Towards Data Science: DAY 9 to 13

Moving on forward in the book, these days were about Categorical Variables, significance testing (t-ratios, F-ratios, ANOVA) moving on to variable selection throughout which I struggled to maintain interest. However, this was extremely crucial knowledge and stressed the importance of intuition to support the math. Luckily, I was able to sum this up through an example on modeling Stock Liquidity using various performance measures.

The case study begins through defining the following criteria through which investors choose their stocks;

1. Expected Return;
2. Riskiness (Volatility);
3. Length of Time of Investment (Varies for Growth and Income Stocks);
4. Liquidity (Ease through which a stock could be sold, measured through its VOLUME in a stock market)

Towards Data Science: DAY 6 to 8

As I progressed on the Edward W. Frees book, these three days were about covering Regression over Multiple Explanatory Variables. Again, this was no different as more or less the same concepts applied to multiple liner regression as well. However, my infatuation with correlations and scatter plots took a hard hit as I learned how deceiving looks can be.

As the book explains this by way of a dataset that lists prices of 37 Refrigerators along with their details and features. The regression attempts to fit Refrigerator Prices to the following explanatory variables:

1.  ECOST: Energy Cost;
2. RSIZE: Refrigerator Compartment Size;
3. FSIZE: Freezer Compartment Size;
4. SHELVES: Number of Shelves;
5. FEATURES: Number of Features.

Towards Data Science: DAY 1 to 5

As a Life Actuary primarily associated with valuations and financial reporting positioned in a very much underdeveloped insurance market, Data Analysis is an area that I never get to experience but do get to hear about more than I would like to. Buzzwords like “BIGDATA” and “R-Programming” further add to that sense of inferiority.

As my response to this growing skillset deficiency, I have resolved to revisit statistics through Edward W.Frees book dedicated to Regression applications in Actuarial and Finance Areas with the aid of R-software. First impressions of R so far have been that its extremely convenient and quick to use with just a few lines of code needed for all kinds of statistical work. As for the book, it’s extremely relatable to the usual problems in Actuarial Science.

Burning Rubber: Failure Story for the most part

I have been trying to get in roots with the “SCIENCE” part of Actuarial Science. Actuarial Exams are absolutely loaded with material on “probability” and entailing Loss Models but the world has now moved on to bigger things like Big Data and GLMs, Extreme Value Theory and Solvency II (it’s probably all they can tweet about).

It’s all the more important to get the amassed knowledge accounted for and get our facts straight but I think it’s equally important to develop our tool kit and use technology at our aid. This is the part that excites me the most and while for the past few months I have been trying to get hands on practice at probability models, model fitting, and some quantitative finance related mathematics, everything while using MAPLE 18 at my aid.

However, usually in my ambition I don’t get much done but rather lose track and go off on a tangent.

DAMAGE CONTROL: PART 1

I have always felt that my less structured pursuance of Actuarial Studies (as for many other Actuaries in the country) will have its toll on me. Taking ACE or ACTEX manuals as the only required ingredient at passing Actuarial exams, the level of mathematical skill and mastery that should be fostered does not happen. However, I also know now that taking Mathematics as a subject in GCE A-Levels and nailing it with a “Too easy A” really meant nothing. I really should have questioned University of Cambridge GCE examination standards when I was commending my own intelligence.

While taking an online Coursera course entitled “Mathematical Methods for Quantitative Finance”, I have realized some embarrassing gaps in my mathematical knowledge: