Optimizing the Investment Process
Optimizing The Investment Process: Leveraging IBOR for alpha generation
By Joshua Satten and David Depew, Sapient Global Markets
The Investment Book of Record (IBOR) has traditionally focused on operations and accounting support to strengthen data utilization, optimize trade support analytics, increase regulatory reporting preparedness and tighten risk mitigation. But is this approach the most comprehensive and effective? In this article, Joshua Satten and David Depew examine the information that should form an IBOR to plug alpha leaks and improve the investment process at a time when profit margins are shrinking and regulatory reporting requirements are expanding.
Investment is the key term that defines an IBOR. All data controlled and consumed is used with the intent of supporting the investment process or the investment itself. But how can firms not only provide trade support, but spur trade success? It is one thing to safeguard and strengthen an architectural model, but it is quite another to coordinate it into one seamless vehicle with a single IBOR engine.
A true IBOR encapsulates a holistic view of a firm’s investments, which is inclusive of all investment data, such as marketing analytics, client and investor reporting on market liquidity, investment ratings, corporate actions, collateral holdings and more.
The IBOR alpha generation framework applies to any asset class traded and spans all markets and geographies a firm may trade in or have regulatory accountability within. It is common for a firm to realize expanded regulatory responsibility by virtue of where their headquarters are located or the requirements for a given investor. Operationally, this adds significant complexity to the investment process in light of the numerous information points needed for capture and handoff.
The effort required to aggregate and keep a consistent view of the alpha process at a firm level is made exponentially difficult in parallel with supporting different business units.
The Benefits of an Effective IBOR
While an IBOR typically supports the operational mechanics of investing, such as corporate action processing and cash flow calculation, a properly optimized investment management system can include many more beneficial elements. To fully support the investment process, it should contain information to analyze and improve the fundamental steps of the investment process.
Typically, if an investment process is top quartile at each step within the context of a peer group analysis, it is more than likely the fund will be top quartile. Figure 1 provides an alternative strategy to achieve alpha. Given significant alpha at the beginning of the process, winning involves plugging alpha leaks in subsequent steps. Unintended bets, bets sized improperly for expected alpha, uninvested cash and excessive turnover are all examples of alpha leaks.
From Alpha Generation to Alpha Preservation
Without alpha generation, any downstream protection is simply damage control. There are many alpha sources in the capital markets. Managers market time, allocate assets, rotate sectors and follow different investment styles (e.g., value or growth). However, security selection is the preferred alpha source for most portfolio managers, although it can entail many different analyses. There is growth versus value and cash flow versus earnings—not to mention earnings quality, ROI and financial leverage.
More specifically, an IBOR should provide data that combines a defined fundamental template (i.e., valuation model), price history and performance with recent news, sell-side analyst reports and internal analyst recommendations. For example, a function can be programmed to highlight stock attributes exceeding specific thresholds, thereby marking them as interesting investment candidates and enhancing the stock selection process with timely, consistent and comprehensive information.
Additionally, an IBOR can deliver the data for backtesting tools for different alpha characteristics used by the portfolio manager. This would allow the manager to better understand performance in different market or economic environments. Backtesting tools can also perform a buy-versus-sell analysis. Cabot Research, which specializes in behavioral finance, has shown that most managers are better at their buy decisions than their sell decisions.
To support asset preservation, the IBOR must feed security selection tools involving quantitative screens based on the manager’s investment philosophy.
Examples of screens include highlighting stocks with earnings growth greater than 10 percent and with a P/E of less than 20. Screens can increase the breadth of an investment process. Increased breadth can generate higher information ratios, according to Grinold and Kahn. Screening can also help firms focus on deeper fundamental analysis.
When properly executed, a robust IBOR becomes a combination of tools and processes that results in a more efficient and transparent alpha process.
Using this data, an analyst can calculate information coefficients (the correlation between a factor’s changes and a security’s performance in some subsequent period) for a manager’s alpha factors. This quantifies the effectiveness of a given alpha factor, and can be done over multiple time periods and within different market environments. This information helps determine what is working and what is not. Senior management can use this information to determine whether a manager is following his or her stated investment philosophy.
Portfolio Construction and Risk Management
Portfolio risk management ensures there is exposure to alpha factors in any given portfolio and outlines the exposures to risk factors, which the portfolio manager may or may not want to minimize. An IBOR can help achieve these goals. It stores all the factor exposures and returns to enable analysts to then analyze a time series of exposures and returns to explain performance. Investment personnel can then modify and adapt exposures going forward. The IBOR should store all needed information to perform historical backtests and bias tests to illustrate the effectiveness of risk models.
Regulatory risk is another challenge for asset managers.
The latest regulatory ask comes from the SEC after issuing their automation of reports regulations. The investment company proposals would enhance data reporting for mutual funds, ETFs and other registered investment companies. With its new regulation, the SEC is requesting two new reports: a monthly portfolio reporting form (N-Port) and an annual census form (the N-Cent). Each of these reports calls for more data to be reported in a structured way. Of the two, N-Port has the greatest data ask as it requires risk and analytics for all positions on a monthly basis (30 days after month end).
Not only do these SEC data requirements underscore the importance of an extended IBOR, they also highlight the need for data accuracy. The best quality assurance (QA) comes from front-office staff who look at data as part of their daily workflow. This QA only happens if the data is synched between functions and groups with an effective IBOR.
When it comes to evaluating an investment process, trading can get overlooked. Few chief investment officers have a trading background or much trading experience.
But trading costs have never been more important. The difference among the different Lipper quartile rankings is very small. The average domestic mutual fund has 100 percent turnover with costs of 75 bps. These costs and turnover imply a trading performance drag of 150 bps. Any efficiencies can translate into significant improvements within Lipper rankings.
Transaction cost analysis (TCA) offers an information set to make trading process improvements. IBOR data can fill this gap with trading studies that define action items and trading strategies to improve the implementation process. One interesting study calculates net (of trading costs) benchmark relative returns. Some of the most profitable trades are at times considered high cost (and therefore unattractive) in a traditional analysis. A net benchmark relative return analysis helps determine if the current trading process is additive to the investment process or not.
Further, more fund managers are using derivatives to express investment views, modify payoff patterns or manage risk. However, not all buy-side firms have the analytical tools to properly analyze different strategies. An IBOR could store what-if analyses to facilitate meaningful dialogue between portfolio managers and traders.
For trading to have a defined effect on fund performance, trading groups need a varied toolkit, real-time information and ex-post analytics. IBOR data can be the input for tools to help trading groups define their value within the investment process.
A tightly held investment philosophy provides discipline and a foundation during a period of volatility. Most commercial performance attribution systems do not have adequate (or comprehensive) derivatives capability.
One solution is to create a system where the derivatives exposures are mapped to the underlying physical securities and then stored in an IBOR. This would allow the constituents of a stock futures index to be included in sector, capitalization or style performance analysis.
It is important to analyze whether exposures and returns are consistent with the portfolio manager’s stated investment philosophy. Many firms do not have the resources to take performance attribution to this next level. A performance attribution system that feeds an IBOR would provide timely information to all participants in a format conducive to effective use, resulting in performance and process improvements.
An IBOR can also provide insight into how the activities and systems of one step interact with the other steps in the investment process. For example, if optimizers are used in portfolio construction, one should be careful to manage the risk associated with risk factors as opposed to the alpha factors from the alpha generation.
Otherwise, the optimizer could inadvertently reduce alpha. The stock selection process could drive up trading costs if analysts and portfolio managers consistently buy and sell stocks with strong negative momentum.
The point is engendering a discipline that builds on the question, “What investment insights and alpha drivers enable funds to outperform?” The value of identified alpha drivers must be proved and can act as a test as to whether an investment team is following their stated game plan. Performance ebbs and flows must be studied specific to a portfolio, market conditions and related timing to better understand the process within the context of the performance history.
An IBOR should provide the foundation for process analysis and improvement. Using an effective and holistically complete IBOR, investment managers can address a number of issues at each step in the investment management process, thereby adding value and preserving alpha.
These changes have the potential to redefine the very nature of how investment operations are performed. The cost of reacting slowly can be significant, especially for undifferentiated firms. This is an opportune time for firms to refine or develop their IBOR strategy. The foundational framework for the alpha process can benefit from design thinking and disruptive innovations— and ultimately enable firms to spring ahead of the pack.
Joshua Satten is a Director of Sapient’s Fintech Practice and leads business development and market strategy with specialties in fintech, OTC operations and business architecture. His fintech oversight includes areas related to robo-advisory, blockchain, machine learning and venture capital. Joshua built his career managing and growing full lifecycle trading operations across OTC derivatives and other structured products combining business architecture, leadership and strategic planning skills. He is an active industry participant and speaker, having worked with and chaired such working groups as ISDA, ISITC, SIFMA AMF, Dodd-Frank, EMIR and MiFID II.
David Depew is a Vice President at Sapient Global Markets who brings over 28 years of experience in fundamental and quantitative investing. David is a subject matter expert in market risk management, performance attribution, quantitative investing and derivatives. He joined Sapient from Putnam Investments, where he was head of fixed income risk management. Additional roles at Putnam included quantitative equity trading strategist and managing Putnam’s international equity fair value process. David holds a Chartered Financial Analyst designation.