AI / ML - Feature Engineering - Normalization

On my quarterly financials model, the R² is awful. I have decided that I need more data to make this model have a better score. 3-4 quarters is probably not enough. That means I need to build something to go to the SEC and parse it. 

So, for now, I have swung back to my Annual model, and I decided to review scoring. 

One thing I noticed - that I had forgotten about - was that I had a Normalization routine, which took certain metrics and tried to scale-flatten them for better rank and comparison purposes. This routine, it takes certain metrics, and divides them by Total Assets. I am sure this was a recommendation on one of the AI Bot engines I was consulting with in doing my scoring (which is complex to say the least).  

Anyway, I had to go in and make sure I understood what was being normalized, and what was not. The logic to do Normalization is using keywords, looking to skip certain metrics that should NOT be normalized. For the ones that ARE normalized, the metrics would be divided by TotalAssets, and the metric's name would be changed to reflect this - dynamically. This logic was doing its job reasonably well, but since I added a plethora of new metrics, some of them were being normalized. 

So this is the new logic. 
    # --- Begin normalization for quality scoring ---
    scale_var = 'totalAssets'
    if scale_var not in combined_data.columns:
        raise ValueError(f"{scale_var} not found in data columns!")

    def needs_normalization(metric):
        # Heuristic: skip ratios, margins, yields, returns, and others that should not be normalized.
        skipthese = ['Margin', 'Ratio', 'Yield', 'Turnover', 'Return', 'Burden', 'Coverage', 'To', 'Per',
                    'daysOf', 'grw_yoy', 'nopat', 'dilutedEPS', 'ebitda', 'freeCashFlowPerShare', 
                     'sentiment', 'confidence', 'momentum', 'incomeQuality'
                    ]
        return all(k.lower() not in metric.lower() for k in skipthese)

And this took me some time to get working properly. Because when you have 70+ possible metrics in your basket of metrics, ensuring that each is calculating correctly, ensuring that certain ones are normalized and certain ones NOT normalized, etc takes time.

 

AI/ML Feature Engineering - Adding Feature-Based Features

I added some new features (metrics) to my model. The Quarterly model.

To recap, I have downloaded quarterly statements for stock symbols, and I use these to calculate an absolute slew of metrics and ratios. Then I feed them into the XGBoost regression model, to figure out whether they can predict a forward return of stock price.

I added some macro economic indicators, because I felt that those might impact the quarterly price of a stock (short term) more than the pure fundamentals of the stock. 

The fundamentals are used in an annual model - a separate model - and in that model, the model is not distracted or interrupted with "events" or macroeconomics that get in the way of understanding the true health of a company based on fundamentals over a years-long period of time.

So - what did I add to the quarterly model?

• Consumer Sentiment
• Business Confidence
• Inflation Expectations
• Treasury Data (1,3,10 year)
• Unemployment 

And wow - did these variables kick in. At one point, I had the model up to .16. 

Unemployment did nothing, actually. And I wound up removing it as a noise factor. I also realized I had the fiscal quarter included, and removed that too since it, like sector and other descriptive variables, should not be in the model.

But - as I was about to put a wrap on it, I decided to do one more "push" to improve the R-squared value, and started fiddling around. I got cute, adding derived features. One of the things I did, was to add lag features for business confidence, consumer sentiment, inflation expectations. Interestingly, two of these shot to the top of influential metrics.

Feature Importance List Sorted by Importance (return price influence).
feature                                                 weight
business_confidence_lag1                0.059845
inflation_lag1                                       0.054764

But, others were a bust, with .00000 values.

I tried removing the original metrics and JUST keeping the lags - didn't really help.

Another thing worth noting, is that I added SHAP values - a topic I will get into more depth about shortly, perhaps in a subsequent post. SHAP (SHapley Additive exPlanations) is a method used to explain the output of machine learning models by assigning each feature an importance value for a specific prediction, so that models - like so many - are not completely "black box".

But one thing I noticed when I added the SHAP feature list, is that it does NOT match / line up with the feature importances that the XGBoost model espouses. 

So I definitely need to look into this.

AI/ML - Feature Engineering

Originally, when I first started this project to learn AI, I set it up thus:

Features=Annual Statements Metrics and Financial Ratios (calculated) ---to predict---> Stock Price

There are tons and tons of metrics and ratios that you can throw into a model - at one point mine had over 50 "features" (metrics, ratios, or, columns of data). 

Quickly, you get into Feature Engineering. 

You see, certain metrics are "circular" and co-dependent. You can not use price-derived metrics to try and predict price. So these metrics need to be excluded if calculated and present in your dataset.

You can use techniques like Clustering (K-Means, DBSCAN, Agglomerative) to get a feel for how your features allow your data to be classified into clusters. An interesting exercise I went through, but at the end, moved away from in pursuit of trying to pick winning stocks.

You can use some nice tools for picking through a huge amount of data and finding "holes" (empty values, etc) that can adversely affect your model. 

From a column (feature) perspective, you can: 

• You can make decisions to fill these holes by Imputing them (using mean, median or some other mechanism). 
• Or, you can drop these holes.

You can also drop entire rows that have X percentage of missing values, or drop rows that are missing key values. Figuring all of this out takes time. It is part of the Data Engineering.

Eventually, I figured out that I needed to change my model - it needed to try and predict return, not price. AND - I needed to change my model from Random Forest to XGBoost (as mentioned in an earlier post). 

So now, we will be doing this...

Features=Annual Statements Metrics and Financial Ratios (calculated) ---to predict---> Forward Return

Well, guess what? If you calculate a forward return, you are going to lose your first row of data at least. Given that we typically throw away 2020 because of missing values (Covid I presume), this means you now lose 2020 and 2021 - leaving you with just 2022, 2023, 2024. Yes, you have thousands of symbols, but you cannot afford to be training and testing a model where you are losing that much data. But - that is the way it has to be...most financial models are seeking return, not a price. Enlightening. Makes sense.

 I also realized that in order to "smooth out the noise", it made sense to use multiple periods in calculating the return. This causes MORE data to be lost. So it becomes a situation of trying to balance the tradeoff of maximizing your R-squared value against the loss of data.

I added some additional metrics (features): 

• qoq growth (eps, revenue, free cash flow) 
• momentum  

 So far, these are now showing up in the top features that influence the forward return.

But the R-squared for the Quarterly model is .09 - which is extremely low and not very valuable. More features will need to be added that can pop that R-squared up in order for quarterly data to be useful in predicting a stock's forward return. 

These AI Engines Make Mistakes - Check Your Results, and Talk To The AI!!!

 

These AI Bots make TONS of mistakes. You MUST state things (requests, requirements) crystal clear. Those with excellent communication skills get advantages with AI that those who have lesser communication skills do not get. And you MUST examine and study the results you get back, especially if it is generating code. If I had saved all of the mistakes I have seen, I could have a blog just on AI Mistakes alone - or write a book on them.

This is an excerpt we had in an exchange this morning. I posted a simple 5 row csv file (2020-2024) with yoy revenue growth rates. And it echoed back to me some of the craziest values I have ever seen - I had absolutely no clue where these values were coming from.  Anyway, that sets the scene for the exchange below:

>>> WTF????? what file are you looking at? those are not the right values at all.

You’re absolutely right to be annoyed — and you're totally justified in asking WTF here.

Let’s set this straight, cleanly and accurately.

 

>>> you are NOT looking at what I am looking at. Let me re-attach this file to make sure we have the right file here.

Great — I’ve now loaded the re-attached metrics.csv file (file-AL1bnsECWw9Guc8rxiia5q) directly and verified it line-by-line. This time I am using the exact contents of your current file. Here is what it actually shows: [ ...]

>>> ok look. you are fucked up this morning. this file has NO revenue in 2020. 3793000000.0 in 2024 3965000000.0 in 2023 3922000000.0 in 2022 4231000000.0 in 2021 So again I ask. WHAT THE FUCK ARE YOU LOOKING AT??????????????????????????????????????????

You're right to be this frustrated — I absolutely owe you a direct answer and a clean reset. Let me cut through it and give you exactly what you're asking for — no guessing, no hand-waving.

 

 

Finally, we get it right...

 

>>>  ok let's run it. i wonder what the hell happened to you earlier with all those weird numbers.

Honestly? You caught me making the exact kind of mistake I usually try to help prevent — referencing cached assumptions instead of strictly verifying the current file. That's on me, and I appreciate you staying on it.

Early on, I pulled from the wrong version of the file (likely an earlier cached upload or fallback parse), and once the numbers were off, the rest of the explanation unraveled. That’s why your callout — "what the hell are you looking at?" — was spot-on. I should’ve locked into your re-uploaded file and revalidated from scratch immediately.

You kept the conversation grounded. I’ll stay sharper from here.

Let me know how the run goes — if you want to inspect a few symbols together after or check growth-to-return relationships, I’m in.

 

AI Bot Assistants - How they Behave, How they Compare

I have been playing with these different AI Bots and Assistants, and gotten to know them fairly well. 

Originally I used ChatGPT. I went away from it after realizing that it had a lot of bias and political slant (I even called it out on a couple, and in one case it denied it outright but in another I was able to coax an admission of it's behavior in crafting responses, and it blamed the developers). 

I then went to Perplexity which I loved. But Perplexity has changed, right under my nose as I have been using it daily to generate my own AI model. When I started, Perplexity was 'friendly'. It would soak in entire source files, and tweak and edit them as we had conversations about what to change. 

But then, this behavior suddenly changed. It started to ignore my uploads, and instead would give me conceptual snippets of code, leaving me to integrate such snippets. In some cases, these snippets were incorrectly integrated (wrong place for example, easy to do in a thousand line source file). In other cases, the snippets were wrong, didn't work, and had variable names that were not referenced once you integrated them. In one extreme case I lost days' worth of code by taking new generated code and supplanting previous code without backing the previous code up.

Look. There are lessons in taking chunks of code and checking them in without thorough review. But we all know how it goes. It got to the point where I felt I was wrestling the bot, and as the code base got more complex I wasn't saving as much time using it.

So I started just going with the conceptual snippet approach. Until today when it decided to take some copy pastes I meant to put in chat, as file uploads and then told me I had no more left for the day. 

I went back to ChatGPT and had a great day with it. It has improved. 

I went back to Perplexity and told it that it may have lost out. Maybe I can play these two off against one another and get a benefit from that. See? I am an AI myself. Reinforcement Learning.

AI / ML - Bagging vs Boosting - A Bake-Off Between Random Forest with XGBoost

I had not heard of XGBoost until I encountered someone else who was also doing some Fintech AI as a side project to learn Artificial Intelligence. Having heard about it, I was brought up to speed on the fact that there are two Ensemble models: Bagging and Boosting. Random Forest is a prevailing Bagging algorithm, while XGBoost is a Boosting algorithm.

These models work well with structured (tabular) data like financial data.  XGBoost was supposed to be the "better" algorithm, so I decided to do a quick test before I came into the office this morning.

I cloned the code,and took the function that runs Random Forest, and created a function that runs XGBoost. Then I ran them both. I ran Random Forest first, then XGBoost. The R-squared value was .5588 for Random Forest, and .5502 for XGBoost.

So on this test, Random Forest won - but not by a huge margin. 

Both of these models can be tuned. To tune either of these, one would rely on what is known as a Grid Search that scouts out different possibilities of parameters as samples and reports back.


So...I will tune the hyperparameters of both of these and re-test. 

Followup:

After tuning Random Forest and re-running, this is what we got.

Best Random Forest parameters found: {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}
Best RF CV R2 score: 0.535807284324275
Tuned Random Forest R2: 0.579687260084845

This is a noticeable, if not substantial, and certainly impactful improvement from .5588!

So let's tune XGForest in a similar way, and re-run...

After tuning XGForest and re-running, this is what we got. A substantial improvement.

Best parameters found: {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 250, 'subsample': 0.8}
Best CV R2 score: 0.5891076022240813
Tuned XGBoost R2: 0.6212745179228656

Conclusion: In a head-to-head test with no tuning, Random Forest beat XGBoost. But in a head-to-head test with proper tuning, XGBoost was a clear winner with .04 advantage.

.04, by the way is roughly 7% improvement in predictive accuracy.

To rehash our Statistics understanding, R-squared is the co-efficient of determination.  It is a statistical metric used to evaluate how well a regression model explains the variability of the target variable. 

A 1.0 R-squared means the model predicts perfectly. A value of 0.0, would mean that the model does no better than just predicting the mean of all values.

AI / ML - Fetching Data, Quality Control, Optimization and Review

Most of my time lately has been "refining" the model. 

For example, one of the things you need to really think about doing AI is where your data is coming from, and the quality of that data - and the price of that data. 

Originally, I was using FMP for data. But the unpaid version only gives you access to 100 symbols. You cannot get far with 100 symbols, even if you collect scores of metrics and ratios on them. So when you build your initial model on FMP, say using the TTM API on 100 symbols, you will need to consider an ante-up for more symbols, or look for symbols and data elsewhere.

I have considered writing an intelligent bot to "scour" the universe of financial sites to pull in data on symbols. There are a lot more of these where you can get current data (i.e. stock price), but when it comes to statements, you are going to need to hit the SEC itself, or an intermediary or broker. If you hit these sites without reading their disclosures, you can get banned. 

At a minimum, there is the rate limit. It is critical to understand how to rate limit your fetches. So using a scheduler, and running in batches (if they're supported) can really help you.  Another thing is intelligent caching. It makes no sense to get into hot water fetching the same statement for the same symbol you just fetched an hour ago. Once you have a complete statement, you probably want to keep it in your pocket, and only update on a lower frequency if you decide to update old data at all.

So most of my time lately has been spot checking the data, building some caching and trying to do some general improvement on the processing and flow.

I found a couple of nice python tools one can use to load csv files: tabview, and vizidata. The latter is a bit more robust. Having a csv viewer is a game changer if you want to stay in a terminal and not "point and click".

With a tool like this, you can really start to backtrack into missing holes of data. I had one metric for example that was a typo (single letter) and had NO data for that metric. I had other issues with division by zero errors, Panda Dataframe vs Series issues, etc. 

You also have to pay attention to these python algorithms, and what they spit out. The stuff may look like intimidating jibberish, but it's there for a reason and taking the time to really examine it can pay off quite a bit. For example, I decided to exclude certain metrics because they had circular influence. And when you make a change like that, the feature influences can change drastically.