ASX 2020-26 Disclosure Archive

Job ID: 40168515

Budget: $30 – $250 AUD

I need a freelancer to collect and organise all publicly available company disclosures for a set of ASX-listed companies for the period 2020–2026 (inclusive). The output must be complete, well-organised, consistently named, and easy to search.

Scope: what to collect (2020–2026)
For each company, download and save all publicly available items in the categories below:
Financial reporting
Annual Financial Report / Annual Report (incl. Appendix 4E where applicable)
Half-Year Report / Interim Report (incl. Appendix 4D where applicable)
Quarterly reports (e.g., quarterly activities/cashflow as relevant)
Market updates & investor material
Investor/analyst presentations (results presentations, investor days, conference decks)
Resources & reserves
Mineral Resource and Ore Reserve Statements
JORC updates, resource/reserve upgrades, annual resource/reserve statements, etc.
Earnings call materials (if publicly available)
Earnings call transcripts (only if free/public on company site, ASX attachments, or other public sources)
Important: Only use public / non-paywalled sources.

Companies (tickers):
ARU (Arafura)
ILU (Iluka Resources Ltd)
BRE (Brazilian Rare Earths)
SMR (Stanmore Resources Ltd)
DVP (Develop Global Ltd)
FFM (FireFly Metals Ltd)
NEM (Newmont Corp)
NST (Northern Star Resources Ltd)
EVN (Evolution Mining Ltd)
RRL (Regis Resources Ltd)
WGX (Westgold Resources Ltd)
VAU (Vault Minerals Ltd)
CYL (Catalyst Metals)
OBM (Ora Banda Mining Ltd)
BGL (Bellevue Gold Ltd)


Deliverable:
A single compressed folder (ZIP or 7z) containing the full PDF set, ready for immediate search and retrieval, plus a brief completion log listing the tickers covered and the total document count per company.

1) Folder structure (must follow exactly):
One master folder: ASX_Disclosures_2020-2026/
Subfolder per company: ARU/, ILU/, etc.
Inside each company folder, subfolders:
01_Annual_Reports
02_Half_Year_Reports
03_Quarterly_Reports
04_Presentations
05_Resources_Reserves
06_Earnings_Calls_Transcripts
07_Other_ASX_Releases (anything not fitting cleanly above but still in scope)

2) File naming convention (must follow exactly):
TICKER_DocType_Period_YYYY-MM-DD_Title.pdf

Examples:
NST_AnnualReport_FY2024_2024-08-19_Annual_Financial_Report.pdf
EVN_Quarterly_4QFY2023_2023-07-27_June_Quarter_Activities_Report.pdf
ILU_Presentation_FY2022_2022-08-10_FY22_Results_Presentation.pdf
ARU_ReservesResources_FY2021_2021-06-30_Mineral_Resource_Statement.pdf
(If the period isn’t clear, use NA and flag it in the index.)

3) Master index file (required):
Create an Excel file: Disclosures_Index.xlsx with one row per document and these columns:
Ticker
Company
Document category (Annual / Half / Quarterly / Presentation / Res&Res / Transcript / Other)
Document title
Release date (YYYY-MM-DD)
Period covered (e.g., FY2022, 1H2023, 3Q2021, etc.)
Source (ASX or Company Site)
Source URL
Saved file name
Notes (e.g., “duplicate removed”, “missing attachment on ASX”, “webcast link only”)

4) Completeness checks (required):
For each ticker, include a short note in the index (or separate QA_Notes.txt) confirming:
ASX announcement list for 2020–2026 has been reviewed
Duplicates removed (same doc attached multiple times)
Any missing/unavailable items flagged with URL and explanation


Quality requirements (important):
PDFs must be downloaded in full (not broken links / partial pages)
Avoid duplicates (common with repeated presentations)
Keep titles clean and consistent
Ensure dates are accurate and in YYYY-MM-DD format
If an item is a webpage only (rare), save a PDF print or an HTML file and include the link in the index

Time / workflow expectations:
I prefer delivery by milestone (e.g., 5 companies at a time) so I can spot-check early.
Please do one ticker first as a sample (any ticker) to confirm naming/folder/index format, then proceed with the rest.

What I’m looking for:
Strong attention to detail and ability to follow a naming convention
Experience working with ASX/company announcements a plus
Comfortable with large volumes of downloads and organising files
May use Python or AI to automate