Interest rates have been the tail that has been wagging the asset prices dog for the last few quarters. What is the Fed/RBI going to do is being discussed on stock chatrooms.

As the interest rate stance of Central banks, in turn, is determined by inflation numbers; inflation has suddenly moved out from the backroom economists' chatter to mainstream market commentary. I have rarely seen inflation numbers being watched with so much focus in my three decades of tracking markets.

This week inflation numbers came out for both India and the US for the month of January.

Detailed analysis below, but the short point is that inflation continues to remain higher than where Central bankers would like it to be - both the headline number and the net of food and fuel, 'core' number.

The recent inflation print is unlikely to bring any good news for the market watchers.

India CPI Update

India's headline Consumer Price Index (CPI) inflation came in at 6.52% year on year (y/y), a 3-month high, vs 5.92% expected, for January. So there has been no real moderation in the numbers.

As had been pointed out earlier, last month's somewhat lower figure was solely due to the drop in food prices, specifically vegetables.

In the latest numbers both the CPI inflation excluding food and CPI inflation excluding food and fuel, the so called core CPI, remains well above 6% that is well beyond not just RBI target inflation of 4% but also its tolerance band of plus minus 2%, which ends at 6%.

This points towards interest rates remaining higher and for longer. The inflation numbers do not provide any leeway to the RBI to stop tightening anytime soon.

Now for the details

Of the 0.46% month on month (m/m) ie January  2023 over December 2022 increase, 0.21% was contributed by food inflation alone with Cereals (+2.6% m/m) & Vegetables (-3.7% m/m) witnessing wild swings.

Clothing & Footwear inflation remained high with m/m annualized number at 5.4% and y/y at 8.8%.

Housing inflation jumped significantly in January with a 0.8% m/m print or 10.3% annualized while y/y remained close to mid-point of 4.6%. If this is not a one-off, then housing inflation will need to watched closely.

In Miscellaneous, Household goods & services, Health and Personal Care remain stubbornly high both on y/y and m/m annualized basis.

When we consider CPI ex-Food it stands at 5.74% m/m annualized or 6.71% y/y, while CPI ex-Food and Fuel & Light stand at 6.60% m/m annualized or 6.11% y/y.

Thus, given that both these measures, which exclude volatile items, remain above or close to 6%, the odds of the RBI delivering another rate hike at the next meeting should increase. Higher (rates) for longer will be the case for the RBI (just as for the Fed), unless and until we see evidence of core inflation receding.

In Government Securities, India 2Year yield currently trades at a cycle high of 7.2% which was hit in Oct 2022 even as the 10Y trades sideways at 7.34%, much below its cycle high of 7.62% hit in June 2022. As you are aware, Government securities or bond prices fall, as yields or interest rates go up.

Source: Ministry of Statistics and Programme Implementation (MOSPI)

US CPI Update

US headline and core inflation came bang in line with estimates on a month-on-month basis, however, they it was higher by a small margin on the year-on-year numbers which could probably be explained by the impact of the revision in Index component weights.

As the stacked graph shows, the big component currently of inflation is services inflation  excluding shelter inflation. This as remain persistently high even as goods inflation has moderated.

Core services ex-shelter inflation seems to be of primary importance given that goods inflation has already receded quite a bit. The trend here seems to be lower on a 3-month annualized basis but still remains quite high at close to 4%, which is consistent with wage data where wages have risen 5% on a 3 month annualized basis. A slowdown in jobs data should aid in bringing down this stickier portion of inflation, however, based on the latest jobs data release that doesn’t seem to be the case yet.

Looking at other components, while shelter inflation remained high at 0.7% m/m & 7.9% y/y, we are well aware that this component comes with a significant lag and that the market-based housing price/rent indices have already started showing negative m/m prints which should begin reflecting in CPI from this summer.

Elsewhere, Apparel inflation surged the most since Sept 2021 (+0.8% m/m), but it could easily be a one-off thing as the trend remains lower. Meanwhile, Used cars & trucks inflation surprised to the downside (-1.9% m/m) given that the Manheim Used Vehicle Value Index released earlier rose 2.5% m/m in January.

In a nutshell, inflation remains in a downward trajectory. However, we would need to see a considerable slowdown in the labor market and hard economic data to get core services ex-shelter inflation to more sustainable levels to justify a lower peak Fed Funds rate than is currently priced in.

As against the current Fed interest rate of 4.5 to 4.75%, the market is pricing in a peak rate of 5.27%.

Source: U.S. Bureau of Labor Statistics,

To recap, inflation remains above comfortable levels both in India and in the US which means that the  RBI and the Fed are likely to continue to raise interest rates and are unlikely to reverse direction in hurry even when  peak rates are reached.

Therefore relief from this angle is unlikely to be forthcoming for the markets.

From the desk of 

Devina Mehra

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 as your wealth advisor, super quick!

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We are all familiar with those Do-it-Yourself investing principles that are supposed to make investing simple and profitable. One of the favourites in this genre is, "Buy monopoly businesses. Buy the largest company in the sector, with the strongest brand. You can't go wrong with the 800 pound gorilla in the business."

But what does history show? Remember Nokia, Kodak, BlackBerry (Research in Motion) - all dominant businesses where magazine covers used to be about whether anyone could ever catch up with them? Where are they now?

You may say that this is the nature of technology businesses.

But it is not as simple as that. In any case when Kodak was running its film based business, nobody thought this was a fast moving high tech area. The issue is far more fundamental…

For one, when a company is the dominant player in the business even if it does nothing wrong at all, any new player in the business will end up taking share and sales away from it.

When you have 60 or 70% market share in a business, as Bajaj Auto did in scooters or Maruti in cars before new foreign players came in, it is a given that the new players will take away some sales from the incumbent.

For the largest player in the market it is near impossible to grow faster than the market whereas for a new or smaller player taking away 1,2,5% share is not such a big deal.

When Tata Motors took over Jaguar Land Rover (JLR) and introduced some great new models in Jaguar, it was able to grow sales far faster than its competitors as it had only 4-5% global market share in luxury cars. Hence it was not that dependent on the overall growth in the  luxury car market.

Another issue is that a new player can target niches. For example, in a paint or dyes business, a new entrant can target a particular type of dye or paint - let us say, an exterior paint only, rather than compete with the dominant player across segments.

In other businesses like detergent, hair oil, tea or confectionery, small players can target certain states or regions, and even tailor their product according to the preferences of that particular region.

That is how many players have nibbled away share in these businesses  where bigger players like Hindustan Unilever or Dabur or Marico have to then pay a premium price to acquire these brands and their market share, as otherwise their own market share keeps getting chipped away.

What also happens is that often the new player cuts prices, gives discounts or gives freebies like free service on vehicles or appliances. This becomes an issue for the Number One company in the business which has to decide whether to follow.

This became clear during a case discussion back in my MBA days where the case study was about a new entrant in a business cutting prices. The professor asked one of us what was the largest player would or should do? The student said it would match the discounts. The professor than asked us to calculate how much of a hit the bigger player would take on its large revenue base if it goes to match the pricing of the smaller player and it became clear that the hit would be huge.

It also happens that smaller and later entrants often have lower cost structures and overheads compared to the old established companies which tend to accumulate costs over a period of time. Basically it becomes a big decision for the incumbent to either follow the smaller player and take a hit on its margins and lose a hefty amount of profits on its high revenue base or cede the market share to the new entrant.

It is, in any case, a good exercise to see how the so called brand value of a company is getting captured in the financials. For example 20 years ago the FMCG companies in India like Hindustan Unilever, Nestle, P&G etc did not have very high EBITA margins but their brand value was captured in their leverage over the distribution chain where they got money in advance from the distributors and had a negative working capital cycle. Now margins are higher but as retail gets more organised in India, the bargaining power of the manufacturers versus the trade is reducing.

Often the biggest player in a market has a choice in terms of pricing and companies can opt for different options that determine their profit and margin trajectory. Amazon, for instance, deliberately keeps pricing and margins extremely low, specially in new businesses, like when it entered cloud computing because it did not want to make the business too attractive for other entrants.

Abbusiness that is growing well or has high margins is a double-edged sword as it becomes attractive for new players as well. Look at Nyaaka, for instance. As I write this in 2023 it is the dominant player in the beauty D2C business but after they have created the business, it is far easier for other players to enter. The exception to these rule is platform businesses, where when a large community of buyers and sellers are on a platform that in itself becomes an entry barrier.

In any event, it is virtually impossible for the dominant player in a market to grow faster than the market itself, whereas that is not a constraint for its small competitor.

It is also mostly true that the big disruptions in a business come from new entrants or smaller players. It is extremely difficult for a giant to do this, especially when it involves destruction of its current cash generating business.

Kodak had the digital camera technology but could never scale it up as it would have destroyed their existing business. Of course we know how that story played out with their business getting disrupted anyway and their going out of business.

There is also some inertia when you have a substantial profitable business and the new business is too small to get top management focus. Microsoft with its cash generating existing businesses missed out opportunity after opportunity in internet browser, search, cloud computing and more - it has caught up only recently in some of these under Satya Nadella's leadership.

Until now we have only been talking of business issues. An additional complexity arises when you buy a big dominant company at the wrong price.

Even if there are no big disruptions to its business, it may still be an underperformer, even over a long period of time.

A good example of this is Coke which, over a period as long as 30 years (1993-2023), has gone up only 12 times - when the S&P 500 has gone up 16.5 times over the same period and Pepsi 19.5 Times.

In India too, large branded companies have underperformed for lengthy periods - a good example being Hindustan Unilever that underperformed for the first decade of this century.

Or Colgate India that saw a stock price decline of 75% over the 9 years from 1993 to 2002!

Or Bata which gave zero returns over a 15 year period from 1994 to 2009.

Moral of the story: Buying companies with large market share and established brands will not lead you to investment Nirvana. A whole lot of additional analysis is needed. And a large market share can actually be a vulnerability, rather than a strength.

From the desk of 

Devina Mehra                                     

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 as your wealth advisor, super quick!

Or WhatsApp us on +91 88501 69753

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India has more than 1.5 lakh people dying each year in road accidents - among the highest in the world, even on the basis of percentage of population.

Now suppose, from tomorrow, all vehicles are changed to self driving ones. Let us say, the death toll drops to 50,000.

What will the newspaper headlines be?

Think carefully before you read further.

Will the headlines be, "Self driving vehicles reduce death toll by two thirds"

Or will it be, "Self driving vehicles kill 50,000 Indians every year"

My guess is, it will be the latter.

Although they do not deal with the specific example, in concept, this is a paradox that Daniel Kahneman, Cass R. Sunstein and Olivier Sibony talk about in their book, 'Noise'.

They give example after example of various areas of human endeavour, even those far away from the conventional number crunching, where well-constructed algorithms consistently outperform human beings - that too human beings with considerable experience and expertise.

For example, whether an accused should be granted bail or not appears to be a problem that only a human being with great judgement (pun intended) should tackle. The test is whether a person out on bail will commit another crime.

Actual studies show that even a very simple algorithm which may take into account only one or two factors, like the age of the accused and their previous crime record, outperforms skilled judges.

So also, cases like corporate recruitment or deciding on insurance premiums. Simple, at times even simplistic, algorithms outperform experienced human beings.

Why does this happen?

One of the reasons is that while we think humans bring expertise and nuanced judgement into the mix, what we forget is that they also bring in 'noise'.

What is noise? In simple terms, noise is undesirable variability, over and above variability which may be due to bias (eg, a judge being biased for or against a gender, race or caste).

Bias is easily understood but even without bias, various judges or insurance professionals will come up with different answers to the same question. Even worse, the same human being will come up with different answers depending on totally unrelated variables like whether they are hungry, what the weather is, what their mood is like and so on and so forth. We all understand this phenomenon at an individual basis that is, our performance is impacted by our own daily mental makeup. This noise is both across different individuals in the same profession or category as well as how a particular individual performs or decides in various instances.

It is this noise that algorithms or machine-led systems reduce and that accounts for their superior performance.

While the machine-led systems do not have all aspects of the expertise of a human professional they also do not have this random variability or noise. The net impact is that the machine-led systems perform better. Just as in our example, the self driving cars performed better than human drivers.

That being the case, what is the issue? Why don't we outsource many of these functions to machine or computer led systems?

The anomaly lies is how we judge the competing systems.

We intuitively know that human beings will make errors but we consciously or not, expect a machine led system, say an Artificial Intelligence based system, to be error free.

We are willing to ditch it at the first mistake: the first wrong diagnosis based on a mamogram or the first accused out on bail who commits a crime. Never mind that doctors and judges are also error prone.

In short, instead of evaluating whether the machine works better than the human being, we expect the machine system to be perfect.

This is completely irrational, as the rational thing we have to test is whether the machine system improves on the alternatives of the existing system, rather than whether it is completely error free on a standalone basis.

This type of thinking leads to wrong choices as we may abandon an algo/ machine even if it's better than what was being done earlier.

That's why we started with the example of self driving cars, where we aren't willing to live with a single fatality even when human drivers make many more errors and cause more deaths.

Moral of the story: when evaluating alternative processes or systems, always pause and think - especially whether you are using the same yardstick to evaluate all the systems or you have unrealistic expectations of one.

What does it have to do with investing? In investing as well, human beings are prone to many limitations like the amount of data they can process plus a huge variety of cognitive biases, like the Recency Bias, the Survivorship bias, Loss Aversion, Endowment Bias, and many more.

All of these limit how well human beings can perform because they tend to drag down the performance over a period of time plus of course is the element of noise.

Try giving the same company details and financials to 5 experienced analysts and see whether all of them come up with the same analysis. It is almost impossible!

That is where a systematic approach helps. Even if the system does in a mechanical fashion what the analyst or fund manager says that they are doing and even if it is somewhat more simplified than what the fund manager claims to be doing, the very fact that it is being done in a systematic manner with no random moves or noise, the system is likely to outperform the human being over a period of time.

However, will the system never make mistakes or not under perform in any time period? No.

But that is the wrong criteria to use to judge whether an artificial intelligence or machine learning system is good.

As in anything, if you ask the wrong question, you can never go right.

If your question is whether switching to an artificial intelligence system will eliminate mistakes? Therefore if it doesn't, you will not use that system.

What you need to do is change the question and ask: Is this system an improvement on the earlier or existing systems? That is when you will get the right answer.

Therefore, the right way is to evaluate whether the systematic approach works better than the human approach over a period of time rather than expecting the non human approach to be perfect and to have all the answers all the time which is anyway never possible in the market as there are things which cannot be known in advance, no matter how much information you gather and how well you analyse it.

For example, we at First Global, use a human plus machine model where most of the heavy lifting is done by the machine.

Does that have a perfect track record? No, but the answer to the question: Is it doing a lot better than what human fund managers are doing specially on a risk adjusted basis? is a unequivocal YES.

If you ask a wrong question you can be lead astray. Hence, always stop and think on what your end objective is and whether you are asking the right question to get to that.

Elsewhere in the book have written more about how human plus machine systems work.

To learn more about human/machines/ combo systems and how to use them for investing:

From the desk of 

Devina Mehra

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 as your wealth advisor, super quick!

Or WhatsApp us on +91 88501 69753

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The Federal Reserve hiked interest rates by 25 basis points (bps) yesterday to 4.75% (upper bound), stating that, it “anticipates that ongoing increases in the target range will be appropriate”.

Earlier the Fed suggested that three months of continued disinflation (reduction in inflation, not to be confused with deflation which is a drop in prices) would be a sign of progress, allowing them to think about downshifting the pace of hikes and potentially pausing.

Later, one of the Fed’s most closely watched hawks, Governor Christopher Waller suggested that “From the risk management side — I need six months of data, not just three.”

Looking at the data, the core personal consumption expenditures index rose 2.2% in the three months through December on an annualized basis, and 3.7% over the past six months, a slowdown from its 4.4% pace in the last 12 months.

With the current rate hike, the real policy rate has finally turned positive as the target rate range is above core inflation for the first time since 2019.

Thus, if the Fed is data dependent, as Chair Powell alluded to yesterday when asked about the rate cuts being priced in by the market in the second half of 2023, then it does not fit well with an 85% chance of a rate hike in March (data as per CME FedWatch tool).

While there a possibility of a hike next time, the 85% chance pricing seems quite high.

Chair Powell broadly stuck to his comments from the previous meeting i.e. the Fed needs to see core-services ex-shelter inflation come down and that they need to avoid any pre-mature loosening (rate-cuts).  However, Chair Powell adds that they have finally begun to see the process of disinflation begin and that they welcome it.

The surprising fact was that Chair Powell didn’t provide any significant pushback to the recent loosening of financial conditions which has taken place on the back of an everything rally over the past three months, from government bonds to credit to equities.

This was the final nail in the coffin for US dollar longs. The US dollar index (DXY) tanked 0.9% and was last trading sub-101 levels while the Euro hit a fresh 10-month high of $1.1000 ahead of an ECB meeting, where the quantum of rate hikes priced for 2023 is much higher than the Fed.

While it is prudent to observe and confirm the price action that takes place on volatile central bank meeting days, we did see US 10-year yields drop by 9 bps to 3.42%, the lower end of the 2-month range, while US 2-year yields went from +5 bps to -9 bps on the day to settle at 4.11%.

The rate cut pricing for H2 2023 remained close to 50 bps, according to the Fed Fund futures market. Now we look ahead to the Bank of England and European Central Bank meetings today.

From the desk of 

Devina Mehra                                                               

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 as your wealth advisor, super quick!

Or WhatsApp us on +91 88501 69753

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Now we have all the Nifty (or for that matter S&P 500) forecasts for end 2023, from every stock market expert and securities house.

Hope you are keeping them safe to check in December.

As an aside, you will have to admit that none of the estimates talk about what that expert had predicted last year and how it turned out.

The truth? It's in a category called 'Objective Ignorance' by Daniel Kahneman - something that CANNOT be known.

Yet, 'Experts' don't even talk probabilities, they give a single number - usually with remarkable confidence.

Treat this forecast game as entertainment only!

Nifty predictions are only one example of humans forecasting things which can't be known. And having a great deal of conviction and confidence in the forecast.

As Daniel Kahneman writes along with co-authors in Noise, "We maintain unchastened willingness to make bold predictions about the future from little useful information."

Once again time for my favourite aphorism, "Confidence does not equal Competence."

And this is not even really about competence, because this is a number that is inherently unknowable.

Even without the benefit of having read Daniel Kahneman, I have refused to give a Sensex or Nifty projection on Diwali or Newyear, for the last 30 years 😊

From the desk of 

Devina Mehra

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 as your wealth advisor, super quick!

Or WhatsApp us on +91 88501 69753

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I am fascinated by ChatGPT.

What it does just weeks from the launch is truly amazing!

I see that a couple of years from now, it will be as much part of our life as say road direction Apps like Google Maps.

When was the last time you looked for offline directions? Exactly.

It is already as good as an intern or a non-expert, like say a content writer at a social media agency.

Ask it create a draft blog with a sketchy outline or question or, on the other hand, compress an old 2500 word article into 750 words and there you have it. Instantly!

Tell it about your business in 3 lines, ask it to make a draft presentation. The PPT slide topics appear immediately.

Ask to give details for possible approaches to one slide, say what to highlight when marketing your services. And done.

Give some hints/ outline and ask it to write a children's book. Done.

And of course, students are already using it not just to answer questions but even to write essays.

Truly mind-boggling.

Sure it isn't perfect.

Not as good as an expert? That's like saying it got only 85% marks in the exam.

Too expensive? Remember what the first iteration of anything looked like? The size of the first mobile phones, for example?

Remember it has been launched only WEEKS ago.

Right now there are all sorts of reasons being given why it's no threat to Google/ Alphabet, but that isn't true in my opinion.

Any incumbent, especially one behaving like a monopoly, leaves uncovered flanks.

Google itself gained as Yahoo was trying to keep visitors on its own site.

Instead of pushing them to click onto a link.

Guess what? Google is trying the same thing now, leaving room for a newcomer.

Other monopoly like behaviour includes penalising a company for plagiarism because they have similar sites in 2 geographies because it can't be bothered. Instead the site owner has to do the hard work and unnecessary work of writing different content for various countries even when they would prefer to have a consistent approach in the first place.

Amazon is the same for sellers where a seller can be taken off fairly arbitrarily.

All monopolies create opportunities as it is very difficult to disrupt your own, cash-generating model.

It's rumored that Google has similar tech, but will it disrupt its current buisnesses?

Or go the Kodak route - it built the digital camera but couldn't jump in whole-heartedly.

It isn't even about ChatGPT, maybe the winner will be someone else - but ready or not, AI is going to take over a lot of our lives.

And we will need to adapt. Education is an obvious area where, ready or not, this technology is and will be available to students. Old ways of teaching, learning, testing etc will all need to evolve to take this into account.

The important takeaway for me is that there are exciting times ahead!

Here's a good overview.

From the desk of 

Devina Mehra

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 as your wealth advisor, super quick!

Or WhatsApp us on +91 88501 69753

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