New frontiers

Operational efficiency and its impact on portfolio performance

Read this article and learn about:

  • Why operational efficiency spurs investment performance
  • The detrimental impact of shortfalls in your operational setup
  • Why an IBOR is fundamental to an operational framework
  • Enabling profitable growth for asset managers
  • Forecasting operational performance to build your ROI case

About the author:

Chito Jovellanos Journal 70x70Chito Jovellanos, President and CEO, forward look inc., Boston, USA.

Prior to heading up forward look, inc., he held executive management posts with Instinet, ScotiaMcLeod, Thomson Financial, and ISI Emerging Markets. Chito Jovellanos participates in industry groups on derivative processing and commodity trading, publishes in refereed journals, and speaks regularly on information quality in the global securities industry.

About forward look, inc.

forward look, inc. is a Boston and San Francisco based advisory firm that enables investment managers to systematically improve portfolio performance by minimizing implementation shortfalls stemming from suboptimal investment operations. It works exclusively with asset managers and plan sponsors with AUM ranging from USD 5bn to 500bn.


 

A new case study on leveraged loan portfolios further demonstrates how the quality of investment operations contributes directly to portfolio performance. Well-established analytic tools applied to data already on hand highlighted the value of an investment book of record (IBOR). Analytics also enabled forecasting of operational performance as a means to sustain growth in asset management, and evaluate potential return on IT investments.

 

In our 2013 Journal article1, we described how minimizing portfolio implementation shortfalls within investment operations prevented the slippage of between 51-242 basis points (bps) of alpha inherent in the manager’s strategy.2

Analytics were applied to:

a) identify the sources of portfolio performance degradation due to issues arising within investment operations (e.g. cash management), and

b) clarify pathways for remediation (e.g. better variation margin reporting and simpler prime broker workflows).

Although asset selection is the primary driver of alpha3, expressing and retaining that alpha over the life of a portfolio is determined largely by the quality of investment operations.

Although asset selection is the primary driver of alpha , expressing and retaining that alpha over the life of a portfolio is determined largely by the quality of investment operations.Chito Jovellanos, President and CEO, forward look inc., Boston, USA.

Since that initial article, we have had the opportunity to monitor additional real-world portfolios. The new information gave us the latitude to move from a purely descriptive approach to a more inductive posture, such as forecasting on-going sources of implementation shortfalls as inputs into risk management models and firm-wide strategic planning.

As an example of our inductive approach, we will present a recent case study that highlights:

  1. the sequential and recurring nature of implementation shortfalls,
  2. why continuous operational vigilance is the best guarantor of attaining alpha; and
  3. how analytics enable actionable insights into investment operations processes.

Case study: Leveraged loans ... higher yield, higher risk

Since 2010, asset managers have begun to invest increasingly in leveraged loans. This strategy is a response to rising rates and renewed inflation in a ‘new normal’ of muted returns across a broad spectrum of asset classes. Leveraged loans offer higher yields but are complex structures4 with equally complex servicing needs (e.g. the mean time to settle in 2013 was 23 days).

Between 2012 and 2015, we worked with three institutional managers who ran high-yield strategies that were driven mainly off leveraged loan portfolios. Our analysis of the operational workflows in these leveraged loan portfolios highlighted three essential outcomes.

1. Sources of portfolio implementation shortfalls emerge in sequence and often in cascades (see upper line in Figure 1).

For leveraged loans, the implementation shortfalls that depressed portfolio performance (as expressed via our DOT metric5) included basic data management (e.g. issuer and credit quality attributes); reconciliation accuracy; cash management lags; and breaks in manual processes.

Data Operability Threshold figure

Figure 1. Data Operability Threshold (DOT values) and monthly returns (percent).

2. To preserve the inherent alpha over the duration of a strategy, constant vigilance is required to offset the impact of implementation shortfalls (see lower line in Figure 1).

Many shortfalls are non-recurring given that the investment landscape is constantly shifting (e.g. new issuers with novel reset terms rushing to market in advance of an anticipated rate hike, giving rise to reconciliation problems early on). However, many are also in the realm of déjà vu (e.g. data management of issuer and security attributes). As shown in the lower line of Figure 1, portfolio performance reverts to its true potential once the source(s) of the implementation shortfalls are addressed.

3. Portfolio implementation shortfalls can be forecast and addressed in near time.

We further leveraged our analytics to enable short-term forecasts (i.e. a one-month horizon) of potential implementation shortfalls as expressed in the DOT metric (see Figure 2). We accomplished this goal by running Monte Carlo simulations of an asset manager’s workflows and evaluating the potential range of DOT values projected by our model 30 days out. The relative shifts month-to-month in DOT values correlated well with the observed changes (both degradation and improvement) in portfolio returns. For asset managers in particular, pre-empting shortfalls is critical. Growth can be sustained only if their strategic choices in market positioning (e.g. new asset classes and strategies) and investments in distribution capabilities (i.e.  sales ‘alpha’) are consistently validated through positive product performance.6

Data Operability Threshold

Figure 2. Data Operability Threshold (DOT values) – observed and forecast values.

Accessible analytics

If you survey the current literature on FinTech analytics, you will be shocked by the variety, volume, and velocity of new management paradigms (e.g. data lakes), new tools (e.g. Spark Streaming), and the inevitable call for significant spending (‘investment’) to transform one’s firm into a ‘totally awesome disruptor’.

…working with information that you already have on-hand, and using established analytical methods will pay immediate dividends in the quest to apply analytics to investment operations and sustain portfolio performance.Chito Jovellanos, President and CEO, forward look inc., Boston, USA.

However, our real-world experiences strongly suggest that working with information that you already have on-hand, and using established analytical methods will pay immediate dividends in the quest to apply analytics to investment operations and sustain portfolio performance.

Data at hand

Early on, we gathered every scrap of operational data that we could on the naive assumption that more would be better. We soon realized we were needlessly expending too much effort. For example, the Investment Management Agreement (IMA), which is central to every asset management engagement, was actually the organizing principle that we already had on hand but continuously underutilized. The IMA, as a legally binding document, described in detail the objectives (e.g. relative return benchmark), constraints (e.g. tracking error; permissible hedges), and rewards (e.g. fees and performance bonuses) that shape the asset manager’s decisions and behaviors going forward.

We therefore took the IMA and abstracted its details into an XML array. We then used this machine-readable structure as the guiding framework for our analytic model, which resulted in a more focused and simpler data collection protocol - e.g. which security types should be closely tracked; which set of SWIFT messages should be inspected; which internal data flows needed to be monitored; and so on.

Analytic methods

The tools we utilize have withstood the test of time and real-world use across numerous business domains. For example, we use graph network models (the first paper on this topic came out in 1736) to analyze the structure of workflows within a firm and with its counterparties. Optimization methods for network models (linear programming; maximum flow; minimum cut) were spurred by logistics challenges in the prior century (e.g. WW II military deployments; Ford’s production lines; railway scheduling). Likewise, Monte Carlo simulations are already familiar to fixed income analysts.

So how much might it cost to independently apply the techniques we describe in our papers? An analytics team within a firm with a basic systems infrastructure already in place (i.e. front and back office systems; data warehouse / mart; core scripting languages) is looking at the equivalent recurring cost of a year’s subscription to just one fully-loaded Bloomberg trading terminal. As we asserted in our earlier article in this Journal, ‘state of the art’ does not equate to the latest and greatest, nor to large expenditures.

Another vote for IBOR

Only one of the three firms we worked with in this case study had an explicit IBOR implementation, and that firm’s experiences were profiled in the case study.

The other two firms claimed that their system architectures were such that they effectively enabled an ‘IBOR-view’ of their portfolio. However, the toll of having multiple systems (in both the trading and fund accounting areas) readily became evident in brittle operational workflows and sub-optimal data quality.

The true IBOR implementation provided one clear advantage: “time-to-insight” was significantly faster compared to the two pseudo-IBOR firms.Chito Jovellanos, President and CEO, forward look inc., Boston, USA.

The true IBOR implementation provided one clear advantage: “time-to-insight” was significantly faster compared to the two pseudo-IBOR firms. There were simply less validations and reconciliations that had to be performed to ensure data integrity. For example, one of the pseudo-IBOR firms constantly posted incorrect exposure information when performing look-throughs for certain classes of derivatives. These had to be detected and amended, and therefore delayed the availability of certified data from the firm.

Looking ahead

We hope our analytics will be employed to evaluate alternative improvements to the operational workflows that a firm typically applies. To help clarify the decision process, a firm can leverage DOT metric forecasting, and weigh the outcomes of each alternative in terms of potential impacts to portfolio performance. In so doing, you achieve a more structured and strategic perspective to measuring potential return on investment.


  1. /en/insights/journal/operational-efficiency-and-portfolio-performance
  2. Jovellanos, C. The Impact of Investment Operations on Portfolio Performance. J of Investing. 2011. Vol. 20, No. 3: p. 40–52
  3. /en/insights/journal/quants-meets-technology-quantifying-asset-management-softwares-role-in-performance
  4. http://www.bloomberg.com/news/articles/2014-09-17/dirty-secret-of-1-trillion-loans-is-when-do-you-get-money-back
  5. The Data Operability Threshold (DOT) metric quantifies the effort required to accurately move data across systems (and eventually to people) so that the information can be understood and applied in a timely way to optimize investment operations processes. Our earlier research established that the better the DOT metric (due to better data interoperability and reduced information latency), the better portfolio performance also becomes. Lower DOT values are better. For example, DOT = 0 (also known as ‘nirvana’) implies perfect information parity across all systems both within the firm and its external counterparties.
  6. McKinsey & Company. 2012. Searching for Profitable Growth in Asset Management: It’s About More than Investment Alpha. 36 p.

 

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