Why Relying Solely on Accuracy Could Bankrupt Your Financial Analysis!

Imagine pouring in millions into a stock, only to watch it crash because your model claimed a 95% accuracy! The horrors of missed opportunities or heavy losses aren’t just hypothetical nightmares; they could be a reality if you’re naively hanging your hat on just one metric. Dive deep with us as we expose the seductive trap of accuracy and explore the compelling world of precision, sensitivity, and the dire need for a balanced metric system in finance.

Daniel Wiczew

Published in

DataDrivenInvestor

4 min readAug 29, 2023

1. The Critical Role of Metrics in Machine Learning:

Before we delve into the specifics, let’s understand why we even need metrics. At their core, metrics give us a tangible means to gauge the performance of our models. Imagine setting off on a treasure hunt without a map. The chances of stumbling upon the treasure are, while not impossible, quite slim. Similarly, without metrics, we’re blindly navigating the vast and unpredictable seas of data.

In financial contexts, stakes are high. A minor misjudgment can lead to drastic consequences — like misreading market trends, undervaluing assets, or even missing potential high-yield opportunities. Metrics, thus, are not just tools; they are our safeguards against erroneous financial judgments.

2. Decoding Accuracy, Precision, and Sensitivity:

Accuracy: It is the ratio of correct predictions (both positive and negative) to the total predictions made. It’s the most intuitive metric and the first one most analysts get familiar with.

Accuracy formula — basically all that is True prediction divided by everything (False prediction and True prediction/Bad prediction and Good prediction)

Precision: Precision deals with the correctness of positive predictions. In finance, think of it as the probability that a recommended stock will indeed yield positive returns.

Precision formula — So how much did you hit the answer right with your model divided by sum of not-right hits and right hits.

Sensitivity (Recall): It refers to the proportion of actual positives that are correctly identified. For a stock analyst, sensitivity would imply how many of the total booming stocks were successfully predicted by the model.

Sensitivity formula — the difference with precision is the “False Negatives” in the denominator. This makes it small, when you predict too many “Positive” outcomes, even for those that were supposed to be False.

3. Comparing Accuracy, Precision, and Sensitivity:

While all three metrics sound invaluable, they serve distinct purposes.

Accuracy provides a holistic view of the model’s performance but can be misleading, especially when dealing with imbalanced datasets (like rare stocks that surge).
Precision zeroes in on the positive predictions. For a financial analyst, a high precision means that the stock tips they provide are more likely to be hits rather than misses. However, it doesn’t account for missed opportunities.
Sensitivity addresses these missed opportunities. A model with a higher recall ensures that fewer potential profit-making stocks slip through the cracks. Yet, it might also suggest more stocks than practical, some of which may not be profitable.

4. The Dangerous Game of Relying on One Metric:

Let’s say a model predicts that only one stock will rise and gets it right. That’s 100% accuracy! But what if there were 100 other stocks that also rose but weren’t predicted? This is where accuracy falls short.

For financial analysts, it’s crucial not to miss out on potential opportunities (high sensitivity) while ensuring that the opportunities they do bet on are likely to be profitable (high precision). Blindly focusing on one metric could lead to heavy losses or missed profits.

5. Cross-Validation and Testing Set — The Unsung Heroes:

While metrics guide us, how we apply them matters. Cross-validation involves dividing data into subsets, ensuring that our model performs consistently across different data segments. This is pivotal in finance, given the vast diversity in stock behaviors across sectors and timelines.

Moreover, a testing set, a subset of data the model hasn’t seen during training, helps validate our model in near-real-world scenarios, ensuring we don’t overfit to historical data, which can be disastrous in the ever-changing financial world.

6. Takeaways:

Don’t Be Blinded by Accuracy: High accuracy doesn’t always mean a reliable model, especially in imbalanced datasets.
Embrace a Holistic Approach: Leverage precision and sensitivity to strike a balance between quality stock recommendations and not missing out on potential opportunities.
Regularly Validate: Always cross-validate and test on unseen data to ensure your models are robust and adaptable.

In the turbulent waters of financial securities analysis, metrics are your compass and anchor. But remember, no one tool is infallible. Use them wisely, in tandem, and always be ready to adapt. After all, in finance as in life, there’s no reward without risk!

Disclaimer: This article is not a financial advice, but is only for educational purpose

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