Have you ever found yourself staring at the NEPSE index, watching it swing wildly, and wondered if there was a deeper, more fundamental force at play beyond the daily news cycles and technical charts? I certainly have. Not long ago, I was grappling with how to make sense of Nepal's market volatility. We've explored advanced strategies like risk parity portfolio optimization, but even the most sophisticated models are only as good as the underlying data and our understanding of its drivers. It struck me: remittances, a colossal and consistent inflow into Nepal's economy, must have a profound impact. But how precisely do they relate to stock market movements? This question became my recent obsession. This post is for data scientists, finance professionals, and discerning investors who want to move beyond surface-level analysis and understand the nuanced relationship between a critical economic indicator and market performance. I'll walk you through how I built a system to collect, process, and analyze real-world NEPSE and remittance data, demonstrating how this often-overlooked correlation can significantly inform your investment decisions and risk management strategy in Nepal.
Key Takeaways
- Remittance data, despite its less direct availability, is a critical macro-economic indicator that can offer valuable insights into NEPSE index movements.
- Effective correlation analysis requires meticulous data harmonization, including resampling and alignment, to accurately compare disparate time series.
- While correlation does not imply causation, identifying significant relationships between remittances and NEPSE can inform more robust portfolio strategies and risk assessments.
- Time series forecasting, even with simpler models like ARIMA, provides a forward-looking perspective on economic drivers, enhancing predictive capabilities for market analysis.
- Accessing reliable, historical economic data for emerging markets often involves creative solutions, such as carefully curated manual extraction or proxy datasets, demanding transparency about data limitations.
The Problem: Beyond the Ticker Tape
For investors in Nepal, the NEPSE index is the primary barometer of market health. Yet, relying solely on daily price movements or company fundamentals can leave you feeling reactive rather than proactive. Nepal's economy is unique, heavily influenced by external factors, chief among them being remittances from its vast diaspora. These inflows directly impact liquidity, consumer spending, and potentially, investment sentiment. My hypothesis was simple: there must be a measurable relationship between the volume of remittances entering the country and the performance of our stock market. The challenge wasn't just *if* a correlation existed, but *how* to quantify it, and then, *how* to leverage that understanding to make more informed investment decisions. This meant building a system that could fetch, clean, and analyze these two very different time series datasets.
Data and Sources
To tackle this, I needed two core datasets:
- NEPSE Index Data: I sourced historical NEPSE index data using the
yfinancelibrary, which provides a convenient interface to Yahoo Finance's market data. Whileyfinancecan sometimes be finicky with specific local indices, it's generally reliable for widely tracked markets. - Remittance Data: This was the trickier part. The Nepal Rastra Bank (NRB) is the official source for remittance statistics, typically published in their monthly/quarterly macroeconomic reports. A direct, easy-to-consume historical API is not readily available for programmatic access over extended periods. For this analysis, I've manually extracted and curated a small, representative sample of monthly remittance figures (in NPR millions) from publicly available NRB macroeconomic reports. In a production system, this would likely involve a custom web scraper or direct data agreements.
Data accessed on 2026-07-07. NEPSE data from Yahoo Finance via yfinance. Remittance data from Nepal Rastra Bank (NRB) macroeconomic reports (e.g.,