Custom Pre-Trained Deep Learning Predictions

Description

Leverage Gravity Investments’ 20+ years in machine learning and artificial intelligence by taking our deep learning A.I predictions and adapting them to fit your investment philosophy and brand ethos.

This is a perfect add-on to your gsphere license as the predictions will be automatically integrated. Using an A.I generated return prediction has many advantages over legacy methods for generating Capital Market Assumptions (CMA) You will also have and access the pre-trained .pth pytorch model that you can query for other sources / uses. The results would be compatible with most any optimizer. We will host the model for you in Amazon AWS Sagemaker.

Our foundation in A.I predictions is trust. before any model can be evaluated as producing good predictions or any investment performance outcomes, we have to be able to trust it. This is much harder than it may appear. We have done the hard work that developed the process to ensure, monitor and safeguard trust, so that when your investors put their trust in you and you put your trust in us, that trust stands to be rewarded.

Here is how you are able to customize the prediction model:

• Define the Investment Universe (up to 1000 U.S. listed securities)
• Set a Time Horizon for the Predictions
• Set The Objective Function (total return, risk adjusted return, alpha, omega etc.)
• Set a Hurdle Rate
• Set an investment style such as value, momentum, GARP, defensive, dividend.

 

Note: This is not for time horizons under one day.

As usual, you Chair the investment committee, we do the work. We will configure, tune, train and test the model. We will provide you with the Excel files with the results of the training. We also include our regular decile charts we use internally to evaluate our models. (see link below)

If the final pre-trained, out of sample, validation set does not beat the benchmark (default is average return of universe)- you do not pay.

We support stocks, ETF’s, currencies and futures. It can also be applied to mutual funds, but stocks is recommended.

To learn about our base model – just reach out. this page helps, but is probably dated: https://advisors.portfoliothinktank.com/ai/.

You do not need to know programming, python, data science, a.i (artificial intelligence) or machine learning to customize your own deep learning model.

Does it work? What are reasonable expectations? We can guide you. It may be instructive to learn that we switched from our quant models to the deep learning model in 2023 for our discretionary assets. It was a data driven decision.

Speaking of data; the machine learning models are feed by multiple data sources from some of the worlds most trustworthy sources such as the Federal Reserve, World Bank, NASDAQ,

our current feature count is about 500 of the most relevant, predictive investment metrics we have discovered and learned over decades of investment experience.

. This includes a cadre of macro-economic data, price and quant data and company filings including financial statements such as balance sheet, income statement, profit and loss and cash flows. We are regularly adding data and can do specific requests for clients as available. As our customer, you wont have to mess with any of this.

This price includes quarterly consults during which we can discuss and assimilate new data, research and features that we have developed in our core model.

We will offer this as a standalone, but you would be crazy, not to pair it with the optimization suite.

For > 1000 monthly inference requests or for more than 100 Million in Assets you will need an Enterprise agreement, but you can start here.