Investigation Report · April 2026 · Complete

AI-Powered NEPSE Stock Prediction Platform

Combining five years of NEPSE historical data, NRB macroeconomic indicators, and NLP-driven Nepali financial news sentiment to deliver explainable Buy, Hold, and Sell signals for retail investors.

Live Market Data Research
Gaurab Nepali · NP069821 BSc (Hons) IT · APU/LBEF CT052-3-3-IR
NEPSE INDEX · LIVE LIVE DATA
2,786.35
+27.86+1.01%
TURNOVER
NPR 4.49B
TRADES
85,879
SCRIPS
242
LIVE MARKET DATA

NEPSE Listed Stocks

Fetching live NEPSE data…
Symbol / CompanySectorLTPChange% ChangePrev CloseVolumeTrend
01 · THE PROBLEM

Why NEPSE needs intelligent analytics

PROBLEM 01
Technical analysis ignores macro reality
RSI, Bollinger Bands and moving averages can't capture NRB rate changes that move the market.
0.5% NEPSE per 0.1% inflation rise
PROBLEM 02
No integrated analytical platform
Nothing combines NEPSE prices, NRB macro data and Nepali news sentiment for retail investors.
PROBLEM 03
Structural information asymmetry
85% of participants are retail investors without institutional tools; ~60% lose money.
60% of retail investors lose money
PROJECT VISION
An ML platform built for Nepal's market

Linear Regression models macro correlations while a Decision Tree captures non-linear reactions. An NLTK sentiment module adds 10–12% accuracy, producing clear Buy/Hold/Sell signals — aligned with UN SDG 8, Target 8.10.

Prof. (Dr.) R.N. Thakur
Primary Supervisor · Dean, LBEF
Mr. Ramesh Suwal
Co-Supervisor · System Development
UN SDG 8 · Target 8.10 · Financial Inclusion
02 · LITERATURE REVIEW

Findings that validate this project

Macro factors drive NEPSE
Inflation, rates and GDP are validated predictors — macro inclusion raises accuracy ~15%.
+15% accuracy
NLP sentiment helps
Positive Nepali news lifts the index ~0.11% next day; NLP adds 8–12% in emerging markets.
+0.11% next-day
Explainability builds trust
59.4% trust ML only with explanations — Decision Trees give human-readable rules.
Bloomberg costs $24k/yr
No tool covers NEPSE data, retail access, macro and Nepali sentiment together.
Lightweight ML fits Nepal
Limited bandwidth and devices make LSTM/Transformer impractical.
NEPSE lacks analytics
The official portal shows only raw prices — no predictions or sentiment.
03 · TECHNOLOGY STACK

Every tool chosen with evidence

Python 3.11
LANGUAGE
Scikit-learn, Pandas, NLTK ecosystem.
Scikit-learn
ML FRAMEWORK
Linear Regression + Decision Tree.
NLTK
NLP / SENTIMENT
Nepali financial lexicon scoring.
BeautifulSoup · pdfplumber
DATA COLLECTION
News scraping + NRB PDF extraction.
React.js 18
FRONTEND
Responsive dashboard (62.5% mobile).
Flask 3.0
BACKEND API
REST API + SQLAlchemy.
PostgreSQL 16
DATABASE
Time-series + JSONB sentiment.
AWS · Docker · Redis
DEPLOYMENT
Containerized EC2 + caching.
04 · PRIMARY RESEARCH

32 investors surveyed — the data speaks

87.5%
Want Buy/Hold/Sell signals
Most-requested feature — confirms UR-01 as essential MVP.
68.8%
Dissatisfied with current tools
Can't predict movement after macro announcements.
62.5%
Primarily use smartphones
Confirms UR-08: must be mobile-responsive.
78.1%
Use only basic indicators
No access to integrated sentiment.
59.4%
Trust AI with explanation
Confirms Decision Tree selection and UR-07.
14
Validated requirements
From survey, interviews, literature and PSF.
05 · RESOURCES

Data sources & tools

NEPSE
nepalstock.com
Nepal Rastra Bank
nrb.org.np
Sharesansar
sharesansar.com
Scikit-learn
scikit-learn.org
NLTK
nltk.org
NEPSEAI
ML PREDICTION SYSTEM

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