Alpha2 (Alpha Squared)
Alpha2 or Alpha Squared is the name derived from the notion of alpha returns from better financial instrument design coupled with operational alpha, using simulation modeling as the common platform for achieving both.
The concept demonstrates Generating Outsized Returns in Private Equity with Two Layers of Analytics
Private Equity General Partner (GP) firms are under ever increasing pressure to deliver supernormal returns from their portfolios. They are using every conceivable avenue to get there, applying creative ideas from many other industries.
We have a suggestion: simultaneously apply science to the design of the financial instruments and the operations of the portfolio companies. This paper explains how to do that.
The diagram below illustrates the concept of Alpha2.
The multiplicative property of this strategy comes from the firm’s ability to take the results of a given simulation and “loop” that backwards into an alternative design of the instrument, or the operations or both, iteratively until the desired outcome is realized1. This is founded on the assumption (verified by numerous conversations with GPs) that intuition and experience alone are not likely to find the optimal strategy for a transaction given the complexity of financial instruments and the vast range of operational methods that could be brought to bear in this modern age. Rather, firms looking for an edge wish to supplement the skills and experience of their experts with parallel analytics capability.
Financial engineering techniques have brought us a methodical means of computing the returns from a particular instrument through the creation of a mathematical model. Quant teams within firms do an excellent job of financial engineering; what they do less well is model visualization and integration with operational models. A core part of the Alpha2 concept is the integration of financial engineering for instrument design with an overall strategy for the transaction.
A core part of the Alpha2 concept is the integration of financial engineering for instrument design with an overall strategy for the transaction.
For example, the timing of the application of a particular instrument in a transaction’s life cycle is an entirely separate design consideration from the parameters of the instrument itself.
Other design considerations might include:
1. On the debt side, what layers (Senior, Junior, Mezzanine, …) should we participate in, and to what extent?
2. Where does our tax minimization strategy enter in to this transaction?
3. What optionality can/should be interwoven with our participation?
The principle behind Operational Alpha is simple: find a means to take assets that perform at one level, pre-transaction, and induce them to operate at a higher level, post transaction. In practice this can be a challenge, as legacy owners have likely been aggressive in devising smarter ways of conducting operations, and any potential scope or scale efficiencies are already priced into the assets.
Enter simulation modeling. By capturing the portfolio company’s operations in the form of a model, a series of experiments can be run on the company to assess a wide variety of performance-based interventions. The GP already has industry/operations expertise in house or retained? Even better—models allow these experts to more quickly identify the efficacy of a host of candidate performance improvements.
Case examples from our own work in the application of modeling to operational alpha include:
• De-bottlenecking the supply chain for significantly reduced delivered cost and growth capacity
• Optimizing the product development pipeline so that less promising products are killed off sooner and more promising products are brought to market faster
• Changing a subjective, human-driven pricing process across 500,000 SKUs to an algorithmic one that uplifts margins
• Building a reliable customer demand forecast that minimizes waste and smoothens the production process
The second cousin to simulation modeling is automation. Automation represents one of the most promising arenas for realigning and reducing the labor content of a given level of production. Almost every firm of any size has hotspots of human labor intensity that can be feasibly and economically automated given the range of tools available today. In the line between those tasks that are best performed by humans and those performed by machines is moving in favor of machines at a rapid pace. Functions that many said “could never be automated” —medical diagnosis, seismic data interpretation, pricing products—are succumbing to automated processes underpinned by smart algorithms2.
So how are simulation and automation related? It turns out that the steps one takes to develop a simulation model of a system are the very same steps one would take to design the automated equivalent of that system. Simulating the operations of a company in the first stage, then automating specific pieces of the operations in a second stage is considered best practice among the leaders in Private Equity.
Putting it all Together
A simulation-based “laboratory” for investigating both forms of Alpha simultaneously is the crux of the Alpha2 concept. Note in the diagram introduced at the beginning of this paper that models employed to investigate instrument design alongside operational alpha allow for a feedback loop—a countless number of changes to the instrument design and timing and the uses of those funds through the configuration of operational alpha initiatives can be assessed in
a quantitative manner. We would envision such a scenario planning capability to be applied pretransaction during structuring and negotiation, and again post transaction for portfolio company growth and performance.
,,,the steps one takes to develop a simulation mofel of a system are the very same steps one would take to design the automated equivalent of that system.
What This Means to the Firm
Apply Alpha2 to the firm does not change what the firm does—the business of structuring and executing transactions and nurturing acquired assets continues as before. What does change is how each of these functions are implemented. Our goal is to move to a point where the leaders in the firm devote more time on design and interpretation of the analysis, and less time on model building. Alpha2 is really about being deliberately systematic in everything the firm does—making a science of decision making through model development. It sets the stage for firms to take the excellence that they already have and make it work in a more consistent, frictionless, and datadriven manner than before.
Alpha2 is really about being deliberately systematic in everything the firm does – making a science of decision making through model development.
Many GPs are growing increasingly interested in monetizing and operationalizing the firm’s Intellectual Property (IP) in a formal way, rather than relying solely on the knowledge and experience of human beings, all of whom will eventually leave the firm. That IP also includes the vast historical data the company has amassed from past transactions whose underlying patterns may turn out to be valuable for future assets or transactions. In all of these cases, simulation models naturally and quietly enforce a discipline around the strategy of the firm and its associated logic, which turns out to form the core of the IP.
Interaction between GPs and LPs is becoming more comprehensive as LPs grow more active regarding the nature of their investments. They wish to “see” more precisely the logical flow of their particular investment on to the portfolio companies. Applying Alpha2 to the candidate transaction not only aids the internal GP team at the design level but also creates visual transparency to the transaction for the LPs and for the management in the portfolio firms. Practiced well, Alpha2 makes the whole fund stand out from competing funds that lack such rigor and transparency.
About Business Laboratory
Business Laboratory builds simulation models and analytical solutions for companies worldwide. Our first book, Profit from Science, published in 2015 by Palgrave, evangelizes the idea of using analytics to compete in complex markets.
Principal offices located in Houston, TX