惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Recorded Future
Recorded Future
人人都是产品经理
人人都是产品经理
Google DeepMind News
Google DeepMind News
Google DeepMind News
Google DeepMind News
雷峰网
雷峰网
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
S
Secure Thoughts
酷 壳 – CoolShell
酷 壳 – CoolShell
SecWiki News
SecWiki News
W
WeLiveSecurity
N
News | PayPal Newsroom
AI
AI
The Last Watchdog
The Last Watchdog
I
InfoQ
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
B
Blog RSS Feed
Hacker News: Ask HN
Hacker News: Ask HN
Hugging Face - Blog
Hugging Face - Blog
L
LINUX DO - 最新话题
Engineering at Meta
Engineering at Meta
Hacker News - Newest:
Hacker News - Newest: "LLM"
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
www.infosecurity-magazine.com
www.infosecurity-magazine.com
腾讯CDC
Attack and Defense Labs
Attack and Defense Labs
博客园 - Franky
Recent Commits to openclaw:main
Recent Commits to openclaw:main
Apple Machine Learning Research
Apple Machine Learning Research
Stack Overflow Blog
Stack Overflow Blog
Forbes - Security
Forbes - Security
Martin Fowler
Martin Fowler
D
Docker
Last Week in AI
Last Week in AI
H
Hackread – Cybersecurity News, Data Breaches, AI and More
D
Darknet – Hacking Tools, Hacker News & Cyber Security
P
Privacy & Cybersecurity Law Blog
U
Unit 42
H
Help Net Security
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
S
Schneier on Security
博客园 - 司徒正美
S
SegmentFault 最新的问题
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Schneier on Security
Schneier on Security
P
Palo Alto Networks Blog
Jina AI
Jina AI
V
Vulnerabilities – Threatpost
P
Proofpoint News Feed
大猫的无限游戏
大猫的无限游戏
WordPress大学
WordPress大学

ashishb.net

A day in Luxembourg - the richest country in the world I was asked to install malware during a fake interview Book summary: Breakneck - China's quest to engineer the future by Dan Wang Book summary: How to Teach Your Baby to Read Book Summary: The Discontented Little Baby Book by Pamela Douglas Introducing Amazing Sandbox - run third-party tools and AI agents securely on your machine Why software outsourcing gets a bad reputation? Book summary: The Natural Baby Sleep Solution by Polly Moore A day in Antwerp, Belgium Journey of online influencers Two days in Brussels, Belgium Shortcuts - when we love them and when we don't A visit to Rakhigarhi Three days in overhyped Paris Empty Japan, crowded Tokyo The real lock-in in GitHub is not the code, but the stars 11-day Norwegian Breakaway East Caribbean cruise Sanskrit and Sri Lankan Air Force Use REST with Open API The Achilles heel of American capitalism Costa Rica in 4 days At a juice stall in Sri Lanka A short stay at Warsaw, Poland Best practices for using Python & uv inside Docker Two days in Vilnius, Lithuania How IntelliJ IDEs waste disk space Pregnancy Why there aren't many digital nomads from India Two days in Riga, Latvia To keep your machine secure, run third-party tools inside Docker Family Ties in Your DNA: Some relatives are closer than others Doctors per capita Two days in Tallinn, Estonia Ship tools as standalone static binaries Made in America Two days in Helsinki, Finland Maintaining an Android app is a lot of work The land of good deals Two days in Oslo, Norway FastAPI vs Flask performance comparison Google Search is losing to Perplexity Two days in Dublin, Ireland Continuous integration ≠ Continuous delivery World's simplest project success heuristic London in 5 days It is hard to recommend Python in production Inflation, IRS, Credit cards, and Vendors Temu and the Chinese approach Things to do in Miami Florida Revenue vs Cost Axis Language learning as an adult The unanchored babies of the green card limbo Price variance in the United States A day in Louisville, Kentucky A surprisingly positive experience with Air India Unhospitable Airports Android: Don't use stale views USA = Union of Sales and Advertisement A day in Nashville, Tennessee Minimize Javascript in your codebase A day in Birmingham, Alabama In defense of ad-supported products Real vs artificial world The science behind Punjabi singers Hiking Mt. Fuji The Indian startup bubble is insane Repairing database on the fly for millions of users Book Summary: One up on Wall Street by Peter Lynch It is hard to recommend Google Cloud At the Prague airport Kyoto in three days Migrating from WordPress to Hugo Book summary: Sick Societies by Robert B. Edgerton Statistical outcomes require statistical games Illegal immigrants to Europe via Cairo Tokyo in three days Mobs are Status Games Writing Script matters as much as the spoken language Sri Lanka in 5 days LLMs: great for business but bad business Mac shortcut for typing Avagraha symbol On a bus with an asylum seeker Nicaragua in 5 days When to commit Generated code to version control Why I always buy a local SIM in a foreign country Use Makefile for Android Four days in Guadalajara, Mexico Android Navigation: Up vs Back Hotels vs Airbnb vs Hostels Currency issues in Argentina Abstractions should be deep not wide Some data on podcasting Always support compressed response in an API service A day in El Calafate - Patagonia, Argentina Hermetic docker images with Hugging Face machine learning models American Elections The sound of "ch" API services should always have usage Limits Hiking in El Chaltén - trekking capital of Argentina Natural Laws vs Man-made Laws
Book Summary: Safe Haven by Mark Spitznagel
Ashish Bhatia · 2024-06-30 · via ashishb.net

Principles

  • Investing is a sequential process
  • The only goal is to maximize wealth over time. Do not “narrow frame” it to focus only on annual returns.
  • A risk mitigation strategy must lower risk and hence increase CAGR (Compounded Annual Growth Rate)

Another example would be a merchant sending 10,000$ worth of goods with a 5% chance of pirates taking it over. Let’s say the merchant has a net worth of 3,000$ and the rest is financed. The expected loss is ~5%. So, an insurance of 800$ might feel expensive.

The better way to look at it is the geometric outcome. Without insurance, the expected per-trip return from 100 trips is ((3000 + 10000)^95 * 3000^5)^1/100 = 12,081$

However, with insurance, the merchant always loses 800$ and gets 10,000$ (either from the sale or from the insurance), so, the returns are 3000 + 10000 - 800 = 12200$! On the other hand, the insurer makes 800 - 500 = 300$ as well!

Insurance is not a zero-sum business. The insurance buyer is playing a game of geometric returns while the insurance provider is playing a game of arithmetic returns, so, both can be making money simultaneously.

One of the book’s core ideas is that investing is a game of sequential investments.

Consider, for example, a simple game where you throw a dice and the returns are

1
2
3
4
5
6
-50% on 1
+ 5% on 2
+ 5% on 3
+ 5% on 4
+ 5% on 5
+50% on 6

The expected outcome is average of all and that’s 20%/6 ~ +3.3%. So, playing this game is a good idea, right? Imagine you play this game repeatedly. Statistically, you will hit all the numbers ~1/6 times each, so, the final returns will be 0.5 * 1.05 * 1.05 * 1.05 * 1.05 * 1.50 ~ 0.91! So, +3.3% arithmetic returns lead to -9%/6 ~ -1.5% geometric returns.

In the game of investing, draw-downs matter a lot.

The only return that matters is geometric and not arithmetic.

Leverage in most cases increases arithmetic returns at the expense of geometric returns and that’s how Long Term Capital Management blew up.

The reverse happens when you keep cash on the side. For example, the previous game of dice can be played again. This time, however, you play each round with a fraction of the money you started with. For example, only bet 40% of your wealth in each round and you will end up with a +1.1% arithmetic return and +0.64% geometric return. The 60% cash on the side does not make money but does not lose in extreme scenarios either. This store of value saves you from a major drawdown.

Another even better approach is insurance. Consider this scheme - “invest 91% in the dice game, 9% in an insurance that pays 5X if 1 comes”. This scheme lowers arithmetic return to 3% but increases geometric returns to 2.1%.

The arithmetic return can go down while geometric returns can go up as the variance in outcomes shrinks.

Finding safe havens that produce positive returns in the down market is not hard. Finding safe havens that are cost-effective is hard.

Using S&P500 returns for the past 120 years as an example, the author demonstrates that a 99.5% S&P500 with 0.5% allocated to insurance performs better than the S&P500 itself. Gold, bonds, 3-month T-bills don’t even come close! So, what’s that “secret” insurance scheme, well the author does not reveal that. But there are multiple threads on the Internet about what it looks like. For example, this one and another one point out that Spitznagel is buying deep out-of-money PUT options that are cheap enough as insurance.