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PATENT-PENDING QUANTITATIVE FINANCIAL INTELLIGENCE

Know which stocks deserve your attention and why.

Stocpenta turns complex market data into understandable, preference-aware stock intelligence using a proprietary probabilistic engine. No guesswork. No stock tips. Just institutional-grade quantitative clarity.

🔒 Proprietary mathematical model · Illustrative metrics and company names for demo purposes
Market Data INPUTS Financial Data SIGNALS Derived Data FEATURES ENGINE STOCPENTA QUANT ENGINE CORE TECH Regime PREFERENCE Performance SCORE + RANK AI Explanation PLAIN LANGUAGE
QUANTITATIVE PIPELINE DATA → MODEL → RANK → EXPLAIN
The Market Reality

Indian investors deserve better than guesswork

Retail investors in India are caught between superficial social media tips and overwhelmingly dense financial screeners. Stocpenta bridges the divide.

01 / INSTITUTIONAL MONOPOLY

Institutional investors monopolize quant

Institutional funds deploy proprietary statistical tensor models and multi-dimensional factor architectures, leaving individual Indian investors with lagging screeners and crude guesswork.

02 / FRAGMENTED TOOLCHAINS

Too many tools, zero conviction

Screeners, broker reports, technical oscillators, and news feeds scattered across multiple platforms generate overwhelming data volume but negative signal clarity.

03 / GENERIC AI LIMITATIONS

AI that summarizes isn't AI that analyzes

Investors need more than isolated figures. Stocpenta brings company fundamentals, business performance, and relevant financial ratios into a clearer research experience, alongside comparative, regime-aware stock rankings.

Interface Experience

Card-based intelligence engineered for the next generation

Explore equities through swipeable cards sequenced by quantitative signal strength — not bloated dashboards. Click Deep Research to explore company fundamentals, business performance, and relevant financial ratios.

LOW VOLATILITY (1 of 20)
RANK #01

Aura Financial Corp

AURAFIN · NSE
₹1,742.40
+1.12%
Market Cap Large Cap
Industry Private Banking
Sector Financials
PERFORMANCE INDEX Percentile vs Sector
Low Risk
94 GOOD
High Growth
68 MODERATE
Valuation
76 GOOD
AI FUNDAMENTAL SUMMARY
India's private banking bellwether delivers steady return on assets with conservative credit provisioning. Its low beta provides a solid defensive foundation.
Design Philosophy

Simple by default. Depth belongs to the engine.

Retail investors shouldn't need a PhD in statistics to consume institutional quant signals. Our mobile-first card interface displays clarity upfront, with complete factor telemetry available on demand in the bottom sheet.

PRIMARY LAYER · THE CARD

Instant Decision Architecture

At a glance, see the stock's current regime fit, relative sector ranking (#01), three-dimensional Performance Index, and plain-language summary without cognitive fatigue.

SECONDARY LAYER · THE BOTTOM SHEET

Company Fundamentals & Financial Research

Tap Deep Research to reveal the underlying quantitative factors — ROA, Gross NPA, P/B multiples, CASA ratios, and index sensitivity — benchmarked against NSE 500 sectoral quintiles.

INTERACTION MODEL

Swipe to discover. Tap to audit.

Traditional platforms confuse density with intelligence. Stocpenta concentrates multi-dimensional statistical computing into high-conviction decision cards.

Low Volatility Growth Regime More Regimes
DEEP-TECH ARCHITECTURE

Explore the Patent-Pending Quant Engine

Discover how relevant market and company information supports preference-aware rankings, deeper fundamental research, and clear explanations for investors.

User Journey

From investor preference to execution in 3 steps

A transparent workflow that separates market noise from genuine statistical edge.

STEP 01

Select Your Regime

Choose your active investment lens: Low Volatility Regime for capital defense, Growth Regime for earnings acceleration, or expand to More Regimes as market conditions change.

STEP 02

Review Prioritized Cards

Browse equities ordered by probabilistic fit. Examine the Performance Index, sector percentiles, and automated plain-language synthesis without having to decipher terminal tables.

STEP 03

Deep Research & Ask AI

Explore company fundamentals, business performance, and financial ratios, then use AI summaries to make complex financial and analytical information easier to understand.

Product Architecture

A unified, progressive investment intelligence platform

Simple enough for self-directed investors to scan quickly, with deeper company research and financial context available when needed.

REGIME-BASED RANKINGS

Preference-Aware Stock Rankings

Instead of one generic 'Buy' rating, stocks are ranked within specific investment regimes. Aura Financial ranks #1 in Low Volatility, but high-beta stocks surface in the Growth Regime. Context determines attractiveness.

#1 Aura Financial Corp 94.6 Percentile Score (Low Vol)
#2 Solis Infra Ltd 91.2 Percentile Score (Low Vol)
PERFORMANCE INDEX

Multi-Dimensional Quantitative Index

Every stock is scored across 3 primary quantitative dimensions: Low Risk (price variance & drawdown stability), High Growth (momentum & cash acceleration), and Valuation (optimised P/E metrics).

LOW RISK
94
GROWTH
68
VALUATION
76
Progressive Disclosure Architecture

Two layers of conviction: Surface clarity, engine depth

Move from quick stock discovery to deeper research into company fundamentals, business performance, and financial ratios, with clear summaries available when needed.

LAYER 1 · SURFACE CLARITY

The Intelligence Card

Instant decision-making without sensory overload: rank, regime fit, and three-pillar index.

LOW VOLATILITY (1 of 20)
RANK #01

Aura Financial Corp

AURAFIN · NSE
₹1,742.40
+1.12%
Market Cap Large Cap
Industry Private Banking
Sector Financials
PERFORMANCE INDEX Percentile vs Sector
Low Risk
94 GOOD
High Growth
68 MODERATE
Valuation
76 GOOD
AI FUNDAMENTAL SUMMARY
India's private banking bellwether delivers steady return on assets with conservative credit provisioning. Its low beta provides a solid defensive foundation.
LAYER 2 · FACTOR TELEMETRY

Deep Research: Company Fundamentals

Full transparency: multi-factor statistical proofs benchmarked to sector quintiles.

Aura Financial Corp — Deep Research

Company Fundamentals, Financial Ratios & AI Summaries

×
STOCPENTA AI SUMMARIES
Aura Financial Corp generates an exceptional 16.8% return on equity while maintaining non-performing assets well below 1.2%. Because investors value this consistency, its valuation multiple is reasonable compared to historical five-year medians.
COMPANY FUNDAMENTALS & FINANCIAL RATIOS
Factor & Plain Descriptor Company Value Sector Avg
ROA / Asset Yield
Return on Assets
 1.95% 1.20%
Gross NPA / Credit Safety
Bad Loan Ratio
 1.24% 2.85%
P/B / Book Valuation
Valuation vs Equity
 2.7x 2.4x
CASA / Deposit Safety
Low Cost Deposit Base
 38.2% 33.5%
Beta / Price Volatility
Sensitivity to Index
 0.72 1.08
Factor signals benchmarked against NSE 500 sectoral quintiles.
Data Comparison

Raw Terminal Data vs. Stocpenta Intelligence

See how isolated financial statistics become intuitive, preference-aware context.

WITHOUT STOCPENTA (RAW FINANCIAL DATA)
Company: Apex Consumer Corp (APEXCON)
P/E Ratio: 34.8x
ROE: 19.2%
Beta: 0.68
EV/EBITDA: 21.4x
Debt-to-Equity: 0.12

Isolated statistics without comparative peer context or regime weighting. The investor must manually calculate if 34.8x is attractive relative to current macroeconomic volatility.

WITH STOCPENTA (QUANTITATIVE INTELLIGENCE)
Rank #03 · Low Volatility Regime
PERFORMANCE INDEX: LOW RISK 92 · GROWTH 64 · VALUATION 72
“High stability characteristics are active. Downside variance ranks in top 5% of FMCG sector. Strong operating cash flows support defensive resilience despite elevated consumer multiples.”
PATENT-PENDING INTELLECTUAL PROPERTY

Not an AI wrapper. A proprietary quantitative intelligence engine.

Stocpenta’s patent-pending statistical framework supports objective-led filtering and comparative ranking across relevant investment universes, data inputs, and time durations, with clear explanations for investors.

Input
Multi-source Market Data INPUT
Financial Statement Telemetry FUNDAMENTALS
Derived Statistical Signals FEATURES

Patent-Pending
Stocpenta Quant Engine

PROBABILISTIC INTELLIGENCE CORE

Statistical Filtering Multi-Criteria Machine Learning Probabilistic AI Natural Language
Intelligence
Investor Preference Regimes IN-ENGINE
Performance Index Scoring 3-ATTRIBUTE
Comparative Equity Ranking OUTPUT
Technology Flow

How raw market data becomes actionable intelligence

The mathematical transformation pipeline that ingests continuous exchange feeds and outputs preference-aware Rankings.

REGIME-AWARE EVALUATION Expandable Regimes
Low Volatility Regime
Growth Regime
More Regimes
STOCPENTA
QUANT CORE

A regime is a formal, patent-pending statistical framework for evaluating and comparatively ranking securities against a defined investment objective. Different combinations of data, time duration, and evaluation parameters can produce distinct ranked sets.

OBJECTIVE-LED REGIME PROCESS

Five-Stage Computational Pipeline

01 · Market Data Ingestion INPUT

Ingests market and financial data from multiple sources, providing the underlying information for quantitative evaluation.

02 · Statistical Signal Identification SIGNAL PROCESSING

Applies the underlying statistical methodology to identify and process relevant quantitative signals across the available data.

03 · Regime-Specific Filtering REGIME CORE

Applies the selected regime's filtering methodology according to its defined objective, such as prioritising low volatility or high growth.

04 · Probabilistic Evaluation & Ranking SCORING

Evaluates securities comparatively under the active regime and generates relative scores and rankings across the selected universe.

05 · AI Summaries & Explanations EXPLAINABILITY

Translates quantitative outputs into understandable explanations and supports transparent inspection of the factors behind a stock's evaluation.

One Framework, Multiple Ranked Universes

Same intelligence architecture, different investment universes.

Stocpenta's framework can support different combinations of data units, evaluation periods, and market universes to produce comparative rankings for different analytical objectives.

Data Units Layer
Price
Volume
Charts
Financial Ratios
Other Market Data
Time Durations Layer
Short TermDays–Weeks
Medium TermMonths–1 Year
Long Term1–5 Years
Custom PeriodFlexible Range
Investment Universe Layer
EquitiesActive
CommoditiesArchitecture Ready
SectorsArchitecture Ready
Sub-sectorsArchitecture Ready
Geographical MarketsArchitecture Ready
Live Engine Rankings

Context-Aware Stock Rankings (E.g. Low Volatility Regime)

Illustrative rankings generated by the engine under the Low Volatility Regime, showing percentile scores alongside company research context.

RANK ILLUSTRATIVE COMPANY SECTOR PERCENTILE SCORE RESEARCH
#01 Aura Financial Corp
(AURAFIN · NSE)
 Financials (Banking) 94.6%
#02 Solis Infrastructure Ltd
(SOLISIN · NSE)
 Utilities & Power 91.2%
#03 Apex Consumer Corp
(APEXCON · NSE)
 FMCG & Consumer 88.7%
#04 Zenith Pharmaceuticals
(ZENPHAR · NSE)
 Healthcare 85.4%
#05 Kinetix Telecom Networks
(KINETEL · NSE)
 Telecommunications 82.1%
Model Telemetry

Multi-dimensional Performance Index

A single rating collapses critical nuance. The Performance Index evaluates Low Risk, High Growth, and Valuation independently.

ILLUSTRATIVE PROFILE

Performance Radar

MODEL TELEMETRY
HIGH GROWTH VALUATION (OPTIMISED P/E) LOW RISK (PRICE STABILITY)
Illustrative score profile NOT FINANCIAL ADVICE
LOW RISK · STABILITY 94.0

Evaluates price volatility characteristics and downside drawdown resilience. Low volatility yields high defensive scores.

HIGH GROWTH · MOMENTUM 68.0

Measures revenue momentum, earnings velocity, and positive cash flow acceleration signals across quarters.

VALUATION · OPTIMISED P/E 76.0

Assesses valuation attractiveness relative to historical sector distributions using an optimised price-to-earnings metric.

Category Differentiation

How Stocpenta compares

Quantitative intelligence is fundamentally distinct from static boolean screeners, generic language models, and broker notes.

Evaluation Dimension Stocpenta Traditional Screeners Generic AI Chatbots Broker Research
Underlying Engine ✓ Proprietary probabilistic quant engine Static boolean filters Off-the-shelf LLM text Subjective analyst notes
Regime Awareness ✓ Low Volatility, Growth, & More Regimes ✗ Static rules ignore context ✗ Cannot back-test or score Manual periodic notes
Multi-Criteria Index ✓ 3-attribute Performance Index Single metrics in isolation Summarized text paragraphs Price targets & consensus
Reasoning Layer ✓ AI-translated factor model audits ✗ None Generic unvalidated text Lengthy PDF reports
User Accessibility ✓ Progressive card depth for all levels Overwhelming terminal noise Requires prompt engineering Heavy jargon & disclaimers
Educational Hub

Learn to invest with quantitative intelligence

Plain-language explainers, deep-tech research notes, and glossary definitions powered by first-principles quantitative thinking.

Reference Dictionary

Stocpenta Quantitative Glossary

Clear, precise definitions of quantitative investing terminology.

Investment Regime
A defined quantitative framework for ranking stocks based on prioritized investor preferences — such as Low Volatility or Growth.
Performance Index
Stocpenta's multi-attribute scoring framework that rates equities across Low Risk, Growth, and Valuation dimensions simultaneously.
Probabilistic Model
A mathematical evaluation system that outputs continuous likelihoods and preference percentiles rather than rigid binary pass/fail cutoffs.
Progressive Disclosure
An interface design methodology that presents intuitive decision cards upfront while enabling deeper company research through bottom sheets on demand.
Company Fundamentals & Financial Ratios
A deeper look at a company's fundamentals, business performance, and relevant financial ratios to help investors understand its financial position and performance.
AI Summaries
Concise, understandable summaries that help investors interpret complex financial and analytical information.
Research Library

Research Notes & Explainers

Deep-dive essays and technical perspectives on multifactor modeling, backtesting, and systematic equity analysis.

Market Signals

Beyond Price and Ratios: Understanding the Signals Behind a Stock

Why nominal price alone cannot answer if a stock is worth buying, and how multidimensional signals reveal true fundamentals.

Read Article →
Scoring Architecture

Why One Stock Score Doesn't Tell the Whole Story

How compressed single scores obscure critical differences in volatility, cash flow resilience, and regime behavior.

Read Article →
Quant Theory

Investing Is Probabilistic, Not Predictable

Navigating market uncertainty through likelihood estimation and continuous evidence rather than false binary certainty.

Read Article →
Model Validation

From Quantitative Rankings to Historical Performance: How Do You Test a Model?

Evaluating strategy efficacy through backtesting, benchmark comparisons, and navigating the realities highlighted by SPIVA India.

Read Article →
Decision Framework

From Stock Research Overload to a Clearer Shortlist

Transforming terminal clutter and hundreds of competing metrics into a focused, preference-aligned research starting point.

Read Article →
Technology Comparison

Rules vs. Quantitative Models: Two Different Approaches to Evaluating Stocks

Why static boolean screeners drop high-conviction opportunities and how probabilistic multifactor models solve for nuance.

Read Article →
Quant Concepts

What is an Investor Preference Regime?

How an evaluation lens transforms the entire equity universe into preference-aware rankings.

Read Article →
Technology

Why Comparative Ranking Beats Pass/Fail Screeners

Why quantitative signals are far more effective when normalized into relative comparative percentiles.

Read Article →
AI Architecture

AI Summaries for Financial Research

How AI summaries turn complex financial and analytical information into clear, understandable investor takeaways.

Read Article →
Our Thesis

Democratizing institutional quantitative intelligence

Stocpenta was founded on a simple observation: retail Indian investors have been left with tools built for a prior generation. We are building the quantitative intelligence layer for what comes next.

Leadership

Meet the Founders

Pioneering probabilistic financial modeling, deep-tech architecture, and operational scale.

Avishek Nag, Co-founder and Chief Scientist at Stocpenta

Avishek Nag

Co-founder, Chief Scientist

Leading the mathematical formulation, patent-pending probabilistic modeling core, and regime-aware algorithmic architecture powering the Stocpenta Quant Engine.

Connect on LinkedIn
Prantik Das, Co-founder and COO at Stocpenta

Prantik Das

Co-founder, COO

Spearheading operations and financial planning, while driving product strategy, go-to-market and commercialization to establish Stocpenta in the investment intelligence ecosystem.

Connect on LinkedIn
FOUNDING MOTIVATION

The Institutional Monopoly on Quantitative Edge

Institutional asset managers analyze equities using high-dimensional probabilistic tensors, multi-criteria factor weighting, and dynamic regime models. Self-directed Indian investors, by contrast, are left with basic screeners with rigid thresholds or speculative tips on social media.

Stocpenta bridges this divide with a proprietary quantitative engine that performs heavy computational evaluation behind the scenes, presenting the outputs through an intuitive card-based interface with on-demand company research.

Brand Identity

What Stocpenta is — and what it is not

Clear boundaries on our mission and role in the financial ecosystem.

What We Are NOT

  • ✗ Not a stock-tip service: We do not provide speculative buy/sell tips or guaranteed returns.
  • ✗ Not a broker: We do not hold client capital or execute direct proprietary trades.
  • ✗ Not a generic AI wrapper: We do not rely on standard LLMs to hallucinate stock evaluations.
  • ✗ Not a static screener: We do not ask users to manually configure complex SQL filters.

What We ARE

  • ✓ Quantitative intelligence platform: First-principles mathematical equity models.
  • ✓ Regime-aware ranking engine: Low Volatility, Growth, and expanding regimes.
  • ✓ Multi-criteria scoring layer: Performance Index across Risk, Growth, and Valuation.
  • ✓ Fundamental research & AI summaries: Clear explanations of company performance and financial information.
EARLY ACCESS REGISTRATION

Join the Stocpenta Waitlist

Register for private pilot access, technical research briefs, or institutional enterprise partnerships.

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CORPORATE & PARTNERSHIP DETAILS
REGISTERED ENTITY
Stocpenta Labs Private Limited
RESEARCH & INQUIRIES
LOCATION
Kolkata, West Bengal, India