The rapid advancement of AI development necessitates a well-considered framework for decision-making and utilizing tools to enable investors to navigate effectively.

Amidst a myriad of emerging data sources and ongoing excitement surrounding ChatGPT and similar models, the investment management sector is a buzz with the transformative possibilities of generative artificial intelligence (AI). While there are numerous opportunities ripe for exploration, these new capabilities also introduce fresh challenges. We believe that employing a uniform framework can provide guidance in navigating this evolving landscape.

A User-Friendly Framework for Decision-Making


Generative AI, part of the AI toolkit in data science, employs sets of algorithms to produce fresh content. While these tools boast immense power, they might not always be the optimal—or suitable—choice for every issue. In data science, it's crucial to avoid treating any tool as a universal solution.

As we often emphasize, data science endeavors should commence with a query, and establishing these queries early in the process serves as an excellent means to concentrate research efforts. Furthermore, employing an intuitive, broadly applicable decision-making framework aids investors in tackling issues and selecting the appropriate tools for each task. Despite the continual emergence of new tools and methodologies, the decision-making approach remains consistent.



The framework is simple in essence. Upon identifying a question or concern, decision-makers collect a wide range of data to obtain a holistic perspective. Following the synthesis of this information and the extraction of insights, they arrive at a decision and implement the corresponding actions. Continuously monitoring outcomes ensures that insights are utilized to iteratively enhance the process.

Whether decisions are linked to investments or business operations, the majority, if not all, rely on this framework. Consequently, it serves as a valuable method to evaluate the efficacy of advancements in a swiftly evolving domain, encompassing the proliferation of data sources and tools.

The Realm of Easy Wins: NLP, NLU, NLG


Natural Language Processing (NLP), alongside natural language understanding (NLU) and generation (NLG), empowers models to comprehend textual inputs, interpret them, and generate responses. While chatbot applications are the most apparent applications, the potential for investment management extends across various areas.

NLP is adept at managing fundamental tasks that unleash the value of text-based data, allowing asset managers to assess market sentiment, recognize trends, pinpoint sales and investment prospects, extract crucial data and insights from lengthy documents, and generate solutions and responses to queries or concerns.

Common tasks within Natural Language Processing (NLP)


NLP, along with natural language understanding (NLU) and generation (NLG), empowers models to interpret textual inputs and generate meaningful responses. While chatbot applications are the most apparent examples, the applications for investment management are diverse and extensive.

NLP stands out in managing crucial tasks that tap into the potential of text-based data. It empowers asset managers to assess market sentiment, identify trends, pinpoint sales and investment prospects, extract pivotal insights from lengthy documents, and craft responses and solutions to queries or challenges.


Refinement and Prompt Design for the Solution


Undoubtedly, NLP and generative AI offer promising prospects, yet they come with their fair share of hurdles, demanding significant attention from asset managers. While harnessing the right human expertise is evident, the tools themselves pose challenges. Concerns often revolve around data quality, data privacy, and the capability to decipher models, but perhaps most concerning are hallucinations—instances where AI fabricates responses—posing significant obstacles.

This is where the refinement of models and the strategic crafting of prompts become crucial.

Training and customizing NLP models to suit the language and intricacies of particular domains can significantly enhance the efficacy of these tools. For instance, we've adapted generic NLP models to analyze the sentiment in financial text. While the term "creating disruption" might not conventionally convey a positive meaning, our technology analysts might interpret it as an opportunity.

Prompt engineering, an emerging discipline already proving its worth, fine-tunes inputs into large language models to generate improved responses. In the financial realm, we've observed that providing appropriate context at the outset of our queries enhances the quality of outputs. For instance, prefacing a prompt to an AI model with "you are a financial analyst" can yield more insightful results.

Various methodologies exist for integrating AI into portfolio management procedures. We delve into three specific methodologies tailored for fixed income, hedge funds, and equities.


Artificial intelligence (AI) provides investors with specialized instruments to navigate the expansive, intricate, and swiftly evolving financial markets. However, its implementations vary depending on the strategy. Through these case studies, we illustrate the various ways AI is employed to augment the investment process and achieve improved client results across different asset classes.


Case Study 1: Enhancing Systematic Investment in Credit Markets


Artificial intelligence (AI) is rapidly evolving into a crucial tool for proactive credit managers, aiding them in maneuvering through a sea of perpetually shifting data to evaluate opportunities. This makes it an ideal match for credit investment strategies and systematic fixed-income methodologies.

Consider systematic bond investing as a three-part procedure: firstly, discerning which bonds are appealing or unattractive through an impartial ranking system; secondly, refining these findings to construct a portfolio; and finally, executing that portfolio adeptly. AI can enhance the initial and concluding stages by aiding in decision-making regarding purchases and sales, estimating absent data, and identifying areas with attractively priced liquidity.


Leveraging AI for Yield Enhancement in Fixed Income

Systematic fixed-income strategies strive to surpass bond-market benchmarks primarily via the selection of individual securities. Their objective is to assess the securities within a benchmark to pinpoint potential for outperformance (alpha), employing diverse predictive factors derived from different data types or signals, including valuation, momentum, or sentiment.

Machine learning (ML) techniques have the potential to enhance analytics across various valuation factors, uncovering novel signals and amplifying the efficacy of existing ones, thereby bolstering systematic managers’ capacity to rank securities. For instance, AI can generate valuation scores for individual bonds as well as issuers, offering diverse perspectives to detect anomalies in bond prices. Moreover, AI introduces a fresh perspective to default probability analysis, refining conventional academic models by deploying a wider array of analytics empirically across a larger dataset.


AI Glossary: Basic Terms

The comprehensive scope AI offers in research is a crucial aspect of natural language processing (NLP) analysis. A human analyst team can only process a fraction of the vast data available from numerous company reports, struggling to consistently analyze the information conveyed in words and phrases across various companies. In contrast, transformer-based language models in AI can manage the entire dataset with absolute consistency, providing breadth that enhances the effectiveness of active return streams in systematic strategies.

Apart from individual security selection, which we consider the primary advantage of systematic fixed-income portfolios, traditional active management employs alternative methods. These methods encompass sector and industry biases and timing strategies reliant on interest-rate exposure (duration) and credit risk. We believe these strategies can also leverage AI technologies. Our research suggests that ensemble-based machine learning (ML) models offer notable benefits, generating risk metrics and beta timing indicators with robust predictive capabilities. Emerging techniques like topological data analysis, dynamic time warping, and signatures also hold promise as potential sources of alpha.


Improving Execution and Bond Trading Using AI


Analyzing bond prices may seem mundane to many. Sorting through extensive lists of closing prices and attempting to calculate accurate values for absent data can be tedious, repetitive, and prone to errors. However, this task is not mundane for AI. Machine learning (ML) can swiftly and accurately recognize price trends to fill in missing data points, covering the entire dataset. Likewise, AI can supplement absent liquidity data by analyzing trading behaviors across various related securities.

So, what does this translate to in practical terms? For systematic bond strategies focused on trading corporate credit, grappling with the restricted liquidity and tradability of specific bonds poses a challenge in capturing and interpreting intraday movements within the credit market and in deriving precise intraday market signals. AI emerges as an exceptionally effective solution to address these challenges.

At PrimeCredit, we utilize sophisticated machine learning models to calculate up-to-the-minute prices for corporate bonds that have outdated or unavailable prices. We achieve this by referencing real-time prices of liquid counterparts and other pertinent market factors. This approach, supported by model-generated prices, offers us a comprehensive perspective of the trading landscape and acts as the basis for our research on intraday market signals.

Predicting the forthcoming liquidity dynamics throughout the trading landscape is crucial for daily credit trading activities. In this regard, machine learning (ML) tools can assist in uncovering non-linear correlations between the attributes of bonds and their anticipated liquidity trends. Grasping the likelihood of executing trades for specific bonds at a desired volume with a satisfactory spread is pivotal for effectively managing a systematic portfolio. We believe that interpretable ML models can yield robust outcomes in this domain.

Case Study 2: Constructing an AI-Powered Hedge Fund

Picture a portfolio manager who has graduated from the most prestigious universities globally, boasting a photographic memory and an in-depth grasp of macroeconomics and geopolitics. She avidly consumes news and holds vast information on stocks, bonds, and currencies. Her mind effortlessly sifts through the nuances of her foundational knowledge to pinpoint the most promising assets influenced by the ramifications of ongoing events.

Human-like Decision-Making with Unparalleled Speed and Scale


Naturally, such a superhuman investor is purely hypothetical. The human brain lacks the capacity to store vast amounts of diverse information in a structured and instantly accessible format, triggered by specific keywords. However, we posit that AI-driven models and systems have the potential to mimic human decision-making on a machine scale, enabling more informed investment decisions free from behavioral biases.

To unearth opportunities, the AI-driven investment approach must extend beyond conventional data reservoirs and quantitative methodologies. Human cognition may err by conflating correlations with causal relationships. Conversely, an AI-driven investment model should discern noteworthy occurrences and enduring causal connections, elucidating the potential development of contagion chains.




This involves concentrating on significant narratives and identifying financial assets that could be influenced in less apparent manners. Essentially, the AI-driven investment approach should ascertain which equities, bonds, and currencies have not yet factored in the repercussions of unfolding events. Confirming the timing and quantifying the anticipated alpha necessitates complex, multidimensional machine learning models that account for nonlinear dynamics and cross-asset interactions.

Unique NLP Applications


Numerous investment teams leverage NLP technology to analyze texts and generate sentiment indicators. However, as these signals are swiftly integrated into securities, they often align with momentum strategies, in our assessment. To unearth alpha opportunities, an AI-driven investing brain needs to detect enduring patterns with sustained efficacy over the long term, fostering alpha potential that remains uncorrelated with other asset classes.

This necessitates training a model that encompasses a specialized yet varied array of subjects, such as geopolitics, technology, finance, and macroeconomics, which might influence asset prices. The AI system is regularly supplied with information from reputable and unbiased sources, ranging from the International Monetary Fund to the World Trade Organization and global central banks. Continuously evolving, it currently comprises tens of millions of nodes (i.e., words and word combinations) in its knowledge graph.

Leveraging the primary themes, this dynamic knowledge graph actively engages with contemporary events as they unfold. It relies on a curated selection of trusted media outlets and assesses news based on its potential for sustained impact on financial markets, distinguishing it from fleeting trends. The top-ranked news items, deemed potentially most influential, are then employed to pinpoint the most relevant financial assets to invest in corresponding themes.



The Matchmaking Algorithm: Identifying Assets with Alpha Potential

The subsequent step involves linking influential themes to investment prospects by assessing the potential impact of each recognized theme on financial assets spanning a wide spectrum, encompassing stocks, equity indices, government bond indices, commodities, and currencies.

When a human investor identifies a theme, it may not always foresee the secondary and tertiary consequences. For instance, stricter carbon regulations would naturally impact car manufacturers and crucial raw materials employed to minimize emissions. However, the ripple effect is extensive and less apparent; it could extend from alternatives for palladium (utilized in catalytic converters in vehicle exhausts) to the electric vehicle supply chain, to other sectors reliant on lithium from technology firms producing chips and hardware, to renewable energy storage solutions.

An ML model can detect patterns and measure the interconnections among multiple assets, aiding in return prediction by indicating the direction (long or short) and magnitude for each asset. This model assesses the anticipated alpha for each security earmarked as a candidate from a pertinent theme. Naturally, not every identified security is deemed suitable for portfolio inclusion. Quantitative tools can further assist in devising a strategy that seeks to optimize the alpha potential of shortlisted securities while implementing various levels of risk management.s

Case Study 3: Enhancing Equity Research and Portfolio Procedures


Equity portfolios grounded in fundamental processes may not appear to be the most apparent choices for implementing AI technology. After all, stock pickers rely on thorough research, expertise in sectors and companies, and, crucially, human judgment to pinpoint alpha opportunities.

We believe that AI won't supplant human analysts in active equity portfolios in the near future. However, the necessity for advanced tools to handle risk and devise portfolios with robust risk-adjusted returns has become increasingly crucial. In our opinion, AI tools can enhance equity investment processes primarily in three aspects: efficiency, risk management/portfolio construction, and signal generation.

Efficiency: Streamlining Mundane Tasks

For analysts to maximize productivity, their attention should be directed towards probing the significant inquiries armed with the finest data. However, historically, gathering top-tier information in a data-saturated environment was both time-consuming and imperfect. A continuous flow of information emanates from news reports, official filings, and earnings calls. Consequently, investment teams were compelled to concentrate their profound research efforts on a select few high-priority holdings or promising investment prospects.

Presently, ChatGPT can summarize transcripts of earnings calls and events that analysts may not typically attend. If utilized effectively, it's akin to having 10,000 interns assisting analysts in hastening the revelation of fresh information. Internal chatbots can aid analysts in becoming more efficient at extracting information from an extensive array of notes and reports; they can also assist quantitative analysts in coding, expediting crucial data discovery processes.

Risk management and portfolio construction are additional domains where AI can prove highly beneficial.


Cluster analysis, an AI technique we've integrated into our risk management toolkit for some time now, is designed to uncover latent risks hidden within unseen market trends, often overlooked by conventional risk models. This advanced machine learning method categorizes stocks into clusters based on their correlated movements over a specified timeframe. For instance, it assists in distinguishing groups of stocks associated with emerging sub-themes or risks, providing valuable insights to portfolio managers for mitigating unintended exposures.

Beyond numerical data such as stock returns, unstructured text presents another avenue for uncovering connections between companies and trends. Leveraging natural language processing (NLP) and word embeddings, we can systematically analyze various materials like company transcripts, 10-K and 10-Q forms, and Wikipedia data to identify thematic stocks or potential portfolio risks. This approach offers a scalable method to identify stocks linked to overarching themes such as demographics and aging, AI, or urbanization, which may significantly influence risk and return dynamics.

Equity investors need to move past the buzz and focus on companies that have well-defined strategies to effectively capitalize on the advantages offered by generative AI.

In any technology revolution, the transition from hope and hype to productivity and profits is rarely smooth. While commercial uses of generative artificial intelligence (AI) are just starting to emerge, equity investors can map out strategies to find companies that are best positioned to reap business advantages.

Since the public release of ChatGPT in November 2022, generative AI has been hailed as a revolutionary technology. Businesses in various sectors are touting its potential, and investors are eager to identify frontrunners. Projections indicate a significant uptick in spending on AI-related software, services, and infrastructure. However, despite the awe and anticipation surrounding this new innovation catalyst, the route to profitable monetization of the technology remains uncertain.




Strategies for Generating Revenue from Generative AI: Enhancing Productivity versus Adjusting Pricing

There are multiple avenues for companies to generate revenue from generative AI. Firstly, businesses utilizing the technology can enhance productivity within their operations. Secondly, platforms offering generative AI solutions can profit by establishing competitive pricing strategies. Lastly, suppliers of essential hardware required to support the technology, often termed "picks and shovels" providers, can also capitalize on its adoption. These paths to monetization are closely interconnected.

Market dynamics have swiftly identified winners in the realm of "picks and shovels," exemplified by the strong performance of NVIDIA this year, known for producing indispensable graphics processing units (GPUs) crucial for AI applications. Conversely, discerning successful strategies among platforms and users remains challenging. However, we are witnessing these entities adopt diverse methods to translate productivity enhancements into tangible profits.

Several companies anticipate that AI could yield productivity enhancements ranging from 20% to 30%. While some notable instances have highlighted the possibility of staff reductions, the predominant focus lies in augmenting output while maintaining the existing workforce. For instance, AI holds the promise of automating numerous mundane, labor-intensive activities, thereby enabling professionals to allocate their time towards higher-value tasks for their organizations.

The realization of productivity gains will hinge on the affordability of the technology. Consider a scenario where a company aims to enhance the productivity of a $100,000-per-year employee by 25%. The value proposition varies significantly depending on whether the AI solution for that worker costs $5,000 or $20,000. Consequently, in the current phase of the technology's development, many investors are closely scrutinizing how AI vendors will price their offerings.

For AI platforms, determining the optimal price point is influenced by the expense of computing infrastructure. AI-supporting technology is notably costly, given the scarcity of crucial infrastructure components like GPUs. Consequently, AI vendors must strike a balance between meeting their customers' productivity demands and managing the costs associated with servicing them.



Three General Pricing Approaches

While AI's commercialization is still in its early stages, three primary pricing strategies are emerging. Grasping the intricacies of these strategies can assist investors in evaluating the potential profitability of various companies involved in the technology.

Subscriptions: Companies that integrate AI features to enhance existing products gain access to a potentially lucrative customer base instantly. Microsoft exemplifies this by charging $30 per user/per month for a service named Copilot, which imbues AI capabilities into applications within its Microsoft 365 suite. Some investors anticipated a lower pricing point. Why did Microsoft charge more than expected? Are customers willing to pay more because productivity gains surpass expectations? Or is the technology proving costlier for Microsoft than anticipated? It's too early to determine, but it may be a combination of both. Google is following a similar trajectory, recently unveiling a $30 per user/per month pricing for its Duet AI service for G Suite enterprise applications.

A la carte: As more companies embrace AI technologies, they'll require additional computing infrastructure to execute their AI queries. We anticipate that many will opt to utilize the native AI platforms offered by cloud providers like Amazon.com, Google, and Microsoft. Given that their usage may be intermittent and considering the high cost of AI infrastructure, these cloud providers are likely to implement an a la carte pricing model. OpenAI has been a trailblazer in this consumption model, billing enterprise customers based on the number of "tokens" they utilize, with each token equivalent to roughly 750 words. Microsoft, as OpenAI's infrastructure partner (and a minority investor), has indicated that 2% of its Azure cloud growth in the third quarter will stem from generative AI consumption.

As a feature: Certain AI providers may opt to integrate AI capabilities into their products without immediate charges for the enhanced services. Instead, the strategy would focus on augmenting the product's value by incorporating AI as a feature. Over time, the company could implement universal price adjustments, justified by the added value. Adobe has traditionally employed this strategy with its Creative Cloud and Acrobat products. This approach is often most suitable for products marketed to consumers and small businesses, who might resist paying extra for a feature they may or may not utilize. Once they integrate the new AI capabilities into their workflow, it could be simpler for them to accept a subsequent price hike.

The Dilemma for Consumer-Facing Companies

Investors seeking significant returns from consumer-facing chatbots might face disappointment. Foundational query-and-response systems like ChatGPT and Google's Bard are swiftly becoming standardized. Companies leading in consumer-oriented domains—ranging from consumer electronics to internet search engines and social media platforms—will need to demonstrate innovative AI applications that enhance consumer experiences. Apple, for instance, has unveiled intentions to craft its own chatbot, while Google explores AI-driven chatbots to refine search query responses.

Directly monetizing these AI offerings could pose challenges, in our assessment; instead, they are likely to be leveraged for targeted advertising, mirroring current platform practices. Seen from the consumer's viewpoint, these products will appear as enhancements aimed at retaining them within the vendor's ecosystems, rather than introducing a new revenue stream.

As technology progresses, pricing strategies will also evolve. Armed with a roadmap of monetization strategies, investors will be better positioned to distinguish companies adept at marketing from those ready to generate AI-driven profits that can underpin investment returns.

Have bond investors finally found a method that offers appealing, consistent, uncorrelated active returns?


With rising rates, bond markets are once again presenting promising real returns, and with increasingly complex economic conditions, there's a broader landscape for active fixed-income security selection. But how can investors consistently capitalize on these opportunities? We posit that systematic fixed-income investing approaches can offer a solution and provide considerable flexibility.


What Constitutes a Systematic Approach to Fixed-Income Markets


Systematic fixed-income investing represents an active strategy designed to surpass bond-market benchmarks. It relies on a dynamic multifactor process to guide investment decisions, employing predictive factors with established connections to outperformance. Through a quantitative and AI-driven decision-making process, bonds in the market are ranked based on their alignment with these predictive factors, thereby seeking to generate outperformance (alpha) via bottom-up security selection.

This systematic methodology differs from traditional active approaches, which primarily emphasize duration and credit-market exposure (beta) along with sector tilts.

Systematic fixed income approaches stand apart from other solutions in several ways.

Systematic Strategies Prioritize Security Selection While Most Traditional Active Strategies Prioritize Beta



Because systematic approaches rely on distinct performance drivers, their returns are expected to differ from—and complement—those of traditional active strategies. The active returns stemming from security selection in systematic strategies are intentionally largely uncorrelated with both the benchmark and significant risk premiums. Consequently, these strategies can serve as effective diversifiers within a fixed-income portfolio. Moreover, systematic approaches can facilitate competitive fee structures, potentially offering a more cost-effective method to outperform bond benchmarks. Additionally, they can be easily customized, empowering managers to align portfolios precisely with client preferences while preserving performance potential.

How Do Predictive Factors Work?

Risk factors, such as interest-rate risk (duration) or spread duration (the sensitivity of a bond’s price to changes in its credit spread), highlight how market influences can impact security prices. Systematic strategies aim to identify factors with predictive power that consistently pinpoint securities with the best risk-adjusted return potential—referred to as predictive factors. These factors can be derived from market values (such as value and momentum) or fundamental, company-specific attributes (like quality). Insights from predictive factors are systematically applied to analyze extensive historical market data, identifying securities with the appropriate attributes to likely outperform the market.

Managing Factor Strategies in Practice

With our extensive research databases, Prime Credit can identify and access hundreds of proprietary factors. While not all factors are continuously integrated into a systematic portfolio, the manager can rotate them based on market conditions as investment regimes shift and the effectiveness of the factors varies accordingly.

Predictive factor-based methodologies originated in equity markets, where constructing benchmarks is relatively straightforward and pricing is largely transparent. However, factor-based approaches have only recently gained traction in fixed-income markets, which are larger, more intricate, and fragmented across various trading venues. These characteristics make liquidity and pricing more challenging to ascertain in bond markets.

Due to these complexities, advanced technology and analytics are indispensable for implementing systematic approaches in fixed-income markets. While academic research supports the utilization of predictive factors in fixed-income investing, creating successful portfolios requires rigorous testing and practical implementation skills.


Constructing a Systematic Portfolio: Integrating Predictive Factors


Implementing a systematic approach involves assessing each bond within the benchmark across various predictive factors, generating a spectrum of scores for each security. For instance, a bond might receive a high score in terms of value but a low score for momentum. Subsequently, a factor aggregation model consolidates these disparate factor scores into a unified composite total factor score for every security.

The model employs two key criteria in constructing a portfolio based on these factor scores: predictive effectiveness and correlation with other factors. These are weighted using an algorithm determined by a machine learning technique. This algorithm ranks the total factor score for each bond while considering other optimization and risk constraints, such as bond, issuer, sector, ESG, duration, spread, liquidity, and transaction cost limits. Thus, the model aims to achieve superior risk-adjusted returns by striking a balance between predictive efficacy and stringent risk management measures.



Three Critical Success Factors


In this emerging domain, certain providers' systematic fixed-income offerings have underwhelmed, with actual returns not meeting the expectations set by historical back-testing. We identify three common shortcomings: dependence on static factors, utilization of unreliable data, and challenges in accessing liquidity and executing strategies effectively. These underscore the significance of three fundamental elements for successful systematic strategies:

An Evolving Factor Methodology. Market dynamics are in constant flux, with the effectiveness of factors fluctuating across different markets and timeframes. For instance, while carry (yield) might wield significant influence in investment-grade bond markets, its impact could be diminished in high yield settings where default risk takes precedence. Therefore, it's imperative to continuously assess and adapt factors to reflect evolving market conditions.

Rich Data Resources. Dependable data serve as the foundational bedrock of successful systematic strategies. These strategies rely on copious amounts of data that are not only voluminous but also pristine, devoid of irregularities and discrepancies. The process of compiling such data is arduous and research-intensive, necessitating the aggregation of information spanning various bond metrics, comprehensive analytics capturing companies' financial dynamics across diverse parameters, and encompassing a broad spectrum of fixed-income categories worldwide.

Liquidity Evaluation. Enterprises unable to accurately gauge a bond's liquidity will encounter hurdles in actualizing their investment strategies. To remain competitive in a landscape where market responses to new information occur with increasing rapidity, proficient fixed-income managers rely on technology consolidating external fixed-income trading platforms into a unified interface. Securing sufficient liquidity for executing desired trades stands as a prerequisite for effectively managing factor weights within a portfolio. Moreover, ensuring ample liquidity at favorable prices assumes paramount importance, as systematic strategies exclusively execute trades meeting stringent criteria for transaction-cost efficiency.




Healthcare equities have lagged behind the global market performance this year. However, delving deeper into the sector's specifics reveals a more intricate scenario. In pivotal segments, earnings growth projections remain robust, coupled with appealing valuations.

Investors in healthcare shares experienced a letdown in 2023. While mega-cap technology equities propelled global stock markets amid enthusiasm surrounding artificial intelligence (AI), other sectors found themselves in the shadows. By October 31, the MSCI World Index surged by 7.9% in US-dollar terms, contrasting with a 6.0% decline in the MSCI World Health Care Index.

Forecasts for industry growth contradict lackluster returns.

Across the sector, returns have exhibited a broad spectrum. For instance, pharmaceuticals and healthcare technology firms experienced slight declines, whereas shares of life sciences tools and services companies plummeted by 21.6%. Nonetheless, every segment within the global healthcare industry anticipates positive consensus earnings growth for 2024 compared to 2023.

As of the end of October, the MSCI World Health Care index traded at a price/forward earnings ratio (2024) of 16.2x, representing a 5.2% premium over the MSCI World index. Historically, the sector has commanded a larger premium relative to the broader market. Despite this, with declining share prices coupled with positive earnings growth, healthcare stock valuations appear appealing when viewed through a historical lens. Over the past decade, based on price/earnings valuations, most healthcare sectors are valued below the 40th percentile of all monthly observations.



Resilient Enterprises Amid Uncertainty

Active investors have opportunities to discover attractively priced companies positioned to leverage enduring growth trends in the sector. These include diagnostics and life sciences firms focused on enhancing early disease detection, as well as technology enterprises poised to integrate AI-driven healthcare solutions. Additionally, innovative equipment manufacturers are advancing novel approaches to enhance outcomes, such as through robotic surgery and minimally invasive procedures. Given that biotechnology stocks tend to command higher valuations, investors should exercise caution to validate whether a company's business model and growth trajectory justify its price.

Many growth trajectories in the healthcare sector stem from the imperative for efficiency and cost-effectiveness in global healthcare systems. Consequently, we anticipate these trends will endure over time and remain resilient even amid macroeconomic challenges. For equity investors, the pathway to success, in our assessment, lies in focusing on businesses rather than scientific endeavors. This entails seeking healthcare companies with robust business models while refraining from speculating on the outcomes of scientific research and development, a notoriously unpredictable endeavor. Companies with such qualities are aptly positioned to navigate short-term market fluctuations and offer investors reliable sources of innovative growth amidst demanding conditions.

Pharmaceutical companies need not dominate a healthcare portfolio. Equity investors are encouraged to diversify their holdings across the sector to uncover innovation and growth opportunities.


Pharmaceutical firms frequently command attention in the healthcare arena. From COVID vaccines to treatments for Alzheimer's, pioneering pharmaceutical products aspire to cure human ailments and enhance quality of life. Investors are frequently drawn to the potential of a blockbuster drug to eliminate a stubborn illness and yield substantial profits.

As major players in the healthcare sector, big pharma firms frequently hold significant weights, often dominating sector positions within global equity portfolios or standalone allocations. However, an excessive focus on pharmaceuticals may constrain a portfolio's potential. Companies involved in diagnostics, technology, and equipment manufacturing, aimed at tackling the world's pressing medical challenges, are gaining significance in driving progress within the healthcare sector.

Over the past two decades, transformations in the healthcare benchmark highlight this evolution. The proportion of pharmaceutical firms within the MSCI World Health Care Index has decreased from 82% in 2000 to 41% presently (Display). Meanwhile, other sectors have gained prominence, presenting equity investors with a wider range of prospects in segments like life sciences tools and services, as well as technology and equipment.



Diagnostics and Life Sciences Tools and Services—These sectors are pivotal in medical procedures, not just treatments. Sophisticated testing and imaging techniques enable healthcare providers to detect diseases in their nascent stages, enhancing treatment effectiveness and recovery prospects. Moreover, genome sequencing capabilities hold the promise of unveiling new drug potentials and facilitating early disease detection.

When pharmaceutical firms engage in testing new drugs, they often subcontract clinical trials. Eurofins Scientific, headquartered in Luxembourg, stands as one of the major diagnostic companies globally, providing an assortment of services ranging from clinical trials to environmental and food testing. In Japan, Synnex offers diagnostic apparatuses for blood analysis, catering to various needs from basic blood type identification to liquid biopsies for cancer detection and Alzheimer's disease screening.

In biotech research and development, pioneering drug development methods, like gene therapy or mRNA vaccines, have traditionally required substantial capital investment. Biotech firms have had to invest in numerous tanks for manufacturing biologic drugs across various sites. Germany's Sartorius Stedim Biotech has introduced a reusable system for the steel tanks utilized by biotech companies, reducing costs during the R&D phase while also being environmentally conscious.


Technology and Artificial Intelligence (AI)—In comparison to other sectors, healthcare has historically lagged behind in embracing high-tech advancements. However, there's a notable shift underway. Companies adept at integrating new technologies have the potential to revolutionize the delivery and provision of care. Such innovative entities can be found across various segments within the healthcare sector.

AI is being integrated into commercial solutions by firms such as Veeva Systems from the US and ICON based in Ireland. With the advancement and broader adoption of AI for diagnostic purposes, we anticipate a surge in demand for companies providing healthcare-related software as a service. Veeva and Japan's M3 provide a spectrum of software services spanning clinical trials, home health monitoring, and pharmaceutical sales marketing tools.

Equipment and Supplies—From multinational pharmaceutical companies to local clinics, the tools and equipment employed in delivering healthcare solutions are continuously evolving. Innovative equipment utilized in life-saving procedures can significantly enhance patient outcomes. Take, for instance, Edwards Lifesciences, a manufacturer of transcatheter aortic valve replacement (TAVR) devices, which addresses a significant heart condition through minimally invasive procedures. According to company reports, the global market for TAVR equipment is projected to expand from $7 billion in 2024 to $10 billion in 2028.

Surgeons are witnessing a rapid technological evolution. Presently, a growing array of procedures are leveraging robotic tools, empowering surgeons to navigate challenging anatomical regions with precision, through minimally invasive incisions, leading to reduced complications and quicker recovery periods. Intuitive Surgical, based in the US, produces a robotic surgical platform widely utilized in American operating theaters and is experiencing expanding adoption internationally, particularly in Europe, Japan, and China, presenting substantial growth prospects in our assessment.

Three Attributes Define Healthy Growth

Although each industry within healthcare presents unique dynamics, we advocate for investors to seek out three key attributes when identifying compelling healthcare enterprises across the sector.

Initially, prioritize products and services that enhance patient healthcare outcomes. Subsequently, companies positioned to assist financially constrained healthcare systems in reducing costs are likely to experience robust demand. Lastly, products and solutions that simultaneously enhance outcomes and economize expenses should yield profitability for the enterprise.


In our perspective, firms embodying these three traits operate within a virtuous cycle. The dynamics of a robust ecosystem furnish these companies with the groundwork to reinvest cash flows effectively, thereby fostering sustained earnings growth. We advocate that equity investors prioritize business fundamentals over attempting to forecast scientific breakthroughs, a notoriously challenging endeavor.

Certainly, pharmaceutical firms that fulfill the criteria of a robust ecosystem warrant inclusion in a diversified healthcare stock portfolio. However, rather than positioning drug manufacturers as the cornerstone of a healthcare portfolio, the initial focus should be on identifying high-caliber business models, irrespective of their location within the evolving landscape of medical offerings and services.

For many years, globalization has steadily advanced, serving as a fundamental force behind economic expansion, reducing inflation and yields, strengthening corporate profitability, and contributing to the ascent of market valuations. However, in recent times, the globalization trend has encountered challenges, marked by the resurgence of populism and the onset of trade conflicts, leading to the emergence of fissures in its foundation.

As part of Prime Credit’s Disruptor Series, we will delve into shifts occurring in this and other long-standing secular trends that have historically bolstered returns, examining their implications for portfolio construction.

The COVID-19 pandemic and the conflict in Ukraine have underscored the fragility of global supply chains, prompting companies to address vulnerabilities. Moreover, the disproportionate benefits of market surges accruing to capital rather than labor have exacerbated income inequality, fueling a backlash against globalization. These factors have raised questions about whether globalization has reached its zenith, potentially paving the way for deglobalization, which could have significant ramifications for economic growth, inflation, and financial markets.

The Ascendance and Ebbing of the Fundamental Super Blend

The boom in capital markets since 1980 isn't solely attributable to globalization. It's just a component of a fundamental mix that also encompasses influential factors like automation, technology, and what was at the time the largest labor force in history. Moreover, declining inflation rates, which remained low, played a significant role in driving up valuation multiples and propelling markets forward.

The outcomes were remarkable. From 1980 onwards, the S&P 500 has yielded an annualized return of 12.2%, significantly surpassing the 7.9% seen in the approximately three decades preceding 1980. During this period, the conventional 60/40 portfolio consisting of stocks and bonds boasted an impressive annualized return of 10.5%.

The era of the super-cocktail gradually waned around 2013, coinciding with the Baby Boomer generation transitioning away from its prime earning phase. Additionally, interest rates had largely reached their natural lows, and the pace of productivity improvements in developed nations began to slacken, dampening gross domestic product (GDP) growth. Despite escalating household wealth, buoyant markets weathered a succession of crises—such as the tech bubble, the global financial crisis, and the COVID-19 pandemic—followed by recoveries backed by exceptionally accommodating fiscal and monetary measures.

The (Harder) Road Ahead for Investors

As the influence of the super-cocktail diminishes, investors are confronted with a notably altered terrain ahead (Display). Economic expansion rates are tempering and are anticipated to decrease in the foreseeable future, exacerbated by progressively unfavorable demographic trends. While inflation is not expected to persist at its current elevated levels, it is likely to exhibit a structurally higher trajectory moving forward.



As growth slows and labor gains momentum, corporate profit margins will face greater strain, making it highly improbable for market returns to match those of previous decades. Reduced nominal returns, coupled with heightened inflation, will squeeze investors from both ends, resulting in markedly lower real returns. Even if inflation subsides to a 2.5% rate over the long term, US Treasury returns are likely to yield negative real yields. Despite recent yield surges, the current landscape presents considerable hurdles for the traditional 60/40 approach.

Pursuing Genuine Returns—and Optimal Return Sequences


The revised landscape awaiting investors in the years to come may seem somber, yet it's crucial to maintain perspective. Looking ahead isn't about plummeting off a precipice; rather, it signifies a shift from what was a remarkably bountiful era for capital markets historically.



In our perspective, the focus in the upcoming years will be on achieving elevated levels of real returns and possessing the adaptability to access various sources of returns across the board. Crafting portfolios geared towards generating a more advantageous return sequence is paramount for long-term wealth accumulation in retirement.

For the traditional 60/40 investment model, which has long been a mainstay for investors, this adjustment will probably involve increasing exposure to equities. Equities have historically shown the capability to yield positive real returns during periods of higher, yet stable, inflation. It's important to note that the relationship between inflation and equity valuations is not straightforward: extremely high inflation or deflation can be detrimental, but a moderate inflation range of 2.5% to 3% still tends to support positive real equity returns.

Allocations to conventional high-grade bonds may be reduced, considering the possibility of negative real returns and a slightly increased correlation with stocks—though they still serve as a diversification tool. Within the bond segment, it's prudent to explore opportunities across a wider spectrum and combine them strategically. High-yield bonds can also play a role in portfolio construction by mitigating risks associated with a larger equity allocation.

Moreover, it's crucial for investors to explore alternative avenues to identify and incorporate streams of real returns and premiums. Real assets emerge as a primary consideration. Due to the prolonged period of low inflation, many investors have maintained significantly lower allocations to real assets. In an environment marked by moderately elevated inflation, real assets assume heightened importance, albeit with the caveat that investors must exercise discernment—real assets vary considerably in terms of their effectiveness.

Additionally, it's worth contemplating specific factor exposures. Although value investing has encountered obstacles, it has traditionally exhibited a relatively strong correlation with inflation, suggesting a potential role for value strategies in a sustained period of elevated inflation. Moreover, if real yields persist at low levels over the long term, certain segments of growth stocks could retain value, particularly those capable of delivering consistent growth.

Efficiently constructing these exposures is crucial, as is identifying opportunities to generate alpha through active management. Even a modest annual alpha of 20 or 30 basis points can significantly enhance long-term wealth accumulation. The positive aspect stemming from the decline of the super-cocktail and globalization? The ensuing disruptions are likely to introduce greater variability in return patterns across regions, asset classes, and sectors, presenting skilled investors with fresh avenues for both alpha generation and diversification.

Benchmarks play a crucial role in portfolio construction, serving as a yardstick for measuring success. Instead of solely evaluating a portfolio's performance against the traditional 60/40 benchmark, it might be prudent to adopt a different approach. This could involve emphasizing outcome-based metrics, such as enhancing the calculated probabilities of achieving retirement goals.

In essence, investors need to leverage every tool available to generate positive real returns over the long term while also incorporating measures to mitigate downside risk or reduce volatility.