Analytical Platform’s 6-Factor (Groups) Investment Model

6-Factor Investing Model
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Analytical Platform’s 6-Factor (Groups) Investment Model

Factor investing is an investment strategy that focuses on key performance drivers among asset classes, as people who read our previous articles would know. As mentioned previously in our blog, in the classical Fama-French 5-Factor Investing approach, those factors were: value, quality, momentum, size, and minimum volatility. However, in the Analytical Platform, we use a slightly different model for our purposes, let’s call it the 6-Factor (Groups) Model.

Our AP 6-Factor (Groups) Model

Value: Value investing is based on the premise that certain companies are lower than their inherent value and will eventually revert to their fair price. Value stocks often have low prices in relation to their profits, dividends, book value, or other factors. Value investing is a type of contrarian investment since it includes purchasing stocks that are unpopular or out of favor.

Quality: Quality can be defined as the quality of a business model. These are the indicators that basically check if a company has a good business model and proposes growth and quality earnings opportunities.

Momentum: Momentum investing is based on the idea that some stocks have stronger price trends than others and that they will continue to outperform in the near term. Momentum stocks typically have high past returns over a certain period (usually 6 to 12 months) and tend to exhibit positive feedback loops (winners keep winning, and losers keep losing).

Invest in the forces that move stock prices

Leverage the power of factor investing with revolutionary software

Size: Size investing is based on the notion that certain equities have better risk-adjusted returns than others based on their market size. Size companies have small market capitalizations in comparison to the overall market or their rivals. Size investing is a type of diversification investing that includes purchasing stocks that are less connected with the overall market.

Volatility: Investing regarding Volatility is based on the idea that some stocks have lower Volatility than others and that they will provide a smoother ride for investors. Minimum volatility stocks typically have low beta (sensitivity to market movements), low standard deviation (variability of returns), and low correlation (relationship with other stocks). Minimum volatility investing can be seen as a form of risk management investing, as it involves buying stocks that are less exposed to market fluctuations.

Trend: Trend refers to the overall direction of the price of a market, asset, or metric. Trends are identified by trendlines or price action that highlight when the price is making higher swing highs and higher swing lows for an upward trend or lower swing lows and lower swing highs for a downward trend. Many traders opt to trade in the same direction as a trend, while contrarians seek to identify reversals or trade against the trend.

If you are reading our TAOTS, you must have seen these factors mentioned in the articles a lot. That’s because we divide more than 3000 indicators that our AI model uses into these six categories/groups.

For example, in our latest TAOTS, we had these Top moving factors within the 6-Factor (Groups) Model.

Factor Statistics 04.12.2023 – 08.12.2023

Top mover factor within the factor groupMover valueFactor groupGroup total value

Using factors in AI Stock Analysis

Our AI-powered StockPicking Lab is built on the 6-Factor (group) investing approach combined with machine learning.

  1. In the first step, we focus on understanding the relationship between the high/low value of the factor under study for the stocks under consideration and the price movement of these stocks.
  2. Subsequently, we evaluate statistical significance using the P-value and T-statistic to select only the significant factors that we use to build the stock valuation model. There is no AI involved so far. However, this step already eliminates the basic problem of analysts evaluating stocks based on statistically insignificant factors and indicators.
  3. In the next step, we can stack (ideally uncorrelated) factors into our model. This is where AI-based stock analysis comes in, as machine learning and its state-of-the-art methods should be used to select the best-performing uncorrelated factors and build robust stock strategies that work in most market situations. These investment strategies are what the StockPicking Lab provides. Read more in the article Stock Analysis with The Power of AI.