In order to investigate the effect of graphene surface chemistry on the electrochemical performance of graphene/polyaniline composites as supercapacitor electrodes, graphene oxide (G-O), chemically reduced G-O (RG-O), nitrogen-doped RG-O (N-RG-O), and amine-modified RG-O (NH(2)-RG-O) were selected as carriers and loaded with about 9 wt % of polyaniline (PANi). The surface chemistry of these materials was analyzed by FTIR, NEXAFS, and XPS, and the type of surface chemistry was found to be important for growth of PANi that influences the magnitude of increase of specific capacitance. The NH(2)-RG-O/PANi composite exhibited the largest increase in capacitance with a value as high as 500 F g(-1) and good cyclability with no loss of capacitance over 680 cycles, much better than that of RG-O/PANi, N-RG-O/PANi, and G-O/PANi when measured in a three-electrode system. A NH(2)-RG-O/PANi//N-RG-O supercapacitor cell has a capacitance of 79 F g(-1), and the corresponding specific capacitance for NH(2)-RG-O/PANi is 395 F g(-1). This research highlights the importance of introducing -NH(2) to RG-O to achieve highly stable cycling performance and high capacitance values.
The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.
In this paper, we investigate how the cross-correlations between stocks in the Singapore Stock Exchange (SGX) evolve over 2008 and 2009 within overlapping one-month time windows. In particular, we examine how these cross-correlations change before, during, and after the Sep-Oct 2008 Lehman Brother Crisis. To do this, we extend the complete-linkage hierarchical clustering algorithm, to obtain robust clusters of stocks with stronger intracluster correlations, and weaker intercluster correlations. After we identify the robust clusters in all time windows, we visualize how these change in the form of a fusion-fission diagram. Such a diagram depicts graphically how the cluster sizes evolve, the exchange of stocks between clusters, as well as how strongly the clusters mix. From the fusion-fission diagram, we see a giant cluster growing and disintegrating in the SGX, up till the Lehman Brothers Crisis in September 2008 and the market crashes of October 2008. After the Lehman Brothers Crisis, clusters in the SGX remain small for few months before giant clusters emerge once again. In the aftermath of the crisis, we also find strong mixing of component stocks between clusters. As a result, the correlation between initially strongly-correlated pairs of stocks decay exponentially with average life time of about a month. These observations impact strongly how portfolios and trading strategies should be formulated.
With hundreds trillion dollars of capital floating in the stock market, it is extremely important to understand market structures and dynamics of stock markets. In this thesis, we studied the macroscopic and mesoscopic dynamics of financial markets, from the econophysics (a marriage between physics and economics) point of view. When econophysicists study stock markets, they frequently borrow methods developed in other areas of physics. However, because of the nature of their problems, econophysicists sometimes also invent new methods. In this thesis, we have also contributed methodological innovations (MI), to contrast the phenomenological discoveries (PD) that we have also made. The first of these methodological innovations is (MI1) the method of partial hierarchical clustering (PHC), a supervised clustering method with the advantage of using multiple thresholds to determine clusters. Through the PHC results, we demonstrated (PD1) the existence of hierarchical structures in the Singapore Exchange and Hong Kong Stock Exchange: from market sectors, to country markets, and to global markets. Furthermore, we also investigated the dynamics of these hierarchical structures across market crashes. To do this, we (MI2) extend the complete-linkage hierarchical clustering algorithm, to obtain robust clusters of stocks with high intra-cluster homogeneity and high inter-cluster heterogeneity. By visualizing these robust clusters using (MI3) the fusion-fission diagram, we observed that (PD2) when approaching market crashes, the movements of stock prices become synchronized, causing most of stocks to merge into a giant cluster. Right after the crash, this giant cluster fragmented and thereafter mixed strongly. This discovery points us to the fusion-fission processes in the market, which we can exploit to forecast market crashes. We assume that the traders strategies form strategy clusters in the constantly changing strategy space, and the stock price movements are governed by the dynamics of these strategy clusters. Moreover, this dynamic can be described by a statistical physics fusion-fission model: the soup-of-groups model (SoG). We (MI4) derived a mean-field SoG forecasting equation and showed that some market crashes can be predicted. Specifically, by fitting the continuous returns of the component stocks of the Straits Times Index to the SoG forecasting equation, we (PD3) found episodes of heightened crash likelihoods close to the Chinese Correction (27 Feb 2007), beginning of the Subprime Crisis (17 Aug 2007), and the Asian Correction (9 Mar 2009), with early warning four to six months prior to the crashes.
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