The most cost-effective approach is usually:

1) Keep the workflow simple and rules-based

Use a small set of high-signal fundamental factors rather than a complex model at first. For example:

• Valuation: P/E, EV/EBITDA, P/B
• Quality: ROE, ROIC, margins, leverage
• Growth: revenue and EPS growth
• Shareholder returns: buybacks, dividends
• Stability: earnings revisions, cash flow consistency

This reduces:

• data requirements,
• research time,
• model overfitting,
• compute/storage costs.

2) Use one primary global fundamental data platform

Pick a vendor that already offers:

• global coverage
• standardized financial statements
• point-in-time fundamentals
• corporate actions
• identifier mapping
• exchange/country classifications

This is usually cheaper than stitching together multiple niche sources. The biggest hidden cost is data normalization across countries and reporting standards, so a platform that handles this well saves a lot of engineering work.

3) Build a modular screening pipeline

A cost-effective workflow typically has 4 stages:

A. Universe definition

Start with a broad investable universe:

• developed markets only, or
• developed + major emerging markets

Filter by:

• market cap,
• trading liquidity,
• listing status,
• currency,
• sector exclusions if needed.

B. Data extraction layer

Pull only the fields you actually need:

• latest fundamentals,
• trailing and forward estimates if available,
• historical fundamentals for trend checks,
• prices/market cap for ratios.

Avoid loading full datasets unnecessarily.

C. Screening logic

Implement screening in a reproducible way:

• hard filters first,
• then factor ranking,
• then composite scoring,
• then exclusions.

Example:

1. remove microcaps / illiquid names
2. exclude negative earnings if strategy requires profitability
3. rank on valuation, quality, and growth
4. combine into a weighted score
5. output top decile or fixed count

D. Output and review

Save:

• the ranked list,
• factor breakdown,
• country/sector weights,
• exceptions and missing data flags.

This keeps manual review cheap and fast.

4) Prefer cloud-native, batch-based processing

For cost efficiency:

• run screens on a schedule, not continuously,
• store fundamentals in a columnar format,
• use batch jobs instead of always-on servers,
• cache static reference data,
• only refresh what changed.

For many use cases, a simple stack like:

• Python,
• SQL,
• parquet files,
• scheduled jobs,
• object storage,

is enough.

5) Minimize custom data cleaning

A major cost driver is cleaning:

• fiscal year alignment,
• currency conversion,
• restatements,
• multiple share classes,
• ADRs,
• survivorship bias,
• point-in-time accuracy.

If the platform already provides:

• normalized line items,
• historical restated series,
• fiscal calendars,
• PIT data,

you can avoid building a large internal data engineering team.

6) Use a scoring framework instead of heavy ML initially

A weighted composite score is usually the best cost/performance starting point.

Example:

• 40% valuation
• 30% quality
• 20% growth
• 10% shareholder returns

Why this is cost-effective:

• easy to explain,
• easy to backtest,
• easy to maintain,
• less data-hungry than machine learning.

7) Backtest only after adding realistic constraints

To avoid false confidence, include:

• transaction costs,
• liquidity limits,
• rebalancing frequency,
• point-in-time data,
• delisting returns if possible.

This prevents wasting money on a screen that looks good only in theory.

8) Start with one market, then scale

The cheapest path is often:

1. build on U.S. or a single developed market,
2. validate the process,
3. expand to Europe/APAC,
4. then add emerging markets.

This reduces initial complexity around currencies, accounting standards, and trading calendars.

9) Automate governance and monitoring

Add cheap but important checks:

• missing data alerts,
• sudden factor distribution shifts,
• universe drift,
• stale updates,
• identifier mismatches.

This prevents expensive debugging later.

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A practical low-cost architecture

A lean setup might look like:

• Data platform: global fundamentals + prices + estimates
• Storage: parquet files in cloud storage
• Processing: Python + pandas/polars + SQL
• Orchestration: cron, Airflow, or managed scheduler
• Backtesting/screening: simple rule engine
• Reporting: notebooks or dashboard

This is usually much cheaper than a full enterprise data warehouse or a custom data lake from scratch.

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Best cost-saving principle

Buy normalization, build logic.

In other words:

• pay a vendor for high-quality global data cleaning and standardization,
• keep your internal effort focused on screening rules, research, and portfolio construction.

If you want, I can also outline:

1. a minimal viable stack for this workflow, or
2. a vendor selection checklist for global fundamental data platforms.