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Canada unveils auto industry plan in latest pivot away from US

https://www.bbc.com/news/articles/cvgd2j80klmo
1•breve•19s ago•0 comments

The essential Reinhold Niebuhr: selected essays and addresses

https://archive.org/details/essentialreinhol0000nieb
1•baxtr•2m ago•0 comments

Rentahuman.ai Turns Humans into On-Demand Labor for AI Agents

https://www.forbes.com/sites/ronschmelzer/2026/02/05/when-ai-agents-start-hiring-humans-rentahuma...
1•tempodox•4m ago•0 comments

StovexGlobal – Compliance Gaps to Note

1•ReviewShield•7m ago•0 comments

Show HN: Afelyon – Turns Jira tickets into production-ready PRs (multi-repo)

https://afelyon.com/
1•AbduNebu•8m ago•0 comments

Trump says America should move on from Epstein – it may not be that easy

https://www.bbc.com/news/articles/cy4gj71z0m0o
2•tempodox•9m ago•0 comments

Tiny Clippy – A native Office Assistant built in Rust and egui

https://github.com/salva-imm/tiny-clippy
1•salvadorda656•13m ago•0 comments

LegalArgumentException: From Courtrooms to Clojure – Sen [video]

https://www.youtube.com/watch?v=cmMQbsOTX-o
1•adityaathalye•16m ago•0 comments

US moves to deport 5-year-old detained in Minnesota

https://www.reuters.com/legal/government/us-moves-deport-5-year-old-detained-minnesota-2026-02-06/
2•petethomas•19m ago•1 comments

If you lose your passport in Austria, head for McDonald's Golden Arches

https://www.cbsnews.com/news/us-embassy-mcdonalds-restaurants-austria-hotline-americans-consular-...
1•thunderbong•24m ago•0 comments

Show HN: Mermaid Formatter – CLI and library to auto-format Mermaid diagrams

https://github.com/chenyanchen/mermaid-formatter
1•astm•39m ago•0 comments

RFCs vs. READMEs: The Evolution of Protocols

https://h3manth.com/scribe/rfcs-vs-readmes/
2•init0•46m ago•1 comments

Kanchipuram Saris and Thinking Machines

https://altermag.com/articles/kanchipuram-saris-and-thinking-machines
1•trojanalert•46m ago•0 comments

Chinese chemical supplier causes global baby formula recall

https://www.reuters.com/business/healthcare-pharmaceuticals/nestle-widens-french-infant-formula-r...
1•fkdk•49m ago•0 comments

I've used AI to write 100% of my code for a year as an engineer

https://old.reddit.com/r/ClaudeCode/comments/1qxvobt/ive_used_ai_to_write_100_of_my_code_for_1_ye...
2•ukuina•51m ago•1 comments

Looking for 4 Autistic Co-Founders for AI Startup (Equity-Based)

1•au-ai-aisl•1h ago•1 comments

AI-native capabilities, a new API Catalog, and updated plans and pricing

https://blog.postman.com/new-capabilities-march-2026/
1•thunderbong•1h ago•0 comments

What changed in tech from 2010 to 2020?

https://www.tedsanders.com/what-changed-in-tech-from-2010-to-2020/
2•endorphine•1h ago•0 comments

From Human Ergonomics to Agent Ergonomics

https://wesmckinney.com/blog/agent-ergonomics/
1•Anon84•1h ago•0 comments

Advanced Inertial Reference Sphere

https://en.wikipedia.org/wiki/Advanced_Inertial_Reference_Sphere
1•cyanf•1h ago•0 comments

Toyota Developing a Console-Grade, Open-Source Game Engine with Flutter and Dart

https://www.phoronix.com/news/Fluorite-Toyota-Game-Engine
1•computer23•1h ago•0 comments

Typing for Love or Money: The Hidden Labor Behind Modern Literary Masterpieces

https://publicdomainreview.org/essay/typing-for-love-or-money/
1•prismatic•1h ago•0 comments

Show HN: A longitudinal health record built from fragmented medical data

https://myaether.live
1•takmak007•1h ago•0 comments

CoreWeave's $30B Bet on GPU Market Infrastructure

https://davefriedman.substack.com/p/coreweaves-30-billion-bet-on-gpu
1•gmays•1h ago•0 comments

Creating and Hosting a Static Website on Cloudflare for Free

https://benjaminsmallwood.com/blog/creating-and-hosting-a-static-website-on-cloudflare-for-free/
1•bensmallwood•1h ago•1 comments

"The Stanford scam proves America is becoming a nation of grifters"

https://www.thetimes.com/us/news-today/article/students-stanford-grifters-ivy-league-w2g5z768z
4•cwwc•1h ago•0 comments

Elon Musk on Space GPUs, AI, Optimus, and His Manufacturing Method

https://cheekypint.substack.com/p/elon-musk-on-space-gpus-ai-optimus
2•simonebrunozzi•1h ago•0 comments

X (Twitter) is back with a new X API Pay-Per-Use model

https://developer.x.com/
3•eeko_systems•1h ago•0 comments

Zlob.h 100% POSIX and glibc compatible globbing lib that is faste and better

https://github.com/dmtrKovalenko/zlob
3•neogoose•1h ago•1 comments

Show HN: Deterministic signal triangulation using a fixed .72% variance constant

https://github.com/mabrucker85-prog/Project_Lance_Core
2•mav5431•1h ago•1 comments
Open in hackernews

Show HN: I analyzed 49K corporate deaths (1992-2025) using minute-level data

https://github.com/Yusuf34soysal/graveyard-index
1•New_Person•1mo ago

Comments

New_Person•1mo ago
OP here.

I spent the last week analyzing a proprietary dataset of 49,315 delisted US stocks to understand "survivorship bias" from a microstructure perspective. Standard backtests usually ignore these companies, but I wanted to see what the order book looks like right before a firm goes to zero.

I built a pipeline to index 84GB of minute-level OHLCV data and cluster the failures using K-Means.

Key Finding: "Type III: The Zombie Churn." Stocks that have already lost 90%+ of their value, but volume explodes to 48x normal levels while price stays flat. It looks like a distinct signature of retail bag-holding vs. institutional exit.

The repo has the indexer script, the clustering logic, and the "Death Metrics" CSV for the top 1,000 failures (including Lehman Brothers and Enron).

Happy to answer questions about the parquet engineering or the metrics used!

New_Person•1mo ago
UPDATE: Three-Phase Mortality Model VALIDATED

I just completed a comprehensive statistical validation of this dataset using all 49,467 confirmed corporate deaths (1992-2025).

KEY FINDINGS: • 59.0% of delisted stocks lost >50% of their value before death (HIGHLY SIGNIFICANT, p<0.001) • Median price destruction: 66.6% • 33.2% lost >90% of value, 16.7% suffered >99% wipeouts

TEMPORAL TRENDS: • 1990s: 13,709 deaths, 60.8% median destruction • 2000s: 11,985 deaths, 71.7% median destruction • 2010s: 9,858 deaths, 73.7% median destruction ← PEAK SEVERITY • 2020s: 14,915 deaths, 66.7% median destruction ← MOST DEATHS

IMPLICATION: Corporate death is NOT sudden in price space—it's systematically foreshadowed by deep, sustained price deterioration. This validates the Three-Phase Mortality Model and has major implications for: - Risk management & distress prediction - Portfolio construction & drawdown dynamics - Early-warning systems for quantitative equity portfolios

The destruction severity has INCREASED over time (60.8% → 73.7%), suggesting market efficiency improvements make corporate decline more visible and systematic.

Full analysis: 6 cells of statistical validation + 4-panel visualization created in Google Colab, bypassing all Drive mount issues by downloading directly from GitHub.

New_Person•1mo ago
LITERATURE REVIEW COMPLETE: Academic Validation Strong

I conducted a comprehensive literature review on corporate mortality patterns using Perplexity AI. The academic research strongly validates the Three-Phase Mortality Model:

Price Decline Before Failure: Multiple event-study papers document substantial negative abnormal returns in quarters/years leading to bankruptcy. Dawkins et al. report sharp "plunge" behavior around Chapter 11 filings. Japanese/TSE/NASDAQ studies show 1-year buy-and-hold returns before delisting of ~60% losses on average, with median losses of ~65% for main-board firms and ~84% for smaller markets.

Three-Phase Models: Several academic frameworks view failure as multi-phase rather than single-event. Recent JSF research categorizes firms as healthy→depressed→distressed, building three-phase models. Structural Merton-tradition models interpret phases as: asset value drift toward default, equity behaves like deep out-of-the-money option, intensifying volatility sensitivity.

Destruction Severity Across Eras: Literature confirms median pre-delisting losses in 50-70% range. Post-2000s studies show attenuation suggesting information flow evolution. The 2010s exhibiting MORE severe median destruction (73.7%) vs 1990s (~61%) aligns with findings that later-period failures show greater price destruction due to smaller, more distressed marginal firms.

Market Microstructure: Studies since 1990s document how decimalization, electronic trading, and high-frequency activity have altered distress manifestation. Flash Crash work highlights self-exciting dynamics and order flow. Delisting leads to tripled percentage spreads, doubled volatility, large depth drops even with high OTC volume.

KEY INSIGHT: The 59% rate of >50% value loss before delisting and 66.6% median destruction are highly consistent with documented one-year pre-delisting losses and event-window returns across multiple markets. This extends the literature by providing large-scale, multi-decade, all-cause delisting perspective.

Full Perplexity research: https://www.perplexity.ai/search/conduct-a-comprehensive-lit...