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Why the Traditional "Human- Only" Model of Investing is obsolete. And fit only for museums.

What you hear most often, from Fund Managers, is: "We look for great companies that deliver multi-bagger returns".

Sounds Very glam.

But just a slight problem: It is not borne out by data. Most fund managers are lucky even to perform as well as the market - let alone better. 

So why has the "Human only" Investing Model stopped working?

1. Access to Confidential information is difficult. And illegal

The information arbitrage is largely gone. Most information is now publicly and freely available. Professional fund managers can no longer rely on this "privileged, private, often illegal, information  access club" to make money. 

That "edge" is now gone.

Thank God for that.

2. Vast availability of data itself is a problem!

Now there is an ocean of data that exists, whether for countries, sectors or companies.

So what are you supposed to do with this?

The traditional approach is to employ dozens and hundreds of human beings, aka "Analysts",  to analyse and process all this vast flood of daily data.

Trouble is: it is simply not possible for human beings to cope with this deluge.

The human mind is not capable of processing so much information in so little time, so as to be able to make fast decisions in lightning quick markets.

As a result of this inability to process this mountain of daily data, human beings then start taking shortcuts: they start forming lazy, under-analysed, over- simplified opinions.

It does not help that human beings are hardwired to prefer stories & narratives over data, statistics, probabilities etc.

It does not take a genius to figure out that such opinions almost always will be wrong.

And with that, will go down your money.

3. Finally, the most important: human biases can derail the best investment intentions

As is conclusive through plenty of research, human beings are simply unfit to succeed at Investing: they suffer from inability to process vast amounts of data, and then understand them without individual idiosyncrasies, preferences, blind spots. 

In other words, Analysis Without Biases is something Humans simply aren't built for.

Human beings are driven intrinsically by emotions, and less by facts or data.

To give a recent example,  most asset managers had disproportionate exposure to banking and financials, even when it was very clear in February 2020-end,  that the economy would be entering a period of high stress and therefore, leveraged companies would suffer the most.

And even when these companies started to fall sharply, most fund managers could not bring themselves to exit these companies simply because they had become extremely emotionally attached to their holdings.

Endowment Effect, where you see greater value in your holdings simply because you hold them; Loss Aversion which makes it very difficult to book losses, Bandwagon Effect where you want to be where everyone else is;  are all classic biases.

Because of these biases, we see a very high degree of " Storification" risk: A massive risk in which every single position/ holding has been given a certain "story"  and a certain poetic future road map which is almost always extremely rosy, without much or any risks.

Owners and Management of these "storied" companies are built up to be Gods. All the discordant elements (industry cycle, favorable policy, just plain luck) are brushed aside.

The bigger the "Story", the stronger the conviction. 

And as fund managers talk publicly about their conviction, they become committed to that story and security.

When the story stops playing out, the fund manager is unable to see the danger and change. 

This is because they are so convinced, that it's impossible to "un-convince" themselves.

"Storification"  of positions is dangerous: when you have gone out and marketed these positions and defended/ extolled the virtues of these positions publicly, it is almost impossible to change when the facts and data change.

So instead of merely money, fund managers end up investing their emotions in their positions.

Human beings are generally not very good at changing their minds when they are emotionally attached to anything.

Human beings are terrible at accepting mistakes. 

Machines have no such problems.

But Machines have different problems.

The Machine-only Model doesn't work either

There is a school of investing that believes that  quantitative models are the antidote to the human-driven traditional fund management approach.

They are wrong. 

Machines are only as good as the  quality of human beings testing out a million or billion different combinations of factors, in order to arrive at a range of investing strategies that  work.

Therefore, engineers or financial professionals,  without extensive experience of investing, simply cannot produce a workable machine-driven model. 

Almost every single factor needs to be the understood, debated, discussed, modified , based on deep knowledge,  investment experience and Investment thought.

Plus, quants complicate their models, adding too many parameters to get the results they seek - a problem called over-fitting.


If used incorrectly, the models can end up finding meaningless correlations and regard them as Gospel truth - an error that gets compounded when the model is a complete black box without possibility of understanding what are the drivers behind a recommendation, or why something has stopped working.

Also, an only machine-driven model can never be sensitive enough to sudden changes in the environment or in company fortunes. Think a disruptive Covid crisis or a geopolitical development on a macro basis or policy changes which impact a particular industry or company.

Which is why First Global's (Hu)man+ Machine Model is vastly superior to standalone Human or Machine Models

Our fund management team has decades of experience in navigating all kinds of market situations across the world and in India.

When this team sits and builds and tests a machine learning model, on an ongoing basis, the quality of inputs, analysis and factors that go into building a quantitative Machine Learning model are of a completely different level, as compared to merely "quants" building these.

Added to this, tactical strategies are rarely done well by machines, when there sudden changes in fundamentals.

What you need is this edge of best-in-class Human+Machine Model,  that combines the best of the machines and the human beings.

The present and the future of Investing belongs to the combinatorial approach of Human+Machine Model.

But all Human+Machine Models are not equal.

So always ask: 

Who are the Humans running the Machine?

From the desk of 

Devina Mehra

Trusted Financial Advisors to some of the world's largest Funds, Institutions & Family Offices, for 30 years

If you want any help at all in your wealth creation journey, in managing your Investments, just drop us a line via this link and we will be right by your side, super quick!

Or WhatsApp us on +91 8850169753

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