input: Write a function to create a decision tree algorithm for predicting the value of a stock based on a CSV file containing stock data from the last 3 months. The function should read the CSV file, split the data into features and target, train a decision tree model, and print the model's accuracy. The function should be named `construct_decision_tree_for_stock`.
  
  assert construct_decision_tree_for_stock("stockdata.csv")==None
output: def construct_decision_tree_for_stock(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(csv_file)
      X = data.drop('target', axis=1)
      y = data['target']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = DecisionTreeClassifier()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print(f"Model Accuracy: {accuracy:.2f}")