Impactul deficitelor bugetare asupra randamentelor trezoreriei cu XGBOOST

Analiștii lui Charles Schwab au spus că, din punct de vedere istoric, deficitele bugetare au avut un impact minim asupra randamentelor trezoreriei, în principal din cauza dominanței economice a Statelor Unite și a statutului său de emitent al monedei de rezervă a lumii.

Analiza importanței variabile cu modelul XGBOOST de învățare automată confirmă afirmația menționată mai sus.

Cod sursă:

library(tidyverse)
library(tidymodels)
library(tidyquant)

#30-year Treasury yield (^TYX) 
df_yield_30 <- 
  tq_get("^TYX") %>% 
  tq_transmute(select = close,
               mutate_fun = to.monthly,
               col_rename = "yield_30") %>% 
  mutate(date = as.Date(date))

#Federal Surplus or Deficit (-) (MTSDS133FMS)
df_deficit <- 
  tq_get("MTSDS133FMS", get = "economic.data") %>% 
  select(date, deficit = price)

#Merging the datasets
df_merged <- 
  df_yield_30 %>% 
  left_join(df_deficit) %>% 
  drop_na()

#Data split
splits <- initial_time_split(df_merged, prop = 0.8)
df_train <- training(splits)
df_test <- testing(splits)

#Bootstrapping for tuning
set.seed(12345)
df_folds <- bootstraps(df_train,
                       times = 100)
#Model
model_spec <- 
  boost_tree(trees = tune(),
             learn_rate = tune()) %>%
  set_engine("xgboost") %>% 
  set_mode("regression")

#Preprocessing
recipe_spec <- 
  recipe(yield_30 ~ ., data = df_train) %>%
  step_date(date, features = "month", ordinal = FALSE) %>%
  step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
  step_mutate(date_num = as.numeric(date)) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_rm(date)


#Workflow sets
wflow_xgboost <- 
  workflow_set(
    preproc = list(recipe = recipe_spec),
    models = list(model = model_spec)
  ) 


#Tuning and evaluating all the models
grid_ctrl <-
  control_grid(
    save_pred = TRUE,
    parallel_over = "everything",
    save_workflow = TRUE
  )

grid_results <-
  wflow_xgboost %>%
  workflow_map(
    seed = 98765,
    resamples = df_folds,
    grid = 10,
    control = grid_ctrl
  )

#Accuracy of the grid results
grid_results %>% 
  rank_results(select_best = TRUE, 
               rank_metric = "rsq") %>%
  select(Models = wflow_id, .metric, mean)


#Finalizing the model with the best parameters
best_param <- 
  grid_results %>%
  extract_workflow_set_result("recipe_model") %>% 
  select_best(metric = "rsq")


wflw_fit <- 
  grid_results %>% 
  extract_workflow("recipe_model") %>% 
  finalize_workflow(best_param) %>% 
  fit(df_train)


#Variable importance
library(DALEXtra)

#Fitted workflow for KNN
set.seed(98765)

knn_wflow_fitted <- 
  workflow() %>% 
  add_recipe(rec_features) %>% 
  add_model(spec_knn) %>% 
  fit(df_train)

#Processed data frame for variable importance calculation
imp_data <- 
  recipe_spec %>% 
  prep() %>% 
  bake(new_data = NULL) 

#Explainer object
explainer_xgboost <- 
  explain_tidymodels(
    wflw_fit %>% extract_fit_parsnip(), 
    data = imp_data %>% select(-yield_30), 
    y = imp_data$yield_30,
    label = "",
    verbose = FALSE
  )


#Calculating permutation-based variable importance 
set.seed(1983)
vip_xgboost <- model_parts(explainer_xgboost, 
                           loss_function = loss_root_mean_square,
                           type = "difference",
                           B = 100,#the number of permutations
                           label = "")

#Plot VIP
vip_xgboost %>% 
  plot() +
  labs(color = "",
       x = "",
       y = "",
       subtitle = "Higher indicates more important",
       title = "Factors Affecting 30-year Treasury Yield") +
  theme_minimal(base_family = "Roboto Slab",
                base_size = 16) +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, 
                                  size = 14,
                                  face = "bold"),
        plot.subtitle = element_text(hjust = 0.5, size = 12),
        panel.grid.minor.x = element_blank(),
        panel.grid.major.y = element_blank(),
        plot.background = element_rect(fill = "azure"))