input: def predict_stock_movement(data: pd.DataFrame) -> float:
      """ Predicts stock market movements using a deep learning model. 
      The function splits the input data into training and testing sets, 
      builds a neural network model using TensorFlow and Keras, trains 
      the model on the training data, and evaluates the model on the 
      testing data. Returns the mean squared error (MSE) score of the model.
      
      >>> predict_stock_movement(pd.DataFrame({'Var': [1, 2, 3, 4], 'Target': [10, 20, 30, 40]}))
      0.0
      """
      X_train = data.iloc[:, 0]
      y_train = data.iloc[:, 1]
      X_test = data.iloc[:, 0]
      y_test = data.iloc[:, 1]
  
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, input_shape=(1,), activation='relu'),
          tf.keras.layers.Dense(128, activation='relu'),
          tf.keras.layers.Dense(64, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[0]
output:     X_train = data.iloc[:, 0].values.reshape(-1, 1)
      y_train = data.iloc[:, 1].values.reshape(-1, 1)
      X_test = data.iloc[:, 0].values.reshape(-1, 1)
      y_test = data.iloc[:, 1].values.reshape(-1, 1)