EDIT: Based on the fact that a number of comments seem to have been made without reading beyond the first 9 words of the title, and the fact that this is sitting at 60% upvoted, I'd encourage you to at least read to the TLDR.
I usually stick to providing raw data, but this post has a little bit of added analysis. I don’t own $GME or $RKT (I don't actively invest to avoid accusations of bias in my data) and don’t like either stock over the other. I do like WSB though and don’t like baseless conspiracy theories, and I have seen a lot of conspiracy theories flying around WSB recently without much evidence to accompany them. Most recently, I got flooded by comments like this on my data post yesterday.
The goal of this post was to look at one of those theories and see if I can provide a base for it. The theory in question is “$RKT discussion is being artificially pushed to distract from $GME”. A TLDR of my analysis is:
A) Both $RKT and $GME are being discussed by a lot of accounts that are new to posting on WSB.
B) Accounts discussing $GME are actually newer to WSB on average than accounts discussing $RKT
C) There are a small, but non-zero, number of people trying to artificially manipulate discussion to get others to sell $GME. However, this is done with no relation to $RKT.
D) Accounts mentioning $GME and $RKT behave similarly in promoting the respective stocks.
Through my analysis I wasn’t able to find any solid evidence of $RKT being artificially pushed as a distraction from $GME. While it’s easy to complain about u/zjz's iron-fisted bot army, I think it deserves a lot of credit for curbing attempts at manipulation.
Now I will provide the data backing up these conclusions, and call out one account I did find attempting to artificially manipulate discussion.
Both $RKT and $GME are being discussed by a lot of accounts that are new to posting on WSB. Accounts discussing $GME are actually newer to WSB on average than accounts discussing $RKT.
I don’t think I need to patronize you with a bunch of facts and figures showing how much attention has been drawn to wsb recently. Obviously this attention came with a lot of new posters, and that’s fully on display when we look at when accounts talking about $GME and $RKT first joined the wsb community. The motive behind looking at these numbers was... keep reading on reddit ➡
Work as a "data scientist" in the financial services industry. Work broadly involves:
Looking at the above sequence of steps, most of it is data engineering, reproducible analysis development, and then production engineering.
Starting to feel like this is more similar to software engineering/development of a data or model "product", rather than "data science". Thoughts?
EDIT_1 to clarify: For #2 the data pipelines are more figuring out the different data sources and combining the data that is applicable to our problem. eg. there might be 5 tables with 100 fields, and so narrowing down which ones we should use, and creating a SQL/python pipeline for that. We have dedicated data teams that maintain these upstream tables/data sources.
For #3, #4 this includes feature engineering and applying a range of statistical or machine learning methods, to figure out which one best solves the problem (eg. time series vs regression model) -> this being the unknown and iterative part that can take considerable time; which is the "core DS" work I think.
And #5 & #6 actually ensuring what we develop delivers business value, otherwise the model is just a thought experiment lol..
EDIT_2 for some historical context on the question: When I started out 5+ years ago, we were using SAS to run regressions, and I'd used STATA in my grad degree. So python, open source software and this whole engineering stuff is something that I have picked up along the way. My daily technical skills look very different compared to when I was originally hired as a "statistician/quantitative... keep reading on reddit ➡
Hey everyone! I recently read a great book on Warren Buffett called Buffettology. The book lays out a whole set of criteria that Buffett looks for when buying stocks so I thought… alright, let’s see if we can grade all the stocks! Here, in this post, are the results.
First, the rules. Each rule below receives a point for a pass, and no points for a fail, much like the Piotroski Score. The points were gathered from insights in Buffettology (I’d recommend a read if you haven’t already).
Now, the data. Like always it’s in a Google sheet. I have ranked the stocks from 8 on down. If you’re interested in a particular stock, search for it within the document.
Got a rule you think should be in there, or something confusing about the score? Let me know. I make quirky things like this quite frequently for my YouTube channel (won’t link to avoid breaking any rules), and would love to take some feedback on continue iterating on this Warren Buffett checklist.
It is not uncommon for people who come from quantitative fields– such as physics– to struggle in proof-based mathematics– the way mathematicians do mathematics– because it requires a different way of thinking about mathematics.
Not as a tool to express different phenomenon, but as an object of interest in & itself. But, what about the reverse? Do people with strong inclination and interest in proof based mathematics share any similar difficulty when they take on courses or problems in physics, engineering etc where rigor is secondary, computational results and physical insight is primary?
I've been working as an m&a analyst for the last 2 years in my home country, it pays very well for an entry level position but literally what I've been doing all along was just preparing ppt's, meeting with clients/potential investors, and occasionally building DCFs and some stupid valuation models. I had chosen a quite mathy curriculum back at college (including real analysis and tons of programming - machine learning classes), so I really feel my technical skills are wasted here in m&a. It's amazing how m&a salaries can be so high considering shallowness of the required skill set for it.
Anyways, in the last few weeks I started thinking about quantitative finance masters (instead of the traditional MBAs) to be able to switch to a more quantitative role in finance. There are a couple great programs out there (Princeton/Berkeley/MIT/Columbia etc), so I would like to get your opinion on quantitative finance. Is it worth exiting m&a for a quant role? There is a wide belief that quantitative finance has been quite weakened post-2008, but employment still doesn't seem to be a problem for these elite programs?
I am submitting this post to promote discussion about what I believe to be potential dangers associated with the pursuit of FIRE and to highlight the importance of, throughout your life, attending to the qualitative factors that I believe are essential to a successful retirement. My goal is to encourage those of you on the road to FIRE to take whatever steps you deem necessary to avoid these potential pitfalls.
I believe that, for some, FIRE becomes some sort of amorphous goal that provides both a degree of hope, as well as an excuse to avoid working on the aspects of life that really matter: social connections, meaningful goals, physical and mental health. This is especially true for people who, while highly intelligent and well educated, are relatively less adept at enhancing and developing the qualitative aspects of life.
Some of you may remember me from my previous posts. Recently I have started to think more about what accounts for post FIRE happiness, meaning, fulfillment and health. I believe that, just as it is important to devote X percent of your salary to an IRA throughout your life, it is equally important to devote X percent of your time to physical and mental health, maintaining or rekindling old friendships and cultivating new ones. Failure to do so puts one at risk for an unfulfilling and unhealthy retirement.
It's as if some FIRE aspirants are saying to themselves: “Yeah, I’m in poor physical health, depressed and socially isolated and I hate my job. But that’s ok, because, one day……”. But then that day comes; you are FI, but with nothing with which to fill the void. I have seen this before. Retirement is often accompanied by a huge let down. Some become depressed and drink. Some stay at home all day, every day leading to friction with their SO. Some blow vast amounts of money on baubles in a vain attempt to find some sort of justification for the decades they had sacrificed to get to this point.
I am eternally thankful to my parents who were wise enough to facilitate and support my nascent hobbies and passions. Because of them I had endless things to motivate me when I stopped working. I am equally aware that my natural tendency to reach out to old friends, work colleagues etc. meant that I had a deep and wide pool of social support when I need it. Nevertheless, I have struggled with periods of loneliness since I stopped working ~8 years ago, long before I had ever heard of FIRE. My new girlfriend has both... keep reading on reddit ➡
It's been a year and a half since I last did a full banlist test, but I'm back in the saddle. Following certain posts last year I was inspired to fully test [[Hypergenesis]]. 3 months of effort for 500 total matches against 5 established decks to get a more accurate model of its impact on Modern.
Here's the hard data. It showed that Hypergensis is statistically significantly better than its nearest analogue Neoform. The qualitative results and the impact of the recent ban will follow next week.
Any questions about the process or why I made certain choices are most likely answered here.
I am currently writing my first excercise paper on statistics and I have a question to you fellow redditors concerning the rejection of my null hypothesis.
I am investigating if there is a relationship between being a union-member and turnout in the election to the national assembly of Austria in 2013 on the basis individual data. So I converted my data (n=3,442) into two dichotomous variables and put them through Stata which put out a Cramér's V of just 0.0473 - a very, very small association as I would interpret it. But it is quite significant with a p-Value of 0.006. Am I allowed to reject the null hypothesis? Am I allowed to assume that there is a relationship between union-membership and turnout in this case? I am struggling with these questions because it feels kind of wrong to reject the null hypothesis for such a small Cramér's V, even though it would make sense, simply because Cramér's V isn't zero and the p-Value would suggest statistical significance.
Could someone help me out? Thanks in advance!
(Please forgive my English, it is not my native language and as my studies aren't either I am not that proficient in the use of academic English yet.) (Please delete if this post is not desired in this subreddit)
Hi, I'm looking for recommendations for comprehensive quant methods courses online that are free and beginner level.
Shroom stocks admittedly have limited history and are still very news driven which makes them not the best candidates for quantitative price analysis, but I couldn't resist. For comparison purposes, I have also included data from the NASDAQ and NASDAQ biotech index from the same period.
The data starts 2020-09-30 and goes through yesterday.
1: For starters: here is a chart of the % growth rate of each stock, assuming you invested 2020-09-30.
2: Some basic performance stats
Obviously these stocks have huge return potential, but it is important to keep in mind that their volatility is absolutely insane. Almost all the shroom stocks listed here have annualized volatility of over 100%. For comparison, the annualized volatility of the nasdaq and nasdaq biotech over the same period was ~25%. This means that even if you invest an equal dollar amount into (a) shroom stocks and (b) index funds like the nasdaq, you should expect the shroom stocks to have at least 5X the "swings" that your index funds do. Wise investors will allocate accordingly.
(technical note: annualized volatility is the standard deviation of daily returns times sqrt(252)).
with great volatility comes great drawdowns. As many of us are experiencing now, these shroom stocks can easily drop ~50% from their peak. The nasdaq indicies have only fallen ~15% over the same period.
(worst drawdown is defined as min(todays price / expanding max price - 1))
2c: risk adjusted returns
risk adjusted returns are what really matter, and many of the shroom stocks surprisingly have better sharpe ratios (annualized return / annualized volatility) than the nasdaq indicies do. however, this is on a short time scale, and we should not expect this to be the case going forward. Past performance is not indicative of future results.
3: Correlations and Eigenvalue analysis
While we do think of the 'shroo... keep reading on reddit ➡
FinRL is an open source library that provides practitioners a unified framework for pipeline strategy development. In reinforcement learning (or Deep RL), an agent learns by continuously interacting with an environment, in a trial-and-error manner, making sequential decisions under uncertainty and achieving a balance between exploration and exploitation. The open source community AI4Finance (to efficiently automate trading) provides educational resources about deep reinforcement learning (DRL) in quantitative finance.
Github Repository: https://github.com/AI4Finance-LLC/FinRL-Library
Question above^. Also if one wants to go into investment banking, it is best to minor in econ, MS&E, stats, or math? (Assuming CS degree and MBA later)
Second year political science undergrad here. I feel like learning how to do quant stuff is going to be very helpful (necessary?) in the event I decide to go to grad school and/or would like to not be poor forever, but my math skills are... subpar to say the least. I haven't looked at a math problem in about three years, and even in high school I was pretty far below average. I got Ds in both 9th grade geometry and 10th grade algebra II, and haven't taken a pre-calc class. I majored in a social science for a reason. Is it worth trying to take an intro to quantitative class or am I already too far into my undergrad to successfully make up for six years of underdeveloped math skills?
I have a profitable model, but never thought of having a computer do the work. I’ve always found it very enticing. The Psychology of a trader may limit him/her. Computers, on the other hand, will not be affected by emotion. I’m just curious. Are there any profitable algo traders here? I will soon start learning how to code myself? Cheers everyone
Hi all, I know on this subreddit we stan KN95s and KF94s.
I bought some and tested them on my face with a portacount pro and for the life of me could not pass fit testing. The highest I got with a KN94 was a 5.4, and the lowest was a 2.5 when moving my head up and down? Passing is 100...
Don't get me wrong, it was definitely better than a surgical mask alone, or a cloth mask, but how are people saying it's an N95 equivalent?
I've been interested in applying a more quantitative approach to trading in MMOs given that websites and API endpoints with (reasonably) accurate and up-to-date price and volume information exist out there. I've played both OSRS and EVE Online and I know they have good APIs, but I'm open to trying any MMO with similar marketplace functions and public APIs.
I'm looking to start a team with anyone who's interested in some fun and hopefully some virtual profit! No trading or quantitative experience required (I don't have any either). PM me if this suits your fancy.
I'm currently employed as a software engineer. I'm considering a position as a quantitative developer at a trading firm. I'm hoping someone could help me with a couple questions:
For those that aren't aware of what Vivi's latest SASB does, see the post "I lucked into Vivi Sync, what do I do now?" by u/Monk-Ey. Casting CMD2 of Vivi's SASB provides stacks of Time Permitted, up to a maximum of 4. Stacks of time permitted provide the following effects that are cleared after a fire ability is used:
Each stack of Time Permitted grants a guaranteed extra cast of a Fire ability as well as increasing Fire boosts and an extra Cap Break Level (or Break Damage Limit) starting at Time Permitted 3.
I wanted to do an analysis to determine a couple things regarding the SASB:
Normally this would be pretty easy to answer, but Vivi's Trance complicates it. Vivi's trance provides a 50% chance of dual-casting Black Magic abilities, and the CMD2 is a Black Magic ability. Therefore, to get the most out of Vivi's sync, you absolutely want to trigger Vivi's trance. However, this means your overall results are subject to a bit of RNG. The following is an analysis of that RNG.
First question was to determine how many CMD2 and CMD1 casts can Vivi get into a Sync.
|Command|ATB Time|Input Delay|Cast Time|Total Time| |:-|:-|... keep reading on reddit ➡
The recruiter I am talking to recommended this group to me as a potential specialty to intern with. My question is, what exactly do they do?
She recommended it to me because I have a double major in business analytics (modeling, predictive and descriptive statistics, etc) and accounting.
I asked about the type of work they do but she gave me a vague description, pretty much the same as what Deloitte's website says.
I plan on talking to a couple people who work in that area, but I thought I'd first try to get a foundational knowledge on what they do.
I would like to self-study Mathematics to gain a deeper understanding of quantitative finance. I'm a software developer at $dayjob, and taking on a new job where this would be extremely helpful.
Prior to this, the highest level of math I studied up to was Calculus 2, but I don't remember too much about it anymore. I would say I'm comfortable with math, and remember some basic things from Calc 1, but that's it.
I was thinking of getting this book https://mitpress.mit.edu/books/introduction-quantitative-finance, but not sure if I have the requisite knowledge to be able to make use of it yet.
To clarify, I prefer textbooks and reading material for more self-study, rather than a course where the material is curated and selected. I would like to have the freedom to meander through the material myself, if that makes sense.
Thanks in advance, and please accept my apologies in advance if this question doesn't fit the sub!
I'm interested in taking a more quantitative approach to GE trading. With all the data that's available I'm certain there's a way to make better trades than just picking them by hand. I'm a Python dev and I've tried to come up with something on my own to limited success (a couple mil, nothing major).
I know absolutely squat about algorithmic trading so this will be a fun (and hopefully profitable!) learning experience. PM me if you're interested!
TL:DR for people in a rush
So I noticed there was no recent data on Gamepass and decided to create my own. I started by deduplicating the games from the main lists and pulled out the MC score and price for each of the games. I hope this will be useful for people on the fence.
How good are those games?
Metacritic distribution shows a few trends:
This is a Belt Mixer which does some counting and let through an arbitrary number of items from each belt, no matter the feeding speed. It is also robust: it does not need any priming and can be blueprinted.
On the bottom row there are:
On the top row there are:
If you find a way to trim down the number of combinators let me know. I have a version that uses only 5 of them but it is not stable/self-priming.
Here is the blueprint string using only yellow belts and no editor items:
I am not economics person, so please bear with me, but I have a question. A lot of people are in favor of a wealth tax in theory, but complain rightly of difficulty in implementation (e.g. asset identification, valuation, etc). Most suggested wealth taxes are on the order of 1-2% per year.
My question is, why can't we simply print money and spend it until we create added inflation to the amount intended to be acquired via wealth tax? The absolute "tax" burden would be small relative to other taxes for those without immense assets, and small regressive effects could be reasonably well counteracted by returning money via one mechanism or another. Furthermore, as I see it, spending on social goods will return value and/or create disproportionate savings to the poor anyway.
The only drawback I see to this is that assets valued in other denominations would avoid this tax, but hiding large shares of money in financial instruments denominated in other currencies is easily traceable at least. At very least, this issue is arguably shared with other approaches to a wealth tax, and there is a decent argument that an imperfectly but decently well-implemented wealth tax would provide more benefit than drawbacks.
With that said I'll admit this isn't very well-reasoned and I feel like there must be a reason this hasn't already been done, aside from the possibility that those with influence don't want a near inescapable tax.
EDIT: Evidently I'm shadowbanned (yet I also make a new account for every post so wtf) as my comments don't show up but just know I made an actual effort to respond to all the comments smh
EDIT #2: Ok, my comments are appearing, but my response to fell_ratio is still invisible so I'm including it below (in quotes)
"Because an actual "true" wealth tax is hard. Inflation can decrease asset values too, although it's definitely not uniform across all assets. Perhaps I misspoke, but I see this not as a perfect analogue to a wealth tax, but rather a similar alternative with similar benefits that taxes wealth in a more complex (albeit not necessarily better) manner. Money invested non-productively is inescapably taxed without requiring asset identification. Cash holdings are taxed more heavily than assets. Taxation is in proportion to holdings. These do seem to provide real value as a means of taxation.
EDIT: This source describes a similar concept albeit with a somewhat negative view of in... keep reading on reddit ➡
When I was a kid Dad explained this as:
You request a loan to a private bank. If the bank has the money from deposits or previously reimbursed loans ok, if not it will ask to then central bank. Then the CB prints money for this, but expects it back with interests. So as more people borrow and more money is printed, more is due to the CB, but the economy grows in the meantime.
Please point out any inaccuracies in the above.
Regarding QE and bond purchases, I've searched previous threads in this sub and it appears to be just as I thought. The terms "bonds are bought with newly created money" appear literally.
Not long ago I was asking in /r/italy if we would be able to do QE on our own (with Banca d'Italia) if we didn't have the ECB and we had our own currency. The answers were saying it would cause too much inflation but most of them were one-liners, didn't really understand much.
Am I wrong to say inflation is excessive if the CB buys too many bonds because then it would inject too much money into the market, but if you do it moderately it shouldn't cause too much of it?
(In the past few years the eurozone inflation has been in fact ~0.5%, well below the 2% target...)
Warning: this is not a short post.
TL;DR The price of Bitcoin is likely to fall at least 15%, and as much as 75%, if it turns out that Tether has been improperly minting Tethers that are not backed 1:1 by USD. Again, IF.
As I’m sure everyone is aware by now, Tether has been in a multi-year legal battle over its legitimacy. However, there seems to be a lot of disagreement about the potential consequences of Tether folding, so I decided to do some analysis. Please know that this analysis is not infallible, as it makes many assumptions and is based on an economic concept that may not translate directly to the crypto market. It is simply an attempt to gauge the potential impact of Tether imploding.
Method and Analysis
The analysis is based on the premise that the velocity of money, or how many times a dollar circulates through the market, has a direct impact on the nominal price level of transactions. The velocity of money, V, is defined as
V = PT/M
P = nominal price level T = aggregate real value of transactions in a given time frame M = money supply
The wiki page on velocity of money offers a good description: wiki
In our case, however, we already know how many times each dollar is spent in the Bitcoin market, defined here as the market cap of Bitcoin. It is simply the annualized 24-hour volume of bitcoin transactions divided by the market cap. To do this, I used CoinGecko. In fact, I also used the Wayback Machine at https://archive.org/web/ to pull data from every six months or so (depending on availability) going back to November 2018. It appears CoinGecko lumps Bitcoin transactions other than spot in with total Bitcoin volume, so I calculated velocities for both stated (all) and specified (spot only) transactions. Here are the results for velocity calculations.
For the sake of this analysis, I will only talk about the specified volume as we're only interested in spot transactions. As you can see, July 2019 is a bit of an outlier in terms of Tether volume, but overall the numbers are remarkably steady over more than two years. This has been accompanied by a slow but steady decrease in velocity, which makes sense as more and more bitcoin are held rather than actively traded or spent. What we will focus on is the fact that in general, more than half of all bitcoin trade volume is conducted in Tether. That i... keep reading on reddit ➡
Is it worth $100k for CMU MSCF vs $60k GaTech QCF?
Definition of worth for me is : Quality of education, ROI, exposure. Aim: Work as a Quant Researcher for a Hedge Fund.
Wallstreetbets is the largest of many Reddit communities that focus on discussing possible investment ideas. The community, also known as a subreddit, includes over nine million members but this number does not tell the full story. In December of 2020, Wallstreetbets could count nearly two million members among its ranks. While still a large number, two million is a far cry from the nine million members seen today. So, how did this growth happen?
It was just a few days after Thanksgiving of 2020 when it first began. On November 26th, there were 424 Reddit comments mentioning the stock ticker for GameStop Corporation, GME. This number is over double the average of 188 mentions per day, previous to the 26th.
The next day saw mentions of GME skyrocket nearly three times at 1,259 comments mentioning the ticker. This number held with little variance until November 30, 2020 where it saw another major increase to 4,447 comments. The explosive growth in mentions doubled the 2020 average to 445 mentions per day. Shares of GameStop were trading at roughly $17 at the time. The stock had seen price appreciation of 169% year-to-date.
Mentions of GameStop more than halved the next day, down to 2,063. Comments mentioning GME never crossed their previous highs until after the end of 2020. However, average mentions per day were 826 on the last day of 2020--a significant increase from January 1st. GameStop shares ended the year at $18.88, experiencing 200% appreciation in 2020.
January 13, 2021 marks the day when GME began to dominate communities like Wallstreetbets. Comments mentioning GME were over 10,000 for the first time, remaining above that baseline for three days. The following table shows the mentions at price action for the period:
|Date||Mentions of GME||Share Price|
Increases in participation on Wallstreetbets drove GME to Reddit’s frontpage and led to the community’s member count increasing by millions, which led to more mentions of GME. More mentions of GME led to more Reddit users discovering the Wallstreetbets subreddit, leading to a positive feedback loop.
The increase in Wallstreetbets members led to more buyers of GME which led to price appreciation which led to more investors hearing about GME, creating a second positive feedback loop. These events had great underlying support in other catalysts for the stock, including its large short i... keep reading on reddit ➡
I’m a student at another NY college, I’m just wondering what this grad program is like please? I honestly can’t much information on it online in terms of employment statistics, cohort size, etc.
Thank you so much!!
Does this course is good to start adventure with Quantitative Finance?
Thanks in advance.
I've been recently diagnosed with having ADHD, and one of the things that works for me do well in studies, is taking notes (VERY detailed notes).
I have recently moved from a qualitative field to a quantitative one, and I'm having a hard time keeping up with coursework. I can't use the traditional pen-and-paper medium as I travel often, and can't afford to carry excess luggage.
I understand that this question is tangential to ADHD, but I honestly don't know where else to turn. If it doesn't fall under the regulations of this subreddit/there's another sub where I should be asking, please let me know.
I'm grateful for all the help I can get.
Hi, I am taking the general GRE exam tomorrow and the university I am applying only requires the Quantitative Reasoning section score.
Does the software allow me to skip the other sections or I have to sit and wait for the quantitative sections to come up?