Latest in q-fin.st

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Self Organizing Supply Chains for Micro-Prediction: Present and Future uses of the ROAR ProtocolJul 17 2019A multi-agent system is trialed as a means of crowd-sourcing inexpensive but high quality streams of predictions. Each agent is a microservice embodying statistical models and endowed with economic self-interest. The ability to fork and modify simple ... More
Multi-Level Order-Flow Imbalance in a Limit Order BookJul 14 2019We study the \emph{multi-level order-flow imbalance (MLOFI)}, which measures the net flow of buy and sell orders at different price levels in a limit order book (LOB). Using a recent, high-quality data set for 6 liquid stocks on Nasdaq, we use Ridge regression ... More
From quadratic Hawkes processes to super-Heston rough volatility models with Zumbach effectJul 14 2019Using microscopic price models based on Hawkes processes, it has been shown that under some no-arbitrage condition, the high degree of endogeneity of markets together with the phenomenon of metaorders splitting generate rough Heston-type volatility at ... More
Gittins' theorem under uncertaintyJul 12 2019We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based on the the theory of nonlinear expectations. We show that, under strong independence of the bandits and with some relaxation in the definition of optimality, ... More
Distributions of Historic Market Data -- Relaxation and CorrelationsJul 11 2019We show that, for a class of mean-reverting models, the correlation function of stochastic variance (squared volatility) contains only one -- relaxation -- parameter. We generalize and simplify the expression for leverage for this class of models. We ... More
Nonlinear price dynamics of S&P 100 stocksJul 09 2019The methodology presented provides a quantitative way to characterize investor behavior and price dynamics within a particular asset class and time period. The methodology is applied to a data set consisting of over 250,000 data points of the S&P 100 ... More
Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial marketsJul 09 2019We develop a new topological structure for the construction of a reinforcement learning model in the framework of financial markets. It is based on Lipschitz type extension of reward functions defined in metric spaces. Using some known states of a dynamical ... More
Artificial Intelligence Alter Egos: Who benefits from Robo-investing?Jul 08 2019Artificial intelligence, or AI, enhancements are increasingly shaping our daily lives. Financial decision-making is no exception to this. We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage ... More
Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatilityJul 05 2019Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information ... More
Financial Time Series Data Processing for Machine LearningJul 03 2019This article studies the financial time series data processing for machine learning. It introduces the most frequent scaling methods, then compares the resulting stationarity and preservation of useful information for trend forecasting. It proposes an ... More
Elicitability and Identifiability of Systemic Risk Measures and other Set-Valued FunctionalsJul 02 2019This paper is concerned with a two-fold objective. Firstly, we establish elicitability and identifiability results for systemic risk measures introduced in Feinstein, Rudloff and Weber (2017). Specifying the entire set of capital allocations adequate ... More
Comparative analysis of layered structures in empirical investor networks and cellphone communication networksJul 02 2019Empirical investor networks (EIN) proposed by \cite{Ozsoylev-Walden-Yavuz-Bildik-2014-RFS} are assumed to capture the information spreading path among investors. Here, we perform a comparative analysis between the EIN and the cellphone communication networks ... More
Identification of short-term and long-term time scales in stock markets and effect of structural breakJul 01 2019The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend ... More
Improved Forecasting of Cryptocurrency Price using Social SignalsJul 01 2019Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations ... More
Tracking VIX with VIX Futures: Portfolio Construction and PerformanceJun 29 2019We study a series of static and dynamic portfolios of VIX futures and their effectiveness to track the VIX index. We derive each portfolio using optimization methods, and evaluate its tracking performance from both empirical and theoretical perspectives. ... More
Detailed study of a moving average trading ruleJun 29 2019We present a detailed study of the performance of a trading rule that uses moving average of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports ... More
Dealing with Stochastic Volatility in Time Series Using the R Package stochvolJun 28 2019The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (MCMC) samplers to conduct inference by obtaining draws from the posterior ... More
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvolJun 28 2019Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial ... More
Correlators of Polynomial ProcessesJun 26 2019A process is polynomial if its extended generator maps any polynomial to a polynomial of equal or lower degree. Then its conditional moments can be calculated in closed form, up to the computation of the exponential of the so-called generator matrix. ... More
Lead-lag Relationships in Foreign Exchange MarketsJun 25 2019Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market ... More
Lead-lag Relationships in Foreign Exchange MarketsJun 25 2019Jun 26 2019Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market ... More
Dynamic time series clustering via volatility change-pointsJun 25 2019This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clustering ... More
Against the Norm: Modeling Daily Stock Returns with the Laplace DistributionJun 25 2019Modeling stock returns is not a new task for mathematicians, investors, and portfolio managers, but it remains a difficult objective due to the ebb and flow of stock markets. One common solution is to approximate the distribution of stock returns with ... More
Hybrid symbiotic organisms search feedforward neural net-works model for stock price predictionJun 23 2019The prediction of stock prices is an important task in economics, investment and financial decision-making. It has for several decades, spurred the interest of many researchers to design stock price predictive models. In this paper, the symbiotic organisms ... More
Hybrid symbiotic organisms search feedforward neural network model for stock price predictionJun 23 2019Jun 27 2019The prediction of stock prices is an important task in economics, investment and financial decision-making. It has for several decades, spurred the interest of many researchers to design stock price predictive models. In this paper, the symbiotic organisms ... More
BERT-based Financial Sentiment Index and LSTM-based Stock Return PredictabilityJun 21 2019Traditional sentiment construction in finance relies heavily on the dictionary-based approach, with a few exceptions using simple machine learning techniques such as Naive Bayes classifier. While the current literature has not yet invoked the rapid advancement ... More
Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio AllocationJun 21 2019Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) ... More
Investment Ranking Challenge: Identifying the best performing stocks based on their semi-annual returnsJun 20 2019In the IEEE Investment ranking challenge 2018, participants were asked to build a model which would identify the best performing stocks based on their returns over a forward six months window. Anonymized financial predictors and semi-annual returns were ... More
Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big DataJun 19 2019Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to ... More
Signatures of crypto-currency market decoupling from the ForexJun 18 2019Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum ... More
Multiscale cross--correlations and triangular arbitrage opportunities in the ForexJun 18 2019Multifractal Detrended Cross-Correlation methodology is applied to the foreign exchange (Forex) market. High frequency fluctuations of exchange rates of eight major world currencies over the period 2010--2018 are used to study cross-correlations. The ... More
Machine Learning on EPEX Order Books: Insights and ForecastsJun 14 2019Jun 27 2019This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected ... More
Machine Learning on EPEX Order Books: Insights and ForecastsJun 14 2019This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected ... More
Sparse Approximate Factor Estimation for High-Dimensional Covariance MatricesJun 13 2019We propose a novel estimation approach for the covariance matrix based on the $l_1$-regularized approximate factor model. Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness ... More
Time scales in stock marketsJun 13 2019Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify ... More
From asymptotic properties of general point processes to the ranking of financial agentsJun 12 2019We propose a general non-linear order book model that is built from the individual behaviours of the agents. Our framework encompasses Markovian and Hawkes based models. Under mild assumptions, we prove original results on the ergodicity and diffusivity ... More
Neural Network Models for Stock Selection Based on Fundamental AnalysisJun 12 2019Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference ... More
Selecting stock pairs for pairs trading while incorporating lead-lag relationshipJun 12 2019Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship ... More
Generalized Beta Prime Distribution: Stochastic Model of Economic Exchange and Properties of Inequality IndicesJun 11 2019We argue that a stochastic model of economic exchange, whose steady-state distribution is a Generalized Beta Prime (also known as GB2), and some unique properties of the latter, are the reason for GB2's success in describing wealth/income distributions. ... More
Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear FiltersJun 10 2019Jun 29 2019In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also ... More
Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear FiltersJun 10 2019In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also ... More
Clustering Degree-Corrected Stochastic Block Model with OutliersJun 07 2019For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm that includes a penalization term depending on the positive deviation of a node from ... More
Implied and Realized Volatility: A Study of Distributions and the Distribution of DifferenceJun 05 2019We study distributions of realized variance (squared realized volatility) and squared implied volatility, as represented by VIX and VXO indices. We find that Generalized Beta distribution provide the best fits. These fits are much more accurate for realized ... More
Conditional inference on the asset with maximum Sharpe ratioJun 03 2019Jun 09 2019We apply the procedure of Lee et al. to the problem of performing inference on the signal noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used ... More
Conditional inference on the asset with maximum Sharpe ratioJun 03 2019We apply the procedure of Lee et al. to the problem of performing inference on the signal noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used ... More
Optimal Dynamic Strategies on Gaussian ReturnsMay 31 2019Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters ... More
Towards Improved Generalization in Financial Markets with Synthetic Data GenerationMay 24 2019Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ... More
Real-time Prediction of Bitcoin Bubble CrashesMay 23 2019Jun 13 2019In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity ... More
Real-time Prediction of Bitcoin bubble CrashesMay 23 2019In the past decade, Bitcoin has become an emerging asset class well known to most people because of their extraordinary return potential in phases of extreme price growth and their unpredictable massive crashes. We apply the LPPLS confidence indicator ... More
Detection of Chinese Stock Market Bubbles with LPPLS Confidence IndicatorMay 23 2019This paper aims to present an advance bubble detection methodology based on LPPLS confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 ... More
Detection of Chinese Stock Market Bubbles with LPPLS Confidence IndicatorMay 23 2019Jun 13 2019We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the ... More
Diagnosis and Prediction of the 2015 Chinese Stock Market BubbleMay 23 2019In this study, we perform a detailed analysis of the 2015 financial bubble in the Chinese stock market by calibrating the Log Periodic Power Law Singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015. ... More
Diagnosis and Prediction of the 2015 Chinese Stock Market BubbleMay 23 2019Jun 13 2019In this study, we perform a novel analysis of the 2015 financial bubble in the Chinese stock market by calibrating the Log Periodic Power Law Singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015. ... More
Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine LearningMay 21 2019At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, ... More
Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock PredictionMay 18 2019Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, ... More
Cointegration in high frequency dataMay 17 2019In this paper, we consider a framework adapting the notion of cointegration when two asset prices are generated by a driftless It\^{o}-semimartingale featuring jumps with infinite activity, observed synchronously and regularly at high frequency. We develop ... More
Improving Regression-based Event Study Analysis Using a Topological Machine-learning MethodMay 16 2019This paper introduces a new correction scheme to a conventional regression-based event study method: a topological machine-learning approach with a self-organizing map (SOM).We use this new scheme to analyze a major market event in Japan and find that ... More
Approximation of Optimal Transport problems with marginal moments constraintsMay 14 2019Optimal Transport (OT) problems arise in a wide range of applications, from physics to economics. Getting numerical approximate solution of these problems is a challenging issue of practical importance. In this work, we investigate the relaxation of the ... More
Sustainable Investing and the Cross-Section of Maximum DrawdownMay 13 2019We use supervised learning to identify factors that predict the cross-section of maximum drawdown for stocks in the US equity market. Our data run from January 1980 to June 2018 and our analysis includes ordinary least squares, penalized linear regressions, ... More
Is Volatility Rough ?May 13 2019May 17 2019Rough volatility models are continuous time stochastic volatility models where the volatility process is driven by a fractional Brownian motion with the Hurst parameter smaller than half, and have attracted much attention since a seminal paper titled ... More
Is Volatility Rough ?May 13 2019Rough volatility models are continuous time stochastic volatility models where the volatility process is driven by a fractional Brownian motion with the Hurst parameter less than half, and have attracted much attention since a seminal paper titled "Volatility ... More
A Note on Bayesian Long-Term S&P 500 Factor InvestingMay 11 2019We fit a dynamic factor model: monthly inflation-adjusted S\&P 500 returns vs 10-year trailing earnings yield (the inverse of Shiller price-to-earnings ratio), 10-year trailing dividend yield, and 10-year real interest rate. We model these three factors ... More
A Three-state Opinion Formation Model for Financial MarketsMay 10 2019We propose a three-state microscopic opinion formation model for the purpose of simulating the dynamics of financial markets. In order to mimic the heterogeneous composition of the mass of investors in a market, the agent-based model considers two different ... More
Dependencies and systemic risk in the European insurance sector: Some new evidence based on copula-DCC-GARCH model and selected clustering methodsMay 08 2019The subject of the present article is the study of correlations between large insurance companies and their contribution to systemic risk in the insurance sector. Our main goal is to analyze the conditional structure of the correlation on the European ... More
Co-jumping of Treasury Yield Curve RatesMay 04 2019We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using ... More
Relevant Stylized Facts About Bitcoin: Fluctuations, First Return Probability, and Natural PhenomenaMay 03 2019Bitcoin is a digital financial asset that is devoid of a central authority. This makes it distinct from traditional financial assets in a number of ways. For instance, the total number of tokens is limited and it has not explicit use value. Nonetheless, ... More
Determining the number of factors in a forecast model by a random matrix test: cryptocurrenciesMay 02 2019We determine the number of statistically significant factors in a forecast model using a random matrices test. The applied forecast model is of the type of Reduced Rank Regression (RRR), in particular, we chose a flavor which can be seen as the Canonical ... More
Bessel-like birth-death processApr 30 2019We do consider models of the population or opinion dynamics which result in non-linear stochastic differential equations (SDEs) exhibiting spurious long-range memory. In this context, the correspondence between the description of birth-death processes ... More
Empirical facts characterizing banking crises: an analysis via binary time seriesApr 29 2019Various works have already showed that common shocks and cross-country financial linkages caused the banking systems of several countries to be highly interconnected with the result that during bad times, banking crises may arise simultaneously in different ... More
Rough volatility of BitcoinApr 28 2019Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst ... More
Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)Apr 25 2019This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of ... More
Copula estimation for nonsynchronous financial dataApr 23 2019Copula is a powerful tool to model multivariate data. Due to its several merits Copula modelling has become one of the most widely used methods to model financial data. We discuss the problem of modelling intraday financial data through Copula. The problem ... More
On the Time-Varying Efficiency of Cryptocurrency MarketsApr 20 2019This study examines whether the market efficiencies of major cryptocurrencies (e.g., Bitcoin, Ethereum, and Ripple) change over time based on the adaptive market hypothesis (AMH) of Lo (2004). In particular, we measure the degree of market efficiency ... More
Inefficiency of the Brazilian Stock Market: the IBOVESPA Future ContractsApr 19 2019We present some indications of inefficiency of the Brazilian stock market based on the existence of strong long-time cross-correlations with foreign markets and indices. Our results show a strong dependence on foreign markets indices as the S\&P 500 and ... More
A Dynamic Bayesian Model for Interpretable Decompositions of Market BehaviourApr 17 2019We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate ... More
A Dynamic Bayesian Model for Interpretable Decompositions of Market BehaviourApr 17 2019May 09 2019We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate ... More
A Weight-based Information Filtration Algorithm for Stock-Correlation NetworksApr 12 2019Several algorithms have been proposed to filter information on a complete graph of correlations across stocks to build a stock-correlation network. Among them the planar maximally filtered graph (PMFG) algorithm uses $3n-6$ edges to build a graph whose ... More
A memory-based method to select the number of relevant components in Principal Component AnalysisApr 11 2019Apr 16 2019We propose a new data-driven method to select the optimal number of relevant components in Principal Component Analysis (PCA). This new method applies to correlation matrices whose time autocorrelation function decays more slowly than an exponential, ... More
Feature Engineering for Mid-Price Prediction Forecasting with Deep LearningApr 10 2019Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we ... More
Feature Engineering for Mid-Price Prediction with Deep LearningApr 10 2019Apr 15 2019Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we ... More
Fractal Time Series Analysis of Social Network ActivitiesApr 10 2019In the work, a comparative correlation and fractal analysis of time series of Bitcoin crypto currency rate and community activities in social networks associated with Bitcoin was conducted. A significant correlation between the Bitcoin rate and the community ... More
Robust Mathematical Formulation and Implementation of Agent-Based Computational Economic Market ModelsApr 10 2019Monte Carlo Simulations of agent-based models in science and especially in the economic literature have become a widely used modeling approach. In many applications the number of agents is huge and the models are formulated as a large system of difference ... More
On the Co-movement of Crude, Gold Prices and Stock Index in Indian MarketApr 09 2019This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's ... More
A Theory of Information overload applied to perfectly efficient financial marketsApr 07 2019Before the massive spread of computer technology, information was far from complex. The development of technology shifted the paradigm: from individuals who faced scarce and costly information to individuals who face massive amounts of information accessible ... More
Blindfolded monkeys or financial analysts: who is worth your money?Apr 06 2019The efficient market hypothesis has been considered one of the most controversial arguments in finance, with the academia divided between who claims the impossibility of beating the market and who believes that it is possible to gain over the average ... More
Bitcoin Price Prediction: An ARIMA ApproachApr 04 2019Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention ... More
Bayesian prediction of jumps in large panels of time series dataMar 28 2019We take a new look at the problem of disentangling the volatility and jumps processes in a panel of stock daily returns. We first provide an efficient computational framework that deals with the stochastic volatility model with Poisson-driven jumps in ... More
Transaction Cost Analytics for Corporate BondsMar 21 2019Jun 07 2019With the rise of the electronic trading, corporate bond traders have access to data information of past trades. As a first step to automation, they have to start monitoring their own trades, and using past data to build a benchmark for the expected transaction ... More
Stylized Facts on Price Formation on Corporate Bonds and Best Execution AnalysisMar 21 2019The goal of this paper is to establish a benchmark for transaction cost analysis in bond trading for retail investors. Investors can use this benchmark to improve decisions when requesting quotes from dealers on electronic platforms. This benchmark is ... More
Dynamic Hurst Exponent in Time SeriesMar 19 2019The market efficiency hypothesis has been proposed to explain the behavior of time series of stock markets. The Black-Scholes model (B-S) for example, is based on the assumption that markets are efficient. As a consequence, it is impossible, at least ... More
Stylized facts of the Indian Stock MarketMar 13 2019Historical daily data for eleven years of the fifty constituent stocks of the NIFTY index traded on the National Stock Exchange have been analyzed to check for the stylized facts in the Indian market. It is observed that while some stylized facts of other ... More
Deep Learning in Asset PricingMar 11 2019Jun 12 2019We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear ... More
Deep Learning in Asset PricingMar 11 2019We estimate a general non-linear asset pricing model with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. Our crucial innovation is the use of the no-arbitrage condition ... More
Kernel Based Estimation of Spectral Risk MeasuresMar 08 2019Spectral risk measures (SRMs) belongs to the family of coherent risk measures. A natural estimator for the class of spectral risk measures (SRMs) has the form of $L$-statistics. In the literature, various authors have studied and derived the asymptotic ... More
Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE dataMar 05 2019Using the United Nations COMTRADE database we apply the reduced Google matrix (REGOMAX) algorithm to analyze the multiproduct world trade in years 2004-2016. Our approach allows to determine the trade balance sensitivity of a group of countries to a specific ... More
Data-driven Neural Architecture Learning For Financial Time-series ForecastingMar 05 2019Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data modalities often embed different nonlinear relationships which are difficult ... More
Multiscale Features of Cross Correlation of Price and Trading VolumeMar 05 2019Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there is mutual information and entanglement between price and volume. On the other hand, we are curious about scaling features of this entanglement and ... More
Cross-shareholding networks and stock price synchronicity: Evidence from ChinaMar 05 2019This paper investigates the effect of cross-shareholding on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross-shareholdings in terms of centrality in the ... More
Conditional Density Estimation with Neural Networks: Best Practices and BenchmarksMar 03 2019Apr 13 2019Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$. ... More
Conditional Density Estimation with Neural Networks: Best Practices and BenchmarksMar 03 2019Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$. ... More