A Random Walk Down Wall Street

What Hacker News says

1. smileysteveDec 2025
   Stock "trading" is statistically against the odds. "A Random Walk Down Wall Street" was published nearly 50 years ago and remains generally true. No YouTuber or perfect instruction following would make you 100% likely to be profitable.

   The most profitable (and effort efficient way) is to routinely invest in a broad basket of stocks over a long period. Ie Voo and Hold.

   thread
2. daft_pinkApr 2025
   Learned Helplessness - Martin Seligman A Random Walk Down Wall Street - Malkiel The Millionaire Next Door - Stanley Atlas Shrugged - Ayn Rand

   Second the Incerto series by Nassim Taleb

   thread
3. jdlshoreMar 2025
   A balanced strategy is best. I use the advice in A Random Walk Down Wall Street, which is a mix of real estate (VGSLX), bonds (VBTLX), and stocks, with the stocks split 50/50 between US (VTSAX) and international (VTIAX and VEMAX).

   I was prepared to take a bath after seeing the news this past week, but I’m actually up a bit—VTSAX is down, but the others are all up.

   thread
4. firefaxMar 2025
   Random Walk Down Wall Street
   thread
5. seeknotfindJun 2024
   I guess it's time to tell the story online?

   When I graduated college, I spent 3 months as a programmer with my econ friend trying to build exactly this. I started off creating a system to paper trade stocks retroactively. So you imagine you go back in time and pretend it's January 1st, 1982 then have an algorithm look at the stocks then, then move it a day forward, and let it trade for the past 40 years and see how it does.

   We tried linear models, SVMs, neural networks, RNNs, ensembles, genetic algorithms, anything with stock data, news sentiment data, classic quant structures, and everything in-between. Basically, 3 solid months of coding before I started working.

   Anyway, I found out a lot of stuff the hard way, because I didn't have an econ degree.

   First off, you try enough methods, you end up p hacking or hill climbing the past anyway, and it's no good.

   Second off, historical clean data is hard to get. It may or may not have splits in it or other things, so you may inadvertantly supply information from the future when playing back from the past. It's hard to get this right.

   Third off, for many of the models we used, they were almost always competitive in the 80s (even a linear regression), but in the oughts or 2010's, they stopped being competitive. We thought computer based trading was becoming more competitive in hedge funds.

   Fourth, simple models tended to work better. So for instance we may have trained the model on data from 70s-80s, then starting in the 80s, we did online (continuous) training as we moved the model forward in time. There's just not enough data. You can train on all historical stocks or all stocks or related data streams in the industry up to that point, but I think we probably didn't have enough data and the market is competitive.

   Fifth, I wish I read a Random Walk Down Wall Street earlier, or all of Taleb's stuff. These are books that have deep mistrust of quants.

   Sixth, I think to be competitive, you need to have money in the game, many heuristics, and industry experience. Big firms have this and equipment, but it's hard to get in as an individual.

   Seventh, I put several hundred hours into this project and learned a bunch about machine learning and economics. In every way I loved the experience, and I'd encourage you to try it. Probably I'm a n00b here, but I hope some of my notes can help you.

   Show morethread
6. jseligerFeb 2024
   The Millionaire Next Door.

   A Random Walk Down Wall Street.

   For most people, the challenge is spending less than they make, hedonic adaptation, and making more money (e.g. most people probably change jobs less often than is ideal).

   thread