input: def predict_stock_market_movement(data: pd.DataFrame, X_test: pd.Series, y_test: pd.Series) -> float:
      """ Predict stock market movements using a deep learning model. The function splits the data into training and testing sets, 
      builds a neural network model, trains it, and then evaluates the model. 
      The function returns the mean squared error (MSE) of the model's predictions on the test data.
      >>> data = pd.DataFrame({'column0': [1, 2, 3, 4, 5], 'column1': [2, 4, 6, 8, 10]})
      >>> X_test = data.iloc[:, 0]
      >>> y_test = data.iloc[:, 1]
      >>> predict_stock_market_movement(data, X_test, y_test)
      0.0
      """
      X_train = data.iloc[:, 0] #Choosing the independent variable 
      y_train = data.iloc[:, 1]  #Choosing the dependent variable
  
      #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[1]
output:     #Make predictions
      predictions = model.predict(X_test)
  
      #Calculate the mean squared error
      mse = ((predictions - y_test) ** 2).mean()
      return mse