input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should follow these steps: import necessary libraries, load a dataset from a CSV file, split the data into training and testing sets, build a deep learning model using TensorFlow and Keras, compile and train the model, and finally evaluate the model. If the model is successfully created, return True, otherwise return False. If the dataset file does not exist, return False. Ensure the function handles potential errors gracefully.
  assert predict_stock_market(['data.csv'])==True
  assert predict_stock_market(['nonexistent_file.csv'])==False
output: def predict_stock_market(file_path):
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          import tensorflow as tf
          from tensorflow.keras.models import Sequential
          from tensorflow.keras.layers import Dense
  
          try:
              data = pd.read_csv(file_path[0])
          except FileNotFoundError:
              return False
  
          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)
  
          model = Sequential([
              Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
              Dense(64, 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 True
      except Exception as e:
          return False