About

monew

Monewmetrics is centered on three core areas:

Quantitative Research: I analyze financial data and apply advanced statistical models—such as linear regression and autoregressive time series models—to generate predictive insights. Python is my primary tool, given its robust libraries for data analysis, machine learning, and statistical modeling.

Statistics & Probability: I explore a wide range of statistical concepts, I cover foundational topics like data location and dispersion measures and will cover more advanced principles, such as regression assumptions and their applications.

Financial Analysis: Through my LinkedIn or SeekingAlpha,I post comprehensive financial analysis on companies, both quantitatively and qualitatively. This includes 3-statement financial modeling, scenario and sensitivity analysis, DCF valuation, and deeper insights into business models, competitive advantages, industry trends, market positioning and more.

DISCLAIMER!

Before proceeding, please make sure that you note the following important information:

NOT FINANCIAL ADVICE! My content is intended to be used and must be used for informational and educational purposes only. I am not an attorney, CPA, or financial advisor, nor am I holding myself out to be, and the information contained on this blog/notebook is not a substitute for financial advice, None of the information contained here constitutes an offer (or solicitation of an offer) to buy or sell any security or financial instrument to make any investment or to participate in any particular trading strategy. Always seek advice from a professional who is aware of the facts and circumstances of your individual situation. Or, Independently research and verify any information that you find on my blog/notebook and wish to rely upon in making any investment decision or otherwise. I accept no liability whatsoever for any loss or damage you may incur.