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Source: https://www.magnific.com/

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It is a good question, because the answer is not simply a matter of buying the cheapest policy, or the one with the longest list of features. It requires understanding what one already owns, what one stands to gain and what one might lose. Here is how we evaluated Amit’s options.

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Two approaches illustrate how such a rule might work.

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The tax advantage that vanished

MLDs are hybrid instruments — non-convertible debentures whose final payout is tied to the performance of a market benchmark, such as the Nifty 50, Sensex, gold, or a government-bond yield, rather than to a fixed coupon. Some are “principal-protected,” guaranteeing the return of capital regardless of how the benchmark performs; others put the principal itself at risk.

Until 2023, the appeal was mostly fiscal. MLDs held for more than 12 months qualified for long-term capital gains tax at a flat 10%, well below the slab rates applied to interest income from ordinary bonds or fixed deposits. That made them a favourite of high-net-worth individuals (HNIs) looking to dress up equity-like returns as tax-efficient debt.

The Finance Act 2023 ended this. A new provision, Section 50AA, reclassified all MLD gains as short-term capital gains, taxable at the investor’s slab rate — as high as 30% plus surcharge for top earners — irrespective of how long the instrument is held. There was no grandfathering: even MLDs purchased before the change became subject to the new rules on transfer or maturity. A parallel change removed the withholding-tax exemption that listed debentures had previously enjoyed, adding a compliance layer that had not existed before.

By any measure, this was the single biggest reason to buy an MLD. Its removal should, in theory, have killed the product.

Why interest has held up regardless

It hasn’t, for a few reasons.

Access got easier. The minimum investment ticket was cut from ₹10 lakh to ₹1 lakh in January 2023, opening the product to a much larger pool of retail and mass-affluent investors just as the tax advantage disappeared.

The structure still does something bonds, deposits and funds don’t. A principal-protected MLD offers a floor — investors get their capital back even if the linked index falls — while still capturing part of the upside if it rises. That combination doesn’t exist in a fixed deposit (fixed return, no upside) or a mutual fund (no floor). For an investor who wants to bank gains from a volatile equity portfolio without immediately re-risking that capital, MLDs offer a place to park money that still tracks the market to some degree.

The wealth-management industry has repositioned the product. Indian private banks and wealth managers entering 2026 describe a broader shift away from selling individual products on tax merit and toward asset-allocation-led advice, in which structured products, private credit and alternatives all compete for a place in the portfolio on the basis of diversification and risk-adjusted return, not tax arbitrage. MLDs have been folded into that reframing: issuers and distributors now emphasise their low correlation to plain-vanilla debt and equity, rather than their tax treatment, which stopped being a selling point three years ago.

The trade-off is legible enough: MLDs offer a shot at equity-like returns with a partial safety net, in exchange for poor liquidity, higher complexity, and — since 2023 — no tax edge over the alternatives.

A tool for volatile markets, not a core holding

The clearest use case for MLDs today is tactical rather than strategic. When equity markets turn choppy, some investors sell part of their holdings and route the proceeds into a principal-protected MLD rather than into cash or a fixed deposit. The MLD then behaves as a kind of holding pen: capital is protected if the market falls further, but the investor retains some participation if it recovers before the debenture matures — at which point the money can be redeployed into equities, often at more attractive levels if markets have indeed corrected.

This is consistent with the broader direction of Indian wealth management going into 2026, where advisers describe a growing preference for portfolios that are resilient rather than aggressively positioned, after a period in which Indian equities underperformed global peers even as gold and silver rallied. Structured products, including MLDs, are increasingly discussed in the same breath as private credit and alternative investment funds — as instruments for managing volatility and dispersion, not as tax shelters.

What investors get wrong

A few misconceptions persist, often because they linger from the pre-2023 era:

• “MLDs are tax-efficient.” They no longer are. All gains are taxed as short-term capital gains at slab rate, with no benefit for a longer holding period and no indexation.

• “Principal-protected means risk-free.” The protection is only as good as the issuer. MLDs are unsecured claims on the company that issues them — if it defaults, the “protection” is worth nothing.

• “MLDs behave like fixed deposits.” They don’t offer an assured return; what comes back at maturity depends entirely on the pre-set payout formula and how the benchmark has performed.

• “Being listed makes them liquid.” Listing does not guarantee a ready buyer. Secondary-market trading in MLDs is typically thin, and early exit can mean selling at a significant discount, if a buyer can be found at all.

The risks worth weighing

Before investing, four risks deserve particular scrutiny:

1. Credit risk. MLDs are debt claims on the issuer, often a non-banking financial company. Investors should stay within their comfort zone on credit rating — AAA or AA-rated issuers for lower risk tolerance — since a default can wipe out the “protected” principal along with any expected return.

2. Liquidity risk. These instruments are built to be held to maturity. Investors who may need the capital early should treat MLDs as illiquid.

3. Payoff complexity. Caps, participation rates and barriers embedded in the payout formula can materially limit upside even when the underlying benchmark performs well. The offer document, not the marketing pitch, tells the real story.

4. Regulatory risk. The 2023 change is a reminder that tax treatment is not fixed. Rules can shift, and — as happened then — can apply retroactively to instruments already held.

Who should — and shouldn’t — hold them

MLDs are best suited to HNIs and sophisticated investors with a long investment horizon, the risk appetite to accept possible principal loss in non-protected structures, and the patience to hold to maturity. They can also serve first-time or more conservative investors through principal-protected structures, provided they invest with proper guidance given the complexity of the payout terms.

They are poorly suited to investors who may need liquidity before maturity, those in lower tax brackets where slab-rate taxation erodes much of the return advantage, or anyone unwilling or unable to evaluate issuer credit quality and payoff mechanics closely.

Where MLDs fit from here

Commentary from India’s private wealth industry heading into 2026 points to structured products, MLDs included, becoming a more settled — if still niche — feature of diversified HNI and UHNI portfolios, alongside private credit, alternative investment funds and offshore allocations. Their future growth looks tied less to tax advantages, which are gone, and more to product innovation — a widening range of underlying benchmarks, more customisable payoff structures — and to whether the industry can make the instruments accessible without obscuring their complexity and illiquidity from newer investors.

The investment case for MLDs, in short, has not so much strengthened as changed shape: from a tax trade dressed up as diversification, to a genuine — if still niche — diversification tool that happens to carry no tax advantage at all.

Sources

1. Vinod Kothari Consultants — “Shorn of tax benefit, MLDs now face tax deduction on payouts” — https://vinodkothari.com/2023/05/shorn-of-tax-benefit-mlds-now-face-tax-deduction-on-payouts/

2. Grip Invest — “Taxation of Market-linked Debentures (MLDs) in India” — https://www.gripinvest.in/blog/tax-implications-of-market-linked-debentures

3. InCred — “MLDs: Advantages and disadvantages” — https://www.incredpremier.com/blogs-details/mlds-types-features-and-benefits

4. PL Capital — “Market Linked Debentures – Invest in MLD Online in India” — https://www.plindia.com/market-linked-debentures/

5. Dezerv — “Market Linked Debentures – Find the list of all MLDs in India” — https://www.dezerv.in/bonds/mld-market-linked-debentures/

6. Anand Rathi PCG (ARPCG) — “Taxation on Market-Linked Debentures in India” — https://www.anandrathipcg.com/blogs/taxation-on-mlds

7. EquityUnlisted — “Market-Linked Debentures (MLDs) 2025 | Smart Fixed-Income Investment” — https://equityunlisted.com/market-linked-debentures-mlds/

8. Jiraaf — “Are Market Linked Debentures Worth It? MLDs Meaning, Tax & Returns” — https://www.jiraaf.com/blogs/personal-finance/market-linked-debentures-mlds

9. Meta Investment — “Earn More Than Traditional Fixed Income with Market-Linked Debentures” — https://metainvestment.in/2024/05/03/mld/

10. Meta Investment — “Market-Linked Debentures (MLDs)” — https://metainvestment.in/income/mld/

11. Tata Capital — “Market Linked Debentures (MLDs): Types, risks & taxation” — https://www.tatacapital.com/blog/wealth-services/understanding-market-linked-debentures/

12. Taxmann — “Market-Linked Debentures Investment – Impact of Finance Act 2023” — https://www.taxmann.com/post/blog/market-linked-debentures-investment-impact-of-finance-act/

13. Hubbis — “India 2026: Portfolio Discipline, Global Diversification And Advisory Depth In A Maturing Wealth Market” (Aditya Birla) — https://www.hubbis.com/article/india-2026-portfolio-discipline-global-diversification-and-advisory-depth-in-a-maturing-wealth-market

14. Hubbis — “From Product to Portfolio Solutions: Building Resilient Wealth Strategies for India’s Next Phase” (Motilal Oswal Private Wealth) — https://www.hubbis.com/article/from-product-to-portfolio-solutions-building-resilient-wealth-strategies-for-india-s-next-phase

15. Hubbis — “Nuvama Private’s Onkarpreet Singh Jutla on Global Diversification, Alternatives and Risk-Calibrated Growth for India 2026” — https://www.hubbis.com/article/nuvama-private-s-onkarpreet-singh-jutla-on-global-diversification-alternatives-and-risk-calibrated-growth-for-india-2026

16. Hubbis — “The Diversification of HNW and UHNW Portfolios in India” (Motilal Oswal AMC) — https://www.hubbis.com/article/the-diversification-of-hnw-and-uhnw-portfolios-in-india-views-from-a-leading-local-asset-management-expert

17. Hubbis — “Global Diversification Comes of Age: Building Cross-Border Investment Capability for Indian Wealth” — https://www.hubbis.com/article/global-diversification-comes-of-age-building-cross-border-investment-capability-for-indian-wealth

18. Hubbis — “India’s Asset & Wealth Management Crossroads: Incentives, Innovation and the Next Phase of Advisory Evolution” — https://hubbis.com/article/india-s-asset-wealth-management-crossroads-incentives-innovation-and-the-next-phase-of-advisory-evolution

19. Financier Worldwide — “The great portfolio rebalancing: a shift in diversification” — https://www.financierworldwide.com/the-great-portfolio-rebalancing-a-shift-in-diversification

20. IRJAEM — “Structural Transformation In India’s Wealth Management Industry: Trends, Growth Drivers, And Emerging Challenges” — https://goldncloudpublications.com/index.php/irjaem/article/view/1422

That resilience earned its reward. Investors, unnerved by disruption, prized stability over growth, and companies obliged by hoarding cash, shoring up balance sheets and avoiding risk. A survey of American chief financial officers shows that concerns about trade and tariffs have fallen sharply since the second quarter of 2025, while worries about inflation, monetary policy and labour quality have risen — a return, in other words, to the “normal” anxieties of running a business.

That shift in mood is not merely psychological.

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The monthly pattern is the more telling detail. Contributions crossed Rs 26,000 crore in the very first month of the fiscal, reaching Rs 26,632 crore in April 2025, and climbed steadily from there. December 2025 and January 2026 set a then-record of Rs 31,002 crore. Then came March 2026 - the same month in which the Nifty 50 fell 11.31%, its steepest monthly decline since March 2020 - and SIP contributions rose to an all-time high of Rs 32,087 crore.

Part of that resilience is generational. According to a review compiled by HDFC Securities, the share of stock-market investors below the age of 30 rose from 22.6% in March 2019 to 38.4% by February 2026, alongside a broader rise in demat accounts to roughly 21-22 crore nationally - a cohort that industry commentary generally links to digital-first onboarding and a preference for automated, recurring investment over discretionary stock-picking, which fits naturally with the SIP format.

Widening that base further is a specific policy push. AMFI, working with SEBI, introduced “Choti SIP” - a systematic investment plan with a minimum monthly contribution of Rs 250 - reportedly launched at an event attended by the SEBI chairperson specifically to reach first-time investors who might otherwise find conventional SIP minimums out of reach. As described in industry trade coverage, the product carries a minimum five-year (60-instalment) commitment, is capped at three such SIPs per investor across three fund houses, and is restricted to growth-plan equity schemes outside the sectoral, thematic, mid-cap and small-cap categories - with distributors reportedly eligible for a modest incentive once an investor completes 24 instalments, an inducement designed to make it worth an adviser’s time to bring in small first-time savers rather than only larger tickets.

The number of contributing SIP accounts rose to 9.72 crore in March 2026, from 8.11 crore a year earlier - a net addition of 1.61 crore active accounts. SIP assets under management reached Rs 14.83 lakh crore, maintaining a 20% share of total industry AUM (20.11%, against 20.09% a year earlier).

Perhaps the more interesting evidence of investor patience sits in the holding-period data. Within direct plans, the share of SIP assets held for more than five years rose to 20% in March 2026, from 19% a year earlier, while the share held for less than a year contracted to 29%. Regular plans - where an intermediary or adviser is typically involved - show an even more pronounced long-term tilt: 34% of SIP AUM held for more than five years, against just 19% held for under a year. Both India’s top-30 cities and the rest of the country recorded the same rising share of assets held beyond five years between March 2025 and March 2026, with B30 towns showing marginally higher long-term holding shares than the metros throughout - a reminder that patience is not solely a big-city phenomenon.

Sources:

• AMFI Annual Report: Fiscal 2026

• Cafemutual, “AMFI launches choti SIP or Rs.250 SIP” - https://cafemutual.com/news/industry/34255-amfi-launches-choti-sip-or-rs250-sip

• Cafemutual, “All you need to know about the newly launched Choti SIP” - https://cafemutual.com/news/industry/35491-all-you-need-to-know-about-the-newly-launched-choti-sip

• HDFC Securities (Big Review, via HDFC Sky), “Retail Participation Soars as Demat Accounts Hit Record” - https://hdfcsky.com/blogs/media-coverage/retail-participation-and-liquidity-trends-reshape-market-structure

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The proximate trigger, as reported at the time, was the conflict involving Iran, Israel and the United States, which pushed crude oil prices sharply higher and sent India’s VIX volatility gauge — the market’s so-called fear index — from below 12 to a peak of 28.9 by 30 March, before it eventually retreated. According to Outlook Business, foreign portfolio investors swung from net inflows of roughly $2.5 billion in February to net outflows of nearly $6 billion by mid-March, and new investor registrations on Indian exchanges fell 24.5% month-on-month — the steepest such drop of the financial year, suggesting that while mutual funds kept buying, the flow of fresh retail money into direct equity slowed sharply during the worst of the panic.

What made the year noteworthy was not the fall itself but who did the buying while foreign investors did the selling. Foreign portfolio investors (FPIs) sold Indian equities worth Rs 1.81 lakh crore over the fiscal. Domestic institutional investors (DIIs), led overwhelmingly by mutual funds, absorbed that pressure and then some, investing a record Rs 8.09 lakh crore — comfortably the highest figure in at least five years, and roughly four times the previous year’s DII total of Rs 5.05 lakh crore.

Mutual funds’ own contribution to that domestic buying was substantial: net equity inflows into mutual funds stood at Rs 3.47 lakh crore for the fiscal. March 2026 marked the 61st consecutive month in which mutual funds recorded positive net equity inflows — a streak that has now run through multiple market corrections and several bouts of foreign capital flight without breaking.

The mechanics of this shift are visible month by month. Foreign investors turned net sellers in July, August, September, October, December and, most emphatically, March 2026, when they pulled out Rs 1,17,775 crore in a single month — by far the largest monthly outflow of the year. Domestic institutions were net buyers in every single month bar none, and in March, as foreign money fled, DII buying actually accelerated to Rs 1,42,960 crore, comfortably its highest monthly figure of the fiscal.

This is not simply a story of resilience; it is a structural change in who sets the marginal price of Indian equities. Five years ago, in fiscal 2022, DIIs invested Rs 2,20,193 crore against FPI outflows of Rs 1,40,008 crore — already domestic-dominated, but at roughly a third of this year’s scale on the DII side. For retail investors, the practical implication is that systematic domestic flows now provide a buffer against foreign-flow volatility that did not exist at anything like this scale even three years ago. Whether that buffer holds in a genuinely sustained downturn, rather than a single bad month, remains untested. But fiscal 2026 was as close to a live-fire drill as Indian markets have had in years, and the buffer held.

A layered view of the cycle

The table below summarises how each layer of the AI value chain behaves during the boom and bust phases, and where long-term winners are most likely to emerge.

A useful way to analyse any AI investment is to separate the value chain into layers, each with its own boom and bust dynamics.

Infrastructure -- chips, data centres, networking and power -- sees heavy spending during the boom and, in a bust, excess capacity and falling pricing power; the eventual winners are the lowest-cost, most efficient providers. Platforms and foundation models attract enormous investment early on, before consolidating around the handful that prove commercially viable, favouring firms with network effects, proprietary data and developer ecosystems. Applications multiply by the thousand in the boom phase and fail in droves once the bust exposes a lack of differentiation; survivors tend to be businesses solving real problems with recurring revenue. Enterprise adoption follows a similar pattern of wide experimentation followed by abandoned pilots, rewarding firms that actually weave AI into existing workflows. End-users, finally, move from sky-high expectations to the sober realisation that AI is a tool rather than a miracle -- benefiting, in the end, from lower costs and higher productivity.

Five questions help sort the durable from the fashionable. Where does a company sit in the value chain -- chipmaker, cloud provider, data-centre operator, software firm or AI-enabled business -- since each faces different economics? Is demand structural or merely cyclical: data generation and computing needs look structural, whereas buying GPUs ahead of demand may not be. Who captures the economics, given that technological adoption does not always reward the technology’s creators -- electricity generated enormous value even as utilities earned only regulated returns, and the internet enriched users more than most telecoms firms? Does AI strengthen or weaken a firm’s competitive position, boosting those with proprietary data, distribution and loyal customers while eroding businesses whose knowledge can be commoditised? And what happens if capital becomes expensive, since booms typically end when financing conditions tighten and cash flow, not funding rounds, decides who survives?

Historically, this cycle unfolds in four phases: an infrastructure boom (benefiting chipmakers, equipment suppliers and data-centre operators, at the risk of overbuilding); an application explosion (thousands of start-ups, high valuations, little differentiation); a shake-out (falling valuations, consolidation, bankruptcies -- the phase in which the strongest companies typically emerge); and finally a productivity era, in which AI becomes invisible infrastructure and the biggest beneficiaries may not even call themselves AI companies. Railways, electricity, the internet and smartphones all followed variants of this pattern; in each case the lasting wealth accrued not to the miners but to the suppliers of picks and shovels, and to the “towns” that grew up around the goldfields using the new technology to work more efficiently.

Why India will not simply mirror America

This framework travels well. But applying it to India requires adjusting for three features of the Indian economy that have no close American equivalent.

Retail investors could cushion market volatility. India’s equity markets have changed dramatically over the past decade. Millions of households now invest regularly through Systematic Investment Plans, making them an increasingly important source of market liquidity. Annual SIP contributions have risen from under ₹44,000 crore in FY2016–17 to nearly ₹3.5 lakh crore in FY2025–26, and these inflows held up through both the pandemic and subsequent corrections (AMFI, 2026) -- evidence that retail investors have become long-term participants rather than short-term traders. At the same time, direct retail trading now accounts for a smaller share of daily turnover than it once did, as more investors favour mutual funds over individual shares and institutional and algorithmic trading grows (Whalesbook, 2026). The implication is nuanced: an AI-driven correction could still make day-to-day trading choppier, but steady SIP inflows may continue to underpin equity markets, limiting the depth of any prolonged sell-off -- a shock absorber American markets largely lack.

The Reserve Bank has less room to manoeuvre. Where the Federal Reserve can often cut rates aggressively in a slowdown, the Reserve Bank of India must also defend exchange-rate stability and guard against imported inflation. A global AI-driven slowdown could trigger capital outflows from emerging markets and weaken the rupee; a weaker currency would raise the cost of imported oil, electronics and industrial inputs, pushing inflation up even as growth slows. In that scenario the RBI might struggle to cut rates at all, meaning an AI bust could tighten financial conditions in India precisely when the economy needs support -- a far more constrained position than that of advanced-economy central banks.

India’s biggest exposure is as an exporter of talent. For America, AI is chiefly about lifting domestic productivity. For India, it also threatens one of the country’s largest export industries. The IT services and business-process-management sectors employ millions of professionals doing precisely the kind of routine knowledge work -- documentation, coding support, customer service, research assistance, report preparation -- that generative AI is designed to automate. The evidence so far is mixed: NASSCOM reckons AI could add nearly $621bn to India’s economy over time and expects the technology to transform jobs rather than eliminate them, and surveys suggest most employers are redesigning roles rather than cutting headcount (NASSCOM, 2026b; Enterprise Times, 2026), while the RBI has noted that IT services exports remain resilient despite growing concern about AI (New Indian Express, 2026). The deeper risk may be slower to show. If companies keep hiring fewer graduates while leaning more on AI, India’s talent pipeline could thin out gradually, since junior staff typically learn by doing routine work before taking on more complex responsibilities. Shrinking entry-level opportunities today could mean a shortage of experienced professionals a decade from now -- a lag effect that would not show up in this year’s export figures.

What each scenario would mean for India

A boom would lift productivity across manufacturing and services, expand Global Capability Centres, draw fresh investment into the digital economy and push technology firms further up the value chain, boosting demand for AI specialists, cybersecurity professionals and cloud engineers. Consumers would gain from better AI-powered healthcare, education, financial services and public administration. But the gains would not be shared evenly: workers with advanced digital skills would capture a disproportionate share, while routine knowledge jobs would come under mounting pressure.

A bust would look different from a Wall Street correction. Investment in data centres and AI infrastructure could slow sharply, straining the banks that finance such projects. States with already-stretched finances would find it harder to sustain employment if tax revenues weakened. The RBI would have limited room to help if capital outflows hit the rupee and stoked imported inflation. Most consequentially, India’s IT services industry could see slower growth if global clients cut spending on AI and digital transformation. Where American markets might absorb much of the adjustment through asset prices, India’s risks would be spread across banks, state governments and the labour market itself.

The bottom line

The boom-or-bust framework -- infrastructure, platforms, applications, enterprise adoption, end-users; structural versus cyclical demand; who captures the economics -- remains a useful lens for thinking about AI’s future anywhere. But India’s story will not simply mirror America’s. A young workforce, a large informal economy, a bank-led financial system and a globally competitive IT services industry give the country its own distinct set of opportunities and risks. A boom could accelerate productivity, strengthen exports and support long-term growth; a bust would expose structural weaknesses in employment, state finances and bank lending rather than trigger the market dynamics familiar from developed economies.

For investors and policymakers, the lesson is straightforward: analyse AI in India through India’s own economic realities, not assumptions borrowed from elsewhere. The technology may be global. Its consequences will remain deeply local.

References

• Association of Mutual Funds in India. (2026). SIP contribution data.

• Business Today. (2026). IT industry likely to hire 135,000 employees in FY26, projects Nasscom.

• Enterprise Times. (2026). Top skills employers prioritise in 2026 as AI redefines jobs across industries.

• Ministry of Finance, Government of India. (2026). Union Budget 2026–27.

• NASSCOM. (2026a). India’s services sector and the AI opportunity.

• NASSCOM. (2026b). India’s workforce transformation opportunity in the AI era.

• New Indian Express. (2026). Reserve Bank of India says IT services exports resilient despite growing AI concerns.

• Press Information Bureau. (2025). Periodic Labour Force Survey Annual Report.

• PRS Legislative Research. (2025). State of State Finances.

• Rees, D. (2025). AI economic scenarios: Revolutionary growth, or recessionary bubble?

The company argues that its fortunes do not depend on whether a drug eventually secures regulatory approval. Instead, it generates revenue from the scientific work that supports a mole…

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Some of the report’s evidence is hard to dismiss. The correlation between American shares and the Bloomberg US Aggregate Bond index, just 0.13 over the past 25 years, has risen to 0.66 since 2022, undermining the basic logic of stock-bond diversification. Index overlap is real, too: the S&P 500, the Nasdaq Composite and the MSCI World index now share nearly identical top holdings, meaning an investor who holds all three may simply be tripling a bet on a handful of technology giants. And the report’s point about passive drift is well made: years of strong equity returns have nudged many 60/40 portfolios towards 77/23 without anyone deciding to take on more risk.

The valuation argument is plausible but more contestable. Apollo notes that the equity risk premium, though near its long-run average, sits well below its 20-year norm, and that credit spreads are near multi-decade tights. Fair enough -- but “spreads are tight” has been a refrain among credit strategists for several years now, with limited predictive power for when, or whether, they widen. Readers should treat this as a description of current pricing, not a forecast.

What the report does not dwell on is the cost of its own prescription. Private markets are illiquid by design; investors give up daily pricing and ready access to capital in exchange for an illiquidity premium that is not guaranteed. Fees are typically higher than for public funds. And manager selection, which Apollo itself highlights as critical -- citing a 30-percentage-point gap between top- and bottom-decile private-equity managers, against just two points in public equities -- is a double-edged argument. It implies that getting it wrong in private markets is far costlier than getting it wrong in an index fund, not merely that getting it right pays more.

There is also an obvious point worth stating plainly: this is marketing literature from a firm whose revenue depends on growing private-markets allocations, not independent research. The data points are real and mostly verifiable, but the framing -- nine “takeaways,” a confident tone about timing being favourable now -- serves Apollo’s commercial interest as much as the reader’s.

None of which makes the underlying diagnosis wrong. Diversification within public markets probably has weakened, and many portfolios likely have drifted further into equities than their owners realise. The sensible response is to take the data seriously while treating the prescription -- more private markets, ideally through Apollo -- with the scepticism due any pitch from the firm offering the cure.

A company may be growing rapidly but trade at a demanding valuation. Another may be attractively priced but struggle to grow. A third may be a high-quality business that the market has temporarily fallen out of love with.

That is precisely why we developed our proprietary stock scoring system.

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The overlap point is real and worth taking seriously. Index providers score constituents along a continuum of value-like and growth-like characteristics rather than assigning each company to a single camp, so a stock can draw weight in both benchmarks at once. The paper’s examples are well chosen: Alphabet, Amazon and Meta, conventionally filed under “growth,” now generate the kind of free cash flow and capital returns historically associated with value; Coca-Cola and American Express, filed under “value,” have produced growth-like earnings durability. That such firms can be the same business viewed through two lenses is a fair description of how index methodology actually works.

Where the paper is less convincing is in its leap from “the boundary is blurry” to “drift is therefore not really drift.” It frames style deviation as evidence of “process discipline rather than process breakdown,” but this is precisely the distinction any manager who has strayed from a mandate would want to draw. A value fund that increasingly holds stocks the market calls growth might indeed be following sound, repeatable logic -- or it might simply be chasing performance and redescribing the result as philosophy. The paper offers no external test for telling the two apart; it asks investors to trust the manager’s own account of its discipline.

The “Three Circle” framework -- valuation, fundamentals, momentum -- is a sensible enough description of how many active managers actually pick stocks, value-labelled or not. The “two-circle” watch list, holding pre-vetted names that satisfy two of three criteria and await the third, is a reasonable way to stay ready for dislocations. But it is also, structurally, a licence to hold growth-flavoured names on the basis that fundamentals and momentum are good enough, with valuation discipline promised as something that will eventually catch up. That is a defensible investment approach. It is a different thing from a finding that growth and value are not “mutually exclusive economic categories.”

None of this means Boston Partners’ process is poor; a 30-year record, cited in the paper, is at least suggestive that something in the approach works. But the paper is marketing material, explicitly labelled as such, written by a value house at a moment when growth stocks have dominated returns for years and rigid style boxes have arguably cost value managers assets. A philosophical case for flexibility is, among other things, a timely case for not being held to one.

The Nifty 50 Value 20 selects 20 supposedly cheap stocks from the Nifty 50, India’s blue-chip benchmark, and is tracked by six exchange-traded funds and index funds, together holding roughly ₹14.5bn (about $153m at current exchange rates) in net assets:

Nippon India’s index fund alone accounts for more than 60% of the total; the rest is split across smaller offerings from HDFC, ICICI Prudential and Kotak. On June 10th NSE Indices announced that the entire stock-selection formula behind the index would change, alongside its rebalancing schedule and the way it weights its constituents. The revision brings the Nifty 50 Value 20 into line with the Nifty 200 Value 30, a sister index with a wider, mid-cap-inclusive universe that already used the new approach. The result, effective from June 30th, is a meaningfully different index wearing the same name, and one that every rupee in those six funds is now obliged to follow.

What changed, in the accounting

The old formula scored each stock on four factors: return on capital employed (40% of the score), the price-to-earnings ratio (30%), the price-to-book ratio (20%) and dividend yield (10%). It also required stocks to comply with insurance-regulator dividend norms, a small but specific filter. The new formula drops that filter entirely and replaces the old ratios with four different ones, each weighted equally at 25%: earnings-to-price, sales-to-price, book-to-price and dividend yield.

This is a more substantial change than swapping one ratio for another. Return on capital employed measures how efficiently a company turns capital into profit; it says nothing about price. Earnings-to-price and book-to-price, by contrast, are inverted versions of the price-to-earnings and price-to-book ratios already in use, just expressed as yields rather than multiples. Sales-to-price is genuinely new to this index, screening for revenue generated per rupee of market value, a metric that tends to favour low-margin, capital-intensive businesses such as commodity producers and utilities over asset-light ones such as consumer brands or software firms. Dropping return on capital employed removes the only factor that had anything to do with how well a business is actually run; the new four-factor set is concerned purely with how cheap a stock looks relative to its fundamentals, with no allowance for the quality of those fundamentals.

The weighting change compounds this. Under the old scheme, return on capital employed counted for nearly half the score, meaning a company had to be both profitable and statistically cheap to qualify. Under the new scheme, all four ratios count equally, which mechanically gives more weight to a single cheap statistic, since no other measure can offset it. A stock with a rock-bottom price-to-book ratio because the market suspects its assets are about to be written down can now score just as well as one that is cheap and fundamentally sound.

Three further changes accompany the scoring overhaul. The number of stocks guaranteed a place in the index regardless of score, the “compulsory inclusion” list, doubles from the top five to the top ten, which loosens the index’s grip on its highest-scoring names and makes the bottom half of its 20 holdings more sensitive to the new formula. The review and rebalancing calendars, previously annual and quarterly respectively, both move to a shared semi-annual cycle in June and December, which means the index will be reconstituted less often than before. And the weighting methodology shifts from plain free-float market capitalisation to what NSE calls “tilt” weighting, multiplying free-float market capitalisation by the value score, so a company that is both large and cheap by the new metrics gets a disproportionately larger slice of the index than its market value alone would justify.

The reshuffle that followed

The practical effect showed up immediately in the index’s holdings. Bajaj Auto, Cipla, Dr Reddy’s Laboratories, Maruti Suzuki and Tech Mahindra were dropped; Bajaj Finserv, Grasim Industries, HDFC Bank, Reliance Industries and Tata Steel took their places. None of those exits were forced by any change in the companies’ underlying business. They left because a different formula, applied to the same set of companies on the same day, produces a different ranking. That is the clearest illustration of what a methodology change actually does: it does not discover new information about which companies are cheap, it redefines what “cheap” means.

The case for the change

The strongest argument for the new approach is consistency. With the Nifty 50 Value 20 and Nifty 200 Value 30 now sharing one formula, NSE has a single, simpler story to tell investors about what “value” means across its suite of indices, rather than maintaining two competing definitions that could, in principle, recommend opposite things about the same stock. Earnings-to-price and book-to-price are also more conventional value metrics internationally than return on capital employed, which is more commonly associated with quality-factor investing; aligning with standard usage makes the index easier to benchmark against global value indices and academic factor research. The shift to semi-annual rebalancing, meanwhile, should reduce portfolio turnover and the trading costs that come with it, a genuine benefit for anyone holding the index through a fund.

The case against

The drawbacks are not trivial. Removing return on capital employed strips out the index’s only check on business quality, leaving a pure-price screen that is more exposed to value traps, companies that are cheap because the market has correctly priced in genuine trouble, not because it has made a mistake. Equal-weighting the four remaining ratios, rather than letting profitability dominate as before, makes that risk somewhat more acute: a structurally troubled company can no longer be filtered out by a poor score on capital efficiency, since that factor is no longer part of the test. The doubled compulsory-inclusion list and the new tilt-weighting scheme also mean the index’s composition is now less purely a function of valuation and more a function of size multiplied by valuation, which can quietly increase concentration in already-large stocks. And the move to less frequent rebalancing cuts both ways: lower turnover is good for costs, but it also means the index reacts more slowly when a stock’s genuine value characteristics change, whether for better or worse, leaving investors exposed to a stale snapshot for longer between reviews.

None of this makes the new methodology wrong. It is a coherent, internationally conventional way to define a value index, and it is now consistent across two of NSE’s products rather than one. But anyone holding a fund that tracks the Nifty 50 Value 20 should understand that the index they own changed in substance on June 30th, not just in its list of holdings. The ratios behind the name, not merely the names inside it, are what determine what kind of “value” an investor is actually buying.

The report’s central conclusion is remarkably consistent with financial history: productive assets have significantly outperformed stores of value over long horizons. Across the median 200-year dataset, global inflation-adjusted returns averaged approximately 4.9% annually for equities and 4.2% for a traditional 60/40 portfolio, compared with 2.6% for government bonds and just 0.4% for gold. Cash held without earning interest generated negative real returns over time, illustrating how inflation steadily erodes purchasing power.

Economic growth forms the foundation of these returns. According to the study, nominal GDP growth ultimately drives corporate earnings, household incomes, government revenues, and therefore asset prices. However, the authors caution that developed economies are entering a period of structurally slower nominal and real GDP growth, reflecting weaker productivity gains and increasingly unfavourable demographic trends. Many countries are projected to experience shrinking working-age populations over the coming decades, potentially reducing future economic expansion unless offset by higher productivity or technological advances such as artificial intelligence.

Perhaps the report’s most practical insight concerns valuation. Across decades and multiple countries, starting valuations consistently proved to be one of the strongest predictors of long-term investment returns. Markets trading at lower price-to-earnings or CAPE ratios historically delivered substantially stronger returns than expensive markets. Similarly, government bonds purchased when yields were relatively high generated better long-term outcomes than bonds bought during periods of exceptionally low interest rates. Rather than attempting to forecast economic cycles, investors may improve long-term results simply by paying attention to the price they pay for assets.

The study also challenges several common assumptions. Gold, despite its impressive performance in the twenty-first century, has delivered relatively modest real returns over the past two centuries. Likewise, the negative correlation between equities and bonds that investors became accustomed to after the Global Financial Crisis appears to have been the exception rather than the historical norm, particularly in higher-inflation environments.

Ultimately, the report reinforces a timeless lesson. Long-term investing is not merely about choosing the right asset class but about combining patience, sensible diversification, and disciplined valuation. Markets will continue to fluctuate, economies will evolve, and policy regimes will change. Yet over centuries of evidence, investors who remained invested in productive assets while maintaining reasonable entry valuations have consistently been rewarded. That enduring relationship between economic growth, valuation, and compounding remains one of the strongest foundations for successful long-term investing.

Learning: Asset allocation is the key criteria to wealth generation, a single asset class runs different kinds of risk.

Source: www.dbresearch.com/research-institute

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