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- 12 May 2020
- Working Paper Summaries
Elusive Safety: The New Geography of Capital Flows and Risk
Examining motives and incentives behind the growing international flows of US-denominated securities, this study finds that dollar-denominated capital flows are increasingly intermediated by tax haven financial centers and nonbank financial institutions.
- 31 May 2017
Stock Price Synchronicity and Material Sustainability Information
This paper seeks to understand and provide evidence on the characteristics of emerging accounting standards for sustainability information. Given that a large number of institutional investors seek sustainability data and have committed to using it, it is increasingly important to develop a robust accounting infrastructure for the reporting of such information.
- 15 Aug 2016
Liquidity Transformation in Asset Management: Evidence from the Cash Holdings of Mutual Funds
A key function of many financial intermediaries is liquidity transformation: creating liquid claims backed by illiquid assets. To date it has been difficult to measure liquidity transformation for asset managers. The study proposes a novel measure of liquidity transformation: funds’ cash management strategies. The study validates the measure and shows that liquidity transformation by asset managers is highly dependent on the traditional and shadow banking sectors.
- 15 Feb 2016
Replicating Private Equity with Value Investing, Homemade Leverage, and Hold-to-Maturity Accounting
This paper studies the asset selection of private equity investors and the risk and return properties of passive portfolios with similarly selected investments in publicly traded securities. Results indicate that sophisticated institutional investors appear to significantly overpay for the portfolio management services associated with private equity investments.
- 25 Sep 2015
Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning
Dealers who need to price idiosyncratic products--like houses, artwork, and used cars--often struggle with a lack of information about the demand for their specific items. Analyzing sales data from the used-car retail market, the authors of this paper develop a model of dynamic pricing for idiosyncratic products, showing that seller learning has an impact on pricing dynamics through a rich set of mechanisms. Overall, findings suggest a potentially high return to taking a more serious information-based approach to pricing idiosyncratic products.
- 20 Jan 2009
- Research & Ideas
Risky Business with Structured Finance
How did the process of securitization transform trillions of dollars of risky assets into securities that many considered to be a safe bet? HBS professors Joshua D. Coval and Erik Stafford, with Princeton colleague Jakub Jurek, authors of a new paper, have ideas. Key concepts include: Over the past decade, risks have been repackaged to create triple-A-rated securities. Even modest imprecision in estimating underlying risks is magnified disproportionately when securities are pooled and tranched, as shown in a modeling exercise. Ratings of structured finance products, which make no distinction between the different sources of default risk, are particularly useless for determining prices and fair rates of compensation for these risks. Going forward, it would be best to eliminate any sanction of ratings as a guide to investment policy and capital requirements. It is important to focus on measuring and judging the system's aggregate amount of leverage and to understand the exposures that financial institutions actually have. Closed for comment; 0 Comments.
- 30 Aug 2004
Real Estate: The Most Imperfect Asset
Real estate is the largest asset class in the world—and also the most imperfect, says Harvard Business School professor Arthur Segel. He discusses trends toward institutionalization, environmentalism, and globalization. Closed for comment; 0 Comments.
How do Private Equity Fees Vary Across Public Pensions?
As state and local defined-benefit pensions increasingly shift capital from traditional asset classes to private-market investment vehicles, this analysis shows that public pensions investing in the same private-market fund can experience very different returns.
Advanced analytics in asset management: Beyond the buzz
- Article"> Article (PDF -467KB)
News reports and social media have been buzzing with the notion of robots making humans obsolete in a host of industries, including asset management. Most business conversations are peppered with terms like big data and advanced analytics . Indeed, a vast intellectual ecosystem of think tanks, professorships, and consultants has emerged out of an obsession with the impact of artificial intelligence on the future of work and commerce. In 2017, there were almost 14,000 research publications in the asset-management industry that contained big data or analytics as keywords—four times the number in 2012.
Faced with this deluge of opinions and claims, it can be difficult for asset-management leaders to separate fact from fiction and to get a clear perspective on what they actually need to do differently in this new “machine age.” Five years ago, the answer would have been: “Not much.” Granted, some firms—notably hedge funds—have been pursuing analytics-driven quantitative or systematic investing for a while, but most traditional asset managers with fundamental investing teams were content to let other industries take the lead. Some were experimenting with accessing alternative sources of data and building small data-science teams, but little had been achieved at scale to alter the traditional way of delivering value in the industry.
Things are now changing. Over the last couple of years, the application of advanced analytics to specific business problems has started to deliver value for traditional asset managers —not by replacing humans but by enabling them to make better decisions quickly and consistently. A broad set of firms are embracing new analytics methods at multiple points across the asset-management value chain—and beyond the alpha-generating use cases favored by quant firms—from increased sophistication in distribution to better investment decision making to step changes in middle- and back-office productivity (Exhibit 1).
Distribution
Against a backdrop of tepid growth (US organic net flows of 1.1 percent per year between 2013 and 2018, driven almost entirely by passive strategies), asset managers have been questioning traditional “feet on the street” distribution models. Some are now using data and advanced analytics to reinvent their distribution models, while others are using these tools to turbocharge their existing distribution forces and create greater operating leverage. Regardless of the extent of the transformation, the evolution toward a more data-driven approach to sales and marketing is now well underway and continues to gain momentum. At present, asset managers are primarily applying advanced analytics to improve distribution along three main vectors:
- Optimizing distribution and service models. A number of asset managers are building vast data reservoirs of multidimensional client characteristics to design distribution and service models that better enable them to cover the right clients, through the right channels, at the right time. Rather than relying on client type or size to determine whether and how a client should be covered, asset managers are now using data to achieve more fine-grained segmentation: for example, between the digitally savvy financial advisor who almost exclusively follows model portfolios, and the rep-as-portfolio builder who is eager for in-person portfolio construction advice. Our work with asset managers has shown that this type of behavioral-based segmentation of clients and subsequent adaptation of sales efforts can free up 15 percent or more of existing salesforce capacity and increase sales from priority client relationships by up to 30 percent.
- Improving productivity through precision targeting. Asset managers are also investing in analytics to generate actionable client insights to improve the productivity of sales and marketing efforts. Examples range from predictive algorithms that identify specific product cross-sell opportunities to those that identify clients at risk of redemption for specific strategies. These algorithms have proven to have greater than 80 percent accuracy in multiple instances, with sales results up to ten times better than control groups that did not use these analytical tools.
- Enhancing performance management. Advanced analytics is also being used by distribution leaders to effectively manage the performance of their teams. Data provides the transparency that enables executives to closely monitor the effectiveness of sales and marketing activities and campaigns, and quickly address those that are not working. Some leading-edge asset managers are also applying advanced analytics to their talent processes, using it to identify the characteristics of high performers, which are then incorporated into hiring, retention, and professional development processes.
The foundation for these use cases is a robust multidimensional data repository (Exhibit 2) on individual clients that combines the best of external (for example, third-party) and internal data (such as transaction history and customer relationship management).
Investments
On the investments side, some traditional asset managers are now engaging more fully in advanced analytics. These efforts are focused in three areas:
- Debiasing investment decisions. Eliminating systematic biases from the investment decision-making process has long been a topic of interest to investors. The ability to stitch together a broad set of data sources about an individual or team’s trading history, communication patterns, psychometric attributes, and time-management practices allows firms to identify drivers of performance and behavioral root causes at a more granular and individualized level than previously. Managers can then make operational improvements based on these insights ; for example, by flagging trades that fit predefined patterns, and double-checking them before execution.
- Using alternative sources of data to generate alpha. The availability of greater quantities of data is putting a premium on having both data-acquisition capabilities and the data-science skills to stitch these sources together into predictive models that improve decision making (Exhibit 3). This approach is being applied in real estate, to give one example, where the prevalence of location-specific data from a variety of sources is helping investors predict key metrics such as rent and vacancy rates with great precision. At one leading real-estate investment manager, the combination of Yelp reviews, information on traffic flows, and credit-card spending data with traditional property- and market-level characteristics improved the predictive accuracy of three-year forward rent forecasts from 60 to 70 percent to over 95 percent. And while the predictive model was not used to replace the existing underwriting process, it was incorporated as an additional test before investment decisions are made.
- Enhancing research processes. The application of techniques such as natural language processing (NLP) is also helping asset managers process vast amounts of information more quickly than before—for example, by automating the ingestion and analysis of public filings and flagging changes in sentiment that a research analyst can focus on. This is an example of machines complementing the human process instead of replacing it—the technology helps narrow down what is relevant in much the same way that a recommendation engine on Netflix or Amazon would and allows the investor to spend more of their time on high-value decisions. One leading alternatives asset manager has invested heavily in this concept by building an investment research engine that enables investment analysts to seamlessly record everything about a potential deal or portfolio through a front-end system. These data are then enriched with relevant proprietary historical data and structured data from third-party providers and results in a research and portfolio management tool that provides a rich, real-time view of potential opportunities.
Not all asset managers are embracing big data and advanced analytics in the ways described above. Many trust more traditional processes. Yet, certain firms or portfolio managers are taking this seriously and have begun to make investments in these capabilities.
Middle and back offices
Advanced analytics is also being used to improve productivity in the asset management middle and back offices. As firms contend with the growing complexity of products, legal entities, vehicles, and markets, economies of scale are coming under pressure. In response, asset managers are looking for ways to increase the productivity of their middle- and back-office functions through advanced analytics-driven solutions. Two particular areas of focus are:
- Process automation of time-consuming tasks. Asset management firms are using NLP and other techniques to analyze text and voice communications and to recommend optimal actions for certain processes, such as suggestions for how to deal with policy breaches picked up in conversations and deploying machine-assisted conversations to answer common operational questions. One leading asset manager recently implemented a solution that automatically uploads hundreds of documents into a central repository and uses NLP techniques to transfer relevant information into a customizable and searchable reporting interface. The solution extracts over four million unique data elements and has led to a 60 percent reduction in the time required to generate relevant reports. This type of analytics-driven automation has the potential to significantly improve the efficiency of core functions within asset management.
- Improving quality of risk management. New US trading regulations (for example, those preventing traders from benefiting from old proprietary trades) are leading to the need for heightened compliance in asset management. Some firms are deploying forensic analytics to monitor traders and cross-check transactions with personal data to uncover instances of misconduct, scanning communications for anomalies or breaches of ethical divides, and building data sets across trading data, external data, and personal employee data to increase the flexibility to expand the number of checks or run different scenarios. Asset managers that have implemented these techniques have seen a 55 to 85 percent reduction in time spent on trade surveillance activities and, more importantly, improved risk identification. In one case, an asset manager found that its machine-learning algorithm was significantly better at detecting risks than a seasoned expert reviewing the same underlying materials.
Markers of success
Asset managers that have extracted meaningful value from data and advanced analytics share a number of characteristics:
- Ruthlessly prioritize based on business value. Asset managers that have derived value from analytics begin with a focus on a small set of analytics use cases where there is business demand and potential for measurable business impact. They typically engage multiple stakeholders in a rigorous prioritization of potential use cases against a set of “hard” criteria (such as business value, time to implementation, data availability, and committed business sponsor).
- Recognize that analytics is a team sport. Successful analytics efforts require cross-functional skills (for example, business, data, technology, operations, and compliance) and work best when led by small, agile teams with end-to-end responsibility for delivering an analytics product. Teams are most effective when the product owner is a business person who will be the direct beneficiary or user of the product, and when analytics resources are embedded within and seen as part of these teams, as opposed to operating in a more centralized model.
- Focus on “last mile” adoption. A common pitfall in the development of analytics capabilities by asset managers is focusing on underlying data and model building but treating the adoption of analytics assets by end users as an afterthought. The question of how end users will actually engage with analytics should be addressed at the very beginning of the process. Thinking these questions through, and planning for how analytics will be integrated into existing workflows and what chain of actions they should trigger, significantly increases the likelihood of sustained long-term impact. Visible sponsorship by key influencers (for example, portfolio managers or top sales professionals) is also vital in the change-management effort. The power of advanced analytics is unleashed when data and models are adopted by end users to deliver business impact (Exhibit 4).
- Adopt a “minimum viable product” mentality. Successful data and advanced analytics capabilities rely on a test-and-learn mind-set. Rather than waiting until they have the full set of talent and data resources needed to build a robust advanced analytical model, asset managers that have successfully embraced advanced analytics have a bias to action and are willing to test and learn—and fail—quickly. In other words, firms learn more from playing the game than from standing on the sidelines.
- Invest in next-level data and analytics talent. One of the greatest challenges asset managers face is in recruiting and retaining distinctive data and analytics talent. Those who get it right recognize that business-as-usual analytics resources are typically not sufficient, and that attracting and retaining distinctive talent typically requires a vibrant community and a strong talent plan (for example, career paths and robust professional development).
- Create an integrated target-state vision for data and analytics. The most mature organizations go beyond individual use cases to create a self-sustaining data and analytics engine that drives measurable business value. While the development of a capability typically happens incrementally, having a clear vision of what the integrated target end state looks like—across data management and governance, analytical tools, technology development, and business adoption—helps to avoid duplication and speed development.
In the last few years, the application of advanced analytics in asset management has moved from the realm of science fiction to, simply, science. Leading firms are applying these tools and insights to improve distribution effectiveness, investment performance, and productivity in the middle and back offices. While some firms are using analytics to enhance productivity of existing practices, others are taking advantage of these new capabilities to ask more fundamental questions about their operating models. What could an analytics-driven distribution approach look like? How might research organizations change with the use of new tools and the availability of alternative sources of data? While there is still some uncertainty around the extent and pace with which analytics will impact asset management, it is our view that superior analytics capabilities will be a key driver of success in the industry going forward.
About the author(s)
Sudeep Doshi is an associate partner in McKinsey’s New York office, where Ju-Hon Kwek and Joseph Lai are partners.
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Are you reframing the future of asset management or is it reframing you?
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Jannine Ravens,
Hermin hologan, show resources, the question is no longer whether asset management needs to change, but what that transformation should look like..
- The COVID-19 crisis brought huge disruption, adding to the pressures that have been driving the industry toward an inflection point for several years.
- Over the next five years, the speed of change will accelerate, pushing firms to do more with less.
- This will provide an opportunity for the industry to reframe the future of asset management by strengthening its performance and elevating its purpose.
A s 2020 began, global asset management could look back on a remarkably successful decade. But the industry’s boom years were ending. Asset management was being driven to an inflection point by a combination of structural shifts, including the transfer of responsibility for long-term savings onto individuals; the increasing emphasis on nonfinancial outcomes; the tendency for capital to flow to the cheapest products and strategies; and the impact of technology on distribution, operations and investment management.
Against this background, COVID-19 not only disrupted financial markets, but it also threw the industry’s weaknesses into focus, accelerated its tectonic shifts and created new problems. Asset managers responded fast to maintain operational resilience, reassure clients and transition to remote working. Despite the liquidity and valuation challenges posed by heightened market volatility, most fund structures, including exchange-traded funds (ETFs), performed well. Environment, social and governance (ESG) funds, fixed income and alternatives saw net inflows.
As a result, EY research shows that the industry’s largest firms enjoyed a collective growth of 14.6% in assets under management (AUM) during 2020. But a closer look shows that more than 75% of AUM growth was due to market movements, and that a handful of firms captured the bulk of net inflows. Revenue growth trailed far behind AUM growth at 3.6%, and with expenses growing by 6.1%, there was a decline of 1.7 percentage points in average operating margins.
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The years 2021–25: a challenging outlook calls for decisive action
The next five years will be much tougher for asset managers than the last five.
The COVID-19 crisis is fanning geopolitical uncertainty, and many countries could take years to recover their lost output. Monetary stimuli will keep rates low for now, but higher inflation and increase in rates cannot be ruled out. Financial markets, which diverged from the real economy during 2020, could see a correction or prolonged period of weak performance.
As a result, asset managers are likely to see investors’ demands grow to be ever more complex. Institutional investors will seek a combination of capital preservation, high yields and strong ESG performance. Retail demand for tailored solutions and ESG investing will grow too, along with advice and education. Asset managers will be forced to accelerate diversification, using alternatives to push up returns while making greater use of low-cost options, such as factor investing and enhanced beta.
Firms will also face a growing margin squeeze. Competition and regulation will erode fees in every asset class, and the shift to lower-margin strategies will also reduce income. On top of that, economic and demographic factors will reduce net inflows from historic levels of 3%–4% to around 2% per annum. At the same time, the need to invest in new products and technology will push up spending.
EY modeling shows that these trends will have dramatic effects on profitability. The base scenario for 2021–25, which assumes AUM growth of 15% over five years, expects average operating margins to decrease by 0.8 percentage points. Most firms will see profitability fall faster than this, due to the accelerating “winner takes all” phenomenon. That will make it hard for many asset managers –especially small- and medium-sized firms without a demonstrable source of differentiation – to survive in their current form. Furthermore, EY modeling shows that a pessimistic scenario (with a market correction holding AUM flat over the next five years) would lead to a 7.3 percentage point reduction in average operating margins by 2025.
Asset managers need to make significant changes to their strategies and business models if they’re to succeed in this increasingly fluid and challenging environment. Firms must pursue multiple avenues of growth; invest heavily in data and technology; and take a flexible approach to partnering, collaboration and mergers. There is also an opportunity for firms to offset margin dilution by taking action on strategic cost transformation.
Cost reduction for a typical medium-sized asset management firm
Strategic cost transformation could achieve a reduction of up to 15% in costs, enabling accelerated investment in technology and innovation.
Components of transformation
Start with a clear idea of each firm’s role in the industry of the future.
For asset managers, successful transformation needs to start with a clear idea of each firm’s role in the industry of the future. Which clients will firms serve? How will they reach them? What investment solutions will they provide, and in what way?
Firms should then build on the changes already achieved during the pandemic, using a combination of six key strategic components to shape a multitrack growth strategy, enabled by technology and funded by strategic cost transformation. The ability to manage simultaneous, multidimensional change will be crucial.
You can explore the EY multitrack success strategy for asset managers in the graphic below. Select each track to reveal the underlying components:
Looking beyond 2025
See 10 ways in which asset management could be reframed by 2030.
Asset managers not only need to transform their medium-term performance. If they are to use the disruption of COVID-19 as a springboard to lasting success, they also need to begin actively preparing for the end of current industry paradigms. Firms should imagine radical but plausible scenarios, identify their strategic implications and begin planning their responses while they still have time.
EY professionals have set out 10 ways in which asset management could be reframed by 2030, depending on the enabling factors and structural features that could develop in the industry over the next decade. Firms should ask themselves, for example, how they would respond if:
- The industry’s purpose was to provide every adult in the world with the knowledge and opportunity to participate in the growth of capital markets
- Asset managers’ performance was measured on the long-term value they create for all stakeholders, not just shareholders
- Every investor knew the impact of the assets they held
- Index providers partnered with technology firms to become the largest asset managers
- Reimagining the idea of “work” allowed asset managers to employ more diverse talent in more diverse locations
- Asset management was transformed from the most fragmented to the most concentrated industry
- Fractional ownership removed the need for funds
- A combination of AI and quantum computing could replace human portfolio managers
- Asset management fees were based on the creation of long-term value
- “D2C” – direct to customer – relationships were the norm, not the exception
Reframing asset management
If the world is to prosper, it needs greater financial inclusion, and in turn, more effective asset management.
We believe the industry has an opportunity to rethink its future and elevate its purpose to create and protect long-term value. In our view, that means benchmarking success through four lenses:
- For clients: Solve client needs while providing value for money and exercising fiduciary duties in a transparent, ethical manner.
- For people: Develop a diverse resource pool, foster an inclusive and equality-driven culture, and implement incentive structures that reward people for doing the right thing.
- For society: Share the benefits of investing with a wider constituency, deliver accessible investment education and make sustainability and climate change risk management the new standard for investing.
- For shareholders: Use these six key components of strategy to help optimize financial performance, while preparing for the long-term restructuring of the entire investment value chain.
Global asset management is at a unique moment in its evolution. Incremental change is no longer enough – decisive action is required to build on the advances already made and use COVID-19 as a positive catalyst for change.
The world will recover from the pandemic, but the needs of the future will not be the same as those of the past. Delivering lasting value has never been more important for asset managers and their stakeholders. If ever there was a moment for the industry to reframe its collective purpose, it is now.
For asset managers, successful transformation will start with a clear view of the role they want to play in the industry of the future. That means identifying which clients to serve, how to reach them, what investment outcomes to provide and how to deliver them.
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Successful transformation will start with a clear view of the role firms want to play in the asset management industry of the future. That means identifying which clients to serve, how to reach them, what investment outcomes to provide and how to deliver them.
When it comes to implementation, CEOs not only need to embed the positive changes accelerated by COVID-19; they also need to use an appropriate combination of six key strategic components to boost revenues and control costs. Winning asset management firms will adopt a multitrack growth strategy, funded by strategic cost transformation and enabled by technology.
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The Pitfalls of Asset Management Research
13 Pages Posted: 5 May 2022 Last revised: 15 Jun 2022
Campbell R. Harvey
Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)
Date Written: April 7, 2022
Forthcoming, Journal of Systematic Investment
Keywords: Backtesting, overfitting, p-hacking, research misconduct, research culture, performance fees, trading strategies, alpha
JEL Classification: G11, G12, G14, G17, G40, C58
Suggested Citation: Suggested Citation
Campbell R. Harvey (Contact Author)
Duke university - fuqua school of business ( email ).
Box 90120 Durham, NC 27708-0120 United States 919-660-7768 (Phone)
HOME PAGE: http://www.duke.edu/~charvey
National Bureau of Economic Research (NBER)
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VIDEO
COMMENTS
15 Aug 2016 Working Paper Summaries Liquidity Transformation in Asset Management: Evidence from the Cash Holdings of Mutual Funds by Sergey Chernenko and Adi Sunderam A key function of many financial intermediaries is liquidity transformation: creating liquid claims backed by illiquid assets.
15 Aug 2016 Working Paper Summaries Liquidity Transformation in Asset Management: Evidence from the Cash Holdings of Mutual Funds by Sergey Chernenko and Adi Sunderam A key function of many financial intermediaries is liquidity transformation: creating liquid claims backed by illiquid assets.
Our key contributions are positioning strategic asset management within the vast field of asset management research, describing the nature of strategic asset management research,...
General issues of asset management are explored in the works of I. Smirnov (2020); A. Vorotilov (2013); A. Kovalevich (2012), F. Ripol-Saragossi, E. Ternikova, S. Budylgin; Woodhouse J. (2003)...
Our work with asset managers has shown that this type of behavioral-based segmentation of clients and subsequent adaptation of sales efforts can free up 15 percent or more of existing salesforce capacity and increase sales from priority client relationships by up to 30 percent. Improving productivity through precision targeting.
A s 2020 began, global asset management could look back on a remarkably successful decade. But the industry’s boom years were ending. Asset management was being driven to an inflection point by a combination of structural shifts, including the transfer of responsibility for long-term savings onto individuals; the increasing emphasis on nonfinancial outcomes; the tendency for capital to flow ...
ASSET MANAGEMENT LITERATURE REVIEW AND ... Research and Technology Implementation Office P.O. Box 5080 Austin, Texas 78763-5080 14. Sponsoring Agency Code
The Pitfalls of Asset Management Research by Campbell R. Harvey :: SSRN Add Paper to My Library The Pitfalls of Asset Management Research 13 Pages Posted: 5 May 2022 Last revised: 15 Jun 2022 Campbell R. Harvey Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER) Date Written: April 7, 2022 Abstract