AI Talks

Applications of Machine Learning in Finance

Investment firms are leveraging ML techniques for a variety of purposes, including:

Factor research and model development. ML methods can help identify and extract relevant factors from large and complex datasets, such as market data, news, social media, alternative data. ML models can dynamically learn changing relationships between input features and handle interactions and non-linearity among them. This can lead to more robust and accurate models for stock selection, asset allocation and risk calculation.

Portfolio optimization and rebalancing. ML techniques can help optimize portfolios based on various objectives and constraints, such as expected return, risk, liquidity, diversification. ML algorithms can automate the process of rebalancing portfolios based on market conditions, investor preferences and portfolio performance. This can improve portfolio efficiency and reduce transaction costs.

Investment advisory and customer service. ML applications can provide personalized and tailored investment advice to clients based on their goals, risk profiles, preferences and behaviors. ML systems can enhance customer service by providing chatbots, voice assistants, sentiment analysis, etc. that can interact with clients in natural language and answer their queries. This can improve customer satisfaction and retention.

Fraud detection and compliance. ML methods can help detect and prevent fraudulent activities, such as money laundering, insider trading, market manipulation, by analyzing patterns and anomalies in transaction data, network data, user behavior, etc. ML models can also help comply with various regulations and standards, such as Know Your Customer (KYC) and Anti-Money Laundering (AML), by automating data collection, verification, reporting, etc. This can reduce operational risks and legal penalties.

Performance evaluation.  ML can help evaluate the performance of the investment portfolio, the investment manager and the investment strategy, by using various metrics, benchmarks, and attribution methods. ML can also help identify the sources of alpha (excess return) and beta (market return) in the portfolio, and provide feedback and suggestions for improvement.

Overview of ML Algorithms Used in Finance

In this section, we’ll explore common machine learning algorithms used in investments. In practice, the choice of algorithm depends on factors like the analysis objectives (classification or prediction), data complexity and the desired outcomes, such as interpretability and accuracy. Some of these algorithms operate through supervised machine learning, while others use unsupervised methods.

Supervised learning algorithms, including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), rely on labeled data to analyze patterns and make predictions. They find applications in predicting stock prices, binary classification (e.g., assessing default likelihood), or identifying fraudulent transactions.

On the other hand, unsupervised learning algorithms, such as K-means and Hierarchical clustering, extract insights from unlabeled data, enabling tasks like market segment identification and anomaly detection in financial data.

Now, let’s provide a brief overview of these algorithms.

Logistic Regression is capable of discerning non-linear associations between predictor variables and binary outcomes. This makes it a valuable tool in predicting binary results, such as determining if a transaction is fraudulent.

Support Vector Machines (SVMs) determine a hyperplane in the feature space to classify data into distinct classes. They are often used in categorizing stocks based on attributes.

K-Nearest Neighbors (KNN) makes predictions by identifying the K (number of neighboring) most similar data points. It is effective for detecting anomalies, like unusual financial transactions.

Classification and Regression Tree (CART) makes predictions by dividing the data into subsets based on input feature values. It can help create intuitive and easily interpretable models, such as decision trees to predict outcomes like loan defaults.

Random Forests are ensemble methods that improve predictive accuracy by combining multiple decision trees. They can be effectively used in constructing consistent investment strategies for asset allocation and stock selection.

Principal Component Analysis (PCA) focuses on dimensionality reduction, while retaining most of the variance in the data. It can summarize large datasets, such as stock data, into a more concise set that captures essential information from the original data.

K-means clustering groups data points into a predetermined number of clusters (k) based on their similarity. It can be used for example to categorize companies differently from current standard industry classifications.

Hierarchical clustering constructs a hierarchy of clusters within data. It can be applied to identify relationships between different groups of financial assets or create taxonomies of stocks for portfolio management.

It’s important to highlight that Artificial neural networks (ANN) and Reinforcement learning (RL) algorithms are emerging as powerful ML tools for financial professionals.

Artificial neural networks (ANNs), including deep neural networks (DNNs), are a subset of machine learning algorithms that mimic the structure and function of the human brain. Although they require large amount of data and significant computational power, they excel at uncovering complex patterns in data without the need for explicit rules or domain knowledge. They are widely used in Natural Language Processing (NLP) tasks, such as sentiment analysis, text summarization, and machine translation. In the finance sector, NLP can be leveraged to extract valuable insights from text data like news articles, financial reports, and social media posts. This helps in making better decisions and managing risk.

Reinforcement learning (RL) is a learning method where an agent learns through trial-and-error interaction with an environment to maximize rewards. This method is applicable to trading strategies, where an RL agent, similar to a virtual trader, operates in the market with the goal of maximizing profits.

Challenges in Implementing Machine Learning Techniques in Finance

Now that we’ve explored some popular algorithms used in finance, let’s shift our focus to the challenges of applying or constructing ML algorithms in this domain.

Overfitting is a notable concern in finance due to complex, noisy, and non-linear financial data. The search for patterns can lead to overly complex models that excel on training data but struggle with new data, amplified by limited data availability. To address this, techniques like cross-validation, ensembling (collection of models), and feature engineering, informed by domain knowledge, can be applied.

Limited financial data availability poses challenges for machine learning. It can result from infrequent events, privacy concerns, regulatory restrictions or the sparse nature of historical data updates, often occurring monthly or quarterly. This scarcity hinders ML models that depend on data for learning and accurate predictions. Strategies like data augmentation and cross-validation can help mitigate these challenges.

Financial markets are nonstationary, constantly adapting, and influenced by human behavior, which raises questions about the validity of using historical data to train ML algorithms and adds to the complexity.

Explainability and performance attribution are also significant concerns in applying ML to investments. Complex ML models are often seen as “black boxes,” making it essential to explain how investment decisions are made and attribute performance. Recent work in explainable machine learning offers methods to understand the interaction effects of input features on model output, facilitating performance attribution in ML portfolios.

Conclusion

Machine learning is transforming finance, enabling data-driven decisions, improved portfolio management, and reduced risk. While challenges like overfitting and data scarcity persist, advances in explainable machine learning hold promise for providing transparency into complex models. As the finance industry continues to evolve, machine learning will be a powerful force, creating new opportunities for growth and innovation.

Resources

Coursera, “Machine Learning in Finance: 10 Applications and Use Cases”, July 16, 2023.

Kathleen DeRose, CFA, Matthew Dixon, PhD, FRM, Christophe Le
Lannou, “Quantitative Methods, Machine Learning”, CFA Institute.

Mike Chen, PhD, Weili Zhou, CFA. 2023. “On machine learning applications in investment”, CFA Institute Research Foundation, Handbook of artificial intelligence and big data applications in investments.