Artificial intelligence (AI) and machine learning (ML) have become must-have tools to get a foot in the door for an edge when it comes to quantitative trading. These modern technologies hold the promise to identify latent patterns in massive financial information leading to clearer predictions, and better investment decisions. For increasingly complex AI models, however, they have another chapter to read: the spectacle of explainability.
The Black Box Problem
Some of the most powerful AI models employed in quantitative investing, including deep neural networks, are quite literally “black boxes” This allows these models to analyze massive data sets and make extremely accurate predictions, but their underlying decision-making benchmarks are often inscrutable throughout the human perspective. The absence of transparency creates a host of challenges:
Regulatory Compliance: Financial regulators are requiring more and more investment firms to be able to do elaborate post-mortems explaining why they made their trades, especially any that were reasoned by AI.
Risk Management — Assuming, for simplicity’s sake and please forgive me that this is a dramatization of the process but also not wholly inaccurate: We know where there have previously been accident black spots or heavy traffic data in relation to our bodies; we are obsessed with watching how AI models arrive at their guesses quickly.
Client Trust: Institutional as well retail investors both are very suspicious of investing their capital in strategies that they do not understand fully.
Model Error: You struggle with a lack of understanding of what failed and why it’s difficult to update any poor-performing models.
The Rise of Explainable AI (XAI)
Enter Explainable AI (XAI): A field that has gained steam in recent years, aiming to solve these challenges. XAI is concerned with developing methods and techniques to solve this issue of interpretability in AI models without causing a loss in their predictivity. For quantitative investing, XAI can be the link between these highly complex artificial intelligence algorithms and people.
Key XAI Techniques in Quantitative Investing
SHAP (Shapley Additive exPlanations) Values: A technique from game theory that allows us to calculate each input feature contribution in model output. Transparency in investing: this could show what economic indicators or company metrics have meaningful effects on the model outputs at a macro level.
LIME (Local Interpretable Model-agnostic Explanations): LIME builds interpretable and simplified models that approximate the behavior of complex AI models for one single prediction. This may clarify why a particular stock was chosen to invest in.
By measuring how each input feature contributes to the model’s output, quants can rank them according to their importance in powering your AI.
Partial Dependence Plots: These visualizations explain how potential changes to a single input factor impact the model’s predictions, and in turn enlighten investors about the relationship between different market pillars like forecasted returns.
Decisions Trees and Rules Extracted The Decision trees — though not as powerful as deep learning models are very interpretable. There are methods to extract rule-based interpretation from more complex models like decision tree-lite, trading off between model performance and clarity of explanation.
Balancing Complexity and Transparency
As important as models’ transparency is, there’s always a trade-off with model complexity. Less complex, and therefore more explainable models may not capture some variability in the data; on the other hand, highly accurate AI models are incredibly convoluted to understand.
These priorities are in tension, and they should be balanced by quantitative investment firms. How to achieve this balance some ideas on how do it are:
Hybrid Approaches: Combining a complex “black box” model with more interpretable models can offer both accuracy and some explanation. For instance, a deep learning model could potentially be used for preliminary stock screening, and then another more interpretable approach to make the final selection decisions.
Explainable at Multiple Levels: Different levels of explanations would be possible for different stakeholders Regulators will be given a more fulsome explanation of the technical features, while clients are presented with an intuitive but still powerful description of the investment process.
It can be monitored in runtime (as we have seen too) and validated against simplified explainable benchmark models to observe a higher level of prediction compliance.
A mixing bowl of dough: antibody against the coronavirus developed in Russia to make explanations more natural and easy for investors, it is necessary to develop interpretability using financially relevant concepts/metrics.
The Future of Explainable AI in Quantitative Investing
Explainability will become more important as AI plays a larger role in the quantitative investment process. In the years ahead, a number of things are likely to change.
Legal Environment: Financial regulators may introduce regulations that are more specific to AI interpretability in investment processes.
More Advanced Visualization Tools: There will be new ways of visualizing complex AI decisions, that make the outputs accessible to non-technical stakeholders.
Explainable AI Hardware: This could also extend to building specialized hardware for silicon-accelerated real-time explanation in a high-frequency trading (HFT) environment.
Investment professionals: Solutions like ExFA will help asset managers make better decisions faster while enhancing their understanding of AI capabilities and limitations, Explica(ble) Finance may even necessitate a shift in quant finance education.
Conclusion
We believe that Explainable AI is the next evolutionary step in Quant investing+”_Blank”Optional. Greater transparency into AI models leads to (1) better fiduciary responsibility to clients, (2) meeting regulatory requirements; and as highlighted throughout this subsection provides deeper insights for analyzing the investment strategies. True, balancing complexity and explainability remains a challenge but the continued advance of XAI techniques suggests that certain powerful AI-driven investing strategies will be able to live alongside the human needs for transparency and understanding.
Firms which thread the needle between AI performance and explainability in quantitative investing, are likely to capture a competitive edge with only significant disadvantages.