A dataset of dynamical social map in ancient China: 618-1644.

The data set of this article is related to the paper "Dynamical structure of social map in ancient China" (2022)[1]. This article demonstrates the data of social relations between cities in ancient China, ranging from 618 AD to 1644 AD. The raw data of social associations between elites used to build social maps are extracted from the China Biographical Database. The raw data contain 14,610 elites and 29,673 social associations, which cover 366 cities. The dataset of this article is relevant both for social and natural scientists interested in the social and economic history of ancient China. The data can be used for further insights/analyses on the evolutionary pattern of geo-social architecture, and the geo-history from the viewpoint of social network.

Fluctuation-driven price dynamics and investment strategies.

Investigation of the driven mechanism of the price dynamics in complex financial systems is important and challenging. In this paper, we propose an investment strategy to study how dynamic fluctuations drive the price movements. The strategy is successfully applied to different stock markets in the world, and the result indicates that the driving effect of the dynamic fluctuations is rather robust. We investigate how the strategy performance is influenced by the market states and optimize the strategy performance by introducing two parameters. The strategy is also compared with several typical technical trading rules. Our findings not only provide an investment strategy which extends investors' profits, but also offer a useful method to look into the dynamic properties of complex financial systems.

Dynamic Portfolio Strategy Using Clustering Approach.

The problem of portfolio optimization is one of the most important issues in asset management. We here propose a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: First, select the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, namely degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion. Second, use the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index to the total number of trading days, or the sum of the amplitudes of the trading days with rising index to the sum of the amplitudes of the total trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all possible optimal portfolio strategies based on different parameters to select portfolios and different criteria to identify market conditions, 65% of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market while the proportion is 70% for the Shenzhen A-Share market.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

How volatilities nonlocal in time affect the price dynamics in complex financial systems.

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.

Codon usage biases in Alzheimer's disease and other neurodegenerative diseases.

Establishing codon usage biases are crucial for understanding the etiology of central nervous system neurodegenerative diseases (CNSNDD) especially Alzheimer's disease (AD) as well as genetic factors. G and C ending codons are strongly biased in the coding sequences of these proteins as a result of genomic GC composition constraints. On the other hand, codons that identified as translationally optimal in the major trend all end in C or G, suggesting translational selection should also be taken into consideration additional to compositional constraints. Furthermore, this investigation reveals that three common codons, CGC (Arg), AGC (Ser), and GGC (Gly), are also critical in affecting codon usage bias. They not only can offer an insight into the codon usage bias of AD and its mechanism, but also may help in the possible cures for these diseases.

A novel approach to predict protein-protein interactions related to Alzheimer's disease based on complex network.

A protein domain interaction prediction program was complied using C(++) to predict whether a query protein could interact with other hub proteins. It is indicated that the proteins interacting with AATF (Apoptosis-Antagonizing Transcription Factor) possess a common conservative pattern: I-x(0,1)-E-x(2)-[A/E/N/T]-x-[E/K]. Our method is attempted to integrate the characteristics of protein interaction network with the simplest building blocks-conservative patterns.