input: Write a function to create an AI-based system for predicting stock market movements. The function should import necessary libraries, load the data, split it into train and test sets, build a deep learning model, train the model, and evaluate it. If the data file 'data.csv' does not exist, the function should return "Data file not found."
  assert predict_stock_movement(True)=="Data file not found."
  assert predict_stock_movement(False) is not None
output: def predict_stock_movement(file_exists):
      if not file_exists:
          return "Data file not found."
      
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.preprocessing import StandardScaler
  
      data = pd.read_csv('data.csv')
      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)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
  
      loss, accuracy = model.evaluate(X_test, y_test)
      return accuracy