Is doing a PhD in finance better than doing a PhD in economics and concentrating in finance?
Both have their own merits and demerits!
Doing a PhD is a matter of great honour and will turn you into a scholar-practitioner in your area of work. So if the financial markets are down, you can always move out of the banks/ hedge funds and get into the world of academics and continue to make a living.
That is how I see a doctoral research degree. It is the best hedge you can have during periods of economic volatility and downturn.
Now, which one is better?
Oh, WOW! That's tough to answer.
Well, getting a PhD in Finance has its own advantages in the private sector, where Financial Institutions do hire savvy tight dressed academics as managers, advisors and board members.
Also due to high student enrolment in Finance Programs across business schools, you will get more tenure track research and/or teaching positions that are paying a decent sum compared to the teaching of Economics these days.
But some Econ schools in the first world are offering fantastic PhD Econ Degree Programs.
So if you end up getting a doctorate from places such as the LSE, Chicago, Stanford, Oxford, Yale, or any other top school, you can easily outshine the crowd with those qualifications compared to obtaining a PhD in Finance from a tinpot uni. in the West.
Most of the PhD candidates in Economics get ample opportunities to work in the civil services aka bureaucracy. But they can completely choose between the private sector and public sector segments of the economy.
Even Banks and Funds hire #Economists with a Finance Concentration, so getting a job in the Financial Sector/s won't be a problem.
Depends on really where your interest lies. Whether you would like to overwhelmingly specialize in Applied Finance?
or #Economics with a focus on financial economics?
This is purely a matter of choice and your future employment ambition.
Also, If you would like to work as a Financial Specialist with a PhD in Finance, it is much better.
On the other hand, if you would like to be described as an Economist a PhD in Economics with any concentration of your choice will do the trick, and certainly is the way forward!
What's the point of doing a one-year industrial placement program, in addition to studying for a one year MSc Degree, if you won't get paid by the employer?
Be careful before you entertain con universities, which will charge you more, and trap you!
I see many universities lure international students, from the poorer parts of the world, by offering them greasy deals.
These countries, where students are allowed to work after graduation, do not guarantee paid decent jobs, or any jobs at all.
It is your luck that matters. #CON
Better to invest in your home country, if you have the money in your a/c, and turn into an #entrepreneur, if you have the skills, the ideas, zest, zeal and the risk appetite to do well in life.
Why give your money to a con institution which offers a run-of-the-mill type degree?
Somebody on this forum needs to do a thread on quotes, spreads, risk-neutral probabilities, real-world probabilities, order books, market execution, limit ordering strategies, order cueing, and bid-ask spreads, etc.
Market #Microstructures are key to analyzing liquidity risks.
The first thing we need to include within our risk pedagogy masterclass is how liquidity risks vary across financial markets, order books, volatility term structures, asset classes, premarket, auctions, intra-day, and private placement transactions.
Most of the students in a standard risk management program are asked to do statistics, mathematical modelling, and computer programming straight away!
All good.
But, it's important to learn the process, and product features, that leads to financial product development.
What's wrong with Biostatistics is analogously related to the question that was put by the Queen to the Economists at the LSE after the GFC.
The Queen wondered why didn't the economists see it coming?
of course, health pandemics are different, but, the statistical models are not
Scholars working in the biomedical sciences, epidemiology, and #biostatistics spheres, rely on mathematical and applied statistical computing based on modelling assumptions that rely on historical data set observations.
The past cannot and will predict the future with certitude.
Even with Quantum Computing, Data Sciences, Machine Learning, Artificial Intelligence, or any other form of computer-aided Predictive Analytics, scholars across the domains of natural and social sciences will never be able to capture the emergence of rare #Black#Swan#Risks!
Risk Management is like a Greek Tragedy, where actors laugh to express their sorrow. Hence, here what mimics laughter is the Normal (Gaussian) PDF and its assumptions.
I believe the strongest voice that emerged in the Post - Crisis years was that of Dr Nassim Taleb, who heavily criticized risk management models and techniques that assume the Normality of returns, and its volatility in financial markets.
The Credit derivatives aka structured products that were financially engineered by Wall Street and the City Quants to provide a market-beating returns used such techniques to pool assets having negative correlations and a low joint probability of default or time to default
There is an obscure problem when you join the BIG-4 Advisory or auditing, or consulting side of the profession within the services sector.
Most of the co-workers come from an #Accounting background, which makes things awfully difficult to communicate and interpret.
I remember working with an #Audit Expert, having a BIG-4 background.
The chap didn't understand anything except for debit, credit, and fraud risk.
The assignment required to be sophisticated Actuarial Finance, Mathematics, and Econometric Skill Sets to understand the GAPS.
Hence, the biggest problem in the Financial Risk Management, #ERM, Quantitative Risk Management, and other FE Financial Engineering related risk management processes, when working with a #BIG4 Firm, is the interaction with Accountants and Auditors who have their own Lingua Franca