input: Write a function to create and train a decision tree model for predicting stock values using stock data from a CSV file. The function should load the data, split it into training and testing sets, train the model, and print the model's accuracy.
  
  def construct_decision_tree_model(csv_file_path):
      # Load libraries
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      
      # Load data
      data = pd.read_csv(csv_file_path)
      
      # Split data
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Train/Test Split
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      # Decision Tree Model
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      
      # Model Accuracy
      y_pred = clf.predict(X_test)
      print('Model accuracy: ', clf.score(X_test, y_test))
  
  assert construct_decision_tree_model("stockdata.csv") is None
  assert construct_decision_tree_model("nonexistentfile.csv") is None
output: def construct_decision_tree_model(csv_file_path):
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      
      try:
          data = pd.read_csv(csv_file_path)
      except FileNotFoundError:
          return None
      
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      
      print('Model accuracy: ', clf.score(X_test, y_test))