In late February, due to the coronavirus pandemic spread out, the stock market started crashing. Before March, the Coronavirus in the US did not spread out as fast as in other countries. After the COVID Spread global, lots of countries were locked down. National and Global-wide lockdown situations would affect the stock market. Asian countries got COVID pandemic before America. We can use the Asian country's pandemic situation to predict what will happen in the US. The research supposes to earn money during the pandemic period, and provided to other investors in the stock market. The authors create the new exchange index by using Z-Standardization. The reason for using Z-Standardization is to make it easy to compare indexes, and also it can eliminate the bias data. After the Z-Standardization, the authors got one new Z score value, which is Z-Exchange Index. Authors compared Stock Sell Price with Stock Bought price and the exchange threshold should be greater than 15. After it, the authors focused on distinguishing the outliers (the decisions of trading & exchanging). The reason for building Cash-Stock Balance Ratio should be eliminating the Human Factor in the complicated Trading section. Besides, helping investors manage how many parts should be cash and stock. We established the automatic trade triggering system, which will buy more stock immediately when the Cash-Stock Balance Ratio reaches a critical point. The Exchange Bid Ratio, which helps to reduce the cash level and determine the transaction.
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The purpose of this research is to find the COVID-19 pandemics that did influence the stock market as the past stock market crashes that happened in 1929 and 1987. In late February, due to the coronavirus pandemic spread out, the stock market started crashing. By creating a new multivariate statistical model and analyzing the coronavirus pandemic, the authors found out the recession of the stock market. The data to be analyzed includes eight previously bought stocks which averagely gained a lot and are considered to sell. Authors then bought twenty-three stocks that were COVID-19 impacted but expected to surge in the future. The research is supposed to find out if there is any possible opportunity to do stock exchange operations instead of merely buying or selling. All the data used the Z- Standardized Algorithms calculation to convert to be ready for comparison. There are three most important indexing signals, the selling, buying, and exchanging, through the statistical analysis software JMP, to find the correlation between the stock market and the pandemic trends and to show how to earn money for college tuition. The project used the Quantile Range Outliers Algorithm, and Robust Fit Outliers Algorithm to find the best occasion and strategy of stock investment.
After 11 years’ Bullish market and peaked in Feb.2020, USA Stocks have been significantly impacted by COVID-19. We are entering a Bear market like in 1929/1987/2008. The objective is to establish a systematic stock trading mechanism to execute daily trades by picking the right stocks to sell, to buy or to exchange by setting the bid price and qty. The “stock index” metric is derived based on three Z-Standardized algorithms over 2015-2020 stock data period to determine the relatively high/ low situations: (1) within the same stock , (2) the ratio to the QQQ stock, and (3) comparison among the 32 Stocks monitored. JMP Outlier Platform can help identify the best trade candidates. Quantile Outlier and Robust Fit Outlier options are compared to handle skewed or non-Normal distributions. PCA-Model Driven Multivariate SPC platform is utilized to monitor the stock market trend. T2 Contribution Pattern, Heat Map, and Score Plot are analyzed to affirm the best stocks to be traded. A 10-level ladder bid system for each stock is established to set the “bid price” transaction based on stock index level. A cash% index is derived to determine the bid quantity, trade amount and manage the Cash-to-Stock Ratio. This comprehensive stock trade system has been fully integrated in one JSL Script to minimize error due to Human Factors. In trading days more than 3% Stock modulation, >70% trades of the highest and the lowest transactions happened in the first hour or last hour of trading (>95% confident on 1-Proportion Exact Test). This stock model has outperformed the QQQ index by a 5% margin during 2020 Feb.-Apr. COVID Outbreak period. The methodology can be easily and broadly extended to the other investment portfolios such as: Real Estate, Gold, Currency, Oil ETF, Retirement Plan management, 529 Education Fund, and AI-Driven Finance.
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