input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, read data 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 then evaluate its performance. The function should return the evaluation score of the model. If the data file is empty, the function should return `None`.
  assert predict_stock_movements() == 0.0234  # Example output for score
  assert predict_stock_movements() == None  # Example output for empty data file
output: import pandas as pd
  import numpy as np
  import tensorflow as tf
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  
  def predict_stock_movements():
      try:
          data = pd.read_csv('stock_data.csv')
          if data.empty:
              return None
          
          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 = tf.keras.Sequential([
              tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
              tf.keras.layers.Dense(32, activation='relu'),
              tf.keras.layers.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
      except Exception as e:
          return None