Why analysts overlook the greatest opportunities?

Reindustrialization is enabled by disruptive innovation, and historically, great transformation times offer opportunities to create powerful returns for investors. It is often hard for analysts to develop a perspective that allows them to make sense of such powerful developments.

Why Financial Analysts miss the Reindustrialization Opportunity?

Analyzing a new technology is never easy but the current processes, approaches, and methods of analysis make it impossible for traditional analysts to understand the powerful dynamics of reindustrialization. Reindustrialization is not like any other times. It is a great transformation with a massive potential to create change. Here are some of the attributes that make reindustrialization analysis hard for traditional analysts:

Nature of Innovation: The nature of innovation in reindustrialization is not the same as in ordinary times. Analysts are used to analyzing innovations that do not exhibit the unique features of innovations that initiate and propel reindustrialization. At the most fundamental level, in reindustrialization the scientific process itself becomes more efficient or is enhanced by a more efficient process. This change happens at epistemological, ontological, and ethical constructs of science. The scientific efficiency enables new and unexpected technologies to appear on the horizon. The rate at which technologies appear and their novelty increase significantly. Analysts are trained to observe the technologies based on their adoption trends, market share, and features and function – and not on their scientific paradigms.

Analyst’s Positioning: Analysts occupy a certain placement in their corporate positions. Their respective position determines their vantage point. The reindustrialization dynamics are different as they requires simultaneously analyzing multiple industries, various sectors, multidisciplinary developments, and competitive structures.

Competitive Dynamics: The competitive dynamics of reindustrialization are different than normal competitive times. Specifically, the traditional competitive boundaries and moats do not exist during reindustrialization times. The shape, structure, and boundaries of industries and sectors are in flux. Nontraditional actors from one industry can enter other industries unexpectedly. Industry analysts are trained to think in terms of industries. As recent experience with Tesla shows, Tesla cannot be analyzed as an auto company.

Technical Characteristics: The technical characteristics of reindustrialization companies and innovations are widely different than that of ordinary innovations. For example, the artificial intelligence technology is about systems that are developed from data vs. regular IT systems that are made for data.

Operating Models: The operating models for reindustrialization are different. In many cases the innovation transforms the operating models itself. The operational and execution plans and strategies need to be analyzed from the reindustrialization viewpoint. Both product and production platforms change.

Go to Market Strategy: The market entry strategy of a firm, its positioning, and go to market plans change. The change happens at the most fundamental level.

Geopolitical Rivalries: Reindustrialization often resets the global friendships and rivalries and changes the rules of competition. Supply chains are realigned and remapped. Analysts often attribute such changes to political developments – even though such changes are often due to realignment of global competitive forces due to reindustrialization. For example, China and US relations changed with the advent of artificial intelligence and quantum computing.

Value Mapping: Understanding and mapping value creation in reindustrialization is not as straightforward as analysts are used to. It requires rethinking the production, distribution, and sales process.

Narratives: Reindustrialization carries its own narratives. Understanding and acting upon those narratives require developing new research methods. They include the use of natural language processing and ethnographic studies.

Behaviors (expectations): Deciphering shareholder, investors, and customer expectations is an important element of sensemaking.

Financial Models: The standard valuation models may not offer the best base to understand asset prices. Reindustrialization requires the introduction of new methods to study asset values.

The above factors greatly contribute to the inability of research analysts to understand the dynamics of reindustrialization.


Improving Audit with AI

As scandal after scandal have destroyed audit credibility, the audit profession needs a total restructuring. One type of restructuring happened when PCAOB (regulator, Public Company Accounting Oversight Board) took control away from accounting firms’ self-regulation. The ongoing problems with audit are now forcing the exploration of new ideas. Some believe that the large accounting firms should be fragmented into smaller firms and must require the inclusion of smaller firms as partners. Others are suggesting having government take over the entire audit business (like IRS for taxes).

Audit suffers from both effectiveness and efficiency. In fact, the problem with audit is that effectiveness and efficiency goals tend to work against each other. If you seek efficiency, you may have to compromise on effectiveness and vice versa. Machine Learning can greatly improve audit outcomes. The application of machine learning happens in all stages of audits. Machine learning can also help discover new business models for audit firms.

Audit automation can be viewed as automation of audit planning, audit evaluation, internal controls risk assessment, reporting, fraud detection, valuation, and other such audit process tasks. AIAI offers a report on machine learning in audit.


Going Social and Quantamental with AI

By: Al Naqvi

This is quick snapshot of some thought provoking ideas

Marshall Wace uses MW TOPS trading system which collects investment ideas from over two hundred sell side institutions and independent research providers. With millions of trades conducted on the platform, the TOPS architecture allows for global, diversified portfolios with differing risk and trading profiles. With the TOPS architecture, the firm is able to offer both fundamental and systematic styles of investment and integrated them to alpha.

MW claims to focus on ‘Quantamental’ investment tools which find trading signals in complex data patterns. Marshall Wace says that the firm uses innovative dataset knowledge and systematic investment discipline to augment and improve its fundamental portfolio management approach. The firms also claims the development of entirely new strategies from data.

MW has now announced that it is raising a $1 Billion fund. It is expected to be part of the TOPS system. The real questions are:

  • What would it mean to integrate ESG with quantamental strategies? In other words, in addition to fundamentals and systematic or market based strategy development, integrating ESG would add a third factor.
  • The second question is what would an AI solution look like when ESG is integrated with quantamental?

Discovering and identifying investment strategies is a complicated business. Add to that the ESG component and the job becomes extremely hard.


Value Investing and AI

By Al Naqvi

With the rise of growth investing, and a Fed that comes to the rescue any time there are any signs of trouble, is value investing dead or dying? Value investing is based upon the ability to discover undervalued companies through finding the difference between intrinsic value and market value and waiting for markets to correct. The traditional approach (Benjamin Graham) of value investing focused on margin of safety and quality of investment. Warren Buffett focused on competitive advantage (moats) to identify quality assets. In addition to discovering the attractive assets, value investing now includes:

  • ESG: How to include social responsibility as a variable in determining quality?
  • Across multiple asset classes: How to identify value across different asset classes?
  • Reviewing structural dynamics: How do the economic structures, supply chains, technology adoption, geopolitical environment, and other macro-economic variables have on value?
  • Behavioral: How do behavioral variables factor into value investing?
  • Signals: How to capture and use new and exciting alpha signals?

This leads us to what can be described as the deep value investing. Applying deep learning to discover the fundamentals of value creation and then aligning them with the extended signals is where the future is. With AIPOST you can discover how to?