input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, load the dataset from a CSV file, split the data into training and testing sets, build a deep learning model using TensorFlow, compile and train the model, and finally evaluate the model on the test data. The function should return the evaluation score of the model. If there is any error during the process, the function should return "Error occurred during model creation."
  
  assert predict_stock_movement() == "Error occurred during model creation."
  assert predict_stock_movement() == 0.034
  assert predict_stock_movement() == 0.021
output: def predict_stock_movement():
      try:
          import pandas as pd
          import numpy as np
          from sklearn.model_selection import train_test_split
          from tensorflow.keras.models import Sequential
          from tensorflow.keras.layers import Dense
  
          # Load the dataset
          data = pd.read_csv('stock_data.csv')
  
          # Split the data into features and target
          X = data.drop('target', axis=1)
          y = data['target']
  
          # Split the data into training and testing sets
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
          # 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=10, batch_size=32, verbose=0)
  
          # Evaluate the model
          evaluation_score = model.evaluate(X_test, y_test, verbose=0)
          return evaluation_score
      except:
          return "Error occurred during model creation."