input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, load data from a CSV file, split the data into training and testing sets, build a deep learning model, compile and train the model, and finally evaluate the model. The function should return the evaluation score of the model.
  assert predict_stock_movements()==score
  assert predict_stock_movements()<=2.0
output: 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
  
  def predict_stock_movements():
      data = pd.read_csv('stock_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)
      
      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'))
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      score = model.evaluate(X_test, y_test, verbose=0)
      return score