Using Prophet For Stock Price Predictions

Marcelo Perlin looks at Facebook’s Prophet to see if it works well for predicting stock price movements:

The previous histogram shows the total return from randomly generated signals in 10^{4} simulations. The vertical line is the result from using prophet. As you can see, it is a bit higher than the average of the distribution. The total return from prophet is lower than the return of the naive strategy in 27.5 percent of the simulations. This is not a bad result. But, notice that we didn’t add trading or liquidity costs to the analysis, which will make the total returns worse.

The main results of this simple study are clear: prophet is bad at point forecasts for returns but does quite better in directional predictions. It might be interesting to test it further, with more data, adding trading costs, other forecasting setups, and see if the results hold.

This is a very interesting article, worth reading.  H/T R Bloggers

Related Posts

Measuring Semantic Relatedness

Sandipan Dey re-works a university assignment on semantic relatedness in Python: Let’s define the semantic relatedness of two WordNet nouns x and y as follows: A = set of synsets in which x appears B = set of synsets in which y appears distance(x, y) = length of shortest ancestral path of subsets A and B sca(x, y) = a shortest common ancestor of subsets A and B This is the notion of […]

Read More

Linear Discriminant Analysis

Jake Hoare explains Linear Discriminant Analysis: Linear Discriminant Analysis takes a data set of cases (also known as observations) as input. For each case, you need to have a categorical variable to define the class and several predictor variables (which are numeric). We often visualize this input data as a matrix, such as shown below, with each case being a row and each variable a column. In this […]

Read More


March 2017
« Feb Apr »