A.I. Investing:

This machine learning prediction system is the result of five generations of collaboration with Cornell’s Masters of Applied Statistics program to achieve a commercial outcome. We are now in our seventh year of working with Cornell, and we have worked on machine learning predictions for many years, with many failed attempts along the way. AI Investing

We have had our achievements as well, and we have outlined the schematic of our A.I. forecasts below, taking into account many of our successes while avoiding the errors found along the way. We will avoid using technical jargon because our intended audience is sophisticated investors.

The prediction system is designed using a hierarchical composition of various models. This approach is similar to the prediction scheme discussed by futurist and author Ray Kurzweil in his book “How to Build a Mind,” which focuses on constructing general intelligence. Kurzweil draws strong parallels between successful machine learning (or artificial intelligence) and the hierarchical functioning of the human brain, which gives rise to the neocortex and its capacity for abstract and creative thought.

A similar analogy can be found in weather forecasting. Weather predictions rely on tens of thousands of barometric, wind, humidity, and temperature readings from around the globe. Combined with centuries of recorded weather data, meteorologists can now forecast the weather more accurately than ever before. For example, forecasting the weather in Poughkeepsie differs significantly from forecasting in San Antonio, TX, and predictions for July 25th are distinct from those for December 25th.

Stock market predictions operate similarly. To create a globally effective prediction model, it is essential to build it from successful smaller models.

While stock market predictions may never achieve the same level of accuracy as weather forecasting, it is important to acknowledge our limitations. Remember Benjamin Martin’s advice in the movie “The Patriot”: “Aim small, miss small.”

If you expect machine learning forecasts to make you rich quickly, you will likely be disappointed. However, if your goal is to consistently add a few points of return to a prudent investment strategy, you are in the right place.

The first thing we did was decide that each stock should have its own prediction model. This means that the data used to predict each stock is uniquely trained to the specific dynamics of that stock. The prediction for every stock is a composite of three separate prediction systems. A prediction for the stock market, a prediction for the sector, and a prediction model for the stock’s alpha: Alpha is the return that a stock generates, independent of the stock market’s influence. The logic follows from straightforward investment thinking. Building on the Nobel Prize-winning work of William Sharpe and the Capital Asset Pricing Model (CAPM): Each stock prediction follows a basic regression equation:

Where:

• $\mathrm{ER}$_i = Expected Return of Investment
• $\mathrm{ER}$_m = Expected Stock Market Return
• $R$_f = Risk-free rate of return (yes, Risk-free is a fallacy, but it’s not material so bear with me)
• $\beta$_i = Beta of the investment

Assuming that risk-free assets offer no effective real return (after inflation), we can simplify the equation to: This is called a single-factor model. We are using a two-factor model with both the return on the stock market and the return on the sector. Asset performance is strongly explained by these three factors, so predicting them well can yield a tremendous advantage. Accordingly, our process takes the form of:

In this model, each stock has its own predicted machine learning model, Alpha. Then it shares the predictions. for the sectors and market using its own unique observed betas. That alpha is computed primarily from information in the company’s financial reports. We use about 70 data points from the financial statements and then expand those 70 data elements across ratios and multiple time scales to create about 300 data features for every stock. The machine learning algorithms find the most predictive combinations of these features and discard the data features with no explanatory power. As an update to a simple market forecast, we are predicting the return from both the stocks, sector beta, and the stock market beta, as the two combined offer much more explanatory power. These are powered by a cadre of economic data and performance data

With our extensive background in portfolio optimization, generating expected returns for investment candidates is a critical input. For many years, our various software platforms, including Advisors Portfolio Think Tank and Gravity Investments, have built portfolios where expected returns are produced for any data based on a multi-sampling expert system. This means we use historical performance to predict the future by optimally selecting multiple historical periods previously observed to offer a predictive advantage. This technique has served investors well and has been shown to offer a predictive advantage, as measured by an in-sample, out-of-sample correlation of approximately 0.25%. More than half of this advantage comes from the momentum signal. Momentum is regularly observed to be the factor offering the greatest return and fits well within a diversified portfolio strategy. For a detailed chart on factor performance, see Visual Capitalist.

Our success with these quantitative forecasting methods has set a high bar that any machine learning prediction must surpass to serve customers better than our existing systems.

Additionally, having built one of the best portfolio backtesting engines in the industry, we have been diligent about addressing biases and implementing proper control processes to ensure the utmost integrity of results.

These two traits have served as both barriers and stepping stones, enabling Portfolio Think Tank to take a long view in designing our stock market prediction system.