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 data from a CSV file, split the data into training and testing sets, build a deep learning model using Keras, train the model, and evaluate it.
  
  assert predict_stock_movements() is None
  
output: 
  def predict_stock_movements():
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Load data
      data = pd.read_csv('stock_data.csv')
      
      # Preprocess 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)
      
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      # Build 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='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      # Train model
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
      
      # Evaluate model
      loss, accuracy = model.evaluate(X_test, y_test)
      print(f'Test Accuracy: {accuracy:.2f}')
  