TheAiGuy

I doubt it's all that complicated. WFC is a crummy bank with a history of fraud and BAC is a big, safe bank that will probably give a positive return. Buffett doesn't have a lot of options, especially with banks large enough to invest in size. MTB, is too small, JPM has too much key man risk, CITI is garbage, GS/MS have very different business models, he's at the regulator limit for AXP, V/MA are too expensive... etc. That leaves BAC.

Forgot the link: https://www.quantopian.com/

Wow, so good luck with this? This should be a really, really hard thing to do without a good dataset. I imagine most quant funds (e.g. Renaissance or Bridgewater) have access to large, high quality datasets. I seriously doubt you will do well running LSTMs on widely available datasets (also, don’t use LSTMs - use GRUs) Anyway, if you are serious about this, a good place to start for tools is probably Quantopian. I know one of the principals there and I don’t think I can vouch for their financial market chops but there toolsets are probably pretty good (i.e. their python interfaces)

2017: 34.37% 2016: 12.36% 2015: -2.71% (2012-2015 was around when I started doing this for real, so my returns prior to 2015 match a typical target date fund, which is more or less how it was invested. ) First good year, with JD as my biggest winner, followed by AAPL, MA, V & RYCEY. Losers were: Selling out of BAC too soon(!) and SBUX. LBRDA nicely kept up with the market. ----- Edit: losers should ~really~ include passing on SQ in 2016. Also, I lost like, $50 on Etherium and Litecoin before deciding it was dumb.

My best investment this year has been JD.com. All the bitcoins investors here, though, wow.

Yeah, I also find myself not talking about my biggest holding -- I don't want it to be "mine" so much

Scott is doing a pretty good job of making fun of me. Probs don't take him seriously here. I, however, am asking what one hopes to gain from an undirected search through the filings in contrast to a direct (or hypothesis driven) approach. An aphorism about the benefits of preparation doesn't really address the issue.

8)

I understand that this is sort of a heterodox view, but I basically just glance at fillings, looking for one or two things that I've previously identified as being material to my thesis. For example, I am looking for revenue growth or indications that a company does, in fact, have enough liquidity to survive, etc. That is, I am looking for something specific to support or falsify a previously constructed thesis on drivers of value in the business and don't bother with other things. Curious how other people think, but I'll be damned before I read about Nike's currency hedges again.

Yeah, I think that's what I want but, honestly, that's probably too much work to justify.

Does the aggregate return (IRR) of the stocks I choose differ from chance. I can't figure that one out so I've settled for does the distribution of returns for individual stocks differ from chance. In an ideal scenario, 1) You compute the mean and variance of your individual stock returns. 2) You compute the mean and variance of the S&P 500 individual stock returns. 3) Compare the two distributions using t-test or another appropriate test based the characteristics of the distributions. I don't know an easy way of performing 2), though. There must be a data service (Bloomberg?) where you can obtain the return of individual stocks for a given time period? Ideally what I'd do is compute a distribution IRRs over paper traded portfolios of randomly selected stocks. I'm generally not a huge fan of parametrics.

Does the aggregate return (IRR) of the stocks I choose differ from chance. I can't figure that one out so I've settled for does the distribution of returns for individual stocks differ from chance.

Hi All, I'm trying to figure out a way to evaluate my portfolio's performance statistically, taking the idea that if you want to do something, you should probably measure it. I have the problem of having switched from a portfolio of mutual funds to stocks over time as I've learned more. This creates a challenge evaluating my performance as the portfolio wide metrics are not appropriate (i.e. I'm fully aware that my mutual funding picking skill is shit). So, I have records of each stock I've bought (when, how much, etc) and I've created a paper benchmark for each stock using a paper-traded portfolio of ETFs (i.e. if I bought 100 shares of AAPL on the Jan 1, 2016, I create a paper trade of VTI for the same dollar amount on the same day). This gives me a dollar-weighted benchmark for each stock, meaning that the IRR of each stock can be compared to the IRR of its benchmark. The difference between these two numbers can be thought of my "alpha" for the stock. B/c's these "alphas" are independent(-ish) samples, I want to test them statistically. I think it's appropriate to weigh the alphas by age, (specifically, a "money weighted age" or, log base(1+IRR)[Gross Return] = age) The t-test isn't appropriate, but I think I sign-rank test is fine. I'm not sure on the statistical test, but I'm not aware of a more appropriate model (quick Googling turned up nothing useful). If I do this, I get something reasonable, but any feedback/comments are appreciated

Good talk, thanks for the link

I don't know -- I've made good money in crowded, megacap growth companies. Should I give it back?