It was the summer of 2010, and I had run the exercise of my stock market algorithms on Google sentiment data, colours, Fortune 500 brands, and emotions. I was ecstatic because the algorithm I had created worked brilliantly across alternative data. The algorithm was agnostic to data and did not care whether it was fed stock market data or data from another domain. This something else indicated a data universality, a context that was common to all data sets.

I instil in my 6-year-old a care about resources like water, food, and energy. I tell her about my life 30 years back, without running water. I show her pictures of water tanks and people scrambling to suck water out of it as it chokes with hoses. I show her pictures of impoverished children who need to carry pots of water on their heads. I feel responsible for having contributed to bringing the world to its current state. The firestorms and smoke follows me everywhere. Predicting these fires go a long way in controlling them.

I used to loathe the term perpetual bull because, as a forecaster, I used to relish the times when I accurately predicted the market. I poured pride into those accuracy reports, illustrating cases of anticipated and happened and how it was both about shape and form of market timing. I grew up when I realized the world was not interested in prediction, it loved horse racing in the world of perpetual bulls and risk management was an alien word.

Artificial General Intelligence needs a world model and Generalized Machine Learning, a novel approach to machine learning.

Machines and the Sisyphean Paradigm

“The Sisyphean Paradigm” refers to a concept inspired by the Greek mythological figure Sisyphus, who was condemned to eternally push a boulder uphill only to have it roll back down before reaching the top. The Sisyphean paradigm is often used metaphorically to describe a situation or task that is repetitive, laborious, and seemingly endless.

Weak Artificial Intelligence (AI) relies upon three fundamental assumptions. Firstly, it posits that greater computational power leads to heightened intelligence since the challenge of data processing has diminished compared to a decade ago. Secondly, it acknowledges the presence of human biases that permeate data curation, thereby amplifying the inherent biases within the data. Unfortunately, the prevailing belief suggests that these biases are irremediable, resulting in a tragic outcome for all. Thirdly, AI assumes that the realm of science lies outside its purview and does not pose a significant challenge.

If you thought your passive Index Funds are low risk, think again. Index funds are herding mechanisms that create more risk than what a street investor perceives. The deafening noise about the move to passive is setting us all up for a market failure. Even if Active is inefficient, Index Fund herding is a tragedy of commons that needs collective awakening to iron out.

I run a wealth tech company. In late 2019, I decided to shut down a business line that drove a dozen investment management mandates, running USD 25 million in assets, and decided I had to do something different to scale. My objective was to create impact and alpha for global investors but I saw myself stuck in client mandate customizations.

I won't kill myself with worry about the fast pace of machine intelligence, if I was Hinton. We are living intelligence times. This was the objective we were working on for the last 80 years, when McCullough and Pitts designed the Turing-Complete "artificial neurons" in 1943. So why worry about it now?

As the expectations for AI to change finance forever are increasing, a look back would help Investors understand the limitation of information to forecast. Information may carry all the elements to see the future because it comes from the past, but old information creates new information, old drift creates new drift, so much so that the more we touch the system the harder it becomes to separate the signal from the noise. Markets are a complex system and you can see the strange attractor but forecasting where the butterfly’s flapping wings will land the tornado is an impossibility. Till we grasp this constraint, AI is yet another piece of information, which like many other things, will create the illusion of a forecast, and eventually fail. This is the curse of information, you can mount it like a horse, but the probability that it will always take you to your desired destination is much lower than 50%. AI needs to be trained to understand mechanisms if it has to be of any use in markets.

More than 20 Trillion (T) Dollars are managed passively today. The industry is 25% of the total $100 T investment management industry and has seen an annual growth of 20% since 2007 while the Active industry has grown at 4.4% for the same period at these growth rates. At the current growth rates, Passive should overtake Active in 7.5 years. In 1975 there was no Index fund. In less than 50 years, everything seems to transform into Passive. All these statistics are based on two assumptions. First, Active can never get its act together to beat the S&P 500. Second, The S&P 500 epitomizes passive. These assumptions are erroneous

I have thought about machines that assimilate knowledge for more than a decade. For me, assimilation was always an achievable present, not a future imagination. The 2002 film Time Machine based on H.G. Wells' novel left an indelible mark on me. The hero of the film, an inventor, Alexander Hartdegen communicates with a humanoid holographic artificial intelligence called Vox through a screen. Vox possessed vast knowledge of history and acts as a repository of information. Vox serves as a source of historical information for Hartdegen as he navigates through different time periods. Vox is in some ways an assimilator. He understands, comprehends, perceives, and is not a retriever of information or a navigator like ChatGPT. A navigator does not understand the data like a human. So how do we transform a ChatGPT into Vox, the assimilator?

What is Value?

Investing styles nomenclature has a history going back to the 1920s. Though Graham and Dodd never used the phrase, "value investing”, they are credited for the same. The term was coined later to help describe their ideas. The Graham approach was to recommend low-risk entry inexpensive (cheap) stocks. They advocated a systematic approach to selection (bargains). They sought to buy businesses trading at a discount to net current asset values, what has been referred to as intrinsic value rather than price momentum.

Interest Rate Elephant!

Interest rates like many other things have been rarely understood. Not because they ride on a black swan but because humans are economic beings that are steeped in short-sighted gains driven by instant gratification. This is why the cycles of history keep rhyming because the new generation does not learn from the previous generation. In some ways, history works because of this poor societal memory.

"I cannot teach anybody anything, I can only make them think." 

Socrates

ETFs, your loved investment vehicle, suffers from a Disco Door Fatality, which means they are causing institutional and individual investors more risk and more harm than the convenience they bring.

Abstract

The [3N] Mechanism reconciles the conflict between informational states. This paper takes the idea of the [3N] mechanism and reexplains it as the combination of the Market Capitalization [MCAP] weighting methodology which can be understood as the Rich-Get-Richer, Non-Normal approach [NN] and Value investing, which can be understood as Poor-Get-Richer, Normal approach [N]. The author illustrates how stock markets can be seen as a complex informational state and how a new mathematical approach can create a new S&P 500 beating Index methodology.

Please summarize your understanding of application of AI in building smart beta to beat the market?

Smart beta strategies aim to outperform traditional market capitalization-weighted index funds by using alternative weighting methods, such as fundamental weighting, volatility weighting, or dividend weighting. AI has the potential to play a role in the construction and implementation of smart beta strategies in several ways:

Negroponte’s digital world is here

In "Being Digital," Nicholas Negroponte predicted and advocated for a future in which traditional analog forms of media, communication, and commerce are replaced by digital counterparts. Negroponte suggested that the transition to a digital world will have far-reaching consequences, including the potential to democratize access to information, increase efficiency in industries such as finance and healthcare, and facilitate new forms of commerce. We are living in Negroponte’s world, almost. The only world that has refused to adapt is the world of asset management.