scholarly journals Formation of Investment Portfolios of Two Assets Based on Forecast Returns Using the ARFIMA-GARCH Model

2021 ◽  
pp. 130-136
Author(s):  
Robert Garafutdinov
Keyword(s):  
Programming Languages ◽  
Forecast Error ◽  
Garch Model ◽  
Real Data ◽  
Significant Difference ◽  
R Programming ◽  
Arfima Model ◽  
Russian Stock Market ◽  
Testing Algorithm ◽  
Investment Portfolios

The paper tests the hypothesis that the formation of investment portfolios of two assets based on predicted returns obtained using fractal models with conditional heteroscedasticity (ARFIMA-GARCH) allows to obtain portfolios with better characteristics than those obtained using the ARFIMA model. A computational experiment on artificial data and real data from the Russian stock market was carried out. The software implementation of the hypothesis testing algorithm was carried out using Python and R programming languages. The following results were obtained. Average absolute forecast error of the ARFIMA-GARCH model differs from the ARFIMA model error within the limits of error, statistically significant difference is not revealed (it is true for both model and real data). At the same time, portfolios formed using the GARCH model have, on average, higher returns, and a better return to risk ratio in comparison with portfolios formed using the ARFIMA model. Therefore, the hypothesis about the benefits of fractal GARCH models is not rejected.

Investigation of Timing Anomalies in the Russian Stock Market in the Post-Crisis Period

2019 ◽  
Vol 12 (3) ◽  
pp. 37-47
Author(s):  
I. Ya. Lukasevich
Keyword(s):  
Stock Market ◽  
Information Technologies ◽  
Real Data ◽  
Weak Form ◽  
Fair Value ◽  
Presidential Decree ◽  
Day Of The Week ◽  
Russian Stock Market ◽  
Economic Growth Rates ◽  
Global And Local

The implementation of the May presidential decree aimed at Russia’s joining the top five global economies and achieving economic growth rates above the world’s average while maintaining macroeconomic stability requires a highly developed and efficient stock market ensuring the accumulation of capital and its deployment in the most promising and productive sectors of the economy.The subject of the research is timing anomalies in the Russian stock market in 2012–2018. The relevance of the research is due to the information inefficiency of the Russian stock market and its imperfections leading to significant price deviations from the «fair» value of assets and depriving investors of the opportunity to form various strategies for deriving additional revenues not related to fundamental economic factors and objective processes occurring in the global and local economies and the economy of an individual business entity. Based on the trend analysis of the Broad Market USD Index (RUBMI), the paper demonstrates a methodology for simulating the analysis of price anomalies on large arrays of real data using statistical data processing methods and modern information technologies. The paper concludes that though the Russian stock market lacks even the weak form of efficiency, such well-known timing anomalies as the “day-of-the-week” effect and the “month” effect have not been observed in the recent years. Therefore, investors could not use these anomalies to derive regular revenues above the market average.

Combining Webduino Programming With Situated Learning to Promote Computational Thinking, Motivation, and Satisfaction Among High School Students

2021 ◽  
pp. 073563312110399
Author(s):  
Ting-Ting Wu ◽  
Jian-Ming Chen
Keyword(s):  
High School ◽  
High School Students ◽  
Programming Languages ◽  
Situated Learning ◽  
Learning Strategy ◽  
Computational Thinking ◽  
Learning Motivation ◽  
School Students ◽  
Study Results ◽  
Significant Difference

Many countries have incorporated computational thinking (CT) and programming languages into their science and technology courses. Students can improve their CT ability by learning programming languages. Moreover, situated learning enables students to generate knowledge and master problem-solving skills through interaction with situations. This study incorporated Webduino learning and the situated learning strategy into a programming course and analyzed its impact on high school students’ CT ability, learning motivation, and course satisfaction. A quasi-experimental research method was adopted, wherein the experimental group was subjected to the situated learning strategy and the control group was subjected to a traditional teaching method. The study results revealed that integrating Webduino programming with situated learning could effectively improve five categories of CT skills; moreover, the activity models of situated learning enhanced the value and expectation dimensions of learning motivation. In addition, satisfaction with the course content and self-identity slightly improved. However, because teachers were required to elaborate on stories to promote learner engagement with life situations, the time available for programming was limited. Thus, no significant difference was observed in teaching satisfaction.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

Intuition and mood of investors in financial markets

2017 ◽  
Vol 30 (2) ◽  
pp. 231-248
Author(s):  
Robson Braga ◽  
Luiz Paulo Lopes Fávero ◽  
Renata Turola Takamatsu
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Real Data ◽  
Financial Assets ◽  
Risky Assets ◽  
Content Type ◽  
Binomial Regression ◽  
Investor Behaviour ◽  
Investment Portfolios

Purpose The purpose of this paper is to evaluate investor behaviour related to the timing of selling financial assets based on an intuitive evaluation of the current market trend and growth expectation. Design/methodology/approach The experiment involved 1,052 volunteer participants who made decisions about stock sales in an environment that simulated a home broker platform to negotiate stocks. Zero-inflated regression models were used. Findings The results show that investors’ attitudes, or beliefs, determine whether they will buy or keep risky assets in their investment portfolios; they may decide to sell such assets, even though market shows an upward trend. Such results make a new contribution to behavioural finance within the context of prospect theory and the disposition effect. Originality/value The originality of this paper lies in the use of new and innovative techniques (zero-inflated Poisson and negative binomial regression models) applied to real data obtained experimentally.

scholarly journals A New Strategy of Hybrid Models using ARIMA, ANN, and DWT in Time Series Modelling

2021 ◽  
Vol 25 (1) ◽  
pp. 27-50
Author(s):  
Tsung-Lin Li ◽  
◽  
Chen-An Tsai ◽  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Real Data ◽  
Hybrid Models ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Model Combining ◽  
Time Series Modelling ◽  
Significant Difference

Time series forecasting is a challenging task of interest in many disciplines. A variety of techniques have been developed to deal with the problem through a combination of different disciplines. Although various researches have proved successful for hybrid models, none of them carried out the comparisons with solid statistical test. This paper proposes a new stepwise model determination method for artificial neural network (ANN) and a novel hybrid model combining autoregressive integrated moving average (ARIMA) model, ANN and discrete wavelet transformation (DWT). Simulation studies are conducted to compare the performance of different models, including ARIMA, ANN, ARIMA-ANN, DWT-ARIMA-ANN and the proposed method, ARIMA-DWT-ANN. Also, two real data sets, Lynx data and cabbage data, are used to demonstrate the applications. Our proposed method, ARIMA-DWT-ANN, outperforms other methods in both simulated datasets and Lynx data, while ANN shows a better performance in the cabbage data. We conducted a two-way ANOVA test to compare the performances of methods. The results showed a significant difference between methods. As a brief conclusion, it is suggested to try on ANN and ARIMA-DWT-ANN due to their robustness and high accuracy. Since the performance of hybrid models may vary across data sets based on their ARIMA alike or ANN alike natures, they should all be considered when encountering a new data to reach an optimal performance.

scholarly journals Plotgardener: Cultivating precise multi-panel figures in R

2021 ◽  
Author(s):  
Nicole E Kramer ◽  
Eric S Davis ◽  
Craig D Wenger ◽  
Erika M Deoudes ◽  
Sarah M Parker ◽  
...  
Keyword(s):  
Programming Languages ◽  
Genomic Data ◽  
Data Access ◽  
Manuscript Preparation ◽  
Data Sets ◽  
New Paradigm ◽  
Link Type ◽  
Bioconductor Project ◽  
Invaluable Tool ◽  
R Programming

The R programming language is one of the most widely used programming languages for transforming raw genomic data sets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, aesthetics, and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline.AvailabilityPackage: https://bioconductor.org/packages/plotgardenerCode: https://github.com/PhanstielLab/plotgardenerDocumentation: https://phanstiellab.github.io/plotgardener/

scholarly journals Sparse STATIS-Dual via Elastic Net

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2094
Author(s):  
Carmen C. Rodríguez-Martínez ◽  
Mitzi Cubilla-Montilla ◽  
Purificación Vicente-Galindo ◽  
Purificación Galindo-Villardón
Keyword(s):  
Data Analysis ◽  
Programming Language ◽  
Multivariate Data Analysis ◽  
Real Data ◽  
Elastic Net ◽  
R Programming Language ◽  
Data Tables ◽  
R Programming ◽  
Penalty Technique ◽  
Data Analysis Methods

Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.

scholarly journals Delphi Method Supported by Forecasting Software

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 65
Author(s):  
Marcin Lawnik ◽  
Arkadiusz Banasik
Keyword(s):  
Time Series ◽  
Programming Languages ◽  
Delphi Method ◽  
Software Tool ◽  
Mathematical Tool ◽  
Key Factors ◽  
Programming Library ◽  
Multi Stage ◽  
Virtual Expert ◽  
R Programming

The Delphi method is one of the basic tools for forecasting values in various types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the form of descriptive statistics measures) and returns to the previous group of experts again, thus starting the next round of forecasting. The multi-stage prediction under the Delphi method allows for better stabilization of the results, which is extremely important in the process of forecasting. Experts in the forecasting process often have access to time series forecasting software but do not necessarily use it. Therefore, it seems advisable to add to the aggregate the value obtained using forecasting software. The advantage of this approach is in saving the time and costs of obtaining a forecast. That should be understood as a smaller burden on data analysts and the value of their work. According to the above mentioned key factors, the main contribution of the article is the use of a virtual expert in the form of a computer-enhanced mathematical tool, i.e., a programming library for a forecasting time series. The chosen software tool is the Prophet library—a Facebook tool that can be used in Python or R programming languages.

scholarly journals Forecasting Randomly Distributed Zero-Inflated Time Series

2017 ◽  
Vol 17 (1) ◽  
pp. 7-19
Author(s):  
Mariusz Doszyń
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Real Data ◽  
Economic Time Series ◽  
Times Series ◽  
Ex Post ◽  
Data Regression ◽  
Economic Time ◽  
The Times ◽  
A Company

Abstract The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many economic time series are randomly distributed, so it is not possible to estimate any kind of statistical or econometric models such as, for example, count data regression models. This is why in the article a new forecasting procedure based on the stochastic simulation is proposed. Before it is used, the randomness of the times series should be considered. The hypothesis stating the randomness of the times series with regard to both sales sequences or sales levels is verified. Moreover, in the article the ex post forecast error that could be computed also for a zero-inflated time series is proposed. All of the above mentioned parts were invented by the author. In the empirical example, the described procedure was applied to forecast the sales of products in a company located in the vicinity of Szczecin (Poland), so real data were analysed. The accuracy of the forecast was verified as well.

On the computational estimation of high order GARCH model

2021 ◽  
Vol 8 (4) ◽  
pp. 797-806
Author(s):  
A. Settar ◽  
◽  
N. I. Fatmi ◽  
M. Badaoui ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Monte Carlo Simulations ◽  
Garch Model ◽  
Real Data ◽  
Conditional Variance ◽  
High Order ◽  
Computational Estimation ◽  
Necessary And Sufficient ◽  
The Garch Model

To guarantee the non-negativity of the conditional variance of the GARCH process, it is sufficient to assume the non-negativity of its parameters. This condition was empirically violated besides rendering the GARCH model more restrictive. It was subsequently relaxed for some GARCH orders by necessary and sufficient constraints. In this paper, we generalized an approach for the QML estimation of the GARCH(p,q) parameters for all orders $p\geq 1$ and $q\geq1$ using a constrained Kalman filter. Such an approach allows a relaxed QML estimation of the GARCH without the need to identify and/or apply the relaxed constraints to the parameters. The performance of our method is demonstrated through Monte Carlo simulations and empirical applications to real data.

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