CNNPred: CNN-based stock market prediction using several data sources....
Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural...
View ArticleImproving Stock Movement Prediction with Adversarial Training....
This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that...
View ArticleUsing Deep Learning for price prediction by exploiting stationary limit order...
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new...
View ArticleExpropriations, Property Confiscations and New Offshore Entities: Evidence...
Using the Panama Papers, we show that the beginning of media reporting on expropriations and property confiscations in a country increases the probability that offshore entities are incorporated by...
View ArticleReligiosity and Terrorism: Evidence from Ramadan Fasting. (arXiv:1810.09869v1...
This study examines the effect of religiosity on terrorism by focusing on one of the five pillars of Islam: Ramadan fasting. For identification, we exploit two facts: First, daily fasting from dawn to...
View ArticleAsset allocation: new evidence through network approaches....
The main contribution of the paper is to employ the financial market network as a useful tool to improve the portfolio selection process, where nodes indicate securities and edges capture the...
View ArticleTerm structure modeling for multiple curves with stochastic discontinuities....
The goal of the paper is twofold. On the one hand, we develop the first term structure framework which takes stochastic discontinuities explicitly into account. Stochastic discontinuities are a key...
View ArticleFraming Discrete Choice Model as Deep Neural Network with Utility...
Deep neural network (DNN) has been increasingly applied to travel demand prediction. However, no study has examined how DNN relates to utility-based discrete choice models (DCM) beyond simple...
View ArticleThe Fatou property of law-invariant risk measures. (arXiv:1810.10374v1...
This paper presents several results on the Fatou property of quasiconvex law-invariant functionals defined on a rearrangement invariant space $\mathcal{X}$. First, we show that for any proper...
View ArticleDefining and estimating stochastic rate change in a dynamic general insurance...
Rate change calculations in the literature involve deterministic methods that measure the change in premium for a given policy. The definition of rate change as a statistical parameter is proposed to...
View ArticleThe Case for Formation of ISP-Content Providers Consortiums by Nash...
The formation of consortiums of a broadband access Internet Service Provider (ISP) and multiple Content Providers (CP) is considered for large-scale content caching. The consortium members share costs...
View ArticleA Relaxed Optimization Approach for Cardinality-Constrained Portfolio...
A cardinality-constrained portfolio caps the number of stocks to be traded across and within groups or sectors. These limitations arise from real-world scenarios faced by fund managers, who are...
View ArticleSpanning Tests for Markowitz Stochastic Dominance. (arXiv:1810.10800v1...
We derive properties of the cdf of random variables defined as saddle-type points of real valued continuous stochastic processes. This facilitates the derivation of the first-order asymptotic...
View ArticleHow Not To Do Mean-Variance Analysis. (arXiv:1810.10726v1 [q-fin.PM])
We use the 2014 market history of two high-returning biotechnology exchange-traded funds to illustrate how ex post mean-variance analysis should not be done. Unfortunately, the way it should not be...
View ArticleForecasting of Jump Arrivals in Stock Prices: New Attention-based Network...
The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for...
View ArticleThe Politics of Attention. (arXiv:1810.11449v1 [econ.GN])
We develop an equilibrium theory of attention and politics. In a spatial model of electoral competition where candidates have varying policy preferences, we examine what kinds of political behaviors...
View ArticleA Macroscopic Portfolio Model: From Rational Agents to Bounded Rationality....
We introduce a microscopic model of interacting financial agents, where each agent is characterized by two portfolios; money invested in bonds and money invested in stocks. Furthermore, each agent is...
View ArticlePrice Discovery and the Accuracy of Consolidated Data Feeds in the U.S....
Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor, the data feed consolidating all trades and quotes across the US stock...
View ArticleGeometrically Convergent Simulation of the Extrema of L\'{e}vy Processes....
We develop a novel Monte Carlo algorithm for the simulation from the joint law of the position, the running supremum and the time of the supremum of a general L\'{e}vy process at an arbitrary finite...
View ArticleOn the solution uniqueness in portfolio optimization and risk analysis....
We consider the issue of solution uniqueness for portfolio optimization problem and its inverse for asset returns with a finite number of possible scenarios. The risk is assessed by deviation measures...
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