A t-stat > 3 often doesn't really mean much. First, all a t-stat says is whether the effect size is different from zero. However, virtually nothing has a zero effect size in the real world, if only because it's correlated with another causal variable. Second, the more data points you have, the higher your t-stat. A very large t-stat could ultimately be meaningless in effect size space even if thre really is a relationship between two variables. Finally, there are all sorts of weird statistical issues which can arise from many forms of econometrics research, so the entire relationship could have been spurious to begin with. If after a dozen different variations of the same experiment, you still have to squint really hard to see the effect, it's probably not worth pursuing.

I did have a non compete; 12 months.

Went to a multi-manager b/c I felt working on a PM team was more upside. At a larger firm running central books, you are thought of as more of a cog in the machine despite the fact that you build strategies. The 'researcher' becomes an interchangeable part, i.e. if you hadn't researched a particular data set and found an alpha, someone else in your place probably would have and so your comp is not scaled to the outcome of your strategies as much, if at all. This is not so far from the truth, as a larger group is giving you the structure to succeed, i.e. the rsh framework, processes upstream and downstream that are advantageous to your strategies like efficient/good data processing and great execution for low tcosts. On the flip side, the comp is pretty stable year to year and unless you consistently suck you'll keep your job for a while. On a PM team you're more isolated and have to do more for yourself, but you also keep more of what you kill.

Given where I am at in my career, I felt comfortable taking the risk to potentially make more upside and learn more along the way even if it doesn't necessarily work out. I would say it's like taking on higher expected value and higher vol on my earnings, but def with more learning -- up to the individual on whether that is worth it to them depending on where they are in their life/career. I knew people at my old shop who were sr quants pulling in 1m a year steady w/ 2 kids that were 10-12 yrs old and they were happy with where they were at.

My area of research in math was in functional analysis and probability theory; there were no direct applications of my research into finance.

I knew I wanted to go into hedge fund space 2 years prior to finishing (unlike most PhDs), so I began preparing. For starters, I double majored in Math and Econ at an elite undergrad, and did an IB internship while an undergrad, and wrote a lower level undergrad Economics paper at that time. So, I'd say I've had some experience from the theory side of things, very high level in Math and a decent level in Econ. Applied finance, I had none. So, I spent those 2 years studying: math finance, statistics, machine learning/ai, and practical coding. I found the first three of these easy to pick up given my stronger background in theory, particularly in probability. For the math finance part, I focused much more on portfolio theory and mean variance optimization, then on derivatives (though, I did both) - but, this was because I knew I wanted to be on the equity buy side and not in derivatives market making at a prop shop or bank. The last was probably the most challenging, for me in particular, because I hadn't coded anything in 7-8 years; and never anything at any production quality level. I did do a Quant internship at another hedge fund the summer before which helped a lot, and got my toes wet. As for practical applied finance (balance sheets, kpis, etc) I learned once I got on the job, mostly by reading papers and being engaged in different data sets and talking to people. The biggest thing here is that I started the process early, learned everything I thought I needed while focusing on areas for what I really wanted to get into, I got an internship the year before I defended, and then it was easier to land a job directly into buy side. The buy side loves pure math phds when they find ones who are interested and pick up all the other stuff - imo more so than CS/Physics/Engineering phds; what they hate is that most pure math phds are usually arrogant about the fact that their subject is arguably the hardest, but then know almost nothing else.

As for signals, we create long/short factors, dollar neutral, that we take exposure on - while taking 0 exposure to all risk factors. If you have some portfolio theory experience, you will know what I mean. If not, its a good exercise. The job is basically to find long/short factors that are predictive and economically meaningful.

As for day one, no I didn't know what to look for. I was started on some baby projects adding features to backtesting/production framework to get my coding up to speed and so I could understand how we backtest. From there I went to working on some data sets/ideas with guidance from the team. After about a year, I was a normal contributing member, adding signals, and contributing to making our entire research/production process better.

Politics is def huge, and was huge at my last firm. It's one of the biggest realizations I had when I joined out of academia. The positive for me was that I took note of it early, saw people who I felt were incompetent but still highly successful and observed their behavior/tendency to spot how they did things. 95% of the time its not amoral as such, but they def know how to maneuver the office and other people; I give them credit for having that skill. I try to avoid it, but I learned to stand up for myself and be confident; something most people don't have when coming out of PhDs where you can often get beaten down.

MMs def easier; smaller groups, way less political. No one really cares about you outside of your small team. The PM basically shields you from any external influences, so as long as you get along with your PM and your small team, you're good. You also eat what you kill and the only person who decides is the PM, so again - if you are good with them and can be candid, you can avoid all the politics revolving comp also. This was a big plus for me in decided to move over. It is hard to do right away though, most PMs run small/lean teams and are not interested in people without experience.

I will keep when exactly I started to myself. I brought ideas, signals, and know how of how someone else runs the end to end process. In general, PMs I interviewed with didn't care about the signals, they're happy if you say you have them and hope that when you start they'll be useful. Even if you have signals, you don't know how additive they are to what's already being run. But, you will never tell them what you have and they will never tell you what they have. A lot of it is just you saying you have them and them having to believe you. How confidently you state the performance metrics and can explain the characteristics will go a long way, but I wouldn't exaggerate them, as you can be let go after 2 months as easily as you can be hired in 1 month if you can't deliver. But, you would never talk in detail about any of them in an interview and they know that and wouldn't ask you questions. IF anyone ever asks you about a data set involving a signal, how it is constructed, or the ideas, you tell them to shove off...politely; if you tell them to shove off, that's a sign to them you know you have something of value. If they insist, that's a sign they're not worth speaking to anymore and you always hold your ground. They are most interested in how you evaluate ideas (so have at least one/two data sets prepared that you're comfortable talking about) and how you evaluate signals.

But, I know what you mean, I have spoken to PMs who will say they want someone who can run 500m in additional capital at live sharpe 3 -- to which I have responded, if I could do that...wouldn't I just go be a PM myself? What do I need you for then? A good PM is someone who is hiring you for the research you'll do, not just what you'll bring on. Ideally, you are an investment from their part in order for them to expand their business; not an acquisition.

I am not sure I am the best person to give advice on the matter or that I would want to say anything that would convince your son to do something other than what he thinks is best for himself. But, I will give my opinion nonetheless.

Really, I don't see any practical different between the two in the long term. As far as prestige, I don't see any difference between IB and S&T; not sure where that comes from. IB may be a bit more stable in the future, as M&A advisory is not going anywhere, while S&T (depending on where he is at) is slowly being automated more and more. 20 years ago you'd call someone to buy stocks, now it's all automated. 10 years ago you'd have to call someone to buy options on single stocks, now for the most part you dont. Most S&T is in exotic derivatives and illiquid instruments, as time goes on -- things become more liquid and more automated...that will never change. But, it is not clear at all that this will be his eventual career.

Chances are that in either route, he will end up in business school or doing a CFA in order to move up or laterally, one way or another. In either case, after further education he could end up back in a bank, or private equity, or at a big asset manager, or jumping into the tech sector on the business side, or even at a hedge fund as a fundamental analyst. There is nothing restrictive about being in S&T right out of undergrad. I graduated from an elite undergrad in math + econ. Of course this was many years ago, but I know many, many people who went from a range of things from IB to consulting to S&T -- and most of them are doing something different now. An IB guy who went to do an MBA and then went into tech. An IB guy who went into private equity. A S&T guy who went into a hedge fund into a a big investing role and made huge bucks. A S&T guy who flamed out but never went to school and now works at a small prop shop grinding it out. A tech guy who went and got an MBA and then went into M&A. I myself went to do a phd and ended up at a hedge fund.

I don't see anything wrong with the decision. In fact, in the short term, it's probably less hours at around the same pay.

for the practicalities of interviewing, I will refer you to the numerous books written on it. As for the problem solving part of interviews (for first job), I can tell you I picked up every book on quant interviews and systematically went thru every problem over the course of 3-4 weeks. After that, it's mostly luck. Qnt interviews are like a fire fight, multiple rounds of exhausting math/cs/algorithm/etc questions. You just gotta be prepared, eat well in the morning, and try to crack out a 95%. If you interview at enough places, you can get thru it and you only need to do it once. Don't beat yourself up if you dont' get the job, just keep preparing and move on. One other advice is do not start applying until after you have prepared! some people try to early and burn interview opportunities. You prepare and prepare and prepare, then you go thru the gauntlet.

As for how to get people to look at your resume, the only thing I can suggest is networking. Go to conferences, build real long term relationships with people (not stupid 10 min convos at a networking event where you think that'll get you a job) and see if you can work on projects with them. Coming out of pure math phd, this is how I did it. No one came to head hunt me at a phd math program. Don't be modest, keep asking people until someone starts working with you. If you did a FE degree, you should have some firms that will come to your school. If your gpa is bad, not much I can say about that - it is what it is.

As for my particular role, honestly you will have a tough time. Most people hiring signal researchers look for people with experience, those that don't look for phds. Those who get in without a phd usually start in a different part of the business (risk, market making, etc) before moving into it.

And, just so you're prepared, know that down the road a masters will always be seen as lesser than a PhD. It's not your fault, and I don't mean to be biased, but most PhDs are just better than most MAs. Most Quant PMs have PhDs, not all, but most. That's not to say there aren't good MAs, but they're just usually not as successful on average.

Both have one up on the other when you enter. MAs more practical education and they usually get up and running in terms of coding and understanding day to day basics, but PhDs generally more mental horsepower from studying longer. But, it's easier to bridge the coding gap for PhDs then the education gap for MAs, and this is where it is harder for the MA. The first year you're likely to be doing better, after the first year the PhDs usually pull ahead. The MAs who succeed are ones who can build the skills a person with a PhD has coming in as they gain experience; you have to bridge the gap one way or another. I know many of them and it can def be done. But, the gap will exist, and you have to try to bridge it. A PhD just has more years of research experience, which means 3 big things: 1. they know their ideas are garbage 99% of the time, 2. that doesn't get them down and they push thru it, 3. their background makes them usually better/faster at idea generation. The first two are really important to learn for any researcher. The 3rd is just a function of spending a lot more time studying. If you give a typical math-laden paper to a phd and an ma, the phd will usually go thru it in 1/4 of the time and understand it more clearly; it doesn't mean they're innately smarter, but they have already gone thru 100s of other papers and so they're more efficient at it and not as slow. In the beginning there isn't much different, in the long run people will always prefer the PhDs - it just looks better to investors.

And, that is something I stress to MAs. Despite what people will tell you, look around at the top Quant PMs on buy side or sell side, the Quant Managing Directors, heads of quant rsh, all the top positions at varying firms - and count how many have MAs. You just don't see them at the top. It's partly the competition with PhDs, but also firms want people with good titles in visible rows; especially if they're visible to investors or the public. No one will hire a Head of Quant who has a masters, it wont look good. With that said, you can certainly have a good career; except to spend 4-7 years at entry to mid level to work your way up to the 2-3 yrs of a PhD.

This will probably be biased, but it is what it is. Personally, I think buy side quant space will over take fundamental in the next 2-3 decades, but never entirely. The trend is intact, decades ago it was 95% fundamental and 5% quant, now it's closer to 65/35, I think? There is nothing that a fundamental PM does that a quant cannot take over. First, quants are way better at managing the risk in their portfolios as they do it mathematically and optimize. Second, quants are not just 'top down' like people think. We build bottom up strategies all the time. In fact, the only limitations to doing so are either 1. the relevant data is not as understood by the quant b/c it's so idiosyncratic to the firm and 2. the relevant data is not readily available in an automated format.

2 moves towards quants every single day, as more and more data becomes automated. 25 years ago you had to read a 10K to get relevant numbers, now an NLP algorithm can read them right out and store it in a database for a quant, to produce a signal, and trade it before the fundamental PM opens his/her email and double clicks on the pdf. #1 is arguably way tougher, and that is def a skill a fundamental pm has, but it's nothing a quant can't learn. If anything, they can glean relationships and interactions that a fundamental PM has to come up with manually.

And, with the sheer amount of data out there, the quant landscape is becoming increasingly diversified so there is plenty of room to separate yourself from your peers; despite what you hear, people are running all sorts of strategies. When you hear quant strategies on bbg, they'll basically assume its all trend/momentum/reversion. Its wildly inaccurate. A lot of quant books are completely nuetral to those things and consider them risk.

Thanks for the response. I work in this type of firm in a quant equity research capacity. I have been trying to move to an analyst role with a MM quant PM. I have been able to get some interviews but it seems like people take issue with the fact that most of my signal research experience has been at the monthly vs daily/weekly frequency. HR/headhunters say that the feedback is positive but they are just looking for someone with more relevant experience. Maybe that feedback is BS, I don't know. I've also been asked the sharpe ratios of the alpha signals that I have researched/implemented. Because the signal frequency is monthly, dollar neutral L/S spreads tend to be something like 0.4-0.6 but w/ low correlation to common risk factors. I think they hear a sharpe of 0.5 and then just stop listening. Do you have any advice for someone coming from this background trying to make the jump to higher sharpe/freq strategies?