input: Write a function named `predict_stock_movement` that creates a deep learning model for predicting stock market movements using a given dataset. The function should load the dataset, split it into training and testing sets, build a sequential neural network model using TensorFlow, train the model, and finally evaluate its performance. The function should return the evaluation score.
  
  assert predict_stock_movement('data.csv')==0.0
  assert predict_stock_movement('test_data.csv')==0.0
output: 
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def predict_stock_movement(file_path):
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Build the model
      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'))
      
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      # Evaluate the model
      _, accuracy = model.evaluate(X_test, y_test, verbose=0)
      
      return accuracy
  