Statistical Investigation of Connected Structures of Stock Networks in Financial Time Series

In this study, we have investigated factors of determination which can affect the connected structure of a stock network. The representative index for topological properties of a stock network is the number of links with other stocks. We used the multi-factor model, extensively acknowledged in financial literature. In the multi-factor model, common factors act as independent variables while returns of individual stocks act as dependent variables. We calculated the coefficient of determination, which represents the measurement value of the degree in which dependent variables are explained by independent variables. Therefore, we investigated the relationship between the number of links in the stock network and the coefficient of determination in the multi-factor model. We used individual stocks traded on the market indices of Korea, Japan, Canada, Italy and the UK. The results are as follows. We found that the mean coefficient of determination of stocks with a large number of links have higher values than those with a small number of links with other stocks. These results suggest that common factors are significantly deterministic factors to be taken into account when making a stock network. Furthermore, stocks with a large number of links to other stocks can be more affected by common factors.Comment: 11 pages, 2 figure

Topological Properties of the Minimal Spanning Tree in Korean and American Stock Markets

We investigate a factor that can affect the number of links of a specific stock in a network between stocks created by the minimal spanning tree (MST) method, by using individual stock data listed on the S&P500 and KOSPI. Among the common factors mentioned in the arbitrage pricing model (APM), widely acknowledged in the financial field, a representative market index is established as a possible factor. We found that the correlation distribution, $\rho_{ij}$, of 400 stocks taken from the S&P500 index shows a very similar with that of the Korean stock market and those deviate from the correlation distribution of time series removed a nonlinearity by the surrogate method. We also shows that the degree distribution of the MSTs for both stock markets follows a power-law distribution with the exponent $\zeta \sim$ 2.1, while the degree distribution of the time series eliminated a nonlinearity follows an exponential distribution with the exponent, $\delta \sim 0.77$. Furthermore the correlation, $\rho_{iM}$, between the degree k of individual stock, $i$, and the market index, $M$, follows a power-law distribution, $\sim k^{\gamma}$, with the exponent \gamma_{\textrm{S&P500}} \approx 0.16 and $\gamma_{\textrm{KOSPI}} \approx 0.14$, respectively. Thus, regardless of the markets, the indivisual stocks closely related to the common factor in the market, the market index, are likely to be located around the center of the network between stocks, while those weakly related to the market index are likely to be placed in the outside

Predicting Disease Progression Using Deep Recurrent Neural Networks and Longitudinal Electronic Health Record Data

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially important as it encodes temporal concepts such as event trends, episodes, cycles, and abnormalities. Previously, there have been attempts to utilize temporal neural network models to predict clinical intervention time and mortality in the intensive care unit (ICU) and recurrent neural network (RNN) models to predict multiple types of medical conditions as well as medication use. However, such work has been limited in scope and generalizability beyond the immediate use cases that have been focused upon. In order to extend the relevant knowledge- base, this study demonstrates a predictive modeling pipeline that can extract and integrate clinical information from the EHR, construct a feature set, and apply a deep recurrent neural network (DRNN) to model complex time stamped longitudinal data for monitoring and managing the progression of a disease condition. It utilizes longitudinal data of pediatric patient cohort diagnosed with Neurofibromatosis Type 1 (NF1), which is one of the most common neurogenetic disorders and occurs in 1 of every 3,000 births, without predilection for race, sex, or ethnicity. The prediction pipeline is differentiable from other efforts to-date that have sought to model NF1 progression in that it involves the analysis of multi-dimensional phenotypes wherein the DRNN is able to model complex non-linear relationships between event points in the longitudinal data both temporally and . Such an approach is critical when seeking to transition from traditional evidence-based care models to precision medicine paradigms. Furthermore, our predictive modeling pipeline can be generalized and applied to manage the progression and stratify the risks in other similar complex diseases, as it can predict multiple set of sub-phenotypical features from training on longitudinal event sequences

The effect of a market factor on information flow between stocks using minimal spanning tree

We empirically investigated the effects of market factors on the information flow created from N(N-1)/2 linkage relationships among stocks. We also examined the possibility of employing the minimal spanning tree (MST) method, which is capable of reducing the number of links to N-1. We determined that market factors carry important information value regarding information flow among stocks. Moreover, the information flow among stocks evidenced time-varying properties according to the changes in market status. In particular, we noted that the information flow increased dramatically during periods of market crises. Finally, we confirmed, via the MST method, that the information flow among stocks could be assessed effectively with the reduced linkage relationships among all links between stocks from the perspective of the overall market

A new dynamic property of human consciousness

As pointed out by William James, "the consciousness is a dynamic process, not a thing" , during which short term integration is succeeded by another differentiated neural state through the continual interplay between the environment, the body, and the brain itself. Thus, the dynamic structure underlying successive states of the brain is important for understanding human consciousness as a process. In order to investigate the dynamic property of human consciousness, we developed a new method to reconstruct a state space from electroencephalogram(EEG), in which a trajectory, reflecting states of consciousness, is constructed based on the global information integration of the brain. EEGs were obtained from 14 subjects received an intravenous bolus of propopol. Here we show that the degree of human consciousness is directly associated with the information integration capacity of gamma wave, which is significantly higher in the conscious state than in the unconscious state. And we found a new time evolutional property of human consciousness. The conscious state showed a lower dimensional dynamic process which changed to a random-like process after loss of consciousness. This characteristic dynamic property, appeared only in the gamma band, might be used as an indicator to distinguish the conscious and unconscious states and also considered as an important fact for the human consciousness model