pwc-archive/papers-with-abstracts huggingface dataset:
Using Machine Learning and Alternative Data to Predict Movements in Market Risk
Authors: Thomas Dierckx, Jesse Davis, Wim Schoutens
Date: 2020-09-16 00:00:00
Tasks: Asset Management, BIG-bench Machine Learning, Management

Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years. Many financial variables such as stock price, historical volatility and trade volume have already been through extensive investigation. Remarkably, we found no existing research on the prediction of an asset's market implied volatility within this context. This forward-looking measure gauges the sentiment on the future volatility of an asset, and is deemed one of the most important parameters in the world of derivatives. The ability to predict this statistic may therefore provide a competitive edge to practitioners of market making and asset management alike. Consequently, in this paper we investigate Google News statistics and Wikipedia site traffic as alternative data sources to quantitative market data and consider Logistic Regression, Support Vector Machines and AdaBoost as machine learning models. We show that movements in market implied volatility can indeed be predicted through the help of machine learning techniques. Although the employed alternative data appears to not enhance predictive accuracy, we reveal preliminary evidence of non-linear relationships between features obtained from Wikipedia page traffic and movements in market implied volatility.
The impact of COVID-19 on the stock market crash risk in China
Authors: Zhifeng Liu, Toan Luu Duc Huynh, Peng-Fei Dai
Date: 2020-09-17 00:00:00

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.
Identification and Estimation of A Rational Inattention Discrete Choice Model with Bayesian Persuasion
Date: 2020-09-17 00:00:00

This paper studies the semi-parametric identification and estimation of a
rational inattention model with Bayesian persuasion. The identification
requires the observation of a cross-section of market-level outcomes. The
empirical content of the model can be characterized by three moment conditions.
A two-step estimation procedure is proposed to avoid computation complexity in
the structural model. In the empirical application, I study the persuasion
effect of Fox News in the 2000 presidential election. Welfare analysis shows
that persuasion will not influence voters with high school education but will
generate higher dispersion in the welfare of voters with a partial college
education and decrease the dispersion in the welfare of voters with a bachelors
degree.
Resource Allocation and Dithering of Bayesian Parameter Estimation Using Mixed-Resolution Data
Authors: Itai E. Berman, Tirza Routtenberg
Date: 2020-09-17 00:00:00
Tasks: parameter estimation, Quantization

Quantization of signals is an integral part of modern signal processing applications, such as sensing, communication, and inference. While signal quantization provides many physical advantages, it usually degrades the subsequent estimation performance that is based on quantized data. In order to maintain physical constraints and simultaneously bring substantial performance gain, in this work we consider systems with mixed-resolution, 1-bit quantized and continuous-valued, data. First, we describe the linear minimum mean-squared error (LMMSE) estimator and its associated mean-squared error (MSE) for the general mixed-resolution model. However, the MSE of the LMMSE requires matrix inversion in which the number of measurements defines the matrix dimensions and thus, is not a tractable tool for optimization and system design. Therefore, we present the linear Gaussian orthonormal (LGO) measurement model and derive a closed-form analytic expression for the MSE of the LMMSE estimator under this model. In addition, we present two common special cases of the LGO model: 1) scalar parameter estimation and 2) channel estimation in mixed-ADC multiple-input multiple-output (MIMO) communication systems. We then solve the resource allocation optimization problem of the LGO model with the proposed tractable form of the MSE as an objective function and under a power constraint using a one-dimensional search. Moreover, we present the concept of dithering for mixed-resolution models and optimize the dithering noise as part of the resource allocation optimization problem for two dithering schemes: 1) adding noise only to the quantized measurements and 2) adding noise to both measurement types. Finally, we present simulations that demonstrate the advantages of using mixed-resolution measurements and the possible improvement introduced with dithering and resource allocation.
Fixed Effects Binary Choice Models with Three or More Periods
Authors: Laurent Davezies, Xavier D'Haultfoeuille, Martin Mugnier
Date: 2020-09-17 00:00:00

We consider fixed effects binary choice models with a fixed number of periods $T$ and regressors without a large support. If the time-varying unobserved terms are i.i.d. with known distribution $F$, \cite{chamberlain2010} shows that the common slope parameter is point identified if and only if $F$ is logistic. However, he only considers in his proof $T=2$. We show that the result does not generalize to $T\geq 3$: the common slope parameter can be identified when $F$ belongs to a family including the logit distribution. Identification is based on a conditional moment restriction. Under restrictions on the covariates, these moment conditions lead to point identification of relative effects. If $T=3$ and mild conditions hold, GMM estimators based on these conditional moment restrictions reach the semiparametric efficiency bound. Finally, we illustrate our method by revisiting Brender and Drazen (2008).
Resistance of the cell wall to degradation is a critical parameter for isolation of high quality RNA from natural isolates of Bacillus subtilis
Authors: Jean-Sebastien Guez, François Coutte, Anne-Sophie Drucbert, Pierre-Marie Danzé, Philippe Jacques
Date: 2020-09-17 00:00:00

Natural isolates of Bacillus subtilis are known for their ability to produce a large panel of bioactive compounds. Unfortunately, their recalcitrance to conventional molecular techniques limits their transcript studies. In this work, diYculties to isolate RNA attributed to the cell wall were overcome, Wnally authorising powerful RT-PCR's.
Mean-variance portfolio selection with tracking error penalization
Authors: William Lefebvre, Gregoire Loeper, Huyên Pham
Date: 2020-09-17 00:00:00

This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in case of misspecified parameters, by "fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean-Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.
Marxism, Logic and the Rate of Profit
Authors: Robin Hirsch
Date: 2020-09-17 00:00:00

It is argued that Marxism, being based on contradictions, is an illogical method. More specifically, we present a rejection of Marx's thesis that the rate of profit has a long-term tendency to fall.
Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition
Authors: Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira
Date: 2020-09-17 00:00:00
Tasks: General Classification

This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT.
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network
Date: 2020-09-16 00:00:00
Tasks: image-classification, Image Classification, Unsupervised Image Classification

We present results of a deep photonic spiking convolutional neural network,
based on two-section VCSELs, targeting image classification. Training is based
on unsupervised spike-timing dependent plasticity, whereas neuron
time-multiplexing and ultra-fast response are exploited towards a a reduction
of the physical neuron count by 90%
High Performance Low Complexity Multitarget Tracking Filter for a Array of Non-directional Sensors
Date: 2020-09-16 00:00:00

This paper develops an accurate, efficient filter (called the `TT filter')
for tracking multiple targets using a spatially-distributed network of
amplitude sensors that estimate distance but not direction. Several innovations
are included in the algorithm that increase accuracy and reduce complexity. For
initial target acquisition once tracking begins, a constrained Hessian search
is used to find the maximum likelihood (ML) target vector, based on the
measurement model and a Gaussian approximation of the prior. The Hessian at the
ML vector is used to give an initial approximation of the negative log
likelihood for the target vector distribution: corrections are applied if the
Hessian is not positive definite due to the near-far problem. Further
corrections are made by applying a transformation that matches the known
nonlinearity introduced by distance-only sensors. A set of integration points
is constructed using this information, which are used to estimate the mean and
moments of the target vector distribution. Results show that the TT filter
gives superior accuracy and lower complexity than previous alternatives such as
Kalman-based or particle filters.
Effect Of Weather Conditions On FSO Link
Authors: Saumya Borwankar, Dhruv Shah
Date: 2020-09-17 00:00:00
Tasks: Sand

Free Space Optics (FSO) is a developing technology for Line of Sight communication that uses light propagation in free space that provides various advantages like high bandwidth, high data rate, ease of installation, free licensing and secure communication. Thus, FSO is a developing technology that can be used in numerous applications for Line of Sight Communication. But the diverse effects like attenuation on FSO communication link due to environmental factors and weather conditions like fog, rain, dust, sand storms, clouds, temperature and the other factors like range, effects of physical obstructions are an essential topic for study which is discussed in this paper. We have done the simulation for the effects of fog and rain on the FSO communication link in Opti system software [1]. This is submitted in leu of FOC assignment at Nirma University.
Novel Parameter Estimation and Radar Detection Approaches for Multiple Point-like Targets: Designs and Comparisons
Authors: Pia Addabbo, Jun Liu, Danilo Orlando, Giuseppe Ricci
Date: 2020-09-17 00:00:00
Tasks: parameter estimation

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.
Stability analysis of a signaling circuit with dual species of GTPase switches
Authors: Lucas M. Stolerman, Pradipta Ghosh, Padmini Rangamani
Date: 2020-09-17 00:00:00

GTPases are molecular switches that regulate a wide range of cellular processes, such as organelle biogenesis, position, shape, and signal transduction. These enzymes operate by toggling between an active ("ON") guanosine triphosphate (GTP)-bound state and an inactive ("OFF") guanosine diphosphate (GDP)-bound state; such a toggle is regulated by GEFs (guanine nucleotide exchange factors) and GAPs (GTPase activating proteins). Here we dissect a network motif between monomeric (m) and trimeric (t) GTPases assembled exclusively in eukaryotic cells of multicellular organisms. To this end, we develop a system of ordinary differential equations in which these two classes of GTPases are interlinked conditional to their ON/OFF states within a motif through feedforward and feedback loops. We provide formulas for the steady states of the system and perform local stability analysis to investigate the role of the different connections between the GTPase switches. A feedforward from the active mGTPase to the GEF of the tGTPase was sufficient to provide two locally stable states: one where both active/inactive forms of the mGTPase can be interpreted as having low concentrations and the other where both m- and tGTPase have high concentrations. When a feedback loop from the GEF of the tGTPase to the GAP of the mGTPase was added to the feedforward system, two other locally stable states emerged, both having the tGTPase inactivated and being interpreted as having low active tGTPase concentrations. Finally, the addition of a second feedback loop, from the active tGTPase to the GAP of the mGTPase, gives rise to a family of steady states parametrized by the inactive tGTPase concentrations. Our findings reveal that the coupling of these two different GTPase motifs can dramatically change their steady state behaviors and shed light on how such coupling may impact information processing in eukaryotic cells.
Dynamic causal modelling of immune heterogeneity
Date: 2020-09-17 00:00:00
Tasks: Epidemiology

An interesting inference drawn by some Covid-19 epidemiological models is
that there exists a proportion of the population who are not susceptible to
infection -- even at the start of the current pandemic. This paper introduces a
model of the immune response to a virus. This is based upon the same sort of
mean-field dynamics as used in epidemiology. However, in place of the location,
clinical status, and other attributes of people in an epidemiological model, we
consider the state of a virus, B and T-lymphocytes, and the antibodies they
generate. Our aim is to formalise some key hypotheses as to the mechanism of
resistance. We present a series of simple simulations illustrating changes to
the dynamics of the immune response under these hypotheses. These include
attenuated viral cell entry, pre-existing cross-reactive humoral
(antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally,
we illustrate the potential application of this sort of model by illustrating
variational inversion (using simulated data) of this model to illustrate its
use in testing hypotheses. In principle, this furnishes a fast and efficient
immunological assay--based on sequential serology--that provides a (i)
quantitative measure of latent immunological responses and (ii) a Bayes optimal
classification of the different kinds of immunological response (c.f., glucose
tolerance tests used to test for insulin resistance). This may be especially
useful in assessing SARS-CoV-2 vaccines.
Solving the Optimal Trading Trajectory Problem Using Simulated Bifurcation
Authors: Kyle Steinhauer, Takahisa Fukadai, Sho Yoshida
Date: 2020-09-17 00:00:00
Tasks: Portfolio Optimization

We use an optimization procedure based on simulated bifurcation (SB) to solve the integer portfolio and trading trajectory problem with an unprecedented computational speed. The underlying algorithm is based on a classical description of quantum adiabatic evolutions of a network of non-linearly interacting oscillators. This formulation has already proven to beat state of the art computation times for other NP-hard problems and is expected to show similar performance for certain portfolio optimization problems. Inspired by such we apply the SB approach to the portfolio integer optimization problem with quantity constraints and trading activities. We show first numerical results for portfolios of up to 1000 assets, which already confirm the power of the SB algorithm for its novel use-case as a portfolio and trading trajectory optimizer.
Spatio-Temporal Probabilistic Voltage Sensitivity Analysis - A Novel Framework for Hosting Capacity Analysis
Date: 2020-09-17 00:00:00
Tasks: Sensitivity

Smart grids are envisioned to accommodate high penetration of distributed photovoltaic (PV) generation, which may cause adverse grid impacts in terms of voltage violations. Therefore, PV Hosting capacity (HC) is being used as a planning tool to determine the maximum PV installation capacity that causes the first voltage violation and above which would require infrastructure upgrades. Traditional methods of HC analysis are computationally complex as they are based on iterative load flow algorithms that require investigation of a large number of scenarios for accurate assessment of PV impacts. This paper first presents a computationally efficient analytical approach to compute the probability distribution of voltage change at a particular node due to random behavior of randomly located multiple distributed PVs. Next, the derived distribution is used to identify voltage violations for various PV penetration levels and subsequently determine the HC of the system without the need to examine multiple scenarios. Results from the proposed spatio-temporal probabilistic voltage sensitivity analysis and the HC are validated via conventional load flow based simulation approach on the IEEE 37 and IEEE 123 node test systems.
Globular structure of the hypermineralized tissue in human femoral neck
Authors: Qiong Wang, Tengteng Tang, David Cooper, Felipe Eltit, Peter Fratzl, Pierre Guy, Rizhi Wang
Date: 2020-09-17 00:00:00

Bone becomes more fragile with ageing. Among many structural changes, a thin layer of highly mineralized and brittle tissue covers part of the external surface of the thin femoral neck cortex in older people and has been proposed to increase hip fragility. However, there have been very limited reports on this hypermineralized tissue in the femoral neck, especially on its ultrastructure. Such information is critical to understanding both the mineralization process and its contributions to hip fracture. Here, we use multiple advanced techniques to characterize the ultrastructure of the hypermineralized tissue in the neck across various length scales. Synchrotron radiation micro-CT found larger but less densely distributed cellular lacunae in hypermineralized tissue than in lamellar bone. When examined under FIB-SEM, the hypermineralized tissue was mainly composed of mineral globules with sizes varying from submicron to a few microns. Nano-sized channels were present within the mineral globules and oriented with the surrounding organic matrix. Transmission electron microscopy showed the apatite inside globules were poorly crystalline, while those at the boundaries between the globules had well-defined lattice structure with crystallinity similar to the apatite mineral in lamellar bone. No preferred mineral orientation was observed both inside each globule and at the boundaries. Collectively, we conclude based on these new observations that the hypermineralized tissue is non-lamellar and has less organized mineral, which may contribute to the high brittleness of the tissue.
Robust Asymptotic Growth in Stochastic Portfolio Theory under Long-Only Constraints
Authors: David Itkin, Martin Larsson
Date: 2020-09-17 00:00:00

We consider the problem of maximizing the asymptotic growth rate of an investor under drift uncertainty in the setting of stochastic portfolio theory (SPT). As in the work of Kardaras and Robertson we take as inputs (i) a Markovian volatility matrix $c(x)$ and (ii) an invariant density $p(x)$ for the market weights, but we additionally impose long-only constraints on the investor. Our principal contribution is proving a uniqueness and existence result for the class of concave functionally generated portfolios and developing a finite dimensional approximation, which can be used to numerically find the optimum. In addition to the general results outlined above, we propose the use of a broad class of models for the volatility matrix $c(x)$, which can be calibrated to data and, under which, we obtain explicit formulas of the optimal unconstrained portfolio for any invariant density.