pwc-archive/papers-with-abstracts huggingface dataset:
Transfer pricing manipulation, tax penalty cost and the impact of foreign profit taxation
Date: 2015-08-16 00:00:00

This paper analizes the optimal level of transfer pricing manipulation when
the expected tax penalty is a function of the tax enforcement and the market
price parameter. The arm's length principle implies the existence of a range of
acceptable prices shaped by market, and firms can manipulate transfer prices
more freely if market price range is wide, or if its delimitations are
difficult to determine. Home taxation of foreign profits can reduce income
shifting incentive, depending on the portion of repatriation for tax purposes.
We find that the limited tax credit rule tends to be a less efficient measure,
nonetheless it is the most widely adopted rule by countries, so to spark the
perspective of more powerful approaches for taxation of foreign profits.
Domain-specific queries and Web search personalization: some investigations
Authors: Hoang Van Tien  IMT Lucca, Italy, Spognardi Angelo  IIT-CNR, Pisa,
  Italy, Tiezzi Francesco  University of Camerino, Italy, Petrocchi Marinella  IIT-CNR, Pisa, Italy, De Nicola Rocco  IMT Lucca, Italy
Date: 2015-08-17 00:00:00

Major search engines deploy personalized Web results to enhance users'
experience, by showing them data supposed to be relevant to their interests.
Even if this process may bring benefits to users while browsing, it also raises
concerns on the selection of the search results. In particular, users may be
unknowingly trapped by search engines in protective information bubbles, called
"filter bubbles", which can have the undesired effect of separating users from
information that does not fit their preferences. This paper moves from early
results on quantification of personalization over Google search query results.
Inspired by previous works, we have carried out some experiments consisting of
search queries performed by a battery of Google accounts with differently
prepared profiles. Matching query results, we quantify the level of
personalization, according to topics of the queries and the profile of the
accounts. This work reports initial results and it is a first step a for more
extensive investigation to measure Web search personalization.
A new strain energy function for modelling ligaments and tendons whose fascicles have a helical arrangement of fibrils
Date: 2015-08-17 00:00:00

A new strain energy function for the hyperelastic modelling of ligaments and
tendons whose fascicles have a helical arrangement of fibrils is derived. The
stress-strain response of a single fascicle whose fibrils exhibit varying
levels of crimp throughout its radius is calculated and used to determine the
form of the strain energy function. The new constitutive law is used to model
uniaxial extension test data for human patellar tendon and is shown to provide
an excellent fit, with the average relative error being 9.8%. It is then used
to model shear and predicts that the stresses required to shear a tendon are
much smaller than those required to uniaxially stretch it to the same strain
level. Finally, the strain energy function is used to model ligaments and
tendons whose fascicles are helical, and the relative effects of the fibril
helix angle, the fascicle helix angle and the fibril crimp variable are
compared. It is shown that they all have a significant effect; the fibril crimp
variable governs the non-linearity of the stress-strain curve, whereas the
helix angles primarily affect its stiffness. Smaller values of the helix angles
lead to stiffer tendons; therefore, the model predicts that one would expect to
see fewer helical sub-structures in stiff positional tendons, and more in those
that are required to be more flexible.
In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales
Authors: Kolchyna Olga, Souza Th'arsis T. P., Aste Tomaso, Treleaven Philip C.
Date: 2015-08-17 00:00:00
Tasks: Clustering

We study the power of Twitter events to predict consumer sales events by
analysing sales for 75 companies from the retail sector and over 150 million
tweets mentioning those companies along with their sentiment. We suggest an
approach for events identification on Twitter extending existing methodologies
of event study. We also propose a robust method for clustering Twitter events
into different types based on their shape, which captures the varying dynamics
of information propagation through the social network. We provide empirical
evidence that through events differentiation based on their shape we can
clearly identify types of Twitter events that have a more significant power to
predict spikes in sales than the aggregated Twitter signal.
Web Content Extraction - a Meta-Analysis of its Past and Thoughts on its Future
Authors: Weninger Tim, Palacios Rodrigo, Crescenzi Valter, Gottron Thomas, Merialdo Paolo
Date: 2015-08-18 00:00:00
Tasks: Feature Engineering

In this paper, we present a meta-analysis of several Web content extraction
algorithms, and make recommendations for the future of content extraction on
the Web. First, we find that nearly all Web content extractors do not consider
a very large, and growing, portion of modern Web pages. Second, it is well
understood that wrapper induction extractors tend to break as the Web changes;
heuristic/feature engineering extractors were thought to be immune to a Web
site's evolution, but we find that this is not the case: heuristic content
extractor performance also tends to degrade over time due to the evolution of
Web site forms and practices. We conclude with recommendations for future work
that address these and other findings.
New Scaling Relation for Information Transfer in Biological Networks
Date: 2015-08-17 00:00:00
Tasks: Open-Ended Question Answering, Relation

Living systems are often described utilizing informational analogies. An
important open question is whether information is merely a useful conceptual
metaphor, or intrinsic to the operation of biological systems. To address this
question, we provide a rigorous case study of the informational architecture of
two representative biological networks: the Boolean network model for the
cell-cycle regulatory network of the fission yeast S. pombe and that of the
budding yeast S. cerevisiae. We compare our results for these biological
networks to the same analysis performed on ensembles of two different types of
random networks. We show that both biological networks share features in common
that are not shared by either ensemble. In particular, the biological networks
in our study, on average, process more information than the random networks.
They also exhibit a scaling relation in information transferred between nodes
that distinguishes them from either ensemble: even when compared to the
ensemble of random networks that shares important topological properties, such
as a scale-free structure. We show that the most biologically distinct regime
of this scaling relation is associated with the dynamics and function of the
biological networks. Information processing in biological networks is therefore
interpreted as an emergent property of topology (causal structure) and dynamics
(function). These results demonstrate quantitatively how the informational
architecture of biologically evolved networks can distinguish them from other
classes of network architecture that do not share the same informational
properties.
Beta Distribution of Human MTL Neuron Sparsity: A Sparse and Skewed Code
Date: 2015-08-18 00:00:00
Tasks: Hippocampus

Single unit recordings in the human medial temporal lobe (MTL) have revealed
a population of cells with conceptually based, highly selective activity,
indicating the presence of a sparse neural code. Building off previous work by
the author and J.C. Collins, this paper develops a statistical model for
analyzing this data, based on maximum likelihood analysis. The goal is to infer
the underlying distribution of neural response probabilities across the
population of MTL cells. The response probability, or neuronal sparsity, is
defined as the total probability that the neuron produces an above-threshold
firing rate during the presentation of a randomly selected stimulus. Applying
the method, it is shown that a beta-distributed neuronal sparsity across the
cells of the MTL is consistent with the data. The resulting fits reveal a
sparse and highly skewed code, with a huge majority of neurons exhibiting
extremely low response probabilities, and a smaller minority possessing
considerably higher response probabilities. The distributions are closely
approximated by a power law at low sparsity values. Strikingly similar skewed
distributions have been found in the statistics of place cell activity in rats,
suggesting similar underlying coding dynamics between the human MTL and the rat
hippocampus.
Why is GDP growth linear?
Date: 2015-08-18 00:00:00

In many European countries the growth of the real GDP per capita has been
linear since 1950. An explanation for this linearity is still missing. We
propose that in artificial intelligence we may find models for a linear growth
of performance. We also discuss possible consequences of the fact that in
systems with linear growth the percentage growth goes to zero.
FX Modelling in Collateralized Markets: foreign measures, basis curves, and pricing formulae
Date: 2015-09-14 00:00:00

We present a general derivation of the arbitrage-free pricing framework for
multiple-currency collateralized products. We include the impact on option
pricing of the policy adopted to fund in foreign currency, so that we are able
to price contracts with cash flows and/or collateral accounts expressed in
foreign currencies inclusive of funding costs originating from dislocations in
the FX market. Then, we apply these results to price cross-currency swaps under
different market situations, to understand how to implement a feasible curve
bootstrap procedure. We present the main practical problems arising from the
way the market is quoting liquid instruments: uncertainties about collateral
currencies and renotioning features. We discuss the theoretical requirements to
implement curve bootstrapping and the approximations usually taken to
practically implement the procedure. We also provide numerical examples based
on real market data.
State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems
Date: 2016-03-29 00:00:00
Tasks: State Estimation, State Space Models, Time Series, Time Series Analysis

State-space models (SSMs) are increasingly used in ecology to model
time-series such as animal movement paths and population dynamics. This type of
hierarchical model is often structured to account for two levels of
variability: biological stochasticity and measurement error. SSMs are flexible.
They can model linear and nonlinear processes using a variety of statistical
distributions. Recent ecological SSMs are often complex, with a large number of
parameters to estimate. Through a simulation study, we show that even simple
linear Gaussian SSMs can suffer from parameter- and state-estimation problems.
We demonstrate that these problems occur primarily when measurement error is
larger than biological stochasticity, the condition that often drives
ecologists to use SSMs. Using an animal movement example, we show how these
estimation problems can affect ecological inference. Biased parameter estimates
of a SSM describing the movement of polar bears (\textit{Ursus maritimus})
result in overestimating their energy expenditure. We suggest potential
solutions, but show that it often remains difficult to estimate parameters.
While SSMs are powerful tools, they can give misleading results and we urge
ecologists to assess whether the parameters can be estimated accurately before
drawing ecological conclusions from their results.
Forecasting stock market returns over multiple time horizons
Date: 2016-03-29 00:00:00

In this paper we seek to demonstrate the predictability of stock market
returns and explain the nature of this return predictability. To this end, we
introduce investors with different investment horizons into the news-driven,
analytic, agent-based market model developed in Gusev et al. (2015). This
heterogeneous framework enables us to capture dynamics at multiple timescales,
expanding the model's applications and improving precision. We study the
heterogeneous model theoretically and empirically to highlight essential
mechanisms underlying certain market behaviors, such as transitions between
bull- and bear markets and the self-similar behavior of price changes. Most
importantly, we apply this model to show that the stock market is nearly
efficient on intraday timescales, adjusting quickly to incoming news, but
becomes inefficient on longer timescales, where news may have a long-lasting
nonlinear impact on dynamics, attributable to a feedback mechanism acting over
these horizons. Then, using the model, we design algorithmic strategies that
utilize news flow, quantified and measured, as the only input to trade on
market return forecasts over multiple horizons, from days to months. The
backtested results suggest that the return is predictable to the extent that
successful trading strategies can be constructed to harness this
predictability.
Designating market maker behaviour in Limit Order Book markets
Date: 2015-08-18 00:00:00
Tasks: regression

Financial exchanges provide incentives for limit order book (LOB) liquidity
provision to certain market participants, termed designated market makers or
designated sponsors. While quoting requirements typically enforce the activity
of these participants for a certain portion of the day, we argue that liquidity
demand throughout the trading day is far from uniformly distributed, and thus
this liquidity provision may not be calibrated to the demand. We propose that
quoting obligations also include requirements about the speed of liquidity
replenishment, and we recommend use of the Threshold Exceedance Duration (TED)
for this purpose. We present a comprehensive regression modelling approach
using GLM and GAMLSS models to relate the TED to the state of the LOB and
identify the regression structures that are best suited to modelling the TED.
Such an approach can be used by exchanges to set target levels of liquidity
replenishment for designated market makers.
Implied volatility in strict local martingale models
Date: 2015-08-18 00:00:00

We consider implied volatilities in asset pricing models, where the
discounted underlying is a strict local martingale under the pricing measure.
Our main result gives an asymptotic expansion of the right wing of the implied
volatility smile and shows that the strict local martingale property can be
determined from this expansion. This result complements the well-known
asymptotic results of Lee and Benaim-Friz, which apply only to true
martingales. This also shows that `price bubbles' in the sense of strict local
martingale behaviour can in principle be detected by an analysis of implied
volatility. Finally we relate our results to left-wing expansions of implied
volatilities in models with mass at zero by a duality method based on an
absolutely continuous measure change.
Dynamical System Modeling to Simulate Donor T Cell Response to Whole Exome Sequencing-Derived Recipient Peptides Demonstrates Different Alloreactivity Potential In HLA-Matched and Mismatched Donor-Recipient Pairs
Date: 2015-08-16 00:00:00

Stem cell transplants may be considered as dynamical systems to allow
sequence differences across the exomes of the transplant donors and recipients
to be used to simulate an alloreactive T cell response. Whole exome sequencing
was performed on HLA matched stem cell transplant donor-recipient pairs, and
the nucleotide sequence differences translated to peptides. The binding
affinity of the peptides to the relevant HLA in each pair was determined. The
resulting array of peptide-HLA binding affinity values in each patient was used
to simulate an alloreactive donor derived T cell repertoire. This simulated T
cell repertoire reproduces a number of features of clinically observed T cell
repertoire. The simulated, alloreactive T cell repertoire was markedly
different in HLA matched stem cell transplant donors and recipients and
demonstrates a possible correlation with survival.
Testing randomness for cancer risk
Date: 2015-08-17 00:00:00

There are numerous stochastic models for cancer risk for a given tissue. Many
rely on the following two hypotheses. 1. There is a fixed probability that a
given cell division will eventually lead to a cancerous cell. 2. Cell divisions
are nefarious or not independently of each other. We show that recent data on
cancer risk and number of stem divisions is consistent with hypotheses 1 and 2.
Dynamic Mode Decomposition for Financial Trading Strategies
Date: 2015-08-18 00:00:00
Tasks: Algorithmic Trading

We demonstrate the application of an algorithmic trading strategy based upon
the recently developed dynamic mode decomposition (DMD) on portfolios of
financial data. The method is capable of characterizing complex dynamical
systems, in this case financial market dynamics, in an equation-free manner by
decomposing the state of the system into low-rank terms whose temporal
coefficients in time are known. By extracting key temporal coherent structures
(portfolios) in its sampling window, it provides a regression to a best fit
linear dynamical system, allowing for a predictive assessment of the market
dynamics and informing an investment strategy. The data-driven analytics
capitalizes on stock market patterns, either real or perceived, to inform
buy/sell/hold investment decisions. Critical to the method is an associated
learning algorithm that optimizes the sampling and prediction windows of the
algorithm by discovering trading hot-spots. The underlying mathematical
structure of the algorithms is rooted in methods from nonlinear dynamical
systems and shows that the decomposition is an effective mathematical tool for
data-driven discovery of market patterns.
LIBOR troubles: anomalous movements detection based on Maximum Entropy
Date: 2015-08-19 00:00:00
Tasks: Articles

According to the definition of the London Interbank Offered Rate (LIBOR),
contributing banks should give fair estimates of their own borrowing costs in
the interbank market. Between 2007 and 2009, several banks made inappropriate
submissions of LIBOR, sometimes motivated by profit-seeking from their trading
positions. In 2012, several newspapers' articles began to cast doubt on LIBOR
integrity, leading surveillance authorities to conduct investigations on banks'
behavior. Such procedures resulted in severe fines imposed to involved banks,
who recognized their financial inappropriate conduct. In this paper, we uncover
such unfair behavior by using a forecasting method based on the Maximum Entropy
principle. Our results are robust against changes in parameter settings and
could be of great help for market surveillance.
Memetics and Neural Models of Conspiracy Theories
Date: 2015-08-19 00:00:00

Conspiracy theories, or in general seriously distorted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than science. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotional arousal increases neuroplasticity and leads to creation of new pathways spreading neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal characteristics of the incoming information and the plasticity of the network, memory may self-organize creating memes with large attractor basins, linking many unrelated input patterns. Memes with fake rich associations distort relations between memory states. Simulations of various neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary data lead to the same conclusion: short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins, distorting input pattern representations in associative memory. Such system-level models may be used to understand creation of distorted beliefs and formation of conspiracy memes, understood as strong attractor states of the neurodynamics.
Fast {\large\it Cl-}type inhibitory neuron with delayed feedback has non-markov output statistics
Date: 2017-12-19 00:00:00

For a class of fast {\it Cl-}type inhibitory spiking neuron models with
delayed feedback fed with a Poisson stochastic process of excitatory impulses,
it is proven that the stream of output interspike intervals cannot be presented
as a Markov process of any order.
Cell-cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis
Date: 2015-08-19 00:00:00
Tasks: Sensitivity

Collective cell responses to exogenous cues depend on cell-cell interactions.
In principle, these can result in enhanced sensitivity to weak and noisy
stimuli. However, this has not yet been shown experimentally, and, little is
known about how multicellular signal processing modulates single cell
sensitivity to extracellular signaling inputs, including those guiding complex
changes in the tissue form and function. Here we explored if cell-cell
communication can enhance the ability of cell ensembles to sense and respond to
weak gradients of chemotactic cues. Using a combination of experiments with
mammary epithelial cells and mathematical modeling, we find that multicellular
sensing enables detection of and response to shallow Epidermal Growth Factor
(EGF) gradients that are undetectable by single cells. However, the advantage
of this type of gradient sensing is limited by the noisiness of the signaling
relay, necessary to integrate spatially distributed ligand concentration
information. We calculate the fundamental sensory limits imposed by this
communication noise and combine them with the experimental data to estimate the
effective size of multicellular sensory groups involved in gradient sensing.
Functional experiments strongly implicated intercellular communication through
gap junctions and calcium release from intracellular stores as mediators of
collective gradient sensing. The resulting integrative analysis provides a
framework for understanding the advantages and limitations of sensory
information processing by relays of chemically coupled cells.
The (in)visible hand in the Libor market: an Information Theory approach
Date: 2015-08-19 00:00:00
Tasks: Time Series, Time Series Analysis

This paper analyzes several interest rates time series from the United
Kingdom during the period 1999 to 2014. The analysis is carried out using a
pioneering statistical tool in the financial literature: the complexity-entropy
causality plane. This representation is able to classify different stochastic
and chaotic regimes in time series. We use sliding temporal windows to assess
changes in the intrinsic stochastic dynamics of the time series. Anomalous
behavior in the Libor is detected, especially around the time of the last
financial crisis, that could be consistent with data manipulation.
On Contextuality in Behavioral Data
Date: 2016-03-24 00:00:00

Dzhafarov, Zhang, and Kujala (Phil. Trans. Roy. Soc. A 374, 20150099)
reviewed several behavioral data sets imitating the formal design of the
quantum-mechanical contextuality experiments. The conclusion was that none of
these data sets exhibited contextuality if understood in the generalized sense
proposed in Dzhafarov, Kujala, and Larsson (Found. Phys. 7, 762-782, 2015),
while the traditional definition of contextuality does not apply to these data
because they violate the condition of consistent connectedness (also known as
marginal selectivity, no-signaling condition, no-disturbance principle, etc.).
In this paper we clarify the relationship between (in)consistent connectedness
and (non)contextuality, as well as between the traditional and extended
definitions of (non)contextuality, using as an example the
Clauser-Horn-Shimony-Holt (CHSH) inequalities originally designed for detecting
contextuality in entangled particles.