Fantiki

LuckyPhD, congratulations on your acceptances. I am currently a student at Columbia finance. If you have any questions, feel free to ask me. If you want to come to the campus, let me know and I can (unofficially) show you around and tell about the program.

I am currently in the Columbia Finance PhD program and I got a link to this thread from a prospective student. After Gur Huberman took the charge of the program, passing the prelims which used to be a piece of cake under Bob Hodrick, became a more serious challenge and 1-2 out of 5-7 admitted students were kicked out of the program every year after not passing the prelims for two times in a row (my understanding is that 2 times 1 student was kicked, and 1 time 2 students). I suspect that a person above is one of these individuals. I am not taking a stand on whether this strictening has been a good or a bad thing: there are pros and cons and I fully understand the emotions. However, other critique by the person above is not well justified. 1) At least the following senior professors actively work with the PhD students and are very nice persons (this is not the full list, if you have any questions about specific faculty members please ask me): Empirical Corporate: Wei Jiang, Daniel Wolfenzon, Charles Calomiris Theoretical Corporate: Patrick Bolton, Neng Wang, Suresh Sundaresan Empirical Asset Pricing: Andrew Ang, Geert Bekaert, Robert Hodrick, Paul Tetlock, Kent Daniel, Michael Johannes Theoretical Asset Pricing: Lars Lochstoer 2) Although he is a more senior person, Gur Huberman is an active researcher: https://www0.gsb.columbia.edu/faculty/ghuberman/research.html . You can see that his latest working papers (e.g., 2015 working paper) have been presented at top 10 departments. Also during the seminars he asks many good questions showing his understanding of the literature. Professor Huberman also teaches a Finance Research Seminar for the second year students, where he makes the students to develop a research idea and the implementation, and takes this very seriously by being very critical to the students' work. I think this is a very useful class. 3) Except for the controversial decision about strictening the prelims, Gur Huberman has done a fairly good job as a head of the PhD program. In particular, he introduced the third year paper initiative which made the transition from the courses to the research easier and strengthened the student-faculty interaction. He also introduced the best 4th year paper initiative which made the pre-job market students work harder on their job market paper during the fourth year and not to procrastinate until the summer before the fifth year and the fall of the fifth year. Also the funding for PhD students to go and present at conferences has gone up during his tenure. The quality of the finance PhD courses has remained very high since the times Bob Hodrick was in charge of the program. 4) There are 3 types of the seminars; PhD, internal lunch seminar and regular external seminar. All of these meet weekly (I would not call it infrequently) and the schedule can be seen here: Finance & Economics Seminars | Faculty and Research . Also the attendance is good and all seminars are very active with a lot of questions. 5) Recent academic placements are available here: Placement | Programs & Admissions . In addition, 1-3 students are going to the industry. 6) I have enjoyed my time in the program and I would not call it a punishment. The program is very challenging though and if you do not work very very hard you will suffer. If you have any questions please feel free to ask me.

My experience is that most study groups meet at the designated group study areas, which you can usually find at libraries and in some other places around the campus. A good place to study which I found out during my first year, was just an empty class room, because these are relatively large, quiet, and have a lot of useful equipment like chulk boards and PC projectors. You usually study most of the time when you do not have classes. During the year, most students concentrate heavily on problem sets, which are abundant, and have very little time for "general studying" (or may be it was just me). Depending on your study habits, you do not need to study in the study group all the time. For instance, I usually took a look on a problem set for a couple of hours myself and tried to clear something out (yeah, mostly unsuccessfully, but still:). Then I went to a study group to crack problems together. In my case this was quite smooth to implement, because due to a relatively large program size (20 economics+5 finance), there have almost always been somebody from the first year studying in the library etc. So if you prefer to study yourself at the evening, depending on the habits in your program, it may well be possible. However, while preparing for the exams/qualifiers/certs or whatever your school calls them, it is often better to study in a group all the time, because others will be asking a lot of questions from different areas and my experience is that you will benefit greatly from it in terms of deepening your understanding of material. Well, a lot of questions also come up while solving the problem sets, but these questions tend to be rather similar across students, so you will most likely be thinking about the same question even if you are not studying in the group, so the benefit of being present all the time while working on the problem sets is not that large.

For empirical PhD coursework and research, you will mostly need MATLAB or alternatively R, which are quite powerful (=you can program almost anything you want) and widely used (=a lot of ready to use packages available) but computationally not very efficient (=slow and are not able to handle the largest datasets). If your problem is speed then you will most likely want to use C++/Assembler combo. However, in academic business-school finance research (as TraderJoe pointed above it is very diffierent in computational finance research) speed is seldom a problem as you usually running your program only once and long waiting is ok (compare, e.g., to industry). At the same time, large datasets are a common problem, which is usually solved with SAS. It should be pointed out that the programming logic of SAS is quite different from standard programming languages, as it is much closer to database languages, so learning it as a first language is not a very good idea in my opinion. If you do not have prior programming experience, I would take an introduction to programming course, which usually uses Scheme or Java as a teaching language (which are good general languages). After this you will be able to learn most programming languages quite easily (in a couple of days). If you are already familiar with programming, take a look at either MATLAB or R. At the same time, I would not try to pick up SAS or C/C++/Assembler before I had been sure I need them, because learning to use them efficiently will take you a lot of time. I would not recommend STATA at all: it is easy to start, but at the end of the day is very limited.

If these are undergraduate courses, then certainly topology, as PDE at undergraduate level is very problem-solving oriented and not abstract at all. Graduate level PDE though could be useful if you are doing dynamical macro or asset pricing.

Generally, I agree that you should take what you are interested in. In terms of grad school signalling game theory and time series are probably the best, but if monetary economics is rigorous enough then it is also a very strong signal.

Congratulations on your acceptances as these two schools are both very good in asset pricing and are known for caring about their students! The departments are usually very closely ranked but I would say that UBC is stronger on theoretical side of ap while Emory dominates on empirics.

I would almost say Toronto. Pittsburgh is a good corporate school and I had expression that they place well, but as it was justifiedly pointed out in another thread their placements has gone down during the last couple of years (they easily placed to Top 50 schools in the beginning of the century), and the difference in overall rankings to Toronto is quite large. I understand your concerns about Toronto being much more of asset pricing school, but they still have people like Dyck and Womack. At the same time almost all Toronto job market candidates are asset pricing students (which is understandable given the focus of the department) and thus it is difficult to judge the quality of advising in corporate finance, so I would recommend you to personally talk to faculty you are interested to work with in Toronto and form your own impression.

CMU is much more established and student-caring program specializing in both theoretical and empirical asset pricing, e.g., Green and Hollifield are big stars and based on their placement history good advisors. Toronto is a rising department who has recently hired good junior and senior faculty in empirical asset pricing but their reputation as a PhD program is only in the process of formation.

Regarding the waitlist. I know that this year several economics students got into the program out of the funding waitlist, I am not sure about the overall waitlist though. Contract theory is one of the strongest areas in the department, Bolton, Salanie, and Chiappori are active in both research and with students (and has placed students very well). For me separating IO from applied micro has been always a bit difficult, but I know that Che is active (e.g., running reading circles where many students participate). Houses are old, built in the beginning of the last century, which has its advantages and disadvantages (e.g., heating is quite noisy) but are kept in good condition and prices are reasonable. From my experience, the apartments are quite standard.

kipfilet, congratulations on your acceptance! You should note that the vast majority of recent placements of Columbia has been in micro, and the number of students doing macro started increasing only after the number of macro faculty increased a couple of years ago. As SpookyElectric pointed out there are strong macro students (some of which I have had as my macro TA:s during this year) in years 3 and 4 of the program. I think that Columbia has recently became a place which provides you excellent opportunities for macro research. I know that there are PhD macro workshops where you are required to present starting from the second year and where faculty are active (at least I know that Reis and Woodford are very active there). Also my impression is that macro advisors are good. I had Reis, Schmitt-Grohe and Woodford as professors during my first year and all of them looked very approachable. I also know that Woodford cares a lot about his students (always finds enough time, comments are always very detailed). From what I have heard about Reis, he gives you a lot but at the same time also demands a lot. The overall roster of macro faculty in almost all areas (monetary, RBC, development, metrics) is strong.

19k/year for a couple in London is really tough. Your research interests probably match PSU better but changing them even slightly will be much easier at LSE (as PSU is practically built around micro theory).

It is mainly an empirical corporate finance school. I would say that their placement is slightly above their research ranking.

The placement has improved during the past years (see the list on the previous page). The reason is that the program has been largely different before the renovation in the beginning of 2000:s. The intake is now smaller, and students are incorporated to the research stage earlier, also the interaction between students and faculty should be improved. Regarding the overall conclusions. I mostly agree, but I would point out that the list by taurenchieftain reflects only the statistics on job market candidates and you should be aware that in programs with the intake of 7-8 approximately 3-4 students will make it to the job market, in programs with the intake of 5 the number will be 2-3 and programs who took 3 student will often have only 1 candidate. So it is not exactly the case that a typical student from a top program gets a top 20 placement.

Both programs are known for caring about their students. My understanding is that for theoretical asset pricing and empirical corporate finance LBS is a better fit, while UCLA clearly dominates in empirical asset pricing. Due to its location, LBS has probably better ties with industry, but the research which UCLA professors are doing (empirical asset pricing, behavioral) is very relevant for hedge funds/banking, and so I would think that you will not have hard times picking an industry position coming out of any of your choices.