US Composite stock market analysis (down run)
Are you looking for stocks to avoid holding or to short? This service provides that data along with significant backup information on the entire US Composite set of US-listed stocks that traded on the day of your run. We run the model at/after 7pm ET on that day's market close data.
Do you want to identify inefficient stocks that have more historical price risk than expected return than other stocks?
CQNS DOWN is a quantitative analysis of US listed stocks that an investor might like to avoid or short, while also hedging upside risk. These are inefficient portfolios of stocks of money-losing companies with significantly poor CQNS scores. These have significantly more risk than expected return, and are based on our CQNS DOWN algorithm. Inefficient portfolios are often one or a few stocks, as diversification removed the exceptional level of risk in these stocks.
These stocks are not just inefficient. The companies behind these stocks have negative net income. Currently, stocks of companies with negative net income have 3.5x the price risk as investing in the S&P 500 ETF.
What you receive:
A set of 25 of the worst, most optimized US-listed stock portfolios that are the most inefficient, and have the worst risk-return trade-off. We provide the top 25 of these portfolios, along with their portfolio CQNS score.
In addition to finding and providing a set of optimized stock portfolios by CQNS score, we provide additional analytical deliverables, as follows:
For each stock that passed data validation, over the past 253 trading days:
- Stock variance for each stock, normalized to allow comparison.
- CQNS score for each stock
- Volume for each stock (% average)
- Price Change (% average)
- BETA (versus index ETF used)
- Stocks that meet a threshold for non-normal distributions in fourth-order price distribution (kurtosis) with low or high daily price variance
- Stocks that meet a threshold for non-normal distributions in the third-order price distribution (skewness)
- Dividends paid (% average price)
- Variance of daily closing stock price (value, normalized)
- Expected returns (%)
- Stocks with negative, high, or low BETA values (BETA value).
- Stocks with stock price change (% of average)
Timing:
We initiate the analysis after 7pm ET to ensure complete trading day information.
We email to you (or upload in a pre-agreed way e.g., Google Drive) a management report along with a spreadsheet (.CSV) before the next market open, and often before midnight ET.
What you do:
You provide us with the following information as you place your order:
- Your name, physical address, email address & phone number
- Any special requests, instructions, or actionable objectives
- How you would like to receive your report and spreadsheet (if not by email). We use Google Drive, Slack, Microsoft Teams/Office, and other collaboration technologies.
You may also call or email us with your information.
- Email: jeffrey@quantum-usaci.com
- Cell: +1.312.515.7333
For repeat orders, you may request an invoice. We accept payment via check, ACH, or through an online payment processor (secure).
We will not sell or share your information unless legally required.
Thank you for your order and your business.
Please see our homepage for our brochure and our webpage "Market Questions & Data" for further details.
Disclosure:
Our algorithm, platform and methods are subject to change and continued development. Please perform your own due diligence and research into companies before investing. Investments in equities can and do lose money. We do not guarantee your results.
How we perform the analysis:
We start with all 11,000+ US listed tickers that traded that day and perform data analysis and validation. We then run our quantitative analysis using professionally-sourced market data from Intrinio on our platform after market close for the date purchased. We write the management report sharing the results and our observations once the 'run' is completed.
We guarantee that we will email your report before the next market open or your analysis is free.