input: Write a function to create an AI-based system in Python for predicting stock market movements using a deep learning model. The function should split the data into training and testing sets, build a deep learning model, compile and train the model, and finally evaluate it. If the given data is empty, return a message indicating that the input data is missing.
  
  assert stock_pred_model('data.csv', batch_size=64, epochs=20)==0.0
  assert stock_pred_model('d.csv', batch_size=32, epochs=10)=='Error: Input data file d.csv not found.'
  assert stock_pred_model('', batch_size=64, epochs=20)=='Error: Missing input data file.'
output: def stock_pred_model(file_path, batch_size, epochs):
      if not file_path:
          return 'Error: Missing input data file.'
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from tensorflow.keras.models import Sequential
          from tensorflow.keras.layers import Dense
          import numpy as np
          
          data = pd.read_csv(file_path)
          if data.empty:
              return 'Error: Input data is missing.'
          
          X = data.iloc[:, :-1].values
          y = data.iloc[:, -1].values
          
          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, batch_size=batch_size, epochs=epochs, verbose=0)
          
          loss = model.evaluate(X_test, y_test, verbose=0)
          return loss
      except FileNotFoundError:
          return f'Error: Input data file {file_path} not found.'