Methodology and key: FT Global Masters in Finance 2017

How this year’s ranking was put together
In the ranking of post-experience finance programmes London Business School regains the top place it occupied between 2011 and 2015

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This is the seventh edition of the FT Global Masters in Finance rankings, which lists both pre-experience programmes and post-experience degrees.

The FT defines pre-experience programmes as those aimed at students with little or no professional experience, while post-experience programmes require participants to have prior work experience.

Masters in financial engineering degrees are not included in these rankings as they tend to place greater emphasis on quantitative skills.

Programmes must meet strict criteria to be eligible for these rankings. In particular, the programmes must be full-time, cohort-based and have a minimum of 30 graduates each year. Finally, the schools must be either AACSB or Equis accredited.

A total of 73 schools took part in the 2017 edition of this ranking, 68 in the pre-experience ranking and six in the post-experience ranking. One school took part in both rankings.

The rankings are calculated according to information collected through two separate surveys. The first one is completed by the business schools and the second by their alumni who graduated in 2014.

For a school to be eligible for the ranking, at least 20 per cent of its alumni must respond to the FT survey, with a minimum of 20 responses. This year, the FT surveyed about 5,800 graduates from pre-experience programmes and about 400 graduates from post-experiences programmes. The two surveys achieved a response rate of 33 per cent and 37 per cent, respectively.

The rankings have 17 different criteria. In the pre-experience ranking, alumni responses inform eight criteria that together constitute 58 per cent of the ranking’s total weight. The other nine criteria are calculated from school data and account for the remaining 42 per cent of the ranking’s weight.

The current average salary of alumni has the highest weighting: 20 per cent. Local salaries are converted to US dollars using purchasing power parity rates supplied by the International Monetary Fund. The salaries of non-profit and public service workers, and full-time students, are removed. Salaries are normalised by removing the very highest and lowest salaries reported.

Also, for the first time in the pre-experience ranking, the FT has introduced the increase in salaries as a criterion, with a weight of 10 per cent. It is based on the average difference in alumnus salary between their first MSc-level job after graduation and their salary three years after graduation.

The weight of career progress was reduced to 5 per cent, in line with the post-experience ranking, while the weight for international mobility and international course experience were both reduced to 8 per cent. Finally, the weight of international boards — the percentage of a board whose citizenship differed from the school’s home country — was reduced to 1 per cent.

Data provided by schools are used to measure the diversity of teaching staff, board members and finance students, according to gender and nationality, and the international reach of the programme. For gender criteria, schools with a 50:50 (male:female) composition receive the highest score. When calculating international diversity, in addition to schools’ percentage of international students and faculty — the figures published — the FT also considers the proportion of international students and faculty who are not from the country where the course is based.

The calculations for the post-experience ranking are similar to those of the pre-experience ranking but with different weights. For example, the salary increase category, which is based on graduates’ pre-masters salary, counts for 20 per cent; in the pre-experience course it counts for 10 per cent.

One difference in the post-experience rankings is in the calculations of the “value-for-money” criterion, where the FT takes into consideration the programme length in order to add the cost of not working during the programme.

Overall, alumni data inform 62 per cent of the post-experience ranking and school data 38 per cent. The weights of some of the criteria were slightly revised, including that of female faculty and female students, which were increased to 5 per cent, in order to match those of international faculty and international students.

In both rankings, information collected over the past three years, where available, is used for all alumni criteria except “value for money”, which is based on 2017 figures only. Responses from 2017 carry 50 per cent of the total weight and those from 2016 and 2015 each account for 25 per cent. Excluding salary-related criteria, if only two years of data are available, the weighting is split 60:40 if data are from 2017 and 2016, or 70:30 if from 2017 and 2015. For salary figures, the weighting is 50:50 for two years’ data, to negate inflation-related distortions.

Finally an FT score is calculated for each school. First, Z-scores — formulas that reflect the range of scores between the top and bottom schools — are calculated for each ranking criterion. These scores are then weighted, according to the weights outlined in the key to the 2017 ranking, and added together to give a final score. Schools are ranked accordingly, creating the FT Masters in Finance rankings 2017.

Key: factors in weighting the ranking

Weights for ranking criteria are shown below in brackets — (pre-experience) [post-experience] — as a percentage of the overall ranking.

Salary today US$ (20) [20]: average alumnus salary three years after graduation, US$ PPP equivalent.†

Salary increase (10) [20]: average difference in alumnus salary between graduation (pre-exp) or before their MSc (post-exp) and today. Half of this figure is calculated according to the absolute salary increase and half according to the percentage increase relative.†

Value for money (5) [3]: calculated according to alumni salaries today, course length, fees and other costs.

Career progress (5) [5]: calculated according to changes in the level of seniority and the size of company alumni are working for between graduation (pre-exp) or before their MSc (post-exp) to today.†

Aims achieved (5) [3]: the extent to which alumni fulfilled their goals.†

Careers service (5) [3]: effectiveness of the school careers service in terms of career counselling, personal development, networking events, internship search and recruitment, as rated by their alumni.†

Employed at three months % (5) [3]: percentage of the most recent graduating class that found employment within three months. The figure in brackets is the percentage of the class for which the school was able to provide data.*

Female faculty % (5) [5]: percentage of female faculty.

Female students % (5) [5]: percentage of female students on the masters.

Women on board % (1) [1]: percentage of female members on the school advisory board.

International faculty % (5) [5]: calculated according to faculty diversity by citizenship and the percentage whose citizenship differs from their country of employment (published figure).

International students % (5) [5]: calculated according to diversity of current students by citizenship and the percentage whose citizenship differs from country of study.

International board % (1) [1]: percentage of the board whose citizenship differs from the school’s home country.

Faculty with doctorates % (6) [5]: percentage of full-time faculty with doctoral degrees.

International mobility (8) [8]: based on alumni citizenship and the countries where they worked before their MSc, on graduation, and three years after graduation.†

International course experience (8) [8]: calculated according to whether the most recent graduating MSc class completed exchanges, attended short classes, study tours and company internships in countries other than where the school is based.*

Languages (1) [n/a]: number of extra languages required on graduation.

Course length (months): Average length of the masters programme.

For gender-related criteria, schools with 50:50 male/female composition receive the highest possible score.

† Includes data for the class of 2014 and one or two preceding classes where available.

* Graduated between April 2016 and March 2017.

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