Artificial Intelligence breaks down company historical and forecasted financial fundamentals to gain perspective
on its Cheapness, Profitability, Sentiment, and Momentum.
These four metrics are defined differently in each industry by our AI based on the metrics' predictive value.
Machine learnings methods sort companies into Value, Growth, and Trending firms in each industry. Ideal firms are
the best investments for investors with a twelve month holding period. Charts on the bottom of the page compare
historical returns and our twelve month outlook for similar firms. Peer firms are determined by AI based on trading
activity and revenue sources in prior 60 months.
For users who are interested in motivations for HedgyAnalytics.com - Hedgy is a young Artificial Intelligence
that was grown in a computer lab surrounded by a lot of data and a few nerds with cool sounding PhDs. It applies
machine learning techniques to data to determine which measurements are most predictive in each industry.
Prediction studies use daily, weekly, and monthly data from 1978 to 2020. Hedgy's neural networks considered
many variations of the input data and concluded that four categories of measurements are distinctly separate
and contribute differently to predictive value using its neural networks. Hedgy determined that the best Cheapness
metrics are a ratios of financial value per share to company price. So all Cheapness measurements are yield metrics.
Particular examples of yield metrics are Earnings Yield, Free Cash Flow Yield, and Dividend Yield. The Profitability
metrics are a measure of the firm's money generating power relative to a relevant financial measurement of firm size.
Return on Equity, measured as the ratio of total earnings to book value is an example of a profitability measurement.
Hedgy created dozens of proprietary metrics and determined which metrics are the most predictive in each industry.
The nerds were impressed to see that their creation identified theoretically established metrics grounded in accounting
theory. Hedgy identified Momentum measures based on price and financial momentum. Hedgy tried to answer basic empirical
questions with Momentum: Has the firm experienced earnings growth? Have returns been positive over the past six, twelve,
and eighteen months. Answers to these questions contribute to roughly three dozen Momentum metrics created by Hedgy.
Hedgy identified the most predictive Momentum metrics in each industry to score a firm's Momentum. The Sentiment metric
is driven by near-term market micro-structure, especially non-linear relationships between volume trends and price moves.
Another important component of the Sentiment metric is the activity of the most influential and predictive sell-side
analyst working at global brokerage houses and investment banks. Most reporting on companies involves something called
earnings consensus which is determined by averaging these analysts' earnings forecast. Hedgy evaluated the data to
determine which analysts are the most predictive and has emphasized their forecasts of earnings, dividends, and price
in predictions. Hedgy finished up on the Sentiment metric by studying daily interactions between market micro-structure
drivers and analyst forecast in each industry to find surprisingly consistent Sentiment drivers across industries.
Hedgy agrees with most academic research that Sentiment is most predictive in the short term while Momentum,
Profitability and Cheapness are most predictive in the long term.