Your 1-Week Roadmap to the Highest Return Day 2: Stock Data Analytics (Crunch the Numbers)

 

This session is all about financial data management in the context of BI prescriptive analytics [75]. This is where the market research starts. By evaluating aforementioned financial metrics and ratios (FMR) of past and current stock data, your objective is to measure and compare operational performance against their counter-parties, industry benchmarks, and peers. The financial statement of a company can also be compared to that of one or more other companies within the same industry. This breaks down into the following steps to follow:

1. Let’s evaluate companies based upon the business/financial risk analysis and comparison of SEC registered and publicly disclosed “buy, hold, and sell” credit ratings for financial markets provided by big three and smaller credit agencies (S&P, Moody’s, Fitch, Duff &Phelps, DBRS, Kroll Bond, etc.); rating agencies help investors decide where to invest their money and whether the risk involved in buying a debt security is worth the promised interest rate, but it is imperative to gauge their sample bias, a potential conflict of interest and spot-on differences in opinion;

2. Initially selected stocks (output of step 1) can first be scrutinized using the rule of thumb that the key metrics (the Return on Invested Capital (ROIC), 2:1 Ratio of Liquidity, Sales Growth Rate, EPS, Equity Growth Rate and Operating Cash Flow Growth Rate) are to be greater than 10% for the last 10 years  [75];  

3. In addition to the key metrics (step 2), you can use many other FMR indicators to analyse price trends over a set amount of time, like 50, 100 or 200 days (simple moving average);  

4. You can apply several additional criteria for historical and real-time stock data screening and short-listing companies (e.g. 6-fold price increase in the past six months, 12-fold in past one-year, 20-fold in past two years and 30 fold in past three years);  

5. The #1 Google ranked fair value calculator [76] can be used to compare the relative valuation of stocks all over the world individually, or by regions, countries or industries, and will help you answer questions such as: “How do UK stock prices compare to the rest of Europe?” or “Which are the 10 most undervalued country stock indexes?”

6. Instead of leaving stock picking to professionals, you can narrow down your investment choices using the all-in-one free stock screener [77], premium market screeners [1] and AI-powered robot-advisors [78] that can sift through the tens of thousands listed stocks in a matter of seconds;

7. Explore variety of guided investment options and banking services for individuals - follow their steps to discover and compare returns and costs in the standard, pessimistic and optimistic scenarios [79]; 

8. Combine the use of alternative data outside the company’s control (social media, web traffic, product reviews, etc.) with traditional financial records (financial statements, sales figures, SEC filings, etc.) to help track the underlying financials of a short-listed company [80];

9. Apply a fully-fledged statistical time-series analysis to selected stock prices using Python/R libraries [81] - calculate daily returns and their probability distribution, raw histograms and boxplot to check outliers, the mean/median, STDEV as a measure of the risk, assess the symmetry of the distribution via skewness, the p-value and the |mean-median| residual, verify the null hypothesis that the kurtosis is the same as a normal distribution (which is 0), verify a Q-Q correspondence between the quantiles of a normal distribution and those of stock prices, identify time periods with high/low volatility (aka volatility clustering), and check if the time-series autocorrelation function can be used for prediction purposes (cf. advanced options such as unsupervised/supervised ML/AI algorithms in Appendix C);          

10. Use the entire set of financial and statistical metrics or KPI’s (steps 1-9) to perform competitive benchmarking by comparing your selected companies against  a number of competitors, market indexes and the industry-low, industry-average, ESG Rating and industry-high benchmarks;

11. The proposed TLS ranking, ranging from red (reject) to green (accept) via amber (validate), measures a selected stock’s performance based upon the outcome of step 10, i.e. the accepted stocks are outperforming competitors and the general market;

Finally, you import various TLS data spreadsheets into Google Sheets and Google Data Studio to create a personal portfolio tracker as a part of your investment portfolio on-premise RDBMS or cloud-based data warehouse [82]

Comments

  1. The Bottom Line: This roadmap allows you to centralize all structured tables and unstructured records (e.g. alternative data) that represent cross-channel market data you need for FMR calculations, statistics, historical analysis, visualization, integration, QC, TLS-based ranking and benchmarking.

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  2. This step aims to study, construct and evaluate investment BI strategies in order to understand both current and future stock exchanges. Market data mining techniques will be used to evaluate past stock prices and acquire useful knowledge through the calculation of some financial indicators mentioned earlier.

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