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School of Mathematics and Statistics, University of Sydney, Department of Finance, Asia University, Taiwan,The paper presents a novel analysis of the US spread of the SARS-CoV-2 causes the COVID-19 disease across 50 States and 2 Territories. Simple cross-sectional regressions are able to predict quite accurately both the total number of cases and deaths, which cast doubt on measures aimed at controlling the disease via lockdowns. Population density appears to play a significant role in transmission. This throws in sharp relief the relative e_ectiveness of the at-tempts to risk manage the spread of the virus by flattening the curve' (aka planking the curve) of the speed of transmission, and the effcacy of lockdowns in terms of the spread of the disease and death rates. The algorithmic tech-niques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers, and risk management and deficision making of healthcare by state, regional and national governments in all countries.

This paper features an analysis of President Trump's two State of the Union addresses, which are analysed by means of various data mining techniques including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. In order to provide a contrast and some parallel context, analyses are also undertaken of President Obama's last State of the Union address and Hitler's 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than Hitler's relatively shorter address, which is characterized by a prevalence of negative emotions. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different.

This paper features an analysis of President Trump's two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot's power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama's last State of the Union address and Hitler's 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than is Hitler's relatively shorter address, which is characterised by a prevalence of negative emotions. Hitler's speech deviates the most from common speech, but all three appear to target their audiences by use of non-complex speech. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different.

This paper features an analysis of President Trump's two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot's power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama's last State of the Union address and Hitler's 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than is Hitler's relatively shorter address, which is characterised by a prevalence of negative emotions. Hitler's speech deviates the most from common speech, but all three appear to target their audiences by use of non-complex speech. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different.

The Global Financial Crisis (GFC) provided overwhelming evidence of the problems caused by inadequate credit ratings. Losses and problem loans experienced by banks over this period were staggering. Yet many of the securitized sub-prime parcels which were widely seen as an underlying cause of the GFC, as well as corporate obligors who experienced severe difficulties during the GFC, retained extremely strong external credit ratings. They may have had low perceived risk at the time of rating, but as circumstances changed, the ratings stayed static and became far removed from the underlying risk. A key problem is that the external credit ratings do not fluctuate with changing economic circumstances. Whilst there are models which measure changing default risk, they are not linked to credit ratings and it is often the rating itself, not the underlying risk that drives behavior, such as the purchase of securitized parcels, the pricing of credit risk, and the allocation of capital for credit risk, which under the Basel standardized model for corporates is based on the rating itself. This problem is exacerbated by the fact that these ratings carry descriptors such as "extremely strong capacity". This descriptor may no longer be appropriate for the rated company if the market turns dramatically, yet the rating and descriptor remain unchanged. To overcome this problem, this paper shows how an innovative fluctuating credit ratings model can be generated by linking the Merton structural credit model to a credit ratings framework. The Merton model measures fluctuations in daily asset values and, using a combination of these fluctuating asset values and the capital structure of a company, it measures Distance to Default (DD) and the Probability of Default (PD) associated with each DD. Under the Merton structural model, default occurs when the firm's debt exceeds asset values. Thus as fluctuations in asset values become more volatile, DD also becomes more volatile and PD increases. External raters such as Moody's provide PD's associated with each rating. Thus by using the Merton model, we are able to generate PDs which fluctuate over time and link these PD's to credit ratings. Therefore, as our PD's fluctuate, so do the credit ratings. To illustrate our approach, we apply this model to a French motor vehicle company (Renault) which experienced severe distress during the GFC. We compare the Moody's rating changes that took place for Renault over the 2006 – 2009 period, which captures the events leading up to and during the GFC. Over this period, only three Moody's external ratings changes took place and throughout this period, Renault stayed in the Moody's 'moderate' risk band. Based on this, an investor would likely assume the company was in reasonable financial health, and a bank would not be required to change its capital allocation for this company if it was a borrower. Yet during this period, the company experienced such severe financial problems that it had to be bailed out by the French Government. Our model, on the other hand, recognizes these stresses far quicker, starting with rating downgrades for Renault from August 2007 and moving downwards through several risk bands, from 'moderate' to 'substantial' to 'high' and then to 'very high' credit risk. This downward spiral is far more in keeping with the actual problems experienced by Renault than the static 'moderate' risk tag would indicate. We thus find that the new model responds extremely rapidly to changing economic circumstances to produce ratings which can far more accurately depict the underlying credit risk of a corporate obligor in these times than prevailing external rating methods. The new ratings can benefit bond investors and banks through improved knowledge of the underlying credit risk of bonds and of corporate borrowers. As capital adequacy can also be linked to credit ratings, an improved rating model can assist banks and regulators to better measure required capital adequacy to protect against economic downturns.