You've built a portfolio using a systematic factor signal and sized each position. Now comes the question that most investors get wrong: when do you rebalance? This question gets far less attention than stock selection | but it matters enormously. The same 30-stock portfolio, positioned differently, can have a max drawdown of 30% or 50%. Same stocks, different sizing.
There are four main approaches to position sizing in systematic equity portfolios. Each makes different assumptions about what you know | and more importantly, what you don't know.
28
Days rebalance cadence RupeeCase Nifty 10
5%
Drift threshold I trigger on
0.18%
Avg round trip cost per rebalance
20%
STCG tax drag on frequent rebal
Rebalancing is where returns leak. Every extra rotation pays STT, impact cost, and possibly 20% STCG. Under rebalancing leaks a different way, the factor signal gets stale.
1
Cadence check
Is it rebalance day on the calendar? 1st of month, quarter, or bi-weekly
2
Drift scan
Any position drifted more than 5% from target weight?
3
Signal refresh
Re-rank universe on latest factor score
4
Tax aware trim
Prefer LTCG exits, defer STCG where possible
5
TWAP execute
Spread trades across the session, avoid single print
My RupeeCase Nifty rebalance flow. The drift gate and tax aware trim are the two steps that separate a backtest from a live net of cost portfolio.
Rebalance cost breakdown on 1 lakh portfolio
- STT round trip 42%
- Impact cost 28%
- Brokerage 18%
- Exchange GST stamp 12%
Reason for rebalance over 12 months
- Signal change 55%
- Drift gate 30%
- Corporate action 15%
Over half my rotations are driven by fresh factor score. Drift only triggers rebalance when a single name has run hard. Corporate actions like bonus or split force mechanical adjustment.
There is a clear sweet spot. Bi weekly captures most of the signal refresh benefit before costs and tax drag begin to eat into net returns. Daily rebalance destroys alpha.
From my notebook
2019 I was running a weekly rebalance thinking faster was better. Over 2019 the gross alpha was 4.1%. After STT, brokerage, impact and 20% STCG tax, the net alpha was 0.2%. I had traded for twelve months to pay STT and tax, essentially free consulting for the exchange. I moved to bi weekly, trimmed turnover from 420% annual to 110%, and the next full year the net alpha was 3.6%. Rebalance cadence is a portfolio design decision. Faster is not better. Optimal is bi weekly for momentum, quarterly for quality and low vol.
The four approaches
Equal Weight
Simple
Weight(i) = 1 / N
Every stock in the portfolio gets the same allocation. If you hold 30 stocks, each gets 1/30 = 3.33%. No assumptions about which stock is "better" than another. Maximum simplicity, maximum diversification across selections.
- Requires no additional inputs beyond stock selection
- Naturally overweights small-cap stocks relative to market cap weighting | provides size factor exposure
- Strong academic evidence that equal weight frequently matches or beats optimised weights out-of-sample
- High-volatility stocks get the same weight as low-volatility stocks | can lead to concentration of risk in a few volatile names
✓ RupeeCase default | use this unless you have a specific reason to deviate
Inverse Volatility Weight
Moderate
Weight(i) = (1/σⅧ) / ∑(1/σⅧ)
Each stock's weight is inversely proportional to its 1-year daily volatility. Less volatile stocks get higher allocations. Ensures that each stock contributes roughly equally to portfolio risk, even if they have different absolute volatilities.
- Reduces the "risk concentration" problem of equal weight | a 50% annualised volatility stock no longer dominates portfolio risk
- Empirically: improves Sharpe ratio vs equal weight in most markets and time periods
- More expensive | requires calculating volatility for each stock and recomputing weights each rebalance
- Can inadvertently create high concentration in defensive/low-vol sectors if not capped
✓ Use for multi-factor strategies where vol varies significantly across selected stocks
Market Cap Weight
Moderate
Weight(i) = MarketCap(i) / ∑MarketCap
Weight each stock by its market capitalisation relative to the total portfolio market cap. Larger companies get higher allocations. This is how index funds work | the Nifty 50 and Nifty 500 are market cap weighted.
- Minimises trading | large stocks don't drift as much, so rebalancing is less frequent
- Concentrates portfolio in the largest stocks | can mean 30 to 40% in 5 stocks
- Systematically underweights smaller companies that may have higher factor scores
- For factor strategies: not recommended | defeats the purpose of factor selection by reintroducing market-cap bias
⚠ Avoid for factor strategies | use only if replicating an index
Kelly Criterion / Signal-Scaled
Complex
f* = (p × b − q) / b
The Kelly criterion gives the theoretically optimal fraction of capital to bet based on edge and odds: f* is the optimal fraction, p is the probability of winning, b is the payoff ratio, q = 1-p is probability of losing. In practice, full Kelly is far too aggressive | most systematic traders use "fractional Kelly" (25 to 50% of the Kelly fraction).
- Theoretically optimal for long-run capital growth if edge estimates are correct
- Requires accurate estimates of win rate and payoff ratio for each position | very hard to get right for equity strategies
- Full Kelly leads to extreme concentration and large drawdowns in practice
- Useful conceptually for understanding bet sizing | not practical for multi-stock factor portfolios
✗ Too complex for most factor strategies | conceptual value only
Worked example: equal vs inverse volatility
Let's say your momentum strategy selects 5 stocks with these annual volatilities:
| Stock | Volatility (1Y) | Equal Weight | Inverse Vol Weight |
|---|---|---|---|
| Reliance | 22% | 20% | 25.2% |
| Bajaj Finance | 35% | 20% | 15.8% |
| Tata Motors | 48% | 20% | 11.5% |
| Infosys | 26% | 20% | 21.3% |
| Asian Paints | 20% | 20% | 27.7% |
| Portfolio Vol (approx) | ~30% (dominated by Tata Motors) | ~24% (more balanced) |
In the equal weight portfolio, Tata Motors (48% vol) contributes a disproportionate share of portfolio variance despite being only 20% of capital. Inverse volatility weighting corrects this | Tata Motors drops to 11.5% weight while Reliance and Asian Paints (more stable) get higher weights.
The practical tradeoff: Inverse volatility weighting produces smoother returns but requires a bit more complexity at each rebalance. For portfolios of 20+ stocks with similar volatilities, the difference narrows significantly | equal weight is usually good enough. For concentrated portfolios (10 to 15 stocks) or those that consistently include a mix of volatile growth stocks and stable compounders, inverse vol weight meaningfully improves the ride.
Position sizing constraints
Regardless of the weighting scheme, practical constraints prevent extreme concentration:
- Maximum single-stock weight | typically 10 to 15% for a 20-stock portfolio, 5 to 8% for a 30-stock portfolio. Prevents any single stock from dominating performance or drawdown.
- Maximum sector weight | typically 25 to 35%. Prevents a sector-driven momentum signal from putting 50%+ into, say, IT or banking in a single period.
- Minimum position size | typically 1 to 2% of portfolio. Positions smaller than this generate trading friction that exceeds any informational value.
- Liquidity constraint | position size should not exceed 5 to 10% of the stock's average daily traded volume. Prevents market impact on entry and exit.
Position sizing on RupeeCase
RupeeCase strategies default to equal weight with a single-stock cap of 10% and a sector cap of 35%. The inverse volatility weighting option is available in the Backtester | toggle it on to see how it changes the Sharpe ratio and max drawdown for any strategy. For most Nifty 500 momentum strategies, inverse vol weight improves Sharpe by roughly 0.1 to 0.2 over a 10-year backtest. Explore at invest.rupeecase.com.
Test position sizing on RupeeCase
Compare equal weight vs inverse volatility weight | side by side backtest
See the Sharpe and drawdown difference instantly.
Glossary
Key terms from this module
Equal weight
Allocating 1/N of capital to each stock in the portfolio. Simplest and most robust weighting scheme | each position has equal capital allocation.
Inverse vol weight
Allocating in inverse proportion to each stock's volatility. Less volatile stocks get higher weights, ensuring more equal contribution to portfolio risk.
Kelly criterion
A formula for optimal bet sizing based on edge and odds. Theoretically optimal for long-run growth but requires accurate edge estimates and typically produces extreme concentration.
Risk contribution
The share of total portfolio variance contributed by each position. In equal weight, high-volatility stocks contribute disproportionately more risk than their capital weight suggests.
Sector cap
A maximum weight constraint limiting how much of the portfolio can be in any single sector | typically 25 to 35%. Prevents sector-driven signals from creating extreme concentration.
RC
Want to put this into practice? RupeeCase is the systematic investing terminal built around everything you're learning here, factor scores, strategy backtests, portfolio construction for Indian markets.
Sources & further reading
- → DeMiguel, V. et al. (2009). Optimal Versus Naive Diversification. Review of Financial Studies. (Equal weight vs optimised)
- → Kelly, J.L. (1956). A New Interpretation of Information Rate. Bell System Technical Journal. (Original Kelly criterion paper)
- → Thorp, E. (2006). The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market. Handbook of Asset and Liability Management.
- → NSE India Research — Portfolio Construction Resources
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📍 4.2 Rebalancing Strategies
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4.3 Rebalancing
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4.4 Performance
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4.5 Risk Mgmt
calc-rebalance-cost
Calculator
Rebalance Cadence Cost Calculator
Estimate annual drag from rebalancing too often. Compare the cost of more frequent rebalances against the (typically smaller) edge from a fresher signal.
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