President trump tweets supreme leader Kim Jong-Un on nuclear weapons

A set of 125 tweets about North Korea's Supreme Leader Kim Jong-Un by President Trump from 2013 to 2018 are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their implications about President Trump's understanding and approach to international diplomacy. The results suggest a predominantly positive emotion in relation to tweets about North Korea, despite the use of questionable nicknames such as "Little Rocket Man". A comparison is made between the tweets on North Korea and climate change, madefrom 2011-2015, as Trump has tweeted many times on both issues. It is interesting to find that Trump's tweets on North Korea have significantly higher positive polarity scores than his tweets on climate change

Nonparametric Multiple Change Point Analysis of the Global Financial Crisis

This paper presents an application of a recently developed approach by Matteson and James (2012) for the analysis of change points in a data set, namely major financial market indices converted to financial return series. The general problem concerns the inference of a change in the distribution of a set of time-ordered variables. The approach involves the nonparametric estimation of both the number of change points and the positions at which they occur. The approach is general and does not involve assumptions about the nature of the distributions involved or the type of change beyond the assumption of the existence of the α absolute moment, for some α ε (0,2). The estimation procedure is based on hierarchical clustering and the application of both divisive and agglomerative algorithms. The method is used to evaluate the impact of the Global Financial Crisis (GFC) on the US, French, German, UK, Japanese and Chinese markets, as represented by the S&P500, CAC, DAX, FTSE All Share, Nikkei 225 and Shanghai A share Indices, respectively, from 2003 to 2013. The approach is used to explore the timing and number of change points in the datasets corresponding to the GFC and subsequent European Debt Crisis

Volatility Spillovers from the Chinese Stock Market to Economic Neighbours

This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China’s increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential effects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover effects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacific Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover effects from China to relat

Risk Measurement and Risk Modelling using Applications of Vine Copulas

__abstract__ This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite nancial risk. Copula-based dependence modelling is a popular tool in nancial applications, but is usuall

A Capital Adequacy Buffer Model

__Abstract__ In this paper, we develop a new capital adequacy buffer model (CABM) which is sensitive to dynamic economic circumstances. The model, which measures additional bank capital required to compensate for fluctuating credit risk, is a novel combination of the Merton structural model which measures distance to default and the timeless capital asset pricing model (CAPM) which measures additional returns to compensate for additional share price risk

An entropy based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

This paper features an analysis of the relationship between the DOW JONES Industrial Average Index (DJIA) and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA)1 provided by SIRCA (The Securities Industry Research Centre of the Asia Pacic). The recent growth in the availability of on-line financial news sources such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. Thomson Reuters News Analytics (TRNA)2 is one such data set. In this study we use the TRNA data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index componen

Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Sentiment Series

__Abstract__ This paper features an analysis of the relationship between the volatility of the Dow Jones Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacic). The expansion of on-line financial news sources, such as internet news and social media sources, provides instantaneous access to financial news. Commercial agencies have started developing their own filtered financial news feeds, which are used by investors and traders to support their algorithmic trading strategies. In this paper we use a sentiment series, developed by TRNA, to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. A variety of forms of this measure, namely basic scores, absolute values of the series, squared values of the series, and the first differences of the series, are used to estimate three standard volatility models, namely GARCH, EGARCH and GJR. We use these alternative daily DJIA market sentiment scores to examine the relationship between financial news sentiment scores and the volatility of the DJIA return series. We demonstrate how this calibration of machine filtered news can improve volatility measures

Volatility Spillovers from the US to Australia and China across the GFC

This paper features an analysis of volatility spillover eects from the US market, represented by the S&P500 index to the Australian capital market as represented by the Australian S&P200 for a period running from 12th September 2002 to 9th September 2012. This captures the impact of the Global Financial Crisis (GFC). The GARCH analysis features an exploration of whether there are any spillover eects in the mean equations as well as in the variance equations. We adopt a bi-mean equation to model the conditional mean in the Australian markets plus an ARMA model to capture volatility spillovers from the US. We also apply a Markov Switching GARCH model to explore the existence of regime changes during this period and we also explore the non-constancy of correlations between the markets and apply a moving window of 120 days of daily observations to explore time-varying conditional and tted correlations. There appears to be strong evidence of regime switching behaviour in the Australian market and changes in correlations between the two markets particularly in the period of the GFC. We also apply a tri-variate Cholesky-GARCH model to include potential eects from the Chinese market, as represented by the Hang Seng Inde

Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non- linear models, including smoot

Fake News and Indifference to Truth

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they dier, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identied as being more positive in sentiment than President Obama's 2016 SOUA. This is conrmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research