input: Write a function to predict stock market movements using an AI-based system in Python. The function should import necessary libraries, use a deep learning model, train it on the provided data, and return the evaluation score of the model. If there is no data available, the function should return an appropriate message indicating that there is no data.
  assert predict_stock_movement(pd.DataFrame({'feature': [1, 2, 3], 'target': [4, 5, 6]})) is not None
  assert predict_stock_movement(pd.DataFrame({'feature': [], 'target': []})) == "No data available"
output: def predict_stock_movement(data):
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      if data.empty:
          return "No data available"
      
      X = data['feature'].values.reshape(-1, 1)
      y = data['target'].values
      
      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 = Sequential([
          Dense(10, input_dim=1, activation='relu'),
          Dense(1)
      ])
      
      model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=10, batch_size=1, verbose=0)
      
      score = model.evaluate(X_test, y_test, verbose=0)
      return score