input: def predict_stock_movements(data: pd.DataFrame) -> float:
      """ Predict stock market movements using a deep learning model.
      The function takes a DataFrame containing stock market data,
      splits it into training and test sets, builds a neural network
      model, trains it, and returns the model's evaluation score on
      the test data.
      
      >>> data = pd.read_csv('data.csv')
      >>> predict_stock_movements(data)
      0.0543  # Example output, actual result may vary
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      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)
  
      # Scale the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score