Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

COMPUTER SCIENCE (1305 journals)            First | 1 2 3 4 5 6 7 | Last

Showing 201 - 400 of 872 Journals sorted alphabetically
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 12)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 11)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 1)
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Computational Toxicology     Hybrid Journal  
Computer     Full-text available via subscription   (Followers: 141)
Computer Aided Surgery     Open Access   (Followers: 5)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 19)
Computer Engineering and Applications Journal     Open Access   (Followers: 8)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 26)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization     Hybrid Journal  
Computer Music Journal     Hybrid Journal   (Followers: 18)
Computer Physics Communications     Hybrid Journal   (Followers: 9)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 15)
Computer Science and Information Technology     Open Access   (Followers: 12)
Computer Science Education     Hybrid Journal   (Followers: 16)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Review     Hybrid Journal   (Followers: 12)
Computer Standards & Interfaces     Hybrid Journal   (Followers: 3)
Computer Supported Cooperative Work (CSCW)     Hybrid Journal   (Followers: 8)
Computer-aided Civil and Infrastructure Engineering     Hybrid Journal   (Followers: 9)
Computer-Aided Design and Applications     Hybrid Journal   (Followers: 6)
Computers     Open Access   (Followers: 2)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 12)
Computers & Education     Hybrid Journal   (Followers: 92)
Computers & Electrical Engineering     Hybrid Journal   (Followers: 8)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 9)
Computers & Structures     Hybrid Journal   (Followers: 44)
Computers & Education Open     Open Access   (Followers: 3)
Computers & Industrial Engineering     Hybrid Journal   (Followers: 6)
Computers and Composition     Hybrid Journal   (Followers: 11)
Computers and Education: Artificial Intelligence     Open Access   (Followers: 5)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 7)
Computers and Geotechnics     Hybrid Journal   (Followers: 13)
Computers in Biology and Medicine     Hybrid Journal   (Followers: 10)
Computers in Entertainment     Hybrid Journal  
Computers in Human Behavior Reports     Open Access  
Computers in Industry     Hybrid Journal   (Followers: 7)
Computers in the Schools     Hybrid Journal   (Followers: 8)
Computers, Environment and Urban Systems     Hybrid Journal   (Followers: 11)
Computerworld Magazine     Free   (Followers: 2)
Computing     Hybrid Journal   (Followers: 2)
Computing and Software for Big Science     Hybrid Journal   (Followers: 1)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 31)
Computing Reviews     Full-text available via subscription   (Followers: 1)
Concurrency and Computation: Practice & Experience     Hybrid Journal  
Connection Science     Open Access  
Control Engineering Practice     Hybrid Journal   (Followers: 46)
Cryptologia     Hybrid Journal   (Followers: 3)
CSI Transactions on ICT     Hybrid Journal   (Followers: 2)
Cuadernos de Documentación Multimedia     Open Access  
Current Science     Open Access   (Followers: 117)
Cyber-Physical Systems     Hybrid Journal  
Cyberspace : Jurnal Pendidikan Teknologi Informasi     Open Access  
DAIMI Report Series     Open Access  
Data     Open Access   (Followers: 4)
Data & Policy     Open Access   (Followers: 3)
Data Science     Open Access   (Followers: 6)
Data Science and Engineering     Open Access   (Followers: 6)
Data Technologies and Applications     Hybrid Journal   (Followers: 217)
Data-Centric Engineering     Open Access   (Followers: 1)
Datenbank-Spektrum     Hybrid Journal   (Followers: 1)
Datenschutz und Datensicherheit - DuD     Hybrid Journal  
Decision Analytics     Open Access   (Followers: 3)
Decision Support Systems     Hybrid Journal   (Followers: 13)
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 33)
Digital Biomarkers     Open Access   (Followers: 1)
Digital Chemical Engineering     Open Access  
Digital Chinese Medicine     Open Access  
Digital Creativity     Hybrid Journal   (Followers: 11)
Digital Experiences in Mathematics Education     Hybrid Journal   (Followers: 3)
Digital Finance : Smart Data Analytics, Investment Innovation, and Financial Technology     Hybrid Journal   (Followers: 3)
Digital Geography and Society     Open Access  
Digital Government : Research and Practice     Open Access   (Followers: 1)
Digital Health     Open Access   (Followers: 10)
Digital Journalism     Hybrid Journal   (Followers: 8)
Digital Medicine     Open Access   (Followers: 3)
Digital Platform: Information Technologies in Sociocultural Sphere     Open Access   (Followers: 1)
Digital Policy, Regulation and Governance     Hybrid Journal   (Followers: 2)
Digital War     Hybrid Journal   (Followers: 2)
Digitale Welt : Das Wirtschaftsmagazin zur Digitalisierung     Hybrid Journal  
Digitális Bölcsészet / Digital Humanities     Open Access   (Followers: 2)
Disaster Prevention and Management     Hybrid Journal   (Followers: 30)
Discours     Open Access   (Followers: 1)
Discourse & Communication     Hybrid Journal   (Followers: 26)
Discover Internet of Things     Open Access   (Followers: 2)
Discrete and Continuous Models and Applied Computational Science     Open Access  
Discrete Event Dynamic Systems     Hybrid Journal   (Followers: 3)
Discrete Mathematics & Theoretical Computer Science     Open Access   (Followers: 1)
Discrete Optimization     Full-text available via subscription   (Followers: 7)
Displays     Hybrid Journal  
Distributed and Parallel Databases     Hybrid Journal   (Followers: 2)
e-learning and education (eleed)     Open Access   (Followers: 40)
Ecological Indicators     Hybrid Journal   (Followers: 22)
Ecological Informatics     Hybrid Journal   (Followers: 3)
Ecological Management & Restoration     Hybrid Journal   (Followers: 15)
Ecosystems     Hybrid Journal   (Followers: 33)
Edu Komputika Journal     Open Access   (Followers: 1)
Education and Information Technologies     Hybrid Journal   (Followers: 53)
Educational Philosophy and Theory     Hybrid Journal   (Followers: 10)
Educational Psychology in Practice: theory, research and practice in educational psychology     Hybrid Journal   (Followers: 13)
Educational Research and Evaluation: An International Journal on Theory and Practice     Hybrid Journal   (Followers: 7)
Educational Theory     Hybrid Journal   (Followers: 9)
Egyptian Informatics Journal     Open Access   (Followers: 5)
Electronic Commerce Research and Applications     Hybrid Journal   (Followers: 5)
Electronic Design     Partially Free   (Followers: 125)
Electronic Letters on Computer Vision and Image Analysis     Open Access   (Followers: 10)
Elektron     Open Access  
Empirical Software Engineering     Hybrid Journal   (Followers: 8)
Energy for Sustainable Development     Hybrid Journal   (Followers: 13)
Engineering & Technology     Hybrid Journal   (Followers: 23)
Engineering Applications of Computational Fluid Mechanics     Open Access   (Followers: 23)
Engineering Computations     Hybrid Journal   (Followers: 3)
Engineering Economist, The     Hybrid Journal   (Followers: 4)
Engineering Optimization     Hybrid Journal   (Followers: 19)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Enterprise Information Systems     Hybrid Journal   (Followers: 2)
Entertainment Computing     Hybrid Journal   (Followers: 2)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Environmental Communication: A Journal of Nature and Culture     Hybrid Journal   (Followers: 16)
EPJ Data Science     Open Access   (Followers: 10)
ESAIM: Control Optimisation and Calculus of Variations     Open Access   (Followers: 2)
Ethics and Information Technology     Hybrid Journal   (Followers: 64)
eTransportation     Open Access   (Followers: 1)
EURO Journal on Computational Optimization     Open Access   (Followers: 5)
EuroCALL Review     Open Access  
European Food Research and Technology     Hybrid Journal   (Followers: 8)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 3)
European Journal of Computational Mechanics     Hybrid Journal   (Followers: 1)
European Journal of Information Systems     Hybrid Journal   (Followers: 86)
European Journal of Law and Technology     Open Access   (Followers: 19)
European Journal of Political Theory     Hybrid Journal   (Followers: 28)
Evolutionary Computation     Hybrid Journal   (Followers: 11)
Fibreculture Journal     Open Access   (Followers: 9)
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 5)
Fixed Point Theory and Applications     Open Access  
Focus on Catalysts     Full-text available via subscription  
Focus on Pigments     Full-text available via subscription   (Followers: 3)
Focus on Powder Coatings     Full-text available via subscription   (Followers: 5)
Forensic Science International: Digital Investigation     Full-text available via subscription   (Followers: 319)
Formal Aspects of Computing     Hybrid Journal   (Followers: 3)
Formal Methods in System Design     Hybrid Journal   (Followers: 6)
Forschung     Hybrid Journal   (Followers: 1)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Databases     Full-text available via subscription   (Followers: 2)
Foundations and Trends® in Human-Computer Interaction     Full-text available via subscription   (Followers: 5)
Foundations and Trends® in Information Retrieval     Full-text available via subscription   (Followers: 30)
Foundations and Trends® in Networking     Full-text available via subscription   (Followers: 1)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Theoretical Computer Science     Full-text available via subscription   (Followers: 1)
Foundations of Computational Mathematics     Hybrid Journal  
Foundations of Computing and Decision Sciences     Open Access  
Frontiers in Computational Neuroscience     Open Access   (Followers: 23)
Frontiers in Computer Science     Open Access   (Followers: 1)
Frontiers in Digital Health     Open Access   (Followers: 4)
Frontiers in Digital Humanities     Open Access   (Followers: 7)
Frontiers in ICT     Open Access  
Frontiers in Neuromorphic Engineering     Open Access   (Followers: 2)
Frontiers in Research Metrics and Analytics     Open Access   (Followers: 4)
Frontiers of Computer Science in China     Hybrid Journal   (Followers: 2)
Frontiers of Environmental Science & Engineering     Hybrid Journal   (Followers: 3)
Frontiers of Information Technology & Electronic Engineering     Hybrid Journal  
Fuel Cells Bulletin     Full-text available via subscription   (Followers: 9)
Functional Analysis and Its Applications     Hybrid Journal   (Followers: 3)
Future Computing and Informatics Journal     Open Access  
Future Generation Computer Systems     Hybrid Journal   (Followers: 2)
Geo-spatial Information Science     Open Access   (Followers: 7)
Geoforum Perspektiv     Open Access   (Followers: 1)
GeoInformatica     Hybrid Journal   (Followers: 7)
Geoinformatics FCE CTU     Open Access   (Followers: 8)
GetMobile : Mobile Computing and Communications     Full-text available via subscription   (Followers: 1)
Government Information Quarterly     Hybrid Journal   (Followers: 28)
Granular Computing     Hybrid Journal  
Graphics and Visual Computing     Open Access  
Grey Room     Hybrid Journal   (Followers: 16)
Group Dynamics : Theory, Research, and Practice     Full-text available via subscription   (Followers: 15)
Groups, Complexity, Cryptology     Open Access   (Followers: 2)
HardwareX     Open Access  
Harvard Data Science Review     Open Access   (Followers: 3)
Health Services Management Research     Hybrid Journal   (Followers: 16)
Healthcare Technology Letters     Open Access  
High Frequency     Hybrid Journal  
High-Confidence Computing     Open Access   (Followers: 1)
Home Cultures     Full-text available via subscription   (Followers: 7)

  First | 1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
Computational Economics
Journal Prestige (SJR): 0.433
Citation Impact (citeScore): 1
Number of Followers: 12  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-9974 - ISSN (Online) 0927-7099
Published by Springer-Verlag Homepage  [2469 journals]
  • Portfolio Optimization Via Online Gradient Descent and Risk Control

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      Abstract: Abstract Since Markowitz’s initial contribution in 1952, portfolio selection has undoubtedly been one of the most challenging topics in finance. The development of online optimization techniques indicates that dynamic learning algorithms are an effective approach to portfolio construction, although they do not evaluate the risk associated with each investment decision. In this work, the performance of the well-known Online Gradient Descent (OGD) algorithm is evaluated in comparison with a proposed approach that incorporates portfolio risk using \(\beta \) control of portfolio assets modeled with the CAPM strategy and considering a time-varying \(\beta \) that follows a random walk. Thus, the traditional OGD algorithm and the OGD with \(\beta \) constraints are compared with the Uniform Constant Rebalanced Portfolio (UCRP) and two specific indexes for the Brazilian market, consisting of small caps and the assets belonging to the Bovespa index. The experiments have shown that \(\beta \) control, combined with an appropriate definition of the \(\beta \) interval by the investor, is an efficient strategy, regardless of market periods with gains or losses. Moreover, time-varying \(\beta \) has been shown to be an efficient measure to force the desired correlation with the market and also to reduce the volatility of the portfolio, especially during hazardous bear markets.
      PubDate: 2022-06-30
       
  • Quasi-Monte Carlo-Based Conditional Malliavin Method for Continuous-Time
           Asian Option Greeks

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      Abstract: Abstract Although many methods for computing the Greeks of discrete-time Asian options are proposed, few methods to calculate the Greeks of continuous-time Asian options are known. In this paper, we develop an integration by parts formula in the multi-dimensional Malliavin calculus, and apply it to obtain the Greeks formulae for continuous-time Asian options in the multi-asset situation. We combine the Malliavin method with the quasi-Monte Carlo method to calculate the Greeks in simulation. We discuss the asymptotic convergence of simulation estimates for the continuous-time Asian option Greeks obtained by Malliavin derivatives. We propose to use the conditional quasi-Monte Carlo method to smooth Malliavin Greeks, and show that the calculation of conditional expectations analytically is viable for many types of Asian options. We prove that the new estimates for Greeks have good smoothness. For binary Asian options, Asian call options and up-and-out Asian call options, for instance, our estimates are infinitely times differentiable. We take the gradient principal component analysis method as a dimension reduction technique in simulation. Numerical experiments demonstrate the large efficiency improvement of the proposed method, especially for Asian options with discontinuous payoff functions.
      PubDate: 2022-06-21
       
  • Optimal Limit Order Book Trading Strategies with Stochastic Volatility in
           the Underlying Asset

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      Abstract: Abstract In quantitative finance, there have been numerous new aspects and developments related with the stochastic control and optimization problems which handle the controlled variables of performing the behavior of a dynamical system to achieve certain objectives. In this paper, we address the optimal trading strategies via price impact models using Heston stochastic volatility framework including jump processes either in price or in volatility of the price dynamics with the aim of maximizing expected return of the trader by controlling the inventories. Two types of utility functions are considered: quadratic and exponential. In both cases, the remaining inventories of the market maker are charged with a liquidation cost. In order to achieve the optimal quotes, we control the inventory risk and follow the influence of each parameter in the model to the best bid and ask prices. We show that the risk metrics including profit and loss distribution (PnL), standard deviation and Sharpe ratio play important roles for the trader to make decisions on the strategies. We apply finite differences and linear interpolation as well as extrapolation techniques to obtain a solution of the nonlinear Hamilton-Jacobi-Bellman (HJB) equation. Moreover, we consider different cases on the modeling to carry out the numerical simulations.
      PubDate: 2022-06-17
       
  • Forecasting Forex Trend Indicators with Fuzzy Rough Sets

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      Abstract: Abstract We propose a machine-learning approach for Forex prices that forecasts trends in terms of whether or not the closing price will change for more than a threshold and whether that change is an increase or a decrease. Instead of using the prices as such, we carry out the forecast solely in terms of indicators that are popular among small-scale traders; our goal is to determine whether these convey sufficient information for a precise forecast for different change thresholds and horizons. Fuzzy rough sets are used to represent and select among multiple economic indicators and to construct a classifier to forecast price changes. High-quality forecasts are feasible for short horizons and for small thresholds of change for all fifteen currency pairs studied in the experiments.
      PubDate: 2022-06-15
       
  • A Comprehensive Study of Market Prediction from Efficient Market
           Hypothesis up to Late Intelligent Market Prediction Approaches

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      Abstract: Abstract This paper has scrutinized the process of testing market efficiency, data generation process and the feasibility of market prediction with a detailed, coherent and statistical approach. Furthermore, attempts are made to extract knowledge from S&P 500 market data with an emphasize on feature engineering. As such, different data representations are provided through different procedures, and their performance in knowledge extraction is discussed. Amongst the neural networks, Long Short-Term Memory has not been adequately experimented. LSTM, because of its intrinsic, considers the long-term and short-term memory in its computations. Thus, in this paper LSTM is further examined in return prediction and different preprocessing methods are tested to improve its accuracy. This study is conducted on market data during September-2000 to February-2021. In order to extend the amount of knowledge extracted from financial time series, and to select the best input features, the advantage of Principal Component Analyze, Random Forest, Wavelet and the LSTM’s own deep feature extraction procedure are taken, and 4 models are compiled. Subsequently, to validate the performance of the models, MAE, MSE, MAPE, CSP and CDCP are calculated. Results from Diebold Mariano test implied that although LSTM neural network has gained a lot of attention recently, it does not significantly perform better than the benchmark method in S&P 500 index return prediction. Yet, results from Wilcoxon signed rank test showed the significance of improvement in the predictions performed by the combination of Principal component analysis and LSTM.
      PubDate: 2022-06-14
       
  • Exploring Uncertainty, Sensitivity and Robust Solutions in Mathematical
           Programming Through Bayesian Analysis

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      Abstract: Abstract The paper examines the effect of uncertainty on the solution of mathematical programming problems, using Bayesian techniques. We show that the statistical inference of the unknown parameter lies in the solution vector itself. Uncertainty in the data is modeled using sampling models induced by constraints. In this context, the objective is used as prior, and the posterior is efficiently applied via Monte Carlo methods. The proposed techniques provide a new benchmark for robust solutions that are designed without solving mathematical programming problems. We illustrate the benefits of a problem with known solutions and their properties, while discussing the empirical aspects in a real-world portfolio selection problem.
      PubDate: 2022-06-10
       
  • Classifying the Variety of Customers’ Online Engagement for Churn
           Prediction with a Mixed-Penalty Logistic Regression

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      Abstract: Abstract Using big data to analyze consumer behavior can provide effective decision-making tools for preventing customer attrition (churn) in customer relationship management (CRM). Focusing on a CRM dataset with several different categories of factors that impact customer heterogeneity (i.e., usage of self-care service channels, service duration, and responsiveness to marketing actions), this research provides new predictive analytics of customer churn rate based on a machine learning method that enhances the classification of logistic regression by adding a mixed penalty term. The proposed penalized logistic regression prevents overfitting when dealing with big data and minimizes the loss function when balancing the cost from the median (absolute value) and mean (squared value) regularization. We show the analytical properties of the proposed method and its computational advantage in this research. In addition, we investigate the performance of the proposed method with a CRM dataset (that has a large number of features) under different settings by efficiently eliminating the disturbance of (1) least important features and (2) sensitivity from the minority (churn) class. Our empirical results confirm the expected performance of the proposed method in full compliance with the common classification criteria (i.e., accuracy, precision, and recall) for evaluating machine learning methods.
      PubDate: 2022-06-10
       
  • A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization

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      Abstract: Abstract Features, or contextual information, are additional data than can help predicting asset returns in financial problems. We propose a mean-risk portfolio selection problem that uses contextual information to maximize expected returns at each time period, weighing past observations via kernels based on the current state of the world. We consider yearly intervals for investment opportunities, and a set of indices that cover the most relevant investment classes. For those intervals, data scarcity is a problem that is often dealt with by making distribution assumptions. We take a different path and use distribution-free simulation techniques to populate our database. In our experiments we use the Conditional Value-at-Risk as our risk measure, and we work with data from 2007 until 2021 to evaluate our methodology. Our results show that, by incorporating features, the out-of-sample performance of our strategy outperforms the equally-weighted portfolio. We also generate diversified positions, and efficient frontiers that exhibit coherent risk-return patterns.
      PubDate: 2022-06-07
       
  • Predict Stock Prices Using Supervised Learning Algorithms and Particle
           Swarm Optimization Algorithm

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      Abstract: Abstract Forecasting the stock market has always been one of the challenges for stock market participants to make more profit. Among the problems of stock price forecasting, we can mention its dynamic nature, complexity and its dependence on factors such as the governing system of countries, emotions, economic conditions, inflation, and so on. In recent years, many studies have been conducted to predict the capital stock market using traditional techniques, machine learning algorithms and deep learning. The lower our forecast stock error, the More we can reduce investment risk and increase profitability. In this paper, we present a machine learning (ML) approach called support vector machine (SVM) that can be taught using existing data. SVM extracts knowledge between data and ultimately uses this knowledge to predict new stock data. We have also aimed to select the best SVM method parameters using the particle swarm optimization (PSO) algorithm to prevent over-fitting and improve forecast accuracy. Finally, we compare our proposed method (SVM-PSO) with several other methods, including support vector machine, artificial neural network (ANN) and LSTM. The results show that the proposed algorithm works better than other methods and in all cases, its forecast accuracy is above 90%.
      PubDate: 2022-06-05
       
  • A Deep Learning Based Numerical PDE Method for Option Pricing

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      Abstract: Abstract Proper pricing of options in the financial derivative market is crucial. For many options, it is often impossible to obtain analytical solutions to the Black–Scholes (BS) equation. Hence an accurate and fast numerical method is very beneficial for option pricing. In this paper, we use the Physics-Informed Neural Networks (PINNs) method recently developed by Raissi et al. (J Comput Phys 378:686–707, 2019) to solve the BS equation. Many experiments have been carried out for solving various option pricing models. Compared with traditional numerical methods, the PINNs based method is simple in implementation, but with comparable accuracy and computational speed, which illustrates a promising potential of deep neural networks for solving more complicated BS equations.
      PubDate: 2022-06-02
       
  • House Prices as a Result of Trading Activities: A Patient Trader Model

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      Abstract: Abstract We present a new modeling approach for house price movements as a consequence of the trading behavior of market agents. In our modeling approach, all agents are assumed to assign a personal threshold value to a (standardized) house and update the threshold value permanently by a continuous-time filtering procedure based on observing the quoted house prices and the resulting price movements. The traders then trade according to a threshold price strategy (try to sell if the personal threshold value is lower, try to buy if the personal threshold value is higher than the actually quoted house price). Our modeling approach and its resulting characteristics are illustrated via numerical examples that highlight certain realistic constellations between various traders.
      PubDate: 2022-06-01
       
  • DeepValue: A Comparable Framework for Value-Based Strategy by Machine
           Learning

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      Abstract: Abstract Value relevant analysis is one of the key stock trading strategies of stock investment which is based on financial statement that represents the intrinsic investing value of firms. Human analysts have dominated the value interpretation of companies so far, in spite of the numerous efforts being made by machine learning researchers nowadays. The complexity of hundreds of accounting terms in financial statements and the latent interaction among industrial contextual factors are hard to be integrated into machine learning approaches. The analysis of profitability and potential for specific companies are often unique and not applicable to the others. In this paper, we constitute a unified learning framework, named as DeepValue, for knowledge transfer from and value extraction of collective financial status for value-based investment strategy. We validate four kinds of feature set from 11 years of financial statements of 90 semiconductor companies in TWSE. It incorporates deep Learning surrogate function for feature mapping, Multitask Learning (MTL) for knowledge transfer and feature sharing, and Long Short-Term Memory (LSTM) for value extraction. This value is then used to assess the degree of value-price gaps for stock selection. Experiment results demonstrate that the proposed framework is able to quantify the potential of stocks. The derived recommendation lists significantly outperform market and EPS-based selection.
      PubDate: 2022-06-01
       
  • Menu Optimization for Multi-Profile Customer Systems on Large Scale Data

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      Abstract: Abstract Everyday, a majority of the people, most probably several times, use the banking applications through online applications or physical ATM (Automated Teller Machine) devices for managing their financial transactions. However, most financial institutions provide static user interfaces regardless of the needs for different customers. Saving even a few seconds for each transaction through more personalized interface design might not only result in higher efficiency, but also result in customer satisfaction and increased market share among the competitors. In ATM Graphical User Interface (GUI) design, transaction completion time is, arguably, one of the most important metrics to quantify customer satisfaction. Optimizing GUI menu structures has been pursued and many heuristic techniques for this purpose are present. However, menu optimization by employing an exact mathematical optimization framework has never been performed in the literature. We cast the ATM menu optimization problem as a Mixed Integer Programming (MIP) framework. All the parameters of the MIP framework are derived from a comprehensive actual ATM menu usage database. We also proposed two heuristic approaches to reduce the computational complexity. Our solution can be accustomed with ergonomic factors and can easily be tailored for optimization of various menu design problems. Performance evaluations of our solutions by using actual ATM data reveal the superior performance of our optimization solution.
      PubDate: 2022-06-01
       
  • Optimized Machine Learning Algorithms for Investigating the Relationship
           Between Economic Development and Human Capital

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      Abstract: Abstract In Economic Development, human capital was previously seen as production factors but gradually evolved into endogenous growth theories. Most of the previous studies have examined the relationships between economic development and human capital via econometric models. Since this relationship is usually nonlinear and machine learning (ML) models can resolve it better, this study investigates the relationships by employing ML methods to provide a new perspective. For this purpose, the optimized ML methods, namely Bayesian Tuned Support Vector Machine and Bayesian Tuned Gaussian Process Regression (BT-GPR), were performed to develop the prediction model for economic development. The hyperparameters have been optimized with the Bayes method by using different kernel functions to increase SVM and GPR methods' predictive performance. The Multiple Linear Regression model has been employed to make a comparison as an econometric model. The performance of the models is evaluated using three statistical metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The BT-GPR with the exponential kernel model has superior prediction ability with the highest accuracy (R2: 0.9727, RMSE: 0.4022, MAE: 0.3728 in the testing phase). The study shows that the BT-GPR model increases the accuracy of R2 6.4%, RMSE 10.7%, and MAE 1% compared with other developed models.
      PubDate: 2022-06-01
       
  • Towards Crafting Optimal Functional Link Artificial Neural Networks with
           Rao Algorithms for Stock Closing Prices Prediction

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      Abstract: Abstract Quite a good number of population-based meta-heuristics based on mimicking natural phenomena are observed in the literature in resolving varieties of complex optimization problems. They are widely used in search of the optimal model parameters of artificial neural networks (ANNs). However, efficiencies of these are mostly dependent on fine tuning algorithm-specific parameters. Rao algorithms are metaphor-less meta-heuristics which do not need any algorithm-specific parameters. Functional link artificial neural network (FLANN) is a flat network and possesses the ability of mapping input–output nonlinear relationships by using amplification in input vector dimension. This article attempts to observe the efficacy of Rao algorithms on searching the most favorable parameters of FLANN, thus forming hybrid models termed as Rao algorithm-based FLANNs (RAFLANNs). The models are evaluated on forecasting five stock markets such as NASDAQ, BSE, DJIA, HSI, and NIKKEI. The RAFLANNs performances are compared with that of variations of FLANN (i.e., FLANN based on gradient descent, multi-verse optimizer, monarch butterfly optimization and genetic algorithm) and conventional models (i.e., MLP, SVM and ARIMA). The proposed models are found better in terms of prediction accuracy, computation time and statistical significance test.
      PubDate: 2022-06-01
       
  • The Effect of Including Irrelevant Alternatives in Discrete Choice Models
           of Recreation Demand

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      Abstract: Abstract We measure bias and efficiency of parameter estimates in the conditional logit (CL) and independent availability logit (IAL) models. Our Monte Carlo experiments consider both no choice set formation where individuals choose from the full set of alternatives, and when choice sets are stochastically formed and individuals choose from a subset of all alternatives. We also compare the performance of the two models using empirical data on paddlefish angler preferences and catch-and-release regulations in Oklahoma. Both the CL and IAL work well when their own assumptions hold, but not under the alternative’s assumptions. The IAL approximates the attribute-based cutoff well in empirical data. While neither the IAL nor the CL is universally preferred, based on our findings, we recommend the IAL when the true consideration sets are unknown.
      PubDate: 2022-06-01
       
  • Is Deep-Learning and Natural Language Processing Transcending the
           Financial Forecasting' Investigation Through Lens of News Analytic
           Process

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      Abstract: Abstract This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment index mechanism. Hourly price data has been used in this research as daily data is too late and minutes data is too early for getting the exclusive effect of sentiments. Normally, hourly data is extremely costly and difficult to manage and analyze. Hourly data has been rarely used in similar kinds of researches. To built sentiment index, text analytic information has been parsed and analyzed, textual information that is relevant to selected stocks has been collected, aggregated, categorized, and refined with NLP and eventually converted scientifically into hourly sentiment index. News analytic sources include mainstream media, print media, social media, news feeds, blogs, investors’ advisory portals, experts’ opinions, brokers updates, web-based information, company’ internal news and public announcements regarding policies and reforms. The results of the study indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h. Top ten companies from High-tech, financial, medical, automobile sectors are selected, and six LSTM models, three for using text-analytic and other without analytic are used. Every model includes 1, 3, and 6 h steps back. For all sectors, a 6-hour steps based model outperforms the other models due to LSTM specialty of keeping long term memory. Collective accuracy of textual analytic models is way higher relative to non-textual analytic models.
      PubDate: 2022-06-01
       
  • Need to Meet Investment Goals' Track Synthetic Indexes with the SDDP
           Method

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      Abstract: Abstract This work presents a novel application of the Stochastic Dual Dynamic Problem (SDDP) to large-scale asset allocation. We construct a model that delivers allocation policies based on how the portfolio performs with respect to user-defined (synthetic) indexes, and implement it in a SDDP open-source package. Based on US economic cycles and ETF data, we generate Markovian regime-dependent returns to solve an instance of multiple assets and 28 time periods. Results show our solution outperforms its benchmark, in both profitability and tracking error.
      PubDate: 2022-06-01
       
  • The Geometry of the World of Currency Volatilities

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      Abstract: Abstract Using empirical data and the properties they reveal, we develop a factor that captures changes of both currency implied correlation and volatilities. For this purpose, we apply the Guldin–Pappus theorem in Euclidean space for rotating triangles to construct a specific factor, which we define as gravity radius. This approach allows the construction of a portfolio index aggregating all currency pairwise trades. Our factor, which is a weighted sum of all gravity radius factors in a portfolio, exhibits characteristics that are similar to the well-known turbulence metric defined in the literature and has moderate correlation to the CBOE VIX index. This factor therefore can serve as a risk indicator. We argue that the changes in volatilities impact the gravity radius factor value considerably more than changes in correlations. Portfolio managers and risk managers can use the new metric to identify correlation and volatility changes that dynamically react to new information.
      PubDate: 2022-06-01
       
  • Numerically Pricing Nonlinear Time-Fractional Black–Scholes Equation
           with Time-Dependent Parameters Under Transaction Costs

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      Abstract: Abstract One of the assumptions of the classical Black–Scholes (B–S) is that the market is frictionless. Also, the classical B–S model cannot show the memory effect of the stock price in the financial markets. Previously, Ankudinova and Ehrhardt (Comput Math Appl 56:799–812, 2008) priced a European option under the classical B–S model with transaction costs when dividends are paid on assets during that period. But due to the importance of the trend memory effect in financial pricing, we extend Ankudinova’s and Ehrhardt’s study under the fractional B–S model when the price change of the underlying asset with time follows a fractal transmission system. The option price is governed by a time-fractional B–S equation of order \( 0<\alpha <1 \) . The main objective of this study is to obtain a numerical solution to determine the European option price with transaction costs based on the implicit difference scheme. This difference scheme is unconditionally stable and convergent and is shown stability and convergence by Fourier analysis. Numerical results and comparisons demonstrate that the introduced difference scheme has high accuracy and efficiency.
      PubDate: 2022-06-01
       
 
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