- 0
for(edge in names(results[[mdl.name]])){
total.coefs <- total.coefs + nrow(results[[mdl.name]][[edge]])
significant.coefs <- significant.coefs + sum(results[[mdl.name]][[edge]]$pval<=0.05)
}
print(paste0("Total number of significant coefficients (p <= 0.05) in model ",mdl.name," is ",significant.coefs,", i.e. ",sprintf("%.3f",100*significant.coefs/total.coefs),"%."))
}
# 3.4 Histograms of t-statistics for each coefficient in each model
for (mdl.name in nms) {
coef.names <- rownames(results[[mdl.name]][[names(results[[mdl.name]])[1]]])
edge.names <- names(results[[mdl.name]])
tstats <- matrix(nrow = length(edge.names), ncol = length(coef.names))
rownames(tstats) <- edge.names
colnames(tstats) <- coef.names
for(edge in edge.names){
tstats[edge,] <- results[[mdl.name]][[edge]]$tstat
}
chart.nrow <- (ncol(tstats)-1)/3
png(file=paste0("results/hist_tstats_",mdl.name,".png"), width=6, height=2*chart.nrow, units="in", res=600)
par(mfrow=c(chart.nrow,3), mar = c(4, 2, 4, 2))
for(i in 2:ncol(tstats)){
coef.name <- colnames(tstats)[i]
my_hist <- hist(tstats[,i], breaks = 20, plot = F)
cls <- ifelse(my_hist$breaks < -1.96, "red", ifelse(my_hist$breaks > 1.96, "forestgreen", "gray"))
title <- "" #title <- Tex(paste0("t-stats for ",coef.name,""))
plot(my_hist, border=F, xlab=TeX(paste0("$\\beta_{",i-1,"}$")), ylab="", main = title, cex = 0.5, col = cls)
}
dev.off()
}
# 3.5 Coefficient matrices for each variables vs. each edge in the network
for (mdl.name in nms) {
edge.names <- names(results[[mdl.name]])
coef.names <- rownames(results[[mdl.name]][[names(results[[mdl.name]])[1]]])
for(cn in coef.names){
tbl <- data.frame(matrix(nrow = data$n, ncol = data$n))
colnames(tbl) <- rownames(tbl) <- data$peers
for(edge in edge.names){
c1 <- unlist(strsplit(edge,","))[1]
c2 <- unlist(strsplit(edge,","))[2]
#stars <- ifelse(results[[mdl.name]][[edge]][cn,"pval"]<0.01,"^{***}",ifelse(results[[mdl.name]][[edge]][cn,"pval"]<0.05,"^{**}",ifelse(results[[mdl.name]][[edge]][cn,"pval"]<0.1,"^{*}","")))
stars <- ifelse(results[[mdl.name]][[edge]][cn,"pval"]<0.01,"***",ifelse(results[[mdl.name]][[edge]][cn,"pval"]<0.05,"**",ifelse(results[[mdl.name]][[edge]][cn,"pval"]<0.1,"*","")))
#tbl[c1,c2] <- capture.output(cat("\\begin{tabular}{@{}c@{}}",sprintf("%.4f", results[[mdl.name]][[edge]][cn,"coef"]),stars,"\\\\(",sprintf("%.3f", results[[mdl.name]][[edge]][cn,"se"]),")\\end{tabular}", sep = ""))
tbl[c1,c2] <- paste0(sprintf("%.4f", results[[mdl.name]][[edge]][cn,"coef"]),stars," (",sprintf("%.3f", results[[mdl.name]][[edge]][cn,"tstat"]),")")
#tbl[c1,c2] <- results[[mdl.name]][[edge]][coef.name,"t-stat"]
tbl[c1,c2] <- paste0(sprintf("%.4f", results[[mdl.name]][[edge]][cn,"coef"]),stars)
}
#print(xtable(tbl, type = "latex", digits = 3, caption = paste0("t-statistics for ",cn), label = paste0("tbl:",cn,"_",unlist(strsplit(coef.name,"_"))[2])),
# file=paste0("C:/results/tstats.",cn,"_",unlist(strsplit(coef.name,"_"))[2],".txt"),
# include.rownames=TRUE)
print(xtable(tbl, type = "latex", caption = paste0("Coefficients for ",cn), label = paste0("tbl:",mdl.name,"_",cn)),
file=paste0("results/result_tbl_",mdl.name,"_",cn,".txt"),
include.rownames=TRUE)
}
}
}