Exercise

# Real-time risk management

It's time to use what you've learned about neural networks to perform (almost!) real-time risk management.

A 14-day rolling window of asset returns provides enough data to create a time series of *minimum volatility portfolios* using Modern Portfolio Theory, as you saw in Chapter 2. These `minimum_vol`

portfolio weights are the training values for a neural network. This is a (1497 x 4) matrix.

The input is the matrix of weekly `average_asset_returns`

, corresponding to each efficient portfolio. This is a (1497 x 4) matrix.

Create a Sequential neural network with the proper input dimension and two hidden layers. Training this network would take too long, so you'll use an available `pre_trained_model`

of identical type to predict portfolio weights for a *new* asset price vector.

Instructions

**100 XP**

- Create a Sequential neural network with two hidden layers, one input layer and one output layer.
- Use the
`pre_trained_model`

to predict what the minimum volatility portfolio would be, when new asset data`asset_returns`

is presented.