3 Jul, 17 tweets, 4 min read
What quantitative aptitude do Finance and Risk Management require?
@actuarynews @CQFInstitute @GARP_Risk
Mathematics is the Queen of all Natural Sciences.

However, its applications in Social Sciences and its sub-fields such as Economics and Business Studies is growing all the time.

Finance and Risk are broadly categorized as subfields of Microeconomics.
That's my opinion.
The two subjects (Risk and Finance, which I would like to jointly refer to as Risk Finance) share a lot in common with the Pricing Theory, Utility Theory, Portfolio Theory, Risk Pooling, Risk Financing and Risk Sharing Theories, the Moral Hazard Problem,
and lastly, the Auctions Theory is taught as part & parcel of the Pedagogy undertaken at any standard business and/or economics school.
Of course, there is a lot more that can be added to the tentative academic teaching list!
The modern-day problems in Economics are viewed and interpreted as Mathematical problems.

This may lead to Charlatanism.

Another topic for another day.
From a standard Finance Pedagogical Perspective, the role of Maths is ever-growing and most noticeable. Curriculums are fast embracing General Quantitative Methods such as Matrix Algebra, EconoPhysics, Quantum Mechanics,
Advanced Neural Network Models, Statistical and Machine Learning Data Science Models, and certain Industrial Engineering Principles (Operations Research and Decision Science Tools) to identify and optimize Financial Market and Portfolio Management Problems.
Understanding the Basic TYPE 1 and TYPE 2 Errors, the Alpha and Beta Risk, power of the test and size of the test varies with the Law of Large Numbers, Levy Power Rule, Normal Distribution(Gaussian) model, Other Discrete Random variable distributions,
Bayesian Estimation Techniques, Stochastic Calculus - SDEs (Stochastic Differential Equations) and related Stochastic Processes, Arithmetic (ABM) and GBM - Geometric Brownian Motion Models, Markovian and Stationary increments, Ito’s Lemma and Wiener Process,
Martingale, White Noise Deviations- Serial Dependence / Autocorrelations, Random (Drunken) Walks, Auto-Regressions, ARIMA, ARMA, ARCH and GARCH(1,1)
and Monte Carlo Simulations are the Backbone of many Derivative Pricing Concepts, Model Building Methods and Risk Management Hedging Instruments and the correlated strategies.
These Risk and Finance Concepts are at best investigated and validated using certain Mathematical and Statistical Models.

We go a step forward now! Machine Learning and Big Data.
Converting your theory and model into a pattern recognizing predictive tool which can enable you to develop a prescriptive analytics dashboard require a strong understanding of programming and/or coding.
Hence, everything that was earlier taxonomized as a Mathematical and/or Statistical Theorem (with applications in Financial Markets/ Banking), now ends up as a Machine Learning Predictive Analytical Algorithm in the fast-expanding Universe of #Risk #Finance.
The computing and software programming experts specializing in AI - Artificial Intelligence have taken things beyond Coase Theorem and Game Theory, earlier solved by Tenure- track Professors using chalk and blackboard ingenuity!
Time to rephrase the question, please =>

” How Machine Learning and Big Data influences the subject of modern Risk Finance ?”

That will generate some more interesting answers.

Thank you!

• • •

Missing some Tweet in this thread? You can try to force a refresh

This Thread may be Removed Anytime!

Twitter may remove this content at anytime! Save it as PDF for later use!

# More from @SAH16928046

30 Jun
Every Concept has a Form, as expounded by #Plato.
But, it does not mean, that a complex concept cannot have simple explainable forms?
It can!
All Computational Algorithms that train various Mathematical or Statistical Models are a representation of some theory, which originated as a concept using logical methods of enquiry, having empirical or rational forms.
Model #Parsimony could be best understood in the light of #Occam's Razor.
27 Jun
Top-Down Vs. Bottom-Up Style of Investment Analysis: Risk Versus Research Desk Perspectives
@GARP_Risk @CFAinstitute @CQFInstitute
In the field of Investment Risk and Research Analyses, the research/risk analyst has to make a choice between two asset selection and/or allocation approaches.
The first approach is referred to as the “Top-Down Analysis” and the second is its opposite, the “Bottom-Up Analysis”.
25 Jun
The power of the human mind is the state of Consciousness that exists!
Risk Managers must increase their level of situational awareness to develop a proactive sense of negative and positive events, having single or multiple outcomes.
Psychology of Risk Perception cant be ignored

Listen to this man to understand how Psychology affects events across both Natural and Social Sciences, etc.
23 Jun
Buy Vs. Sell-Side Risk Information: Time to Differentiate between “Your Risk” and “My Risk” Reports
@CFAinstitute @GARP_Risk
Report Sample of Asset Allocation Analytics
Well, we all are accustomed to reading “Buy” and “Sell-Side” Investment Evaluation Reports prepared by Financial Research Analysts at various FIs such as Investment Companies operating in the Financial Markets.
21 Jun
Was speaking to someone regarding the development of transferrable, applicable, and/ or any other learning and development related commutative technical skills during the period of study at any polytechnic or a university.
Of course, the conclusion we drew might help pedagogy.
What kind of skills we develop bifurcates into two directions.
Direction 1- #STEM Based Skill Programs
Direction 2- #Liberal #Arts, #Humanities and other #Social #Sciences based Skill Programs
Over time the divergence particularly in the fields of social sciences and natural sciences has been reduced due to advancements, interfaces, and cross-fertilization of concepts, theories and models, being exchanged.
Hence, Skills are being shared and improved upon both ways!
15 Jun
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.