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Data Dilemmas: How to Handle Missing Values in Machine Learning for Smarter Investments

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Reese Marlowe in Business & Economy December 2025 • 4 min read.

"Unlock the Secrets to Building Robust Financial Models: A practical guide to navigating missing data challenges in machine learning portfolios."


In the fast-paced world of finance, machine learning (ML) has emerged as a powerful tool for building investment portfolios. These portfolios rely on vast amounts of data, often drawing from hundreds of cross-sectional stock return predictors to forecast future performance. However, a hidden challenge lies within these datasets: missing values. This is the problem of incomplete information, where certain data points are absent, threatening the reliability and accuracy of your carefully constructed models.

Imagine building a stock portfolio using machine learning, only to find that a significant portion of your data is missing. This is a common problem. Dropping stocks with missing values is often not feasible as doing so can drastically reduce the size of the dataset, leaving you with insufficient information to train your models. In some cases, applying this practice can eliminate over 99% of the available data. Therefore, learning how to strategically handle missing data is necessary for financial analysis and modelling.

This article explores practical methods for dealing with missing values in machine learning portfolios. We will provide guidance on how to navigate this complex issue, enabling you to build more robust and reliable financial models.

Why Does Missing Data Matter in Financial Machine Learning?

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Missing data can significantly impact the performance and reliability of machine-learning models used in finance. Here is a detailed breakdown:

Reduced Sample Size: The most straightforward approach to dealing with missing data – simply removing any rows (stocks, in this case) that contain missing values – leads to a drastically reduced sample size. Machine learning models thrive on large datasets; reducing the data available can lead to underfitting, where the model fails to capture the underlying patterns in the data and performs poorly on new, unseen data.

  • Bias: Missing data is often not random. There can be systematic reasons why certain data points are missing. For example, smaller companies may be less likely to report certain financial metrics, or data collection processes might be inconsistent across different time periods. Removing incomplete data can, therefore, introduce bias into your analysis, leading to skewed or inaccurate model predictions.
  • Inefficient Models: Even if you choose to retain stocks with missing values, the gaps in the data can create problems for many machine learning algorithms. Most algorithms are designed to work with complete datasets, and introducing missing values can disrupt their internal calculations, leading to less accurate and less reliable results.
  • Spurious Correlations: When imputing missing values (i.e., filling them in with estimated values), there's a risk of introducing spurious correlations. If the imputation method isn't carefully chosen, it might create artificial relationships between variables that don't actually exist, leading the model to make incorrect predictions based on these false patterns.
Given these challenges, a thoughtful and strategic approach to handling missing data is essential for building successful machine learning portfolios. Now, let's take a look at a good way to get past the dilemma.

The Takeaway: Simple Strategies Often Work Best

While sophisticated imputation methods might seem appealing, remember that they can also introduce noise and potentially lead to underperformance. In many cases, simpler methods like mean imputation can provide surprisingly robust results, especially when dealing with the complexities of financial data. By carefully considering the structure of your data and the potential pitfalls of different imputation techniques, you can build machine learning portfolios that are both accurate and reliable.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2207.13071,

Title: Missing Values Handling For Machine Learning Portfolios

Subject: stat.me q-fin.gn stat.ap

Authors: Andrew Y. Chen, Jack Mccoy

Published: 20-07-2022

Everything You Need To Know

1

Why is handling missing values important when using machine learning for financial models?

Missing values can significantly impact the performance and reliability of machine learning models used in finance. Removing data can lead to a reduced sample size, potentially causing underfitting and failing to capture underlying patterns. The absence of data might also introduce bias if the missingness is systematic. Even imputing missing values carries the risk of introducing spurious correlations, leading to incorrect predictions. Therefore, strategically handling missing data is crucial for building successful machine learning portfolios.

2

What challenges arise when missing data is present in financial machine learning models?

Missing data in financial machine learning models can lead to several challenges. Dropping rows with missing values reduces the dataset size, potentially causing underfitting. The missing data may introduce bias if the reasons for the missingness are not random. Introducing missing values can disrupt internal calculations, leading to less accurate and reliable results. When imputing values, there is a risk of creating spurious correlations that do not reflect real relationships, misleading the model.

3

What are some strategies for addressing missing data in financial machine learning, and why might simpler methods be preferred?

Strategies for addressing missing data include imputation methods such as mean imputation, which can provide robust results, and more complex methods. While sophisticated imputation might seem appealing, they can introduce noise and lead to underperformance. Simpler methods like mean imputation may be preferred due to their straightforwardness and effectiveness in maintaining data integrity while addressing missingness.

4

What is the impact of removing stocks with missing data on machine learning portfolios?

Removing stocks with missing data in machine learning portfolios can drastically reduce the sample size. This can lead to underfitting, where the model fails to capture the underlying patterns in the data. In extreme cases, applying this practice can eliminate over 99% of the available data, rendering the model ineffective due to insufficient information for training and validation.

5

How can the introduction of 'spurious correlations' affect financial machine learning models when imputing missing values?

When imputing missing values, there's a risk of introducing spurious correlations, which are artificial relationships between variables that don't actually exist. If the imputation method isn't carefully chosen, the model might make incorrect predictions based on these false patterns. This can lead to flawed investment strategies and reduced performance of the machine learning portfolio, as the model is essentially learning from noise rather than genuine signals in the data.

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