Is Macro Driving the Market? (Part 1 of 3)
By: Jonathan Neitzell - Founder | Managing Partner, Anduril Partners
INTRODUCTION
In a world gripped by debates around macro factors like virus waves, inflation, consumer purchasing health, and sector rotations, it may appear challenging to filter fact from noise. However, growing availability of data, powerful no code tools, and applying consistent and repeatable analytical process will provide significant benefits to decision makers who are bold and prepared.
Join us in this multi-part series as we digest the following challenges and opportunity:
Part 1:
? Challenge #1: With the explosion of data and software tools, how is the decision process changing for stakeholders in the investment and corporate communities?
Part 2:
? Challenge #2: What examples of specific data and tools are available to add clarity in our efforts?
? Challenge #3: How can consumer trend data be used to understand macro impacts like consumer purchasing decisions, adjustments to infection rates, lockdown responses, or even inflation?
Part 3:
? Challenge #4: As the macro thematic changes, how can we compare prior examples like the Covid Delta wave, rising interest rates, and impacts to consumer purchasing decisions to predict reactions to the next exogenous drama?
? Opportunity: We believe that applying a disciplined and analytical process, using sophisticated no code tools and improving input quality, will allow us to move from questions to hypothesis testing and ultimately to decisions.
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Part 1:
CHALLENGE #1
With the explosion of data and software tools, how is the decision process changing for stakeholders in the investment and corporate communities?
The growth in available data and potential from analytics and artificial intelligence (AI) technology, increases both the opportunity — and the challenge — for asset managers, investor relations professionals, and corporate management teams. Analytics can influence corporate decisions in product management, capital allocation, and how equity shareholders impact share pricing.
The size and scope strains comprehension:
Five skills and tools are needed to unlock the value of this data and use it to our advantage:
Thankfully, just as we saw with public cloud adoption, new no code tools and services are becoming available to make the scale and transparency of technology magic available to the business user who understands the core value proposition. Massachusetts Institute of Technology (MIT) calls the insight made possible by this technology “shared intelligence.”
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Early adopters have the opportunity to separate from the pack as Amazon did with its public cloud computing service and successful e-commerce businesses. The COVID-19 pandemic creates a further imperative to take action on these opportunities.
APPLYING PROCESS IN FINANCIAL WORKFLOW
CAN YOU OODA?
If driving performance is your objective, the OODA loop may provide a welcome model. The concept of feedback loops have become increasingly prevalent to digest and filter the mountain of data resources from noise into insight. As an example of applied process amidst an ever acting and reacting competitive environment, the U.S. military recognized that excellence in process may be one of the few sustainable areas of persistent advantage.
Based on this realization, former fighter pilot and Pentagon tactician, John Boyd, created a straightforward framework called the OODA loop, which stands for Observe, Orient, Decide, and Act. “Time is the dominant parameter. The [operator] who goes through the OODA cycle in the shortest time prevails because his opponent is caught responding to situations that have already changed.” Now considered a foundational doctrine, it suggests that regardless of the backdrop, one must quickly and accurately:
This framework hypothesis suggests the team most successfully cycling through this framework will learn and win while opponents are choking on the noise and confusion of exponential information growth. Process matters!
Figure 1: Fusing Data, Discipline and Technology?
For integration into our daily workflow, how can we integrate qualitative (human experience) and quantitative inputs into “shared intelligence?” The diagram above, “Fusing Data, Discipline, and Technology” demonstrates how an asset management group might implement the OODA loop concept. This serves as a decision-making framework for integration into portfolio management and monitoring. Further, it brings process and discipline to measuring and accounting for error rates in inputs as they work their way through calculation and review within workflows.
On the left in the diagram, we have inputs such as SEC filings, internal or external analysis (like consumer spending data), industry relationships, and qualitative experiences. In the next column there are estimates that will adjust to reflect the changing world around us. These input names may change with business model or vertical, but for asset managers, this drives top and bottom-line adaptation to operational key performance indicators (KPIs) and forward estimates. This is then reviewed based on portfolio risk parameters that may be as simple as a gut feel (controversial) or as sophisticated as rule-based or mathematical factor models.
These steps culminate in a buy or sell decision and then the forward performance of the asset begins to show tangible outcomes (real and predicted). If our effort has been recorded in software, now the magic begins — we can check our initial assumptions against actuals and run feedback statistics, error rates, and increasingly complex machine learning on this real-time and growing resource of training data and intellectual property. This allows data and institutional learning to be- come integrated as a tangible asset we can build and grow.
TURNING QUESTIONS INTO PREDICTIONS
PEELING BACK THE VEIL
In a moment of stark honesty, most organizations will admit they have never actually drawn out their decision process. The few that have done so may offer a flowchart to demonstrate that they have a roadmap.
However, if the inputs are not touching software and creating a time series of quantified changes, the effort is incredibly prone to narrative shift, hindsight bias, and lack of objectivity. Consequently, the ability for feedback loops or incremental learning will be severely compromised. It has been said, if software is eating the world, models will run the world. For those humble, confident, and willing to be held accountable, the tailwinds of technology can harness this tremendous potential in transparency, scale, and continued improvement on behalf of your stakeholders.
One of the largest shifts we are likely to see in team discussions during the next five years is toward analytics and data-influenced decisions. To do this, we must take our qualitative, thematic questions and turn them into key performance indicators — hypotheses which can be quantified, tested, and predicted (example shown in the following diagram). This process entails integrating the personal experiences of business users and operators and attaching their primary metrics to data consistently available.
Figure 2: Business KPIs: A Universal Language?
For the financial industry, analysts might answer questions about a company’s equity value by inferring revenue growth based on KPIs such as new customer growth, average spend per transaction, share of industry sales, and cohorts changing purchasing locations between physical and virtual storefronts. These may be seen within transaction records, email receipts, web traffic, or natural language processing queries of customer social media comments.
These discussions are often the same across corporate, private equity, and public equity uses, making a focus on defining, tracking, and predicting KPIs an increasingly universal language. Corporate intelligence and investor relations groups are likely to be a vital bridge between planning for resource allocation and explaining these key components to stakeholders.
To read the full article, visit Anduril Partners.