Analysis of the Relationship Between Commercial Bank Index and NEPSE Index (2003–2025)
June 24, 2025 | Investopaper
1. Introduction
The NEPSE Index is the principal barometer of Nepal’s capital market, reflecting the aggregate performance of the listed companies. Among these, the Commercial Bank Index holds significant weight, given the dominance of commercial banks in the market. This study analyzes the relationship between the Commercial Bank Index and the NEPSE Index over a period spanning July 2003 to April 2025.
The health of the stock market often reflects the underlying economic dynamics of a country. In Nepal, the financial sector, particularly commercial banks, plays a pivotal role in driving capital market trends. Understanding the interaction between this sector and the broader market index helps identify macro-financial linkages that are critical for portfolio diversification, risk management, and policy formulation.
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2. Data Overview
The dataset for the analysis was taken from Nepal Stock Exchange (NEPSE). The time period of the data is from July 17, 2003 to April 13, 2025. Daily trading data of Nepse Index and commercial bank Index were used for the purpose of the study.
The dataset consists of 5,005 daily observations over a span of 22 years.
3. Summary Statistics
The summary statistics of the data is presented in the table below:
| Metric | Mean | Median | Standard Deviation | Min | Max |
| Commercial Bank Index | 878.76 | 847.86 | 479.05 | 181.75 | 2175.19 |
| NEPSE Index | 1096.98 | 925.21 | 763.90 | 195.14 | 3199.03 |
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4. Time Series Visualization
A time-series line chart was created to visualize the progression of both indices. From 2003 to 2025, both indices exhibit upward trends interspersed with volatility bursts, especially around major economic or political events.
The graphical representation makes it evident that while both indices rise and fall in tandem, banking stocks often lead market recoveries due to their sensitivity to interest rate policy and liquidity positions.
5. Correlation Analysis
The correlation between NEPSE Index and Commercial Bank Index is found to be 0.9442. A correlation coefficient of 0.9442 indicates a very strong positive linear association. This means that approximately 94 percent of the time, an increase in the Commercial Bank Index corresponds with an increase in the NEPSE Index.
Such a strong relationship is not purely coincidental, but rather reflects the structural weight and influence of the banking sector on the NEPSE. From a market dynamics perspective, such high correlation could indicate concentration risk, where performance of one sector overly determines the broader market’s movement.
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6. Scatter Plot Analysis
A scatter plot with a fitted linear regression line shows a tight clustering of data points along the regression line. This visually supports the strong correlation observed numerically.
This clustering confirms that the banking sector is a reliable proxy for NEPSE movement, especially useful for short-term forecasting.
7. Regression Modeling
The regression model: NEPSE_Index ~ Commercial_Bank_Index yielded an R-squared of 0.8914. The slope coefficient (1.506) implies that every 1-point rise in the bank index leads to a 1.51-point increase in NEPSE.
This high explanatory power is rare in financial econometrics, indicating that NEPSE performance is significantly driven by commercial banks. The model’s residuals showed no major deviation from normality, suggesting good model fit.
8. Return and Volatility Analysis
The NEPSE Index had a slightly higher average daily return (0.059%) compared to the Commercial Bank Index (0.050%). However, the Commercial Bank Index had a higher standard deviation (1.55% vs 1.27%), indicating greater volatility.
This means that while NEPSE offered slightly higher returns, it also did so with comparatively lower risk—making it a better performer in a risk-adjusted sense.
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9. Moving Averages
Five-day and 200-day moving averages were computed for both indices to observe short-term trends as well as long term trends.
5 Day Moving Average
The 5-day moving average (5-DMA) is a short-term technical indicator used to smooth out price fluctuations over a 5-day (typically one trading week) period. It helps identify short-term trends in the market or a specific stock/index. This tool is particularly useful for technical traders and risk managers for identifying short-term reversal or continuation signals.
200-Day Moving Average
The 200-day moving average (200-DMA) is one of the most widely used long-term trend indicators in technical analysis. If the current price is consistently above the 200-DMA, it generally indicates a long-term uptrend or bullish market sentiment. If the price is below the 200-DMA, it suggests a long-term downtrend or bearish sentiment.
10. Daily Return Visualization
Daily return plots showed several spikes, indicating periods of abnormal returns likely due to policy announcements, political events, or economic shocks.
The behavior of returns in both indices is remarkably synchronized, confirming the interdependence of commercial banks and the broader market.
Distribution of Daily Returns (in Histogram)
11. Summary of Key Findings
| Aspect | Insight |
|---|---|
| Correlation | Very strong (r = 0.9442) |
| Regression R-squared | 89.14% |
| Commercial Bank Volatility | 1.55% |
| NEPSE Volatility | 1.27% |
| Risk-Return Ratio | NEPSE has higher return-to-risk ratio.
NEPSE :0.0467 vs Commercial Banks: 0.0325 |
| Time Period Covered | July 2003 – April 2025 (5,005 trading days) |
12. Conclusion
This analysis conclusively demonstrates the integral role of the Commercial Bank Index in shaping the broader NEPSE Index. For investors, policymakers, and regulators, the Commercial Bank Index serves as a leading indicator.
Moreover, any systemic risk or stress within the banking sector is likely to reverberate across the capital market, warranting closer supervision and stronger regulatory oversight of financial institutions.
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