For over two decades, the Jupiter Systematic Equities team has continually refined its investment process through disciplined research and innovation. Rather than viewing a systematic model as something static, we believe it should evolve alongside financial markets, advances in technology and the increasing availability of new sources of information. Every enhancement introduced into the model follows extensive research, rigorous testing and careful implementation.
The latest evolution of the process is the introduction of a new industry-specific signal which falls within our Sustainable Growth stock selection strategy. It continues a broader evolution in the types of datasets we can incorporate into the model. While many of our signals are applicable across a broad universe of companies, we have increasingly been able to draw on more specialised datasets which offer differentiated and more granular investment insight. The addition of patent data in 2024, for example, allowed us to assess the efficiency of innovation for companies engaged in research and development. Our latest work takes this further by incorporating datasets designed specifically to capture the characteristics and operational drivers that are unique to individual industries.
From signal breadth to industry specifics
Traditional quantitative equity models have historically been built using signals that can be measured consistently across a broad investable universe of stocks. Standard measures of valuation, momentum or earnings quality have universal definitions and can therefore be applied across sectors, regions and market capitalisations.
Broad-based proprietary signals remain a core part of our investment process. Their extensive coverage makes them robust, economically intuitive and well suited to systematic portfolio construction. However, companies do not all operate in identical environments. The drivers of success for an airline are fundamentally different from those affecting an oil producer, a semiconductor manufacturer or a retailer. As a result, some of the most informative indicators of future business performance can be highly specific to individual industries rather than universally applicable.
Historically, this type of information has largely been the domain of fundamental sector analysts whose research frequently focuses on the operational metrics most relevant to companies within their particular industry. The rapid expansion of alternative datasets increasingly allows us to capture these insights systematically, taking us a step closer to the level of granular analysis undertaken by specialist industry analysts.
Importantly, we can do this while retaining the discipline of a systematic process. Rather than relying on the judgement of an individual analyst, industry-specific information can be evaluated consistently across companies using a repeatable, rules-based framework. This allows us to benefit from increasingly specialised information while seeking to minimise the behavioural biases that can influence discretionary decision-making.
Instead of asking whether every company exhibits the same characteristics, the model can therefore ask a more nuanced question: what matters most for companies operating within this particular industry?
By moving beyond broad market signals, the new industry-specific signal captures information unique to individual industries, providing an additional lens through which to assess a company's growth prospects.
Industry intelligence in practice
Within the airline industry, for example, indicators such as passenger load factors, operating efficiency and capacity utilisation can provide valuable insight into underlying demand trends and the strength of an airline's operations. Higher seat occupancy and efficient capacity management can improve margins and earnings resilience, making these metrics useful leading indicators of future profitability that may not yet be fully reflected in traditional financial statements.
For oil and gas companies, operational measures such as drilling activity, well development and production dynamics offer insight into how effectively a company is converting investment into future production. These indicators can help identify businesses that are bringing new wells online efficiently, optimising output and positioning themselves for stronger long-term cash flow generation.
Retail businesses, meanwhile, may be assessed using metrics such as same-store sales growth. Elsewhere, other industries will have their own operational indicators that are unique to the way those businesses generate value.
Together, these examples illustrate the value of moving beyond a common set of measures and identifying the information that is most relevant to understanding the growth prospects of companies within each industry.
Benefits to the investment process
The introduction of the industry-specific signal delivers several important benefits.
Continual model evolution over time
Markets evolve continuously and we believe systematic investment processes should evolve with them. Our philosophy has always been that successful systematic investing depends on continual research rather than a fixed set of investment rules. The incorporation of industry-specific information is the latest step in that evolution.
As new datasets become available and advances in technology create new analytical possibilities, we believe systematic models should evolve to capture information that was previously inaccessible. Industry-specific alternative data is one example of this evolution. We continue to explore additional datasets, new analytical techniques and further enhancements that can improve the quality, breadth and diversification of our stock selection process.
Below is a summary of the latest enhancements made to the investment process over the past few years
The value of active minds: independent thinking
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