Despite AI's impressive successes, its effectiveness in financial tasks such as portfolio management differs from areas where AI has flourished for several distinct reasons. The blog post highlights four key factors that set finance apart from industries where AI has made significant advancements. Understanding these differences is crucial for exploring AI's potential and limitations in the financial sector.
Low Signal-To-Noise Ratio
The key distinction in finance versus other fields where AI excels, like image recognition, lies in the signal-to-noise ratio (SNR). Understanding the concept of SNR is pivotal in distinguishing between systems with high predictability and those dominated by randomness or "noise." This metric essentially measures the proportion of valuable, meaningful information (signal) relative to the background of irrelevant or misleading data (noise). A clearer comprehension of this concept can be illustrated through contrasting examples: image recognition of cats versus the unpredictability of financial markets.
In the realm of image recognition, consider the task of identifying cat images among thousands of photographs. This scenario typically presents a high signal-to-noise ratio. The "signal" in this case refers to the presence of cat images, which are generally easy to distinguish against the "noise" — factors such as image blur, varied backgrounds, and other potential distractions. The predominance of signal over noise enables both humans and machine learning systems to achieve near-perfect accuracy in cat recognition, reflecting a high level of predictability within the system.
Conversely, financial markets exemplify an environment with a low signal-to-noise ratio. Here, "signal" might be information that could predict stock price movements, while "noise" encompasses a vast array of unpredictable factors, including sudden economic changes, political events, and market sentiment. In such markets, even the most informed investment strategies are subject to unexpected fluctuations, underscoring the dominance of noise. The inherent unpredictability is further amplified by the actions of market participants themselves. When traders act on predictive information, their trades adjust the market prices, integrating the signal into the market value and thus eroding the advantage that the initial predictive signal provided. Consequently, the market becomes a reflection of new, unforeseen information — essentially, noise.
This contrast underscores the fundamental difference between high and low signal-to-noise environments. High SNR scenarios are characterized by a clear, discernible signal that stands out against the noise, facilitating accurate predictions and decisions. In contrast, low SNR settings are mired in unpredictability, where noise overshadows the signal, making reliable forecasts challenging. Understanding this distinction is crucial for navigating different fields, from technology and science to economics and finance, each requiring tailored strategies to extract and leverage the signal amidst the prevailing noise.
Ever Changing Markets
The difficulties artificial intelligence faces due to low signal-to-noise ratios are compounded by the ever-changing nature of financial markets. In particular, financial indicators and variables, e.g. price or volatility, are non-stationary, which implies that statistical properties like averages and variances are not constants but time-varying, which is a stark contrast to the majority of standard machine learning settings. Furthermore, the vast majority of machine learning algorithms assume data is independent and identically distributed across training, validation, and testing, which is not holding for a large portion of financial indicators and variables.
When a researcher uncovers a new indicator that hints at an undervalued asset, which could be crucial for price prediction, its effectiveness diminishes as it gains popularity. As more traders leverage this insight, the market quickly adjusts, incorporating the newfound information, thereby altering the underlying data generation process. Similarly, technological advancements can transform economic structures and modify how we engage with markets. Although advancements in machine learning have introduced techniques to navigate such evolving phenomena, these challenges underscore the complexity of finance compared to other areas of machine learning research. Unlike the static nature of identifying cats in images, which remains constant regardless of an algorithm's proficiency, financial markets are dynamic, with new information continuously reshaping the landscape.
Small Data
A significant distinction within finance (and economics at large) lies in the reliance on smaller datasets, contrary to the prevalent "big data" landscape seen in many other fields. Although techniques designed for big data can be valuable, financial and macroeconomic analysis primarily revolves around time series analysis. This approach is particularly evident in endeavors like predicting stock returns. While it's possible to compile increasingly comprehensive datasets for such predictions, the bottleneck is the limited quantity of data points available for the specific variable of interest, such as stock returns. This limitation stems from the fact that new data regarding stock returns can only be accumulated over time.
This scenario illustrates the concept of "small data" — situations where the dataset is too small to employ conventional big data analytics effectively. Small data challenges are characterized by the need to extract meaningful insights from a limited number of observations, which can hinder the ability to make reliable predictions or inferences. In finance, every additional piece of data on stock returns is tied to the passage of time, not merely the ability to gather more information. This constraint emphasizes the importance of time in generating new data points and presents unique challenges in forecasting and analysis, underscoring the need for specialized statistical approaches tailored to these time-sensitive, data-scarce environments
Explainability Requirements
In contrast to someone identifying cat images, who may not be concerned with how the algorithm discerns cats from other elements, asset managers bear a critical additional burden due to their fiduciary responsibilities. They must not only leverage machine learning models for insights but also deeply understand the risks these models introduce to their clients' portfolios. This necessity stems from their obligation to safeguard their clients' interests, requiring a thorough comprehension of the potential risks and the ability to communicate these effectively.
While some machine learning models act as inscrutable "black boxes," making it challenging to grasp their decision-making processes, this lack of transparency is particularly problematic in asset management. Given their fiduciary duty, asset managers need models that offer not just predictive power but also clarity on how conclusions are drawn. This requirement for model interpretability is paramount; it allows managers to evaluate the underlying risks and make informed decisions.
The pursuit of machine learning in finance isn't limited to achieving efficiency and predictive accuracy. It also includes developing models that are interpretable and provide insights into their operation. This dual objective aligns with the asset managers' need to understand the nuances of risk exposure. As such, there's a growing interest in research that explores ways to make financial machine learning models more transparent and intuitive. By focusing on structural approaches that offer both performance and clarity, asset managers can fulfill their fiduciary duties more effectively, ensuring they manage and communicate the risks associated with their investment strategies comprehensively.
