The Hard Reality of Using Alternative Data to Build Outperforming Strategies

Quantitative analysts have long recognized the potential of alternative data to genderate alpha, but realizing consistent results has proved challenging over the years. Many datasets initially promise strong predictive signals but fail to deliver sustained performance, especially as they become broadly available and their alpha-generative edge deteriorates. Furthermore, transforming raw alternative data into clean, actionable signals often takes huge effort and a deep understanding of how KPIs relate to the underlying business drivers for each company — skills traditionally found among fundamental analysts rather than quants. Quantitative analysts thrive on clean, processed, accurate, and predictive data.

We’ve spoken about this and other issues faced by the modern quant in our article "The Ugly Truth About Backtests," highlighting the pitfalls of over-reliance on simplistic, backward-looking performance simulations. But another truth looms just as large: the burden of cleaning and structuring data still presents a persistent challenge for quants. The typical quant investor is no stranger to this pain. They attempt to wrangle disparate datasets into actionable signals, often only to get inconsistent results.

For example, two different alternative datasets might suggest bullish activity for Amazon Prime subscriptions but flag significant slowdowns in AWS Cloud demand. Which do you trust? Two data vendors might present completely divergent views on the same fundamental business metric. Even more fundamental, are subscriptions even the right KPI to analyze? The analyst must first understand if there is a link between the KPI and the stock price, and measure the strenght of that relationship.

Time-consuming research, manual reconciliations, and validations are not particularly loved by quants who are then forced to hunt down answer and fix various inconsistencies in data. This burns resources and introduces potential for human error.

Is there a better way to build high Sharpe strategies with Alternative Data? We think the answer is yes, but it takes combining technology and investment experience in a specific way.

Introducing QTIP: Bridging Man + Machine

At Maiden Century, we believe data itself is never the end—it's merely a means to an end. This is why we built QTIP. Quantitative Tools for Investment Professionals (QTIP) is specifically designed to address the onerous and time-consuming workflows of extracting clean, accurate, and meaningful signals from alternative data. Its main strength is the solid foundation it rests upon. Built on top of Maiden Century’s IDEA platform, QTIP turns messy raw data into highly predictive signals, helping quant investors build high Sharpe, low-correlation, differentiated strategies. Because QTIP sits on top of IDEA, it benefits from industry-trusted and time-tested models that investors have come to rely on for consistent KPI forecasts.

QTIP is not about black-box predictions or data for data’s sake. Rather, it's an integration of human investment expertise with sophisticated machine learning models, turning raw alternative data into actionable insights that directly predict key performance indicators (KPIs) fundamental investors watch closely.

A Step-by-Step QTIP Approach

1. Mapping Data to Businesses:
   • QTIP maps dozens of alternative datasets to thousands of brands, products, subsidiaries, and corporate entities. This granular mapping is foundational, ensuring accuracy from the start of the process.
2. Breaking Down Business Models into KPIs:
   • For each mapped entity, our analysts identify and define KPIs that are genuinely impactful and best describe the fundamental drivers of the businesses.
3. Training Models:
   • Leveraging our understanding of fundamental business drivers, we carefully train machine learning algorithms to predict these KPIs, maximizing the quality of our signals.
4. Predictive Algorithms:
   • By combining multiple datasets intelligently, QTIP achieves a directional hit-rate approaching 70% and rising. Our predictive algorithms are fine-tuned to anticipate not just recent quarters, but also forward-looking management guidance.

QTIP Workflow: A More Sophisticated Approach to Backtesting Alternative Data Strategies

Consider the typical systematic investment workflow pre-QTIP. Without a tool like QTIP, systematic investors typically follow a traditional and relatively simple backtesting process, which is summarized in the figure below. The problem is over-simplification. This process skips several essential steps that we feel as central to extracting optimal value from alternative data.

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Now, compare this to the numerous steps Maiden Century adds to the workflow in order to optimize returns from your alternative data feeds. The contrast is stark.

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The complexity is needed for optimal results. Our process is generally broken out into two ‘phases’. The first phase stems from the work we do for our discretionary (IDEA) clients in translating multiple data feeds into reliable period and metric specific forecasts, benchmarked against Consensus / sell-side estimates. The second phase is specific to QTIP clients, and converts the significant amounts of metadata generated for discretionary clients into synthetized, ticker level point-in-time scores that are primed for backtesting algorithms.

From Messy Data to Meaningful Alpha

Ultimately, QTIP encapsulates the best of both human intelligence and advanced algorithmic processing, providing quant hedge funds with a clear path from messy, complex alternative data to robust, actionable alpha signals. It frees quantitative analysts from the quagmire of data cleaning and normalization, allowing them to focus instead on strategy innovation and alpha generation.

QTIP is not merely a tech innovation; it's a meaningful shift in the quantitative investment workflow. It’s helps elevate your quant investment process beyond the drudgery of data prep and directly into the art of predictive investment analytics.

Ready to transform your quant strategy? Let’s talk.

Contact Maiden Century at hello@maidencentury.com or visit us at www.maidencentury.com