Neural networks: Meaning, Criticisms & Real-World Uses

What Are Neural Networks?

Neural networks, also known as artificial neural networks (ANNs), are a type of machine learning algorithm designed to mimic the structure and function of the human brain. Within the realm of computational finance, neural networks excel at identifying complex relationships and patterns within large datasets, making them invaluable for tasks like predictive modeling and decision-making. These sophisticated algorithms are built from interconnected layers of nodes, or "neurons," which process information and learn from experience, adapting their internal parameters to improve performance over time.

History and Origin

The concept of artificial neurons dates back to the 1940s, with early theoretical work by Warren McCulloch and Walter Pitts in 1943 describing how neurons might function. This foundational idea laid the groundwork for future developments. A significant breakthrough came in 1958 when American psychologist Frank Rosenblatt designed and programmed the Perceptron, widely recognized as the first artificial neural network. Demonstrated publicly on July 7, 1958, in Washington, D.C., the Perceptron was initially simulated on a five-ton IBM 704 vacuum-tube mainframe. This prototype was capable of simple image recognition, learning to distinguish patterns through a trial-and-error process. For instance, it could be trained to recognize which side of a card a black dot was on, improving its accuracy as it processed more examples.⁸

Key Takeaways

Neural networks are computational models inspired by the human brain, designed for pattern recognition and complex data analysis.
They consist of interconnected layers of artificial neurons that process information and learn from data.
Neural networks are a core component of modern artificial intelligence and deep learning, enabling sophisticated analytical capabilities in finance.
Their applications in finance include forecasting, fraud detection, and algorithmic trading.
A significant challenge with complex neural networks is their "black box" nature, where the internal decision-making process can be opaque.

Formula and Calculation

Unlike a simple financial ratio, a neural network does not have a single, universal formula. Instead, its "calculation" involves a series of mathematical operations performed across its layers. The fundamental process for a single neuron involves a weighted sum of its inputs, followed by an activation function.

For a neuron in a neural network, the output is calculated as:

y = f\left(\sum_{i=1}^{n} (w_i x_i) + b\right)

Where:

(y) = The output of the neuron
(x_i) = The (i)-th input to the neuron
(w_i) = The weight associated with the (i)-th input, representing the strength of the connection
(b) = The bias term, which shifts the activation function's output
(n) = The number of inputs
(f) = The activation function (e.g., sigmoid, ReLU, tanh)

During the training process, the network adjusts these weights and biases to minimize the difference between its predicted output and the actual output for a given set of financial data. This adjustment typically happens through an optimization algorithm called backpropagation.

Interpreting Neural Networks

Interpreting neural networks primarily involves understanding their outputs in the context of the problem they are designed to solve. For example, if a neural network is used for credit scoring, its output might be a probability of default. A higher probability would suggest a greater risk, informing lending decisions. In quantitative analysis, a neural network predicting stock prices would provide a forecasted value, which analysts would then evaluate against market conditions and other models.

The challenge in interpretation often lies in the "black box" nature of complex neural networks, where the exact reasoning behind a particular output may not be transparent. Unlike simpler models where specific input variables can be directly tied to an outcome, neural networks learn intricate, non-linear relationships that are difficult for humans to trace. Despite this, their predictive power often makes them valuable tools, with interpretation focusing on the reliability and accuracy of their outputs rather than the granular details of their internal logic.

Hypothetical Example

Imagine a portfolio manager at an investment firm who wants to use a neural network to identify undervalued stocks. Instead of relying solely on traditional valuation metrics, they decide to train a neural network on historical financial data for thousands of companies.

The inputs to the neural network might include:

Price-to-Earnings (P/E) ratio
Debt-to-Equity (D/E) ratio
Revenue growth rate
Profit margins
Industry sector
Macroeconomic indicators like interest rates

The output the neural network is trained to predict could be a binary classification: "undervalued" (1) or "not undervalued" (0), based on subsequent stock performance over a defined period.

Step-by-Step Scenario:

Data Collection: The manager gathers five years of historical data for 500 companies, including the input metrics and whether each stock was considered "undervalued" based on a predefined threshold (e.g., outperforming its sector index by 15% in the next 12 months).
Training: The neural network is fed this labeled data. It adjusts its internal weights and biases to learn the complex patterns that correlate input metrics with the "undervalued" classification.
Testing: The manager then uses a separate set of new, unseen company data to test the trained network.
Prediction: For a new company, "Alpha Corp," the manager inputs its current P/E, D/E, revenue growth, profit margins, industry, and macroeconomic context into the trained neural network.
Action: The neural network outputs a high probability (e.g., 0.92) that Alpha Corp is undervalued. Based on this, the portfolio manager may decide to further research Alpha Corp and potentially include it in their investment strategy. The network effectively acts as an advanced filter, highlighting opportunities that might not be obvious through simpler screening methods.

Practical Applications

Neural networks have a wide range of practical applications across various facets of finance and investing:

Fraud Detection: Financial institutions use neural networks to analyze vast amounts of transaction data, identifying unusual patterns that may indicate fraudulent activity. This includes detecting anomalies in credit card transactions or identifying suspicious money laundering activities.⁷
Algorithmic Trading: Neural networks can analyze market trends, news sentiment, and historical price movements to predict future price directions, informing automated trading decisions. They are used to develop sophisticated investment strategies.
Credit Risk Assessment: Lenders employ neural networks to evaluate the creditworthiness of loan applicants by analyzing diverse financial and behavioral data, providing more nuanced risk management insights than traditional models.
Market Prediction: From forecasting stock prices and commodity movements to predicting macroeconomic indicators, neural networks leverage their pattern recognition capabilities to generate forecasts.
Portfolio Optimization: Neural networks can help construct optimized portfolios by understanding complex correlations between assets and predicting their future performance under various market conditions.
Customer Relationship Management (CRM): In financial services, neural networks can predict customer churn, recommend personalized financial products, and enhance overall customer experience through data analysis of client interactions and preferences.

Limitations and Criticisms

Despite their powerful capabilities, neural networks come with notable limitations and criticisms, particularly concerning their transparency and potential for systemic risks within finance.

One of the most significant drawbacks is the "black box" problem. Complex neural networks, especially those with many layers, can be extremely difficult to interpret.⁶ The decision-making process, while effective, often lacks transparency, meaning that it can be challenging for humans to understand exactly why a neural network arrived at a particular conclusion or prediction.⁵ This opacity raises concerns about accountability, fairness, and the ability to debug errors, particularly in high-stakes financial applications where regulatory oversight and explainability are crucial.

Furthermore, the increasing reliance of the financial sector on a limited number of powerful artificial intelligence (AI) models, often powered by neural networks, presents potential systemic risks. As SEC Chair Gary Gensler has highlighted, if many financial institutions rely on the same underlying AI models or data aggregators, a flaw or miscalculation in one of these central models could trigger widespread financial instability due to network interconnectedness and a "monoculture" of decision-making.⁴,³,² Regulators are increasingly scrutinizing the potential for "AI washing," where companies may exaggerate or mislead investors about their AI capabilities without adequate disclosure of associated risks.¹

Other criticisms include:

Data Dependency: Neural networks require vast amounts of high-quality, labeled financial data for effective training. Poor data quality or insufficient data can lead to inaccurate or biased models.
Overfitting: Without proper validation techniques like backtesting, neural networks can sometimes "memorize" the training data, leading to excellent performance on historical data but poor generalization to new, unseen data.
Computational Cost: Training large neural networks can be computationally intensive, requiring significant computing power and time.

Neural Networks vs. Deep Learning

While the terms "neural networks" and "deep learning" are often used interchangeably, deep learning is actually a specialized subset of neural networks.

Neural Networks refer to a broad category of artificial intelligence models inspired by the human brain. They consist of interconnected layers of nodes (neurons) that process information. Simpler neural networks might have only one or two hidden layers between the input and output layers.

Deep Learning, on the other hand, specifically refers to neural networks that have multiple hidden layers—often dozens or even hundreds. This "depth" allows deep learning models to automatically learn intricate, hierarchical representations of data directly from raw inputs, removing the need for manual feature engineering. For example, in image recognition, an early layer might detect edges, a middle layer shapes, and a final layer objects. This ability to learn complex patterns across many layers is what gives deep learning its powerful capabilities in areas like image processing, natural language processing, and advanced predictive modeling.

The confusion often arises because the most impactful and widely discussed applications of neural networks today, especially in fields like finance, typically involve deep learning architectures due to their superior performance on complex tasks. However, not all neural networks are "deep."

FAQs

What is the primary purpose of neural networks in finance?

The primary purpose of neural networks in finance is to identify complex patterns and relationships in financial data for tasks such as forecasting, risk management, and anomaly detection. They can process vast datasets to uncover insights that might be missed by traditional analytical methods.

How do neural networks learn?

Neural networks learn through a process called training, where they are fed large amounts of data. During training, the network adjusts the strengths of connections between its artificial neurons (known as weights) and bias terms to minimize the difference between its predictions and the actual outcomes. This iterative adjustment process allows them to improve their pattern recognition and predictive accuracy.

Are neural networks a form of artificial intelligence?

Yes, neural networks are a fundamental component and a powerful technique within the broader field of artificial intelligence. They are designed to enable machines to learn from data and perform tasks that typically require human intelligence, such as recognizing speech or making complex predictions.

What is the "black box" problem with neural networks?

The "black box" problem refers to the difficulty in understanding the internal reasoning or decision-making process of complex neural networks. Because these models learn intricate, non-linear relationships, it can be challenging for humans to trace how specific inputs lead to a particular output, making their operation somewhat opaque.

Can neural networks be used for investment decisions?

Yes, neural networks are increasingly used to support investment strategy by providing insights for investment decisions. They can analyze market data, sentiment, and economic indicators to predict future asset prices, identify trading opportunities, and optimize portfolio allocations. However, they are typically used as tools to inform, rather than solely dictate, human investment choices.