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 using TensorFlow, compile and train the model, and finally evaluate the model. If the model is successfully trained and evaluated, return True; otherwise, return False.
  
  assert predict_stock_movement()==True
output: 
  def predict_stock_movement():
      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
      from tensorflow.keras.optimizers import Adam
  
      # Load data
      data = pd.read_csv('stock_data.csv')
      X = data.drop('target', axis=1).values
      y = data['target'].values
  
      # Split data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Build model
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      # Compile model
      model.compile(optimizer=Adam(learning_rate=0.001), 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)
  
      return accuracy > 0.5
  