Since its inception, the global financial system has evolved to manage the increased complexity of efficient, while still retaining its fundamental role as a Pareto-efficient resource allocation facilitator. The successful allocation of resources has become a major driver of the creation of negative externalities, particularly environmental degradation, poses significant risks to future economic and social development.
In this blog post, we present an advanced framework for seamlessly integrating “Augmented Intelligence” into your investment decision-making process. By exploiting the symbiotic relationship between human intelligence, artificial intelligence (AI), and sustainability, augmented intelligence seeks to redefine the investment management paradigm.
What is the purpose of financial markets?
Financial markets are complex adaptive systems (Lo, 2004). Their essential purpose consists in promoting efficient allocation of resources between participants (Mishkin, 2018; Ross & Westerfield, 2016; Fabozzi & Modigliani, 2009). This purpose has remained the same since Luca Pacioli introduced double entry bookkeeping in 1494. The first stock exchange began in Amsterdam in 1602. Or, efficient allocation interpretations are standardized, Harry Markowitz et al. 1952.
What has changed throughout the history of financial markets is the degree of complexity participants had to master to achieve efficient allocation. This level of complexity is determined by the extent of the system and the dynamics within it.
Over time, humanity has expanded the range of factors considered for efficient allocation decisions. Financialization, globalization and digitalization were dominant drivers in expanding this range. Today, market participants can allocate resources to $795.7 trillion (Vacchino, Periasamy, & Schuller, & Schuller, & Schuller, & Schuller, & Schuller), which is unprecedented in human history.
To increase the increase in dynamics within the system, market participants adapt interactions, evolve traditional belief systems about the market, and create more insightful assessment techniques that seek to understand market complexity. I applied it.
This change focused on which actions contributed most to the integration of various sources of evidence into decisions at the time of allocation. Inference has been inductively varied from the ductive (Schuller, Mousavi, & Gadzinski, 2018), leading to a more accurate assessment of dynamics within the financial system.
Complex systems produce properties that can only be studied at a higher level. Complex, nonlinear interactions between the components of complex systems create new, often unexpected characteristics or behavior that cannot be explained simply by examining individual parts of the system. Thus, appearances are a natural result of complexity, with the whole being greater than the sum of its parts.
The major emergency property in the history of financial markets is humanity's domination over nature, following the scientific revolution of the late 15th century. This advantage leads to the density of unprecedented breakthroughs by humanity, providing more refined and scalable tools to master complexity.
Learn planetary time through the financial system
As is common in complex adaptive systems, what began as a side effect – negative externalities – has now become the dominant factor affecting the system. Today, the financial system is learning how to integrate factors beyond human-centered worldviews. We have entered an era when time no longer distributed discriminantly along the scales of human and non-humans.
Planetary time represents the synchronization of human and ecological temporality. This is an essential concept for addressing climate change and resource exploitation. As capital flow facilitators, financial markets are set up on their own to promote this peer. This requires a paradigm shift from maximizing short-term profits to sustainable, long-term value creation.
As humanity needs to be reintegrated into the homeostasis of planet Earth, the purpose of the financial system, namely promoting the efficient allocation of resources between participants, is set in a new context. Does this lead to the question of how to design financial systems that employ extended intelligence (AI, human intelligence, sustainability) to master the era of planetary time? Academia and practitioners deal with these three elements in silos, and to act too slowly and break through these barriers, they cannot be integrated into the overall decision design. What is the current state of each silo?
Human intelligence in investment management
Over the past 40 years, behavioral finance has advocated evidence-based decision-making. We know a lot more now about the amount of bias and why we tend to make investment decisions that are full of noise and bias. However, while not doing enough to help participants in the global financial ecosystem bridge knowledge gaps, this is essential to accelerate the spread of innovation. Professional investors tend to talk more about behavioral finance than exploiting that insight, or removing cognitive biases has only temporary effects (Gadzinski, Mousavi, & Schuller, 2022).
What has become more academically more prominent is its focus on applicable behavioral considerations, such as the construction of behavioral design. The intent is not only to raise awareness of cognitive dissonances and their effects, but also to make it easier for decision-makers to improve such structures with low cognitive effort.
Awareness training has proven ineffective because its impulses are too superficial to promote behavioral change (Fleming, 2023). Alternatively, high performance principles for designing investment decision support systems that generate evidence-based decision-making (Schuller, 2021).
Sustainability in Investment Management
A sustainability consideration in the financial system is an expanded, intelligent gateway to impact the real-world economy needed to reintegrate humanity into homeostasis along with planet Earth. These considerations include the history of history in finance, although not very impactful.
Many investment leaders have recently embraced Sustainable Development Goals (SDG) driven investments, which are essential for excellent investment management practices. The road to necessity took decades to build (Townsend, 2020). However, compliance-driven approaches often leave sustainability to management burdens rather than core investment strategies.
A recent acceptance of policymakers and regulators is that they cannot become the main driver for initiating, promoting and encouraging capital deployment directions to enable SDGs to be achieved. The actual relocation of large capital must be done by market participants themselves by creating value for stakeholders through evidence-based assessments of opportunities set in risk/return profiles. This translates to the scale when front office professionals are incentivized to look for opportunities to generate more profits for sustainability.
Asset allocation for 3rd generation
The current state of investment management does not result in the investment decision design to achieve the necessary seamless integration of extended intelligence, as it addresses components in both academic and practically fragmented ways.
Traditional asset allocation models, rooted in static optimization and linear extrapolation, are becoming increasingly inadequate in the face of complex and dynamic market conditions. The third generation asset allocation method informed by Andrew Lo's Adaptive Market Hypothesis (AMH) highlights causal, inductive, and adaptive methodologies. These approaches are consistent with the principles of extended intelligence and provide a framework for integrating sustainability into portfolio structures.
Unlike the first and second generation models, which prioritize forecasting and prioritize future value discounts, the third generation methodology focuses on real-time causal analysis. By incorporating evidence-based assessments and advanced AI tools, these models allow investment professionals to navigate uncertainty and complexity significantly more effectively.
In short, this new generation will allow for adaptive, inductive, causal, and future investment decision support systems to seek rational decisions. Therefore, we reverse the traditional modeling approach of reality, and the model follows the model following the model that follows reality.
Impact on investment experts
The shift to intelligence expanded through third-generation asset allocation methods requires cultural change within the investment management industry. This shift includes the breakdown of silos between academia, regulatory agencies and industry practices. Investment teams need to prioritize cognitive growth and maintain a human-centric approach while leveraging AI tools to enhance their decision-making processes.
Furthermore, the slow adaptability of the industry must be addressed through targeted training, regulatory incentives, and the development of comprehensive investment decision support systems. These systems need to integrate human and artificial intelligence to optimize capital allocations that are consistent with planetary time.
Key takeout
The question for stakeholders of the global financial system is how can we design financial systems that integrate AI with human intelligence to establish augmented intelligence and master the planetary age.
You need to disassemble the conceptual and practical silos. Third generation asset allocation technology is young, but already lays the foundation for what such symbiotic relationships look like.
The next step in our industry is to conceptualize investment decision support systems based on the framework of third generation principles.
From then on, it's upwards.
