Asset Allocation & Tax-Aware Rebalancing

1. What you'll learn and why it matters

The two chapters before this one were about the building blocks: what mutual funds and ETFs are and how to pick one (chapter 14), and how each kind of fund is taxed (chapter 15). This chapter is about the whole portfolio. It answers three questions that matter more to your long-run result than any single fund choice:

1. How much in each asset class? An asset class is a group of investments that behave alike because the same things drive their returns: Indian shares, foreign shares, bonds, gold, property, cash. Asset allocation is the split of your money between them, for example 60% equity, 30% debt, 10% gold.
2. How to plan for specific goals: an emergency fund, a child's education in 12 years, retirement in 25.
3. How to keep the mix on target (rebalancing) without paying more tax than you need to.

You will learn, with real NAVs to 30 Sep 2026: the long-run return, volatility, worst falls and correlations of each Indian asset class; how much each one helped in the 2020, 2022, 2024–25 and 2026 equity falls; how to size an emergency fund and fund a goal with a step-up SIP (systematic investment plan: a fixed monthly investment into a fund); the main allocation models and their weak points; whether valuation-based ("tactical") shifts helped; what hybrid funds do for you; whether rebalancing adds return or only reduces risk, before and after tax; a tax-aware rebalancing playbook with a fully worked ticket; how to write your own Investment Policy Statement; and how withdrawals in retirement interact with the order of returns.

This chapter builds on earlier ones rather than repeating them: diversification maths is in chapter 11, part 3; the market's P/E and bond yields in chapter 8, part 14; the core/satellite "three buckets" of your own framework in chapter 17, part 3; listed-equity tax in chapter 13, part 7; and the harvesting of long-term capital gains (LTCG; gains on assets held long enough to count as long-term, as opposed to short-term capital gains, STCG) in chapter 13, part 14 (for funds, chapter 15, part 10).

2. The asset classes open to an Indian investor

Varsity's asset-allocation chapter starts from a simple idea: you cannot avoid owning assets, because cash itself loses value to inflation; and since nobody can predict which asset will do best next decade, the sensible answer is to own a little of several whose returns come from different drivers. Owning five large-cap funds is not diversification. It is sometimes called "diworsification": more funds, same driver.

Here are the main classes, what drives each, and how a fund holding it is taxed. The tax class matters for rebalancing, because it decides what a sale costs you. Chapter 15, part 3 has the full rules; the short version for 2026-27 is in the last column.

Three points beginners miss:

• Your EPF, PPF and house are part of your allocation. If ₹8 lakh of your ₹20 lakh of savings is in EPF and PPF, you already hold 40% "debt" before you buy a single debt fund.
• Debt is there to be stable, not to earn the most. Varsity's review chapter advises sticking to high-quality (government and AAA) bonds and short-to-intermediate maturities, and avoiding credit-risk funds unless you are an expert in debt. Chapter 14, part 8 explains duration and credit risk.
• International equity diversifies two ways: different companies (US markets are dominated by technology, India's by financials and older-economy businesses), and a different currency. When the rupee weakens, rupee returns on a US fund go up.

Two smaller classes, measured on real data: the Nippon India Silver ETF returned 30.4% a year from its launch on 4 Feb 2022 to 30 Sep 2026, but with a 44.4% fall along the way and 37% annual volatility. Embassy Office Parks REIT, India's first REIT, returned 10.85% a year from 1 Apr 2019 including distributions (Yahoo's distribution-adjusted price), with a 33% maximum fall. Both are optional extras; neither is needed for a sound plan.

3. Thirteen years of real data: return, risk and drawdowns

To compare asset classes fairly, we use one fund per class with a daily NAV history from 2 Jan 2013 (when "direct" plans began) to 30 Sep 2026: 13.74 years. A fund's NAV (net asset value per unit, defined in chapter 14) already includes reinvested dividends and is net of the fund's expenses, so it is the return you would actually have had. The funds are listed in the Sources.

Four measures, each defined once:

• CAGR (compound annual growth rate): the steady yearly rate that turns the start value into the end value, \( \text{CAGR} = (V_{end}/V_{start})^{1/\text{years}} - 1 \). See chapter 1.
• Volatility: the standard deviation of monthly returns, scaled to a year by multiplying by \( \sqrt{12} \). It measures how bumpy the ride is.
• Maximum drawdown: the largest fall from a previous peak to a later low, on daily NAVs. It measures how bad it can feel.
• Worst 12-month and worst 3-year return (annualised), from month-end NAVs: the worst outcome for someone who needed the money after one or three years.

How to read these numbers

• The past decade is one sample. The Nasdaq 100's 24.3% a year combined a historic US tech boom with a weaker rupee: the dollar went from ₹54.68 to ₹96.05 over the period, 4.2% a year, and every rupee of that is in the ETF's return. Planning on it repeating is how people get hurt. Varsity's own planning chapters use deliberately conservative assumptions (about 10–11% for equity), and so will we in part 7.
• Gold is not a steady asset. It lost money in 2013, 2014 and 2015 (−6.6%, −9.3%, −7.5%), was below its January 2013 level until 2019, then gained 71.8% in 2025 alone. Its 11.0% CAGR depends heavily on the end date.
• Debt funds can fall. The gilt fund dropped 11.9% from its peak when interest rates rose. Only liquid and short-duration funds were close to "always up".
• Worst 3-year returns tell you the honest minimum horizon: the Nifty 50 fund still lost money over some 3-year windows (−1.1% a year) and the midcap ETF lost 11.6% a year. Money you need within three years does not belong in equity.

4. Correlation, and what each asset did in four falls

Correlation (ρ, "rho") measures how two assets' returns move together, from +1 (always together) through 0 (unrelated) to −1 (always opposite); chapter 11 has the maths. Diversification works when you combine assets with low or negative correlation, because one's bad months are partly offset by the other's.

The formula from chapter 11 shows why gold helps despite being as volatile as the Nifty. For two assets with weights \(w_1, w_2\), volatilities \(\sigma_1, \sigma_2\) and correlation \(\rho\):

Correlations are averages, and they change. What matters most is what an asset does when you need it: in a sharp equity fall. The chart measures each fund from the Nifty 50 fund's peak to its trough in the four falls of the last seven years.

• Mid and small caps fall at least as hard as large caps in a crash. They are more equity, not a diversifier.
• International equity is not a hedge: in March 2020 and in 2022 it fell with India; in 2024–25 and 2026 it held up. Over the whole calendar year 2022 the Nasdaq 100 ETF lost 25.8% in rupees while the Nifty 50 fund made 5.4%. It diversifies across years more than across crashes.
• Gold was the most reliable cushion in these four episodes, and short-duration debt the most reliable "dry powder" (money ready to buy equity cheaply).
• Varsity's warning stands: as more assets become easily traded (ETFs, leverage), correlations can rise exactly when you need them low. Diversification lowers risk; it does not remove it.

Why not let an optimiser pick the "best" mix?

Varsity's Module 9 shows how to use the same formula, extended to many assets with a correlation matrix, to find the mix with the highest expected return for a given risk: portfolio optimisation, which traces the "efficient frontier". We tried it with Nifty 50, Midcap 100, Nasdaq 100, short-term debt and gold, using past average returns as the "expected" returns and asking for the best mix at the volatility of a 60/30/10 portfolio (no short selling):

• 2013–2026 data: 0% Nifty 50, 14% midcap, 48% Nasdaq 100, 25% debt, 13% gold.
• 2013–2019 data only: 0% Nifty, 10% midcap, 53% Nasdaq, 38% debt, 0% gold.
• 2020–2026 data only: 0% Nifty, 17% midcap, 32% Nasdaq, 11% debt, 41% gold.

The optimiser simply loads up on whatever did best in the sample it was given, and its answer swings wildly when the sample changes (gold from 0% to 41%). The 2013–2019 answer happened to keep working, because the Nasdaq kept winning; nobody could have known that in 2020. Optimisers are very sensitive to the expected returns you feed them, and those are exactly what nobody can forecast. For a personal portfolio, round and robust weights you can stick with (part 8) beat "optimal" weights estimated from one decade.

5. Why allocation drives most of the outcome

A portfolio's return is the weighted sum of its parts, which Varsity's retirement chapter calls the "sum product":

That is the weighted average of the three CAGRs above, for a 60/30/10 mix of the Nifty fund, the short-term debt fund and GOLDBEES. (The realised CAGR of the actual portfolio differs a little, depending on rebalancing; see part 12.) Risk, by contrast, is less than the weighted average: the same mix had a volatility of 9.59% computed from the measured correlations, against a weighted average of the three volatilities of 11.53%.

The famous "90%" claim, and its caveats

In 1986 Brinson, Hood and Beebower studied 91 large US pension funds over 1974–1983 and found that the policy mix (how much in shares, bonds and cash) explained 93.6% of the variation in each fund's quarterly returns over time. Stock selection and market timing explained little. This is often repeated as "asset allocation explains 90% of returns". In 2000 Ibbotson and Kaplan clarified what the number means:

• About 90% of the ups and downs of one portfolio over time come from its allocation, mostly because all portfolios rise and fall with the markets they hold.
• Only about 40% of the differences between two investors' returns is explained by their different allocations; fund choice, costs and timing explain the rest.
• On average about 100% of the return level comes from the allocation, because active choices, after costs, roughly cancel out.

The practical lesson: get the big split right first, keep costs low, and do not expect fund-picking to rescue a wrong allocation. Varsity's review chapter puts it in one line: asset allocation, risk management and behaviour determine your returns, not picking the "best fund".

6. Risk capacity, tolerance and need; the emergency fund first

Three different questions decide how much equity you should hold. Planners keep them separate:

Your equity share should respect the lowest of the three. A 25-year-old with a secure job has high capacity, but if she sold everything in March 2020 her tolerance was lower than her plan. Someone who has already saved enough for retirement has low need and gains little from taking more risk. Your human capital (the present value of your future earnings, which Varsity's review chapter calls your biggest asset) matters too: a salaried government employee's income behaves like a bond, so she can hold more equity; a trader or business owner whose income already depends on markets should hold less.

A quick test of tolerance using part 3: in early 2020 the Nifty 50 fund fell 38.4% in ten weeks. With ₹10 lakh in a 60/30/10 portfolio, the equity part would have gone from ₹6 lakh to about ₹3.7 lakh. If that thought makes you want to sell, lower the equity share now, while markets are calm.

The emergency fund comes before any allocation

Varsity's portfolio chapter makes two assumptions before building any fund portfolio: you have term life and health insurance, and you have an emergency fund, money for job loss, repairs or hospital deposits that you can reach in a day. Common advice is 3 to 12 months of expenses: fewer if your job is secure and you are young, more if your income is irregular. Varsity's point is that liquidity matters more than return here, and that you can build it up with a SIP rather than all at once.

Where to keep it: part in your savings account, the rest in a liquid fund (a debt fund holding instruments of up to 91 days). Many liquid funds offer instant redemption of up to ₹50,000 (or 90% of the holding, if lower) per day, and liquid funds charge only a tiny exit load for the first six days.

7. Goal-based buckets and a worked goal plan

Varsity defines a financial goal by three things: the amount needed, the time available and your age (which signals how much risk you can carry). Instead of one big portfolio, many investors keep a bucket per goal, each with an allocation that suits its deadline:

Two formulas from chapter 8's time-value-of-money section do all the work. First, inflate today's cost to the goal date at the goal's own inflation rate (education and healthcare usually run above general inflation):

Then find the monthly SIP that grows to that amount. With a monthly rate \( i = (1+r)^{1/12} - 1 \) for an expected yearly return \( r \), and \( N \) monthly instalments paid at the start of each month:

A step-up SIP raises the instalment by a fixed percentage each year, in line with your pay. Varsity's review chapter shows why this matters: a ₹10,000 SIP growing 10% a year at 9% beats a flat ₹10,000 SIP at 13% over 30 years. Your savings rate, which you control, matters more than the return, which you do not.

8. Allocation models: age rules, fixed mixes, glide paths, core-satellite

Age-based rules ("100 minus age")

The old rule says hold (100 − your age)% in equity: 70% at 30, 40% at 60. It is easy, and it captures one truth: your horizon shortens as you age. Its weaknesses: it ignores your goals (a 30-year-old saving for a house in 3 years should not hold 70% equity for that goal), your other assets (EPF, a paid-off house, a pension), your income stability and your tolerance. With longer lifespans some use "110 or 120 minus age". Treat any of them as a starting point, then adjust for parts 6 and 7.

Fixed strategic mixes

A strategic allocation is a long-term target you keep through good and bad markets, such as 60/30/10 equity/debt/gold. Varsity's beginner advice for anyone unsure is simple: a few big, liquid classes (a large-cap index fund, a mid-cap fund, US equity via a cheap S&P 500 fund, a short-term government/PSU bond fund, gold, perhaps REITs), with roughly equal or sensible weights, and then stick to it. The value of a fixed mix is behavioural: it is a decision made calmly, in advance.

Glide paths and SEBI's Life Cycle Funds

A glide path lowers equity on a schedule as a goal approaches, which protects the corpus against a crash just before you need it (the "sequence risk" of part 18). Varsity's portfolio chapter suggests starting to move a 10-year goal's money into debt from about year 8. In February 2026 SEBI created a new fund category that does this for you: Life Cycle Funds, target-date funds of 5 to 30 years (in multiples of 5) that must follow a prescribed glide path, and that replace the old "solution-oriented" retirement and children's funds (which stopped taking new money).

Two features to note: an exit load of 3% in the first year, 2% in the second and 1% in the third, to discourage early exits; and the fund's internal shifts from equity to debt are not taxable events for you. How the fund itself is taxed on your exit depends on its average equity share, which falls over time; check the factsheet's tax note, and see chapter 15.

Core-satellite

A core-satellite portfolio keeps most money (the core, say 80–90%) in a low-cost, diversified, rebalanced allocation, and a small part (the satellite) for active ideas: stock picks, a sector bet, or your swing-trading account. This is exactly the structure of your framework's three buckets in chapter 17, and the reason for the portfolio-heat and correlation limits in chapter 11. The core's allocation is decided by this chapter; the satellite's size is capped so that its failure cannot hurt a goal.

9. Tactical allocation using valuations, tested honestly

Tactical asset allocation means moving away from your strategic mix for a while based on a view, for example holding more equity when the market looks cheap and less when it looks expensive. Chapter 8 showed that the Nifty 50 P/E (price-to-earnings ratio) has some link with the next year's return, and warned that its history is short: NSE switched to consolidated earnings on 31 Mar 2021, so only the series since April 2021 is comparable.

We tested a simple rule on that comparable period: hold 70% equity when the P/E at the previous month-end was below 21, 60% between 21 and 24, and 50% above 24, the rest in the short-term debt fund, rebalanced monthly. The static alternative is a constant 60/40.

If you want a valuation tilt, keep it small (±10 points around your strategic weight), decide the rule in advance, write it into your Investment Policy Statement (part 17), and carry it out mainly by steering new money, not by selling. Varsity's review chapter suggests a gentler version: when rebalancing back into equity after a fall, put more of it into the sub-class that has fallen most.

10. Hybrid funds: rebalancing done for you

A hybrid fund holds more than one asset class inside one scheme, and the fund manager keeps the mix in range. SEBI's categorisation circular of 26 Feb 2026 sets the ranges:

The big advantage is tax: the fund's internal rebalancing is not a sale by you, so it creates no capital gain in your hands. You pay tax only when you redeem, under the fund's tax class. If a BAF qualifies as equity-oriented, you get equity taxation on what is effectively a mix of equity and debt, which is often better than holding a debt fund taxed at slab. The disadvantages: one manager's model controls your risk, the expense ratio is higher than a DIY index-fund mix, and you cannot see or control the exact allocation.

Read this table carefully, because the obvious conclusion ("hybrids win") is not safe:

• Selection bias. These are four large, surviving funds chosen because they have long histories. Weaker hybrid funds that were merged or closed are not in the list.
• "Balanced" names hide very different risk. HDFC Balanced Advantage fell 34.2% at its worst, which is close to the Nifty's 38.4%. SBI Multi Asset fell only 17.6%. Compare the drawdown, not the label.
• Different ingredients. The hybrids held active stock picks, mid caps and (in multi-asset funds) commodities and arbitrage; the DIY mix held only the Nifty 50. Part of their lead is active management having a good decade, which the SPIVA evidence in chapter 14, part 9 says does not reliably persist.

When a hybrid fund makes sense: smaller portfolios where the simplicity is worth more than the extra expense ratio; investors who will not rebalance themselves; and debt-like allocations where the equity-oriented tax class is valuable. A BAF can be the "debt plus a bit of equity" bucket for a 3–7 year goal, for example.

11. Rebalancing: why, and three ways to do it

Left alone, a portfolio drifts: the asset that rose most takes a bigger share. Varsity's review chapter gives the example of a 60/30/10 portfolio that becomes 70/25/5 after a good year for equity. Its risk is now that of a 70% equity portfolio, which you never chose. Rebalancing means trading back towards the target: selling some of what is overweight and buying what is underweight. Two reasons to do it:

1. Risk control: keep the risk you chose. This is the main reason.
2. A built-in "buy low, sell high": you trim what has risen and add to what has fallen. Whether this adds return depends on whether the assets take turns (it helps) or one keeps winning (it hurts). Part 13 tests both cases.

Define the drift of asset \(i\) as its current weight minus its target weight, and the trade that restores the target:

The three methods

A common band rule is the "5/25 rule": rebalance when an asset is more than 5 percentage points away from target or more than 25% of its own target away, whichever is tighter. For 60% equity the band is 55–65% (5 points is tighter than 15). For 10% gold it is 7.5–12.5% (2.5 points is tighter than 5). Varsity's review chapter suggests an annual check with a 5% tolerance band.

Cash-flow rebalancing splits each new instalment \(C\) in proportion to how far each asset is below target after adding it:

12. Does rebalancing pay? The before-tax backtest

We tested a 60/30/10 portfolio built from three real funds: the UTI Nifty 50 Index Fund (direct), the ICICI Prudential Short Term Fund (direct) and GOLDBEES. ₹10 lakh was invested on 2 Jan 2013 and held to 30 Sep 2026, with the actual daily NAVs, purchase stamp duty, STT, ETF exchange charges and an assumed 0.05% spread on each ETF trade. The engine keeps every purchase as a separate lot (a batch of units bought on one date) and sells them first-in-first-out (FIFO), as the law requires (chapter 13). First, the before-tax picture:

What the before-tax test says:

• Rebalancing reduced risk: volatility fell from 10.3% to about 9.4–9.6% and the worst fall from −25.4% to about −23.2%.
• It also added a little return in this sample, 0.2–0.4 points a year, because Indian equity, debt and gold took turns: gold was bought cheaply in 2013–2018 and trimmed after its rallies; equity was bought after March 2020.
• Starting in January 2015 instead gives the same ordering with smaller gaps: 10.05% (never), 10.15% (annual), 10.43% (bands) a year.
• Trading was light: 2–3% of the portfolio a year. Costs over 13¾ years were a few hundred rupees.

13. After tax and costs: five methods for a SIP investor

Now the realistic case: ₹10 lakh on 2 Jan 2013 plus ₹20,000 on the first trading day of every month (₹42.8 lakh paid in), a 30% slab, and every sale taxed under the 2026-27 rules from chapter 15 as if they had always applied: equity fund 20% short-term, 12.5% long-term above ₹1.25 lakh a year (with "grandfathering" for units bought before 1 Feb 2018: gains up to their 31 Jan 2018 value are not taxed); debt fund at slab for units bought from 1 Apr 2023, and 12.5% after 24 months for older units; gold ETF 12.5% after 12 months, slab before; 4% cess; losses set off and carried forward as the law allows. Tax due on a sale is paid out of the portfolio on the day. At the end we also compute the exit tax: what you would owe if you sold everything on 30 Sep 2026.

The five methods: never rebalance; annual (December); bands; cash-flow only (all SIPs to the underweight assets, never sell); and tax-aware: cash-flow SIPs, plus a check each March in which overweight assets are sold only from lots that are already long-term (and equity only up to the year's unused ₹1.25 lakh exemption), plus harvesting of equity LTCG up to the exemption each March (sell, buy back the next day; chapter 13).

XIRR (defined in chapter 14, part 12) is the annual return on your own cash flows. "Rebalances" counts the dates on which a sale was made to restore the mix; the cash-flow method never sells, and the tax-aware method's yearly harvests are not counted.

What explains the differences, honestly

• Rebalancing taxes were small: ₹69,000–₹1.02 lakh over 13¾ years, against an exit tax of ₹6.3–7.9 lakh that every method carries. The fear that "rebalancing will kill me in tax" was not borne out for this low-turnover portfolio, partly because equity sales mostly fell inside the yearly ₹1.25 lakh exemption.
• The month you choose matters as much as the method. The same annual rule done each March instead of each December ends at ₹1,07,47,068 after tax, ₹3.70 lakh more. The chart below repeats the lump-sum test for all twelve months: before tax the result ranges from ₹39.80 lakh (December) to ₹41.89 lakh (March). That spread is luck, not skill.
• The tax-aware method's lead splits into two parts. Without the harvesting step it ends at ₹1,07,61,105, almost the same as plain March rebalancing (₹1,07,47,068) but with 3 rebalancing dates instead of 14 and less tax paid along the way (₹1.08 lakh against ₹1.26 lakh). Harvesting then adds about ₹1 lakh, mostly by lowering the exit tax from ₹7.14 lakh to ₹6.28 lakh. The harvesting is the part that reliably helps; the rest is timing.
• Cash-flow rebalancing alone kept risk almost as low as the others (9.4% volatility, −23.2% worst fall) with zero tax along the way. Its limit showed late: by 2025–26 the portfolio was over ₹1 crore and a ₹20,000 SIP could not offset gold's surge.

14. The tax-aware rebalancing playbook

The goal is to get close to your target allocation while paying the least tax, in roughly this order of preference:

1. Use new money first. Direct SIPs, bonuses and dividends to the underweight asset (cash-flow rebalancing). No sale, no tax, no exit load.
2. Do not trade small drifts. Use bands (part 11) and skip any trade smaller than, say, 1% of the portfolio or ₹10,000. Small trades add work, lots and tax records for no benefit.
3. Rebalance in February–March, inside the tax year's exemption. Equity sales that produce long-term gains up to ₹1.25 lakh in a tax year are tax-free (shared with your share sales; chapter 13). If you must sell equity, size the sale so that the long-term gain fits in what is left of the exemption.
4. Sell long-term lots, and wait for the 12/24-month mark when it is close. FIFO decides which units go (you cannot choose lots within one folio; see chapter 15, part 5), so check the date of the oldest lot. A gold ETF lot held 11 months is taxed at your slab; once it has been held more than 12 months (exactly 12 months is still short-term, so wait a few days past the anniversary), at 12.5%. For a gold or international FoF the line is 24 months.
5. Use losses. A short-term loss can absorb any capital gain; a long-term loss only long-term gains (chapter 15, part 9). If a holding is below cost, redeeming it and reinvesting in a similar but different fund banks a loss to offset the gain on what you trim. Under FIFO, losses in recent lots are stuck behind older gains in the same folio; chapter 15, part 11 shows the trap and the rules.
6. Prefer trimming the asset with the lowest tax per rupee sold. Tax per rupee = (gain ÷ sale value) × rate. Equity inside the exemption: zero. A gold ETF lot that has doubled: 50% gain share × 13% = 6.5% of the sale. A post-2023 debt fund at 30% slab with a 15% gain share: 4.7%.
7. Avoid exit loads. Most equity funds charge 1% if redeemed within a year; Life Cycle Funds 3/2/1%. Check before every sale.
8. Consider hybrid funds for the part you would rebalance most (part 10): their internal rebalancing creates no tax for you, but check their tax class and costs.
9. Think about asset location across the family (below).
10. Harvest every year anyway. Even when you do not need to rebalance, book equity LTCG up to ₹1.25 lakh each March and buy back the next trading day (chapter 15, part 10). In the backtest this was the single most reliable tax saving.

Asset location across family members, and clubbing

Asset location means choosing whose name (or which account) holds each asset so the family's total tax is lowest. Each adult has a separate ₹1.25 lakh equity exemption, a separate basic exemption and slab, and a separate ₹12 lakh rebate limit for slab-rate income. Debt-fund gains are slab-rate income, so the rebate can cover them, unlike STCG/LTCG on equity (chapter 13, part 5).

15. A worked rebalancing ticket on 30 Sep 2026

A "ticket" is a written record of a rebalancing decision: where you are, where you want to be, the trades, the lots, the tax and the alternative you rejected. Here is one for a portfolio built from real NAVs: ₹10 lakh invested on 1 Oct 2021 in 60/30/10 (UTI Nifty 50 Index Fund, ICICI Pru Short Term Fund, GOLDBEES), then ₹25,000 on the first trading day of each month, split 60/30/10, never rebalanced. ₹24.75 lakh paid in by 1 Sep 2026.

16. Implementation: one fund per asset class, and a review rhythm

Varsity's portfolio chapter warns against the "10–12 funds for one goal" portfolio (overlap is measured in chapter 14, part 14): three large-cap funds from different fund houses hold mostly the same stocks, so you pay three expense ratios for one exposure. A clean portfolio has one vehicle per asset class, sometimes two.

Fund or ETF?

• Index fund: easy SIPs and withdrawals on any amount, units bought at NAV, no demat needed. Stamp duty 0.005% on purchases.
• ETF: often a lower expense ratio, bought and sold on the exchange during the day, needs a demat account; stamp duty 0.015% on purchases, a small spread, and the risk of paying a premium above NAV.

Review rhythm

• Monthly (5 minutes): SIPs went through; note the allocation.
• Quarterly: any asset outside its band? If yes, plan the rebalance (new money first).
• Yearly (February–March): harvest, rebalance, review each fund against its benchmark over 3–5 years, not 1. Varsity's review chapter suggests a red flag only if an active fund trails its benchmark by 5–10% over a short period, or after a strategy, manager or ownership change.
• On life events (marriage, child, job change, inheritance): revisit goals and risk capacity, then the allocation.

17. Your Investment Policy Statement and yearly calendar

An Investment Policy Statement (IPS) is a one-page document that records your allocation and your rules before markets test them. When the Nifty is down 15% (as in March 2026), you follow the page, not the news. Copy this template, fill in the blanks, sign and date it, and review it every April.

The yearly calendar

18. Retirement: withdrawals, buckets and sequence risk

Systematic withdrawal plans (SWP) are tax-efficient

An SWP redeems a fixed amount from a fund every month. Each redemption is a sale of units, FIFO, so only the gain portion of each withdrawal is taxed, at the fund's capital-gains rate. The alternative, the IDCW option (income distribution cum capital withdrawal, the old "dividend" option), pays out money that is taxed in full at your slab rate (chapter 15, part 7).

Sequence-of-returns risk

While you are saving, the order of good and bad years does not change your final CAGR. Once you are withdrawing, it does: a crash early in retirement forces you to sell more units at low prices, and those units are not there for the recovery. This is sequence-of-returns risk, the "sequence risk" of Varsity's asset-allocation chapter.

How long did different withdrawal rates last? For retirements starting each January from 2008 to 2012, here is what was left after 14 years, deflated at 6% a year to the starting year's money (₹1 crore = 100 lakh at the start):

The bucket strategy for retirement

A common way to manage sequence risk is three buckets: bucket 1 holds 1–2 years of withdrawals in liquid or money-market funds (the SWP comes from here); bucket 2 holds the next 3–5 years in short-duration debt or a conservative hybrid / BAF; bucket 3 holds the rest in equity for growth. Refill bucket 1 from bucket 3 after good equity years and from bucket 2 after bad ones, so you are never forced to sell equity in a crash. It is rebalancing with a timetable, and the tax playbook of part 14 applies to every refill.

19. In your framework, and try it yourself

All snippets read files from the course repository's data/ folder. Save each as a .py file and run it from the repository's root folder with .venv/bin/python snippet.py. Snippets 1–3 need only pandas (snippet 2 needs nothing).

1. Return, risk and correlation of the asset classes (reproduces the table in part 3):

import numpy as np
import pandas as pd

nav = pd.read_csv("data/extra/ch16_navs.csv", index_col=0, parse_dates=True)
nav = nav.loc["2013-01-02":"2026-09-30"]
days = pd.bdate_range(nav.index[0], nav.index[-1])
nav = nav.reindex(nav.index.union(days)).ffill().reindex(days)   # same calendar for all

for col in ["NIFTY50", "MIDCAP", "NASDAQ100", "GILT", "SHORTDEBT", "LIQUID", "GOLD"]:
    s = nav[col].dropna()
    years = (s.index[-1] - s.index[0]).days / 365.25
    cagr = (s.iloc[-1] / s.iloc[0]) ** (1 / years) - 1
    m = pd.concat([s.iloc[:1], s.resample("ME").last()])
    vol = m.pct_change().std() * np.sqrt(12)
    mdd = (s / s.cummax() - 1).min()
    print(f"{col:10s} CAGR {cagr:6.1%}  vol {vol:5.1%}  max drawdown {mdd:6.1%}")

monthly = nav.drop(columns="SMALLCAP").resample("ME").last().pct_change().dropna()
print(monthly.corr().round(2))
# NIFTY50 CAGR 11.1%, vol 15.7%, max drawdown -38.4% ... GOLD 11.0%, 15.4%, -25.5%; corr Nifty-gold -0.11

2. A goal calculator (future cost, level SIP, step-up SIP):

def sip_needed(target, annual_return, months):
    i = (1 + annual_return) ** (1 / 12) - 1
    return target * i / (((1 + i) ** months - 1) * (1 + i))     # paid at the start of each month

def stepup_fv(first_sip, annual_return, years, step):
    i = (1 + annual_return) ** (1 / 12) - 1
    total, amt = 0.0, first_sip
    for y in range(years):
        for m in range(12):
            total += amt * (1 + i) ** ((years - y) * 12 - m)
        amt *= 1 + step
    return total

goal = 20_00_000 * 1.08 ** 12                    # Rs 20 lakh today, 8% inflation, 12 years
print(f"goal Rs {goal:,.0f}")                     # 5,036,340
print(f"level SIP at 8.55%: Rs {sip_needed(goal, 0.0855, 144):,.0f}")          # 20,469
print(f"10% step-up, first SIP: Rs {goal / stepup_fv(1, 0.0855, 12, 0.10):,.0f}")  # 12,597

The step-up figure from this snippet uses a constant 8.55%, so it is lower than the ₹13,352 of part 7, which used the glide path's falling returns.

3. A rebalancing ticket for your own holdings (drift, band check and the trades; replace the values with yours):

target = {"equity": 0.60, "debt": 0.30, "gold": 0.10}
values = {"equity": 17_96_436, "debt": 9_55_400, "gold": 6_02_204}   # Rs, from your statement
next_sips = 6 * 25_000                                                # new money you will add soon

total = sum(values.values())
for a in target:
    w = values[a] / total
    band = min(0.05, 0.25 * target[a])
    flag = "OUTSIDE band" if abs(w - target[a]) > band else "ok"
    print(f"{a:7s} {w:6.1%} (target {target[a]:.0%}, band +/-{band:.1%}) {flag}")

# full rebalance today
for a in target:
    print(f"full rebalance: {a:7s} trade Rs {target[a] * total - values[a]:+,.0f}")
# tax-smarter: new money to the most underweight asset first, then trim only what is still outside its band
after = dict(values)
after["equity"] += next_sips
tot2 = sum(after.values())
trim_gold = max(after["gold"] - 0.125 * tot2, 0)
print(f"sell gold Rs {trim_gold:,.0f} (vs Rs {values['gold'] - 0.10 * total:,.0f} for a full rebalance)")
# sell gold Rs 164,199 (vs Rs 266,800 for a full rebalance)

4. The framework's lot-level rebalancing backtest:

import sys; sys.path.insert(0, ".")                 # run from the repository root
import pandas as pd
from framework.funds import rebalance_backtest

px = pd.read_csv("data/extra/ch16_navs.csv", index_col=0, parse_dates=True)
px = px.loc["2013-01-02":, ["NIFTY50", "SHORTDEBT", "GOLD"]]
classes = {"NIFTY50": "equity_oriented", "SHORTDEBT": "specified_debt", "GOLD": "other"}
for method in ["none", "calendar", "band"]:
    res = rebalance_backtest(px, {"NIFTY50": 0.6, "SHORTDEBT": 0.3, "GOLD": 0.1}, method=method,
                             freq="YE", band=0.05, tax_classes=classes, listed={"GOLD": True},
                             slab_rate=0.30, initial=1_000_000, monthly_sip=20_000)
    m = res["metrics"]
    print(f"{method:9s} CAGR {m['cagr']:.2%}  vol {m['vol']:.1%}  maxDD {m['max_drawdown']:.1%}  "
          f"tax paid Rs {m['tax_paid']:,.0f}  after exit tax Rs {m['post_tax_value']:,.0f}")
# none: CAGR 10.50% ... after exit tax Rs 10,261,184; band: CAGR 10.89% ... Rs 10,730,292

These figures differ a little from the table in part 13 (bands: ₹1,07,30,292 against ₹1,06,36,972 after exit tax; never rebalance: ₹1,02,61,184 against ₹1,02,57,467). The framework uses a flat ±5-point band for every asset, where the table uses 5 points or 25% of the target, whichever is tighter (±2.5 points for gold), and it measures volatility on a time-weighted index. The methods come out in the same order.

Exercise: add sip_to_underweight=True to the "none" run to test cash-flow rebalancing, change the slab to 0.20, or swap "GOLD" for "NASDAQ100" and watch what rebalancing does to a trending asset. To rebuild every number in this chapter, run course/scripts/ch16_data.py --refresh (downloads NAVs) and then course/scripts/ch16_figures.py.

20. Practical checklist

• Term and health insurance in place; emergency fund of 3–12 months' expenses in savings plus a liquid fund, outside the allocation.
• Every goal written down with amount (today's money), inflation, date and its own allocation.
• EPF, PPF, FDs and gold jewellery counted in the debt and gold totals.
• Strategic allocation chosen from risk capacity, tolerance and need, whichever is lowest, and written into an IPS.
• One fund or ETF per asset class; direct plans; tax class, TER and tracking checked (chapter 14).
• Glide path written for each goal with a date; money for goals under 3 years away out of equity.
• Bands set (5 points or 25% of target, whichever is tighter); checked quarterly.
• New money directed to the underweight asset before any sale.
• Before any sale: exit load, 12/24-month dates of the oldest lots, this year's LTCG used so far, available losses.
• February–March: equity LTCG harvested up to ₹1.25 lakh; any rebalancing sales sized to the exemption; buy back the next trading day.
• Family asset location considered; clubbing rules respected; a CA consulted for gifts or large moves.
• ETF premium to iNAV checked before buying international or thin ETFs.
• April: SIP step-up; IPS reviewed; after 1 February each year, tax rules re-checked and framework/config.py updated.
• In retirement: 1–2 years of withdrawals in a liquid bucket; SWP rather than IDCW; withdrawal rate reviewed yearly.

21. Key takeaways

22. Quiz and exercises

23. Sources

Zerodha Varsity, Module 11, Personal Finance – Mutual Funds (concepts; explained here in our own words, with figures updated where Varsity's are older; for example Varsity's review chapter still mentions the ₹1 lakh exemption and debt-fund indexation, both since replaced): 2. Personal Finance Math (Part 1) · 3. Personal Finance Math (Part 2) · 4. The Retirement Problem (Part 1) · 5. The Retirement Problem (Part 2) · 26. The Mutual Fund Portfolio · 28. Asset Allocation, An Introduction · 29. Exchange-traded Funds (ETF) · 31. Personal Finance Review (Part 1) · 32. Personal Finance Review (Part 2). Module 9, Risk Management & Trading Psychology: Risk (Part 2) – Variance & Covariance · Risk (Part 4) – Correlation Matrix & Portfolio Variance · Expected Returns · Portfolio Optimization (Part 1).

Law and regulation (checked 2 Oct 2026): Income-tax Act, 2025 as amended by the Finance Act, 2026 (text): s.2(101) short-term capital asset (12/24 months), s.76 specified mutual fund (old 50AA), s.92(2)(m) and (3) gifts (old 56(2)(x)), s.99 clubbing (old 64), s.108–111 set-off and carry-forward (old 70–74), s.196 STCG (old 111A), s.197 LTCG (old 112), s.198 LTCG on equity and equity-oriented fund definition (old 112A) · ClearTax: old vs new section mapping · Outlook Money: MF tax reckoner 2026-27 (no change to fund capital-gains rates in Budget 2026) · AMFI: tax regime for mutual funds · ClearTax: SGB capital gains from 1 Apr 2026 (exemption only for original subscribers held to maturity) · SEBI circular HO/24/13/15(2)2026-IMD-RAC4/I/5764/2026, 26 Feb 2026: Categorization and Rationalization of Mutual Fund Schemes (hybrid ranges, Life Cycle Funds, end of solution-oriented schemes) · PPF 7.1% for Oct–Dec 2026 · EPF 8.25% for 2025-26 · ICICI Pru Short Term Fund: exit load nil · UTI Nifty 50 Index Fund: exit load nil.

Research: Brinson, G., Hood, L. R. and Beebower, G. (1986), "Determinants of Portfolio Performance", Financial Analysts Journal 42(4) · Ibbotson, R. and Kaplan, P. (2000), "Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance?", Financial Analysts Journal 56(1).

Data: daily NAVs from AMFI via mfapi.in (UTI Nifty 50 Index Fund Direct 120716; Motilal Oswal Nifty Midcap 100 ETF 114456; Motilal Oswal Nifty Smallcap 250 Index Fund Direct 147623; Motilal Oswal Nasdaq 100 ETF 114984, split-adjusted; HDFC Gilt Fund Direct 119116; ICICI Prudential Short Term Fund Direct 120754; SBI Liquid Fund Direct 119800; HDFC Balanced Advantage 118968, ICICI Pru Balanced Advantage 120377, ICICI Pru Multi-Asset 120334, SBI Multi Asset Allocation 119843 (direct); regular plans UTI Nifty 50 Index 100822, ICICI Pru Short Term 101758; Kotak Gold ETF 106193; Nippon India Silver ETF 149758), saved in data/extra/ch16_navs.csv, ch16_hybrid_navs.csv, ch16_long_navs.csv and ch16_extras.csv; GOLDBEES, MON100 and EMBASSY prices from NSE via Yahoo Finance (two GOLDBEES bad prints in Dec 2019 removed); Nifty 50 P/E from NSE via craytheon.com (data/extra/ch08_nifty_pe_pb_dy_weekly_craytheon.csv). Downloads: course/scripts/ch16_data.py; all calculations and figures: course/scripts/ch16_figures.py with ch16_lib.py (numbers saved to data/extra/ch16_numbers.json). Examples are historical analysis for education, not recommendations or tax advice.