Summary Statistics
Policy Events
26
2015–2025
Rate Cuts
18
69% of decisions
Rate Hikes
8
31% of decisions
Surprise Events
14
54% were surprises
Avg Same-Day Return
−0.27%
Slight negative on avg
Avg Pre-Period Return
+0.14%
5-day pre-announcement
Repo Rate & CPI — 2015 to 2025
The rate cycle: aggressive cuts through COVID, then a sharp hiking cycle in 2022, now returning to easing
Decision Breakdown
Hike vs Cut, and Expected vs Surprise classification
Same-Day Nifty Return per Event
Green = positive return, Red = negative. Bars annotated by policy type
CPI vs Same-Day Return — Scatter
Does inflation level predict the day's market move? Each dot = one policy event
📡 Transmission Chain
Prior papers (Mascarenhas et al., 2024; Hussain, 2024) confirm CRR and Interest Rates Granger-cause BSE Sensex and Nifty 50. This study extends that by separating the expectation phase from the actual transmission.
🎯 Expectations Matter
Markets don't wait for RBI. Pre-period returns of −7 to 0 days capture investor positioning BEFORE the decision. This expectation channel is entirely absent from existing literature.
⚡ Surprise Premium
Surprise decisions (CPI signal mismatched with actual decision) generate larger same-day absolute returns than expected decisions — consistent with EMH that only new information moves prices.
📈 Short vs Long Term
Report 2 found no significant immediate (15-day window) effect. This study's lag analysis (Day 0, +7, +15, +30) reveals the distinction between panic and recovery that emerges over time.
Event Window Analysis — −7 to +30 Days
Avg Pre-Return (−7 to 0)
+0.14%
Expectation pricing phase
Avg Same-Day Return
−0.27%
Announcement day
Avg Post-Return (0 to +7)
+0.19%
Short-term reaction phase
Avg Month Return (+30)
+1.14%
Long-term drift
Average Market Movement Across the Event Window
Shows the typical Nifty 50 return at each phase: before (expectation), day-of (announcement), and after (reaction). Positive pre-period suggests markets price in expectations early.
Pre vs Post Return — All Events
Comparing the 5-day pre-period return to the 7-day post-period return per event. Is there reversal?
Day-Before vs Same-Day Return
Final positioning (day before) vs the actual announcement day reaction. Strong day-before moves suggest last-minute expectation adjustments.
📊 Key Finding: Pre-Period Evidence
The average pre-period return is positive (+0.14%), but this masks volatility. Looking at individual events, you can see markets often move in the OPPOSITE direction of the eventual decision — consistent with the "buy the rumour, sell the news" pattern.
🔄 Post-Period Recovery
Average post-return (+0.19%) slightly exceeds same-day return in magnitude, suggesting that after an initial shock, the market partially absorbs and adjusts. This echoes Report 2's finding of no significant 15-day difference.
Expected vs Surprise Policy Classification
Expected (No Surprise) — CPI trend aligned with decision
Surprise — CPI trend contradicted actual decision
Expected Events
12
CPI aligned with decision
Surprise Events
14
CPI contradicted decision
Avg |Same-Day| Expected
0.58%
Smaller reaction
Avg |Same-Day| Surprise
0.84%
Larger reaction (+45%)
Same-Day Return: Expected vs Surprise
Blue bars = Expected policies (small reaction). Orange bars = Surprise policies (larger reaction). Confirms that only new information moves markets.
Absolute Returns Comparison — All Windows
Average absolute return at each lag window, split by Expected vs Surprise. Surprises consistently generate larger moves.
Month-Ahead Return by Policy Type × Surprise Classification
30-day drift after the announcement, broken out by whether the decision was a hike/cut AND whether it was expected or a surprise
✅ EMH Confirmation
Surprise policies generate ~45% larger same-day absolute returns than expected ones. This directly confirms the Efficient Market Hypothesis: only unanticipated information causes repricing.
📉 COVID Outlier
The March 2020 emergency cut (−75 bps during COVID) is classified as Surprise (CPI was rising, not falling). Its month return of −26% is the most extreme event — consider treating it as a structural break.
📈 2022 Hiking Cycle
The 2022 hikes were largely classified as Surprise (RBI kept hiking despite falling CPI). Yet monthly returns were mixed — suggesting the market had already priced in global rate dynamics independently of domestic CPI signals.
Lag Analysis — Hike vs Cut Comparison
Average Returns by Lag Window — Hikes vs Cuts
For each policy type, how does the average Nifty 50 return evolve from Day 0 through +30 days? Short-term panic vs long-term recovery pattern visible here.
|Absolute| Average Returns by Lag — Magnitude of Reaction
Stripping direction: which lag window creates the LARGEST market movement? Useful for identifying when policy impact peaks.
30-Day Forward Return Distribution — All Events
Each event's month-ahead return, ordered chronologically. COVID outlier (March 2020) is clearly visible. Also shows the 2022 hiking cycle's broad market pressure.
Pre-Period vs Month Return Relationship
Does the market's pre-announcement positioning predict the subsequent month's return? Scatter by event type.
Rate Change Magnitude vs Same-Day Return
Larger rate changes (±50, ±75 bps) vs smaller ones (±25 bps). Does the size of the move matter?
⏱ Short-Term: Cuts Dominate
Rate cuts show larger positive same-day and post-period returns on average. This reflects the liquidity relief narrative — cheaper borrowing lifts equity valuations immediately.
📆 Long-Term: Mixed Signal
By the +30 day mark, both hikes and cuts show high variance. The 30-day return is driven more by macro events (oil prices, global risk) than the rate change itself — consistent with Report 2's weak long-term significance.
🔢 Size Matters (Marginally)
Larger rate moves (≥50 bps) tend to generate larger same-day reactions, but not proportionally — a 75 bps cut doesn't cause 3× the reaction of 25 bps. Markets focus on direction and surprise, not precise magnitude.
Full Event Dataset — 26 RBI Policy Dates
| Date | Repo | Change | Type | CPI | CPI Δ | Surprise? | Pre-5d Ret | Day Before | Same Day | Post-7d Ret | Month (+30) |
|---|
Research Design & Methodology
01
Data Foundation
Built on findings from three peer-reviewed papers using data from RBI, NSE, and macroeconomic databases covering 2001–2025. This study extends their 2001–2020 window through 2025.
02
Lag Analysis
Market returns computed at 4 windows: Day 0 (same day), +7 days, +15 days, +30 days. Pre-period: −5 days and −1 day. Captures short-term panic and long-term drift.
03
Event Window
RBI policy date as center (Day 0). −7 to 0 = expectation phase. 0 = announcement. 0 to +7 = reaction phase. This captures the "expectations priced in" phenomenon missed by prior studies.
04
Expected vs Surprise
Classification: if CPI_Change > 0, expected decision = Hike; if CPI_Change ≤ 0, expected = Cut. Mismatch between expected and actual = Surprise. Simple, replicable, consistent with market microstructure theory.
Prior Literature Summary
Report 1 — Mascarenhas et al. (2024)
Period: 2001–2020 | Method: Granger Causality
Key: CRR + Interest Rate → Nifty/Sensex. SLR + Reverse Repo + Interest Rate → GDP. Used as foundational evidence for transmission mechanism.
Key: CRR + Interest Rate → Nifty/Sensex. SLR + Reverse Repo + Interest Rate → GDP. Used as foundational evidence for transmission mechanism.
Report 2 — Monetary Policy & Nifty Sectors (2011–2016)
Method: Multiple Regression + t-test (±15 days)
Key: No significant short-term (±15 day) difference BUT significant long-term effect. Banking sector most sensitive. This study's lag analysis directly tests this window.
Key: No significant short-term (±15 day) difference BUT significant long-term effect. Banking sector most sensitive. This study's lag analysis directly tests this window.
Report 3 — Inflation & Sectoral Returns
Method: Pearson Correlation
Key: Selective impact — Pharma + PSU Banks most significant. IT, Metal, Energy NOT significant. Justifies using overall CPI as inflation proxy while acknowledging sectoral heterogeneity.
Key: Selective impact — Pharma + PSU Banks most significant. IT, Metal, Energy NOT significant. Justifies using overall CPI as inflation proxy while acknowledging sectoral heterogeneity.
This Study's Contribution
Adds the Expectations Layer that all three prior papers miss. Inflation → (Market Expectations of RBI) → RBI Decision → Market Reaction. First study in this dataset to separate pre-announcement expectation pricing from post-announcement transmission.