Each sector's return for the last 12 calendar months.
Sector ↕
How much each sector's own return beat or lagged the broad market over the same window, not a forecast, just what already happened. Nifty 500 is the broader, cap-weighted benchmark; Nifty 50 is large-cap only, so a sector can look strong against one and weak against the other if it's a mid/smallcap-heavy index.
Sector ↕
3 Month
6 Month
12 Month
N50 ↕
N500 ↕
N50 ↕
N500 ↕
N50 ↕
N500 ↕
Each sector's price strength relative to the broad market (Nifty 500): RS-Ratio (x-axis) is the current relative-strength level, RS-Momentum (y-axis) is whether that relative strength is improving. The trailing line shows each sector's path over the last 10 trading days. This describes where money has been rotating recently, it isn't a forecast.
Leading: outperforming & strengthening
Weakening: outperforming but losing steam
Lagging: underperforming & still falling behind
Improving: underperforming but catching up
Sector ↕
Quadrant ↕
RS-Ratio ↕
RS-Mom ↕
1M ↕
PE %ile ↕
Positions are computed from sector price history (refreshed periodically), benchmarked against Nifty 500. Money-rotation patterns described here are historical tendencies, not guarantees of future direction.
Understanding the signals
PE Percentile - How cheap or expensive a sector is compared to its own history (most sectors have ~10 years of data; a few newer ones have less). If it shows 72%, it means the sector is cheaper than 72% of the days in that history. Higher percentage = cheaper than usual.
How we calculate it
Rank today's PE against every historical daily PE reading for that sector and take the percentile of days with a higher PE than today. It updates as new daily data comes in; it isn't fixed to a rolling calendar window.
vs 200DMA - The 200DMA is the long-term price trend. When a sector is below it, prices have fallen below the trend, which can sometimes signal a buying opportunity.
How we calculate it
Average of the last 200 daily closes, compared to today's price, shown as a % above/below. We use real price history for every sector; where that history is too short we fall back to a rough estimate from 1-month and 1-year-ago prices, which is noted internally but not shown per-row.
PE Trend 3M - Is the sector getting cheaper or more expensive over the last 3 months? "Cheapening" means PE is falling (prices dropped or profits grew).
How we calculate it
% change between today's PE and the PE from ~60 trading days (roughly 3 months) ago. Above +5% is labeled "getting expensive", below -5% is "cheapening", in between is "stable".
Forward PE - What the PE is expected to be in 12 months based on how fast profits are growing. If forward PE is lower than current PE, it means profits are growing faster than the price.
How we calculate it
Current PE ÷ (1 + estimated EPS growth). It inherits the same estimate used for EPS Growth below, so it carries the same caveats.
EPS Growth (Est.) - How fast profits (EPS) are growing in this sector. Higher growth can justify a higher price. This is an estimate, not a reported company figure: it's derived by backing out implied EPS from price ÷ PE at various points in time and comparing to today, not from company-disclosed earnings.
How we calculate it
Implied EPS = price ÷ PE, both today and at 1/2/3 years ago. We take the CAGR between today and each of those points, then use the median of the available CAGRs (more stable than any single year). If there isn't enough history, we fall back to a rough 1-year estimate blended 60/40 with a fixed per-sector assumption, and if even that's unavailable, we just use the fixed assumption. The result is floored at 5% and capped per sector (usually 30%, lower for IT/FMCG/Metal/Energy/Commodities/Consumption). Treat it as a directional gauge, not a precise number.
PE Discount - How much cheaper (or expensive) the sector is right now vs its usual price level.
How we calculate it
% difference between today's PE and the median PE of that sector's full available history. -20% means it's 20% cheaper than its own historical median.
Understanding Sector Behaviours
Cyclical Sectors (Metal, Realty, Auto, Infrastructure)
These sectors go through boom-and-bust cycles. Their profits swing a lot depending on the economy. The tricky part: they look cheapest (low PE) when profits are at their highest, often just before a slowdown. And they look most expensive when profits are at their lowest, which might actually be a good time to invest. Don't use PE alone here. Look at whether demand in that industry is rising or falling.
Defensive Sectors (FMCG, Pharma, IT)
These companies sell products people need regardless of the economy (medicines, food, software services). Their profits are steady, so their PE stays in a predictable range. When PE shows "cheap vs history" for these sectors, it's a more reliable signal because earnings aren't swinging wildly.
Banking & Financials (Bank, Private Bank, PSU Bank, Financial Services)
Bank profits can jump around because of loan losses (NPAs) and one-time write-offs. So PE can be misleading in any single quarter. The PE percentile still helps compare where the sector stands vs its own past, but also pay attention to whether loan growth is healthy and bad loans are under control.
Market-cap Segments (Midcap, Smallcap, Microcap)
These group companies by size, not industry. They tend to rise together when markets are optimistic and fall together when fear rises. Smallcaps can look cheap for long periods simply because investors prefer safety during uncertain times. A low PE here doesn't always mean opportunity, it might just mean the mood hasn't shifted back to risk-taking yet.
Thematic Indices (CPSE, PSE, Defence, India Manufacturing)
These are built around a theme (government companies, defence spending, manufacturing push). They often move based on government policies and budgets rather than pure business performance. Many of these indices are relatively new (started after 2020), so there isn't enough history to draw strong conclusions from PE percentiles. Treat signals here with extra caution.
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