input: 
  def predict_stock_market_movement(X_train, y_train, X_test, y_test):
      """Predicts stock market movements using a deep learning model.
      The function takes in training and testing data for independent (X) and dependent (y) variables,
      builds a neural network model, trains it, and evaluates the model.
      >>> data = pd.DataFrame({'X': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]})
      >>> X_train = data['X']
      >>> y_train = data['y']
      >>> X_test = data['X']
      >>> y_test = data['y']
      >>> prediction = predict_stock_market_movement(X_train, y_train, X_test, y_test)
      >>> round(prediction, 2)
      0.0
      """
      #import necessary libraries
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      #create a model and add layers
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      #compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      #train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[0]
  
output:     # Make predictions
      predictions = model.predict(X_test)
      
      # Calculate the mean squared error between predictions and actual values
      mse = tf.keras.losses.MeanSquaredError()
      error = mse(y_test, predictions).numpy()
      
      return error