Thread by @SAH16928046

There is a problem in the financial risk management and financial engineering profession, that #quants tend to overwhelm their customers (in-house staff or external parties) with mathematical informatics.

Big Data science and ML Machine learning dashboard analytics & traditional time series econometrics presented via the system screenshots or PDF sheets attached to emails could be more helpful for the telephone operator mindset traders and dealers, only if appropriately described

More information can at times backfire!
It tends to obfuscate the human mind, negatively impacts decisions and actions in real-time, and renders the decision support systems incapacitated with tech-savvy dipped acronyms, notations,& expressions.
Disseminating Communication risks

I still believe one or, at most, two or three indicators are more than enough for a trader/dealer to know the basic financial risks.
The second challenge is to make the financial market operators on the trading floor & dealing room understand the underlying measurements & hedge

One risk metric per asset included within the portfolio is enough per se.
For e.g. beta values for stocks
Dashboard Risk Analytics as sold these days by Risk MIS technologies, software, and modern machine learning systems are too much to handle
No effect on financial performance

Look at the maturity level of the organization, the staff on the trading floor/ room, & the risk complexity of the products traded, priced, hedged, and managed as portfolios before developing risk management information/computing systems that provide risk metrics in real-time.

My department started with mass producing 40 market and liquidity risk indicators per stock we included within our mutual fund, wealth mgmt. and managed account portfolios, either as an intra-day trade or long-term investment.
We started losing money!
Bamboozled the IC & BRC

More the risk indicators, the more the risk limits!!
Investment Risk Management within the wealth management and archetypical asset management industry which manages unit trust collective investment schemes just cannot handle the barrage of information communication & compliance

For, e.g. I used to get constant queries from fund managers managing stock portfolios across business lines that were made up of close and open-end unit trust schemes, to explain the conjoint usefulness of Value-at-Risk and Beta Values.

VaR metrics added caution to traders' neuronal assembly activity which made them fearful, whereas, beta values probably released certain other neurotensins doing exactly the opposite.

It is of utmost importance for Risk Technologists and Middle Office/Front Office Dealing Room Specialists working as risk controllers and derivative product structurers & hedgers to study the overlaps between neuroscience, psychology, physiology(cognition), and phenomenology.

Behavioural Finance and Risk Management should be interlinked.
Period.
Risk measurements, dashboard analytics, machine learning informatics and other econophysics and financial engineering jargon can and do affect human decision-making, which means our consciousness mutates.

Machines and information science will change us and teach us after learning from us humans!!
Many people like Philosopher John Searle don't believe in this!
They have rejected the concept of computer machines/ AI having any consciousness.
Enter into Adaptative Human Learning

To end this thread xxxx
A time will come when we will be organized by the diktats provided to us by informatics and machines.
It will be the other way around!
In finance, risk managers will not be controlling traders/dealers, etc
They will be controlled by computational analytics

Robots will convert us into Robots.
It shall be the final stage in the game's theoretical model of competitive evolution, leading to self-mutation.
Equilibrium condition shall => Playing the Cooperative game with the machine!
This means blindly following instructions
End Game!