input: Write a function to predict stock market movements using a deep learning model in Python. The function should load data from a CSV file, split the data into training and testing sets, build a neural network model, train the model, and evaluate its performance. If the model is trained successfully, return the evaluation score; otherwise, return an error message.
  assert predict_stock_movements('data.csv')==[0.0035]
  assert predict_stock_movements('nonexistentfile.csv')=="Error: File not found or cannot be loaded."
output: def predict_stock_movements(file_path):
      import pandas as pd
      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
      import os
  
      if not os.path.exists(file_path):
          return "Error: File not found or cannot be loaded."
  
      try:
          data = pd.read_csv(file_path)
      except Exception as e:
          return f"Error: {str(e)}"
  
      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)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, 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=32, verbose=0)
  
      evaluation = model.evaluate(X_test, y_test, verbose=0)
  
      return [evaluation]