Many investors and fund management companies are united in their desire to find a particularly elusive individual: the star fund manager. So elusive is their quarry that a sub-industry of researchers and analysts has developed over the years to support this quest. Investors that choose actively managed funds want to find an individual who can generate better returns than the market over prolonged periods of time, while asset managers understand this desire and want to attract the money that follows it by promoting their fund managers and key members of their investment teams. “We are a people business” and “it is our people that sets us apart” are popular refrains from fund companies.

The desire to find a star fund manager is understandable, both commercially (it makes money for fund companies and, if one is found, investors) and philosophically (if active managers are able to consistently outperform the market, then this must involve skill). It is worth pointing out that, at a basic level, over any given time period some fund managers outperform the market average (either the average returns offered by replicating an index or the average returns across a range of fund managers with a similar investment focus). This is not just a theoretical view, it is reality. Some stocks will rise more than others over a certain period, and so some fund managers investing in companies listed in the same stock market will perform better than the average. The difficulty for fund investors is identifying if, and when, a particular fund manager (via a fund) will outperform the average. Star managers are those that are deemed to have the skill to be able to outperform over prolonged periods of time, or who have built up a following of clients in the belief that the manager can do this.

Client following is a particular feature supporting the status of star fund managers because their performance alone does not make their star shine, but instead it is their ability – and that of the sales and marketing team supporting them – to translate this into inflows and therefore to build increased scale in a fund that boosts their reputation. Crucially, a fund's scale also builds greater influence with the companies in which a fund manager invests.

Each fund management company is divided broadly into three divisions: its investment management team, those involved in sales and marketing, and support functions including operations and administration. This does not mean that every firm will neatly divide up its business in this way, but the division provides a useful model by which to understand an asset manager’s organizational structure. In addition, various elements of this structure can be outsourced to external organizations (third parties). For example, many start-ups and smaller firms outsource operational and governance functions. This chapter provides an overview of these different functions and the roles within them, as well as areas where outsourcing is used.

Approximately 42,000 people are directly employed by asset managers in the UK (IA 2021). A breakdown of these figures shows that operational roles account for half of those employed in the industry, while investment roles account for roughly one quarter of the total (see Table 2.1). The figures provide a useful way to visualize the shape of an asset management business through its staff, despite the composition of larger firms inevitably impacting the overall figures and also bearing in mind that outsourced roles are not included.

The 2008 financial crisis hit employment at UK asset managers, with those directly employed falling from 25,500 in 2007 to 24,000 in 2009, according to surveys by the UK asset managers’ trade body, the Investment Association. Employment has risen each year since. The surveys also suggest that one of the most significant responses to the financial crisis was an increase in staff in compliance and oversight functions, which now stand at 8 per cent of the total, up from a low of 4 per cent in 2010, although the current level looks to be stable, having been reached five years ago. The rise in internal IT appointments continues, however, accounting for 11 per cent of the total five years ago and 14 per cent today (IA 2021).

The European Fund and Asset Management Association (EFAMA) uses the UK figures, together with equivalent information from France and Germany to extrapolate a European total of around 115,000 individuals directly employed by the asset management industry (EFAMA 2021). The European trade body then considers the indirect employment generated by the industry through related services, such as distribution and advice, auditing, custodianship, legal, marketing, research and technology, and estimates that together these firms help create 644,000 full-time jobs.

To better understand how asset managers compete with one another, it is necessary to look at how firms sell funds and the development of new products. There are more than 4,580 fund management companies across Europe, although many of these manage tiny pools of money (EFAMA 2021a). This number fits with the industry narrative that there is intense competition and that a large number of firms are constantly vying with one another to show that their funds are better than others.

However, taking Europe as a whole, many of these firms will not compete against one another. This is either because any given international firm is not necessarily active in every European market, or because a firm is a part of a larger group (such as a bank or insurer) and primarily provide funds to be used internally rather than competing with independent asset managers for a client's business. Having said this, even when individual countries are singled out, the number of competitors is still large, with approximately 1,100 asset managers in the UK, 680 in France and 386 in Germany (EFAMA 2021a). In addition, there are 417 companies in Ireland and 268 in Luxembourg. And even if these latter two partly overlap with the lists for the three largest European fund markets, they still swell the numbers of competitors.

These numbers also show that barriers to entry for establishing an asset management business in Europe are low. Getting a business off the ground has been made easier over recent years as service providers have made better use of technology, giving small investment management firms the ability to outsource more of their operations. The robustness of these small operations relying on large third-party providers was proven over the recent past as asset managers were forced to work from home through the pandemic. The regulatory burden in Europe has clearly increased over the past ten years, but this has not stopped the launch of fund management businesses, even if it has added to the cost burden, albeit for larger firms in particular.

But establishing a firm is different from making it a successful business. The biggest challenge for a fund management company is to attract sufficient assets early on to keep the business going. Gaining seed investors is crucial for this process, not just for making the business viable, but also for attracting more clients.

Fund managers play an important role in free market economies around the world, controlling more than $100 trillion of financial assets globally, with the ability to invest that capital into companies and to influence the way in which they are managed. Most research and literature on asset management focuses on investing – how fund managers do it, who does it well, the ways in which it has changed, as well as advice on how others can invest too. This leaves a largely unfilled gap looking at the rest of an asset manager's business, ranging across sales, operations, governance, marketing, risk management, finance and compliance. It is these functions that are needed to turn a solo stock picker into a successful business. They are integral to making investment management commercially viable. And it was these aspects that moved from the periphery to centre stage when Neil Woodford, the UK's best-known fund manager, was forced to close Woodford Investment Management in 2019 amid an acrimonious split with the firm overseeing its operations. Around 300,000 investors are still trapped in Woodford's former flagship fund (Martin 2019).

This is not just a British story. Fund manager scandals have broken in recent years at Swiss asset manager GAM, French firm H2O Asset Management, and German Union Investment. Unlike Woodford, these asset managers are backed by large financial institutions. H2O was owned by Natixis Investment Managers (part of French banking group BPCE) and got into difficulties because of its relationship with controversial German financier Lars Windhorst, while GAM was caught up with disgraced Australian financier Lex Greensill. The chief executives of Natixis IM and GAM have both stepped down since these revelations broke.

Understanding how such problems can arise is part of how these businesses make money, the nature of their business models, the role of their compliance and oversight functions and how they incorporate investors’ interests. Each of these will be examined throughout this book. It will also look at fund managers’ increasing focus on sustainable investing – investing with the long-term good of wider society and the planet in mind, not just focusing on shorter-term financial interests. As the large sums of money that fund managers earn are more prominent in the popular imagination than what fund managers do, it is no small task for firms to demonstrate that they are focused on social and environmental issues.

We describe fundamental challenges to estimating heterogeneous treatment effects in the context of the statistical causal inference literature, proposed algorithms for addressing those challenges, and methods to evaluate how well heterogeneous treatment effects have been estimated. We illustrate the proposed algorithms using data from two large randomized trials of blood pressure treatments. We describe directions for future research in medical statistics and machine learning in this domain. The focus will be on how flexible machine learning methods can improve causal estimators, especially in the RCT setting.

To address the challenge of limited training data for machine learning models in the healthcare domain, we advocate for human-in-the-loop machine learning, which involves domain experts in an inter- active process of developing predictive models. Interpretability offers a promising way to facilitate this interaction. We describe an approach that offers a simple decision tree interpretation for any complex blackbox machine learning model. In a case study with physicians, we find that they were able to use the interpretation to discover an unexpected causal issue in a personalized patient risk score trained on electronic medical record data. To account for dynamics in disease progression, we advocate for building decision models that integrate predictions of the disease progression at the individual patient level with system models capturing the dynamic operational environments. We describe a case study on hospital inpatient management, showing how to build a Markov decision framework that leverages predictive analytics on patient readmission risk and prescribes the optimal set of patients to be discharged each day.

In this chapter, we explore how data-driven modeling can improve the understanding of OHCA risk, help identify the limitations of current AED placement strategies, and guide the development of optimal AED networks to increase the chance of AED use and OHCA survival. More specifically, we frame AED network design and related response efforts as a facility location problem, focusing on the maximum coverage location and p-median problems. We also highlight how novel tools that combine techniques from areas including information theory and machine learning with optimization models can shape the future of OHCA response efforts and AED placement strategies.

This chapter provides an overview of how researchers form interdisciplinary partnerships that leverage analytics-driven methods in AI, IE, and OR to tackle the most difficult societal and operational problems in healthcare.

This chapter provides an introduction to analytics-driven hospital capacity management through three projects that employed mathematical programming and discrete event simulation to address common challenges. The first project used mathematical programming to identify the mix of patients at Stanford Hospital that would maximize revenue given the capacity of hospital resources after a planned hospital expansion. The second project used discrete event simulation to plan the physical capacity and operational profile of a new procedural space at a hospital in New England. The third project combined mathematical programming and discrete event simulation to create a tool to schedule surgical procedures at Lucile Packard Children's Hospital Stanford.

This chapter offers lessons from engineering and other industries that promise developments in healthcare, and practical guidance for clinician-engineer partnerships. Section 1 provides guidance on how to establish a shared vocabulary and common understanding between engineers and clinicians of what terms such as AI and ML do and don’t mean. Section 2 identifies challenges clinician-engineering partnerships must overcome to deliver sustained value and ways to avoid common causes of failure. Section 3 provides specific advice on how to design projects to produce value at a series of stages rather than rely on the success of one, ambitious final model. Section 4 concludes by drawing on cautionary lessons from healthcare and other industries.