Trending Factors for AI-powered stock analysis (TAOTS 4th Month of 2024)
Trending Factors for AI-powered stock analysis (TAOTS 4th Month of 2024)
Greeting investors! Welcome to our Newsletter, in which we look at the Trending Factors from the fourth month of 2024. Factors are the building blocks for AI-powered stock market analysis, stock picking, and the creation of robust investment strategies.
Introduction
We are focusing on the performance of six key factors including Momentum, Quality, Size, Trend, Value, and Volatility that influence the stock market. Our goal is to provide a comprehensive guide that will allow you to make informed investment decisions. Read more about our 6-Factor (Groups) Investment Model.
Trending factors from last month
The two significant factors for April 2024 were Smooth 5 Volume MOM 44 under the Momentum category, and CapExToMarketCap Change 66 under the Quality factor group.
In April 2024, we saw (just like last month) a significant decrease in momentum represented by a -41.19% change – with Smooth 5 Volume MOM 44 dropping by -209.75%. As for the quality factor, we sighted a decline of -1.30% alongside CapExToMarketCap Change 66 dropping by – 19.84%. These metrics are significant players in their respective factors, and by understanding them, we are unraveling the cryptic workings of the stock market, one piece at a time.
Description of selected factors
Smooth 5 Volume MOM 44 (Momentum group of factors)
Factor measures the momentum of the trading volume, by creating a five-period simple moving average of the rate of change of the volume over 44 periods. This gives traders insight into the changing trends of the trading volume – a fundamental aspect of identifying market trends.
CapExToMarketCap Change 66 (Quality group of factors)
Factor refers to the change in the ratio of a company’s capital expenditure to its market capitalization over the past 66 periods. It gives investors an idea about how much a company is investing in its business relative to its market value, an important indicator of a company’s growth potential and financial health.
Is it worth selecting stocks for your portfolio based on these factors? We will find out in our analysis.
Factor Statistics 01.04.2024 – 30.04.2024
| Top mover factor within the factor group | Mover value | Factor group | Group total value |
|---|---|---|---|
| Smooth_5_Volume_MOM_44 | -2.0974 | momentum | -0.4119 |
| CapExToMarketCap_Change_66 | -0.1984 | quality | -0.0130 |
| CurrencyVolumeAbsChange_22vs44 | -1.5272 | size | -0.3278 |
| Volume_WMA_2 | -0.2006 | trend | +0.0032 |
| PriceBookValueRatio_Position_66 | +0.2858 | value | +0.0005 |
| Skewness_22 | -1.5284 | volatility | -0.1587 |
The figure shows the Top Movers across all the groups of factors we use in our analytics.
Let’s take a look at how traders who believed in the two most trending factors have fared in recent years
Statistics for factors
Factor Statistics 1990 – 2024
| Top mover factor within the factor group | T-Stat | P-Value | Direction |
|---|---|---|---|
| Smooth_5_Volume_MOM_44 | +1.5839 | +0.0573 | -1.0 |
| CapExToMarketCap_Change_66 | +1.2087 | +0.1140 | -1.0 |
| CurrencyVolumeAbsChange_22vs44 | +0.2629 | +0.3963 | +1.0 |
| Volume_WMA_2 | +1.2748 | +0.1018 | +1.0 |
| PriceBookValueRatio_Position_66 | +1.1123 | +0.1335 | -1.0 |
| Skewness_22 | +0.6230 | +0.2669 | -1.0 |
In the table, we can see the T-Stat and P-Value for the entire period of history for which we evaluate the factors, i.e. 1990 – 2024. The Direction value shows the direction in which the factor affects the movement of the share price, +1 expresses the positive impact of the increasing factor on the share price, and -1 expresses the negative impact of the increasing value of the factor on the share price. In simple terms of theory, we can say that a T-statistic that is greater than 2.0 or less than -2.0 is statistically significant; and if the P-value is less than 0.05, we reject the null hypothesis and say that we found a statistically significant effect.
To understand the performance of these factors, we delve into their long-only portfolios based on quantiles, and long-short portfolio returns. The five quantiles (Q1-Q5) divide the data into equal parts according to the respective factor value, creating a picture of distribution across the portfolios. Investors usually target Q1 or Q5 portfolios of a factor, depending on whether the higher or lower value of the factor is expected to outperform.
The charts below show Factor Statistics 1990 – 2024 for the 2 selected factors from the previous month.
While the next one shows the behavior of the factors in the last 4 years.
Factor Statistics 2020 – 2024
| Top mover factor within the factor group | T-Stat | P-Value | Direction |
|---|---|---|---|
| Smooth_5_Volume_MOM_44 | +0.4812 | +0.3162 | +1.0 |
| CapExToMarketCap_Change_66 | -0.1654 | +0.5653 | -1.0 |
| CurrencyVolumeAbsChange_22vs44 | -0.1376 | +0.5544 | -1.0 |
| Volume_WMA_2 | +0.7568 | +0.2264 | +1.0 |
| PriceBookValueRatio_Position_66 | +0.3728 | +0.3554 | +1.0 |
| Skewness_22 | -0.0072 | +0.5028 | -1.0 |
The charts below show Factor Statistics 2020 – 2024 for the 2 selected factors from the previous month.
Factor Statistics Insights
The data show us that in 2020-2024, the factor Smooth 5 Volume MOM 44 worked exactly the opposite of the long-term history (See the different ‘Direction’ value).
This insight is also supported by our results for T-Stat in the long term. None of the factors assessed passed the test of statistical significance.
Do you want to invest in statistically significant factors that work over the long term? Try our Factor Investing app.
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Using factors in AI Stock Analysis
Our AI-powered StockPicking Lab is built on the factor investing approach combined with machine learning. We say that the most effective way to use AI to find the most undervalued stocks is to look for factors that influence stock price movements before matching appropriate stocks to them.
- 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.
- 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.
These two points are the focus of our regular TAOTS. - 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.