input: Write a function to implement an AI-based system for predicting stock market movements using a deep learning model. The function should import necessary libraries, load the dataset, split the data into training and testing sets, build and train a deep learning model, and evaluate its performance. The function should return the evaluation score of the model.
  
  assert predict_stock_movement('data.csv')==0.9876
  assert predict_stock_movement('another_data.csv')==0.8954
  assert predict_stock_movement('empty_data.csv')==None
output: 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 sklearn.metrics import accuracy_score
  
  def predict_stock_movement(file_path):
      try:
          data = pd.read_csv(file_path)
      except Exception as e:
          return None
      
      if data.empty:
          return None
      
      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()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      y_pred = model.predict(X_test)
      y_pred = (y_pred > 0.5).astype(int)
      
      return accuracy_score(y_test, y_pred)