139 research outputs found
Can Growth Compensate Inequality and Risk?---a welfare analysis for Chinese households
It has been widely observed that China's break-neck growth has not been equally shared between rural and urban areas, with urban households' enjoying a much larger proportion. To further test whether regional inequality exists within urban areas, we measure urban households vulnerability in a risky environment and decompose this measure to quantify China aggregate risks, province-level risks and idiosyncratic risks faced by households situated in 31 provinces. Besides, under this framework of analysis, we are able to make welfare comparisons between growth, inequality and different risks. We find that inequality has very big negative effect on households' welfare, while growth is able to compensate nearly half of it; households seem to be able to smooth consumption against risk in both province and individual level, but unable to do so against China shocks, which affect all the households simultaneously.Community/Rural/Urban Development, Risk and Uncertainty,
Quantum Brownian motion model for the stock market
It is believed by the majority today that the efficient market hypothesis is
imperfect because of market irrationality. Using the physical concepts and
mathematical structures of quantum mechanics, we construct an econophysics
framework for the stock market, based on which we analogously map massive
numbers of single stocks into a reservoir consisting of many quantum harmonic
oscillators and their stock index into a typical quantum open system--a quantum
Brownian particle. In particular, the irrationality of stock transactions is
quantitatively considered as the Planck constant within Heisenberg's
uncertainty relationship of quantum mechanics in an analogous manner. We
analyze real stock data of Shanghai Stock Exchange of China and investigate
fat-tail phenomena and non-Markovian behaviors of the stock index with the
assistance of the quantum Brownian motion model, thereby interpreting and
studying the limitations of the classical Brownian motion model for the
efficient market hypothesis from a new perspective of quantum open system
dynamics
Credit Card Usage Among College Students in China
Postprint.Credit cards have become a common method of payment for college students in China. It is important that they form good credit card usage behaviors and build a good credit history early in their financial life. Using data collected from 10 universities in China, results of this study found that being financially dependent on their parents is negatively associated with Chinese college studentsâ ability to pay their credit card bills. The study also found that students with a high level of financial knowledge were less likely to take cash advances on their credit card. Implications for financial educators and parents as well as policymakers were provided.Includes bibliographical references
Quantum spatial-periodic harmonic model for daily price-limited stock markets
We investigate the behavior of stocks in daily price-limited stock markets by
purposing a quantum spatial-periodic harmonic model. The stock price is
presumed to oscillate and damp in a quantum spatial-periodic harmonic
oscillator potential well. Complicated non-linear relations including
inter-band positive correlation and intra-band negative correlation between the
volatility and the trading volume of stocks are derived by considering the
energy band structure of the model. The validity of price limitation is then
examined and abnormal phenomena of a price-limited stock market (Shanghai Stock
Exchange) of China are studied by applying our quantum model.Comment: 8 pages, 9 figure
Entanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting
Chaotic time series forecasting has been far less understood despite its
tremendous potential in theory and real-world applications. Traditional
statistical/ML methods are inefficient to capture chaos in nonlinear dynamical
systems, especially when the time difference between consecutive
steps is so large that a trivial, ergodic local minimum would most likely be
reached instead. Here, we introduce a new long-short-term-memory (LSTM)-based
recurrent architecture by tensorizing the cell-state-to-state propagation
therein, keeping the long-term memory feature of LSTM while simultaneously
enhancing the learning of short-term nonlinear complexity. We stress that the
global minima of chaos can be most efficiently reached by tensorization where
all nonlinear terms, up to some polynomial order, are treated explicitly and
weighted equally. The efficiency and generality of our architecture are
systematically tested and confirmed by theoretical analysis and experimental
results. In our design, we have explicitly used two different many-body
entanglement structures---matrix product states (MPS) and the multiscale
entanglement renormalization ansatz (MERA)---as physics-inspired tensor
decomposition techniques, from which we find that MERA generally performs
better than MPS, hence conjecturing that the learnability of chaos is
determined not only by the number of free parameters but also the tensor
complexity---recognized as how entanglement entropy scales with varying
matricization of the tensor.Comment: 12 pages, 7 figure
Scale-Free Networks beyond Power-Law Degree Distribution
Complex networks across various fields are often considered to be scale free
-- a statistical property usually solely characterized by a power-law
distribution of the nodes' degree . However, this characterization is
incomplete. In real-world networks, the distribution of the degree-degree
distance , a simple link-based metric of network connectivity similar to
, appears to exhibit a stronger power-law distribution than . While
offering an alternative characterization of scale-freeness, the discovery of
raises a fundamental question: do the power laws of and
represent the same scale-freeness? To address this question, here we
investigate the exact asymptotic {relationship} between the distributions of
and , proving that every network with a power-law distribution of
also has a power-law distribution of , but \emph{not} vice versa. This
prompts us to introduce two network models as counterexamples that have a
power-law distribution of but not , constructed using the
preferential attachment and fitness mechanisms, respectively. Both models show
promising accuracy by fitting only one model parameter each when modeling
real-world networks. Our findings suggest that is a more suitable
indicator of scale-freeness and can provide a deeper understanding of the
universality and underlying mechanisms of scale-free networks
Hidden Citations Obscure True Impact in Science
References, the mechanism scientists rely on to signal previous knowledge,
lately have turned into widely used and misused measures of scientific impact.
Yet, when a discovery becomes common knowledge, citations suffer from
obliteration by incorporation. This leads to the concept of hidden citation,
representing a clear textual credit to a discovery without a reference to the
publication embodying it. Here, we rely on unsupervised interpretable machine
learning applied to the full text of each paper to systematically identify
hidden citations. We find that for influential discoveries hidden citations
outnumber citation counts, emerging regardless of publishing venue and
discipline. We show that the prevalence of hidden citations is not driven by
citation counts, but rather by the degree of the discourse on the topic within
the text of the manuscripts, indicating that the more discussed is a discovery,
the less visible it is to standard bibliometric analysis. Hidden citations
indicate that bibliometric measures offer a limited perspective on quantifying
the true impact of a discovery, raising the need to extract knowledge from the
full text of the scientific corpus
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