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Showing posts with label Career. Show all posts
Showing posts with label Career. Show all posts

Thursday, February 18, 2016

Non-commodity professional

To become a non-commodity professional who cannot be replaced by others and/or machines, you must have four skill/mindsets:
1. Knowledge (deep and multidiscipline) and thinking power
2. Decision making (best possible one in a timely manner under various constraints)
3. Action (i.e., implementation)
4. Evaluation (review)

Thursday, December 31, 2015

Features of people who succeed in their professional and personal lives

Features of people who succeed in their professional and personal lives:
1. Emotionally stable
2. Modest
3. Objective and logical
4. Active, action-oriented, not making excuses
5. Neither extrapunitive nor intropunutive, realistic and fact/result-oriented
6. Generous, not trying to control external factors that you cannot change
7. Following dynamics
8. Matching your competitive advantage and what you really like
9. Adaptable to change

Saturday, June 21, 2014

Portfolio Management Job

Why did I choose a portfolio management as my long-term financial professional career in the first place?

I think it boils down to three reasons:

  1. Intellectually Stimulating
    • Portfolio Management is a complicated game of the theory and practice and is still evolving. You have to keep learning theories first and then understand realities by continuously adjusting differences between theories and realities. It never ends and you can't be 100% sure no matter how you spent time. Dynamics in markets always overwhelm us.
  2. Global Profession
    • The world economies are more and more connected and money/labors are moving around the world. You're required to understand other countries/regions.
  3. Fair Competition
    • Portfolio Management is competitive, but a fair game in the long run. You cannot survive in the industry from the long-term perspective unless you understand laws of the competition and cope with changes. Darwin's theory works here.

What do you think about it? Your comments would be greatly appreciated.




Deep Learning (Regression, Multiple Features/Explanatory Variables, Supervised Learning): Impelementation and Showing Biases and Weights

Deep Learning (Regression, Multiple Features/Explanatory Variables, Supervised Learning): Impelementation and Showing Biases and Weights ...