Deep Learning Stock Volatility with Google Domestic Trends

Tensorflow/Keras implementation of the [paper].

Trend displayed in Google Domestic Trends

Status?

Work accomplished so far:

• End-to-end implementation
• Test the data workflow pipeline
• Sanity check of the model
• Train the models
• Reproduction of the results in the paper

Plot

MAPE of the train, val and test along with dummy (benchmark future value = last value)

A new predictor is added every 600 epochs. We start with only the historical volatility as a predictor. Then, at 600 epochs, the second predictor is added: returns. At 1200, we had Trend COMPUT, 1800 Trend CRCARD, 2400 Trend INVEST and so forth.

We realized that we started to overfit after more than 5 predictors. The dataset is indeed incredibly small.

The lowest model MAPE loss on the test set coincide with the lowest MAPE loss on the validation set. They both have a comparable value (around 25), in agreement with the results found in the paper.

As a summary, the 5 best predictors are:

• sigma (volatility)
• returns
• Trend COMPUT
• Trend CRCARD
• Trend INVEST

As a conclusion, I'm a bit curious to know how they trained a model with 30 predictors on a such tiny dataset. They don't mention any information related to the model. It's just composed of a LSTM layer. I'm also a bit skeptical about this approach in general.

How to run it?

# might require python3.6.
git clone https://github.com/philipperemy/stock-volatility-google-trends.git svgt
cd svgt
pip3 install -r requirements.txt
python3 run_model.py