Journal articles on the topic 'Learning – Econometric models'
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Kim, Dong-sup, and Seungwoo Shin. "THE ECONOMIC EXPLAINABILITY OF MACHINE LEARNING AND STANDARD ECONOMETRIC MODELS-AN APPLICATION TO THE U.S. MORTGAGE DEFAULT RISK." International Journal of Strategic Property Management 25, no. 5 (July 13, 2021): 396–412. http://dx.doi.org/10.3846/ijspm.2021.15129.
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This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements. The existing real estate literature has primarily used econometric models to analyze the factors that affect the default risk of mortgage loans. However, in this study, we estimate a default risk model using a machine learning-based approach with the help of a U.S. securitized mortgage loan database. Moreover, we compare the economic explainability of the models by calculating the marginal effect and marginal importance of individual risk factors using both econometric and machine learning approaches. Machine learning-based models are quite effective in terms of predictive power; however, the general perception is that they do not efficiently explain the causal relationships within them. This study utilizes the concepts of marginal effects and marginal importance to compare the explanatory power of individual input variables in various models. This can simultaneously help improve the explainability of machine learning techniques and enhance the performance of standard econometric methods.
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Liao, Ruofan, Paravee Maneejuk, and Songsak Sriboonchitta. "Beyond Deep Learning: An Econometric Example." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp01 (August 28, 2020): 31–38. http://dx.doi.org/10.1142/s0218488520400036.
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In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.
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Salmon, Timothy C. "An Evaluation of Econometric Models of Adaptive Learning." Econometrica 69, no. 6 (November 2001): 1597–628. http://dx.doi.org/10.1111/1468-0262.00258.
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Pérez-Pons, María E., Javier Parra-Dominguez, Sigeru Omatu, Enrique Herrera-Viedma, and Juan Manuel Corchado. "Machine Learning and Traditional Econometric Models: A Systematic Mapping Study." Journal of Artificial Intelligence and Soft Computing Research 12, no. 2 (April 1, 2021): 79–100. http://dx.doi.org/10.2478/jaiscr-2022-0006.
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Abstract Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject. Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications. Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.
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Zapata, Hector O., and Supratik Mukhopadhyay. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing." Journal of Risk and Financial Management 15, no. 11 (November 17, 2022): 535. http://dx.doi.org/10.3390/jrfm15110535.
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Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative bibliometric analysis of this growing literature using Web of Science (WoS) data. We identified trends in the literature over the past decade, the geographical distribution of articles, authorship, and institutional contributions worldwide. The paper also identifies the dominant literature using citations in WoS and discusses computational algorithms that are expanding the econometric frontiers in asset pricing. The top cited papers were reviewed, highlighting their contribution. The limitations of ML learning methods and recent advances in ML were used to provide a conic view to future ML econometric practice.
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Athey, Susan, and Guido W. Imbens. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11, no. 1 (August 2, 2019): 685–725. http://dx.doi.org/10.1146/annurev-economics-080217-053433.
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We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.
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Fan, Jianqing, Kunpeng Li, and Yuan Liao. "Recent Developments in Factor Models and Applications in Econometric Learning." Annual Review of Financial Economics 13, no. 1 (November 1, 2021): 401–30. http://dx.doi.org/10.1146/annurev-financial-091420-011735.
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This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.
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Shen, Ze, Qing Wan, and David J. Leatham. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN." Journal of Risk and Financial Management 14, no. 7 (July 20, 2021): 337. http://dx.doi.org/10.3390/jrfm14070337.
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One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.
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Ifft, Jennifer, Ryan Kuhns, and Kevin Patrick. "Can machine learning improve prediction – an application with farm survey data." International Food and Agribusiness Management Review 21, no. 8 (December 7, 2018): 1083–98. http://dx.doi.org/10.22434/ifamr2017.0098.
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Businesses, researchers, and policymakers in the agricultural and food sector regularly make use of large public, private, and administrative datasets for prediction, including forecasting, public policy targeting, and management research. Machine learning has the potential to substantially improve prediction with these datasets. In this study we demonstrate and evaluate several machine learning models for predicting demand for new credit with the 2014 Agricultural Resource Management Survey. Many, but not all, of the machine learning models used are shown to have stronger predictive power than standard econometric approaches. We provide a cost based model evaluation approach for managers to analyze returns to machine learning methods relative to standard econometric approaches. While there are potentially significant returns to machine learning methods, research objectives and firm-level costs are important considerations that in some cases may favor standard econometric approaches.
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Rondina, Francesca. "An Econometric Learning Approach to Approximate Expectations in Empirical Macro Models." International Advances in Economic Research 23, no. 4 (November 2017): 437–38. http://dx.doi.org/10.1007/s11294-017-9662-8.
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Storm, Hugo, Kathy Baylis, and Thomas Heckelei. "Machine learning in agricultural and applied economics." European Review of Agricultural Economics 47, no. 3 (August 21, 2019): 849–92. http://dx.doi.org/10.1093/erae/jbz033.
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AbstractThis review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.
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Jia, Fang, and Boli Yang. "Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function." Complexity 2021 (February 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5511802.
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Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.
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Ertuğrul, Hasan Murat, Mustafa Tevfik Kartal, Serpil Kılıç Depren, and Uğur Soytaş. "Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models." Energies 15, no. 20 (October 12, 2022): 7512. http://dx.doi.org/10.3390/en15207512.
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The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed.
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Chlebus, Marcin, Michał Dyczko, and Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem." Central European Economic Journal 8, no. 55 (January 1, 2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.
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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
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Shin, Sun-Youn, and Han-Gyun Woo. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms." Energies 15, no. 13 (July 2, 2022): 4880. http://dx.doi.org/10.3390/en15134880.
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In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data.
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Ulussever, Talat, Hasan Murat Ertuğrul, Serpil Kılıç Depren, Mustafa Tevfik Kartal, and Özer Depren. "Estimation of Impacts of Global Factors on World Food Prices: A Comparison of Machine Learning Algorithms and Time Series Econometric Models." Foods 12, no. 4 (February 18, 2023): 873. http://dx.doi.org/10.3390/foods12040873.
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It is a well-felt recent phenomenal fact that global food prices have dramatically increased and attracted attention from practitioners and researchers. In line with this attraction, this study uncovers the impact of global factors on predicting food prices in an empirical comparison by using machine learning algorithms and time series econometric models. Covering eight global explanatory variables and monthly data from January 1991 to May 2021, the results show that machine learning algorithms reveal a better performance than time series econometric models while Multi-layer Perceptron is defined as the best machine learning algorithm among alternatives. Furthermore, the one-month lagged global food prices are found to be the most significant factor on the global food prices followed by raw material prices, fertilizer prices, and oil prices, respectively. Thus, the results highlight the effects of fluctuations in the global variables on global food prices. Additionally, policy implications are discussed.
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Ni, Zhehan, and Weilun Chen. "A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction." BCP Business & Management 34 (December 14, 2022): 879–90. http://dx.doi.org/10.54691/bcpbm.v34i.3108.
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Forecasting the future price trend of a stock traded on a financial exchange is the aim of stock market prediction. In recent decades, stock market prediction has been a fascinating topic in the domain of Data Science and Finance. In reality, the stock movement is ambiguous and chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At the same time, accurate enough forecasting of stock price movement leads to substantial benefits for investors. This paper provides a comprehensive review of the application and comparison of Machine Learning (ML) algorithms and Econometric Models in stock market prediction. The mentioned models are categorized into (i) ML algorithms, including Linear Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). (ii) Econometric Models, including Autoregressive Integrated Moving Average (ARIMA) Model, Capital Asset Pricing Model (CAPM), and Fama-French (FF) Factor Model.
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Zholudeva, Vera V. "Econometric modeling of the higher education system in Yaroslavl region." Open Education 22, no. 4 (August 28, 2018): 12–20. http://dx.doi.org/10.21686/1818-4243-2018-4-12-20.
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The objective of the study is to analyze the models that describe the processes, running in the education. The article concludes that currently there are important changes and new trends in the sphere of higher education in Russia: the development of higher education is carried out in the conditions of the effective use of modern information technologies. The author emphasized the analysis of the use of distance learning technologies in the higher education system, which is especially important for our country because of the vast territory, the remoteness of many regions from the centers of educational services, due to the growing high cost of these services.The development of Internet technologies, multimedia in conjunction with the growing popularity, the Internet makes it possible to promote education to a new level. That is why today the demand for distance learning in Russia is equal, and in some universities has exceeded the demand for full-time education. In the near future distance learning will take on the main burden in the system of professional training and retraining of specialists due to its mobility, mass, availability and relative cheapness.Also in this article the basic quantitative regularities of the market of higher education of the Yaroslavl region in relation to the economy are determined. In the article, econometric modeling is chosen as a tool for management in the field of vocational education. This is due to the fact that it is able to identify trends and patterns of changes in the indicators of education development in the region, to determine the consequences of a development strategy that contributes to the understanding of the processes taking place in the higher education system. Econometric models, used for forecasting in the education system are analyzed; their advantages and disadvantages are revealed. Some of them are disclosed in the paper on the example of modeling the system of higher education in the Yaroslavl region.As the result of analyzing the statistical data of the regional office of Federal State Statistics Service in Yaroslavl region the following models were developed: a model that shows how the application of distance technologies in higher education is related to socio-economic indicators; the regression model of correlation between the system of higher education and the economy (GRP); the model of forecasting the number of students in different educational categories; the econometrical model of connectivity between the education expenditures and economic factors. The paper evaluates the impact of educational and demographic indicators on the education level index of the Yaroslavl region. The econometrical models, constructed in the research, represent the informational basis for modernization of regional higher education system and elaboration of social-economic strategies of the regional development. The proposed statistical tools of evaluation and forecasting education system development can be used for decision-making and planning on the regional level.
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Jang, H., and J. Lee. "Machine learning versus econometric jump models in predictability and domain adaptability of index options." Physica A: Statistical Mechanics and its Applications 513 (January 2019): 74–86. http://dx.doi.org/10.1016/j.physa.2018.08.091.
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Ganicheva, Antonina Valerianovna, and Alexey Valerianovich Ganichev. "Modeling of Trajectories of Obtaining and Assimilation of Knowledge." Journal of Pedagogical Innovations, no. 3 (October 16, 2022): 16–24. http://dx.doi.org/10.15293/1812-9463.2203.02.
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The article discusses new, visual, rather simple from a computational point of view, methods for calculating the individual trajectories of trainees. Indicators characterizing the effectiveness of the learning process are introduced: the volume and pace of knowledge acquisition, the student’s abilities. These indicators can be used to form individual educational trajectories. Econometric models have been constructed for these indicators. It is shown how to build models using dummy variables. Based on such models, it is possible to assess the presence of structural changes in the educational process. The aim of the study is to develop indicators that characterize the formation of individual educational trajectories of students and the construction of econometric models of regression dependences of these indicators on a factor sign (number of study hours). The models developed in the article can be used to monitor the educational process with the possibility of its adjustment, management, as well as to predict its effectiveness. These indicators can be used to form individual educational trajectories.
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Valier, Agostino. "Who performs better? AVMs vs hedonic models." Journal of Property Investment & Finance 38, no. 3 (March 26, 2020): 213–25. http://dx.doi.org/10.1108/jpif-12-2019-0157.
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PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.Practical implicationsAVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.Originality/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.
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Hlushak, Oksana M., Svetlana O. Semenyaka, Volodymyr V. Proshkin, Stanislav V. Sapozhnykov, and Oksana S. Lytvyn. "The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects)." CTE Workshop Proceedings 7 (March 20, 2020): 210–24. http://dx.doi.org/10.55056/cte.354.
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This article demonstrates that mathematics in the system of higher education has outgrown the status of the general education subject and should become an integral part of the professional training of future bachelors, including economists, on the basis of intersubject connection with special subjects. Such aspects as the importance of improving the scientific and methodological support of mathematical training of students by means of digital technologies are revealed. It is specified that in order to implement the task of qualified training of students learning econometrics and economic and mathematical modeling, it is necessary to use digital technologies in two directions: for the organization of electronic educational space and in the process of solving applied problems at the junction of the branches of economics and mathematics. The advantages of using e-learning courses in the educational process are presented (such as providing individualization of the educational process in accordance with the needs, characteristics and capabilities of students; improving the quality and efficiency of the educational process; ensuring systematic monitoring of the educational quality). The unified structures of “Econometrics”, “Economic and mathematical modeling” based on the Moodle platform are the following ones. The article presents the results of the pedagogical experiment on the attitude of students to the use of e-learning course (ELC) in the educational process of Borys Grinchenko Kyiv University and Alfred Nobel University (Dnipro city). We found that the following metrics need improvement: availability of time-appropriate mathematical materials; individual approach in training; students’ self-expression and the development of their creativity in the e-learning process. The following opportunities are brought to light the possibilities of digital technologies for the construction and research of econometric models (based on the problem of dependence of the level of the Ukrainian population employment). Various stages of building and testing of the econometric model are characterized: identification of variables, specification of the model, parameterization and verification of the statistical significance of the obtained results.
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Basu, Rounaq, and Joseph Ferreira. "Understanding household vehicle ownership in Singapore through a comparison of econometric and machine learning models." Transportation Research Procedia 48 (2020): 1674–93. http://dx.doi.org/10.1016/j.trpro.2020.08.207.
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ADAM, KLAUS. "LEARNING TO FORECAST AND CYCLICAL BEHAVIOR OF OUTPUT AND INFLATION." Macroeconomic Dynamics 9, no. 1 (February 2005): 1–27. http://dx.doi.org/10.1017/s1365100505040101.
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This paper considers a sticky price model with a cash-in-advance constraint where agents forecast inflation rates with the help of econometric models. Agents use least-squares learning to estimate two competing models of which one is consistent with rational expectations once learning is complete. When past performance governs the choice of forecast model, agents may prefer to use the inconsistent forecast model, which generates an equilibrium where forecasts are only constrained rational. Output and inflation then display persistence, inflation responds sluggishly to nominal disturbances, and the dynamic correlations of output and inflation match U.S. data surprisingly well. The rational expectations equilibrium instead has great difficulty in matching any of these features.
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Lei, Bolin, Boyu Zhang, and Yuping Song. "Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model." Mathematics 9, no. 4 (February 5, 2021): 320. http://dx.doi.org/10.3390/math9040320.
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The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.
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Casinillo, Leomarich F., and Emily L. Casinillo. "Econometric Evidence on Self-Determination Theory in Learning Calculus Among Agribusiness Students." Indonesian Journal of Social Studies 3, no. 1 (July 15, 2020): 1. http://dx.doi.org/10.26740/ijss.v3n1.p1-12.
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Self-determination theory is a study of individual interest and motivation which plays a vital role in good and productive performance in learning. This study focus on the agribusiness students’ motivation in learning Calculus as part of their curriculum. Using econometric models, this study identified some statistically significant factors of motivation. The study employed 121 agribusiness students as respondents using stratified random sampling in the first semester of SY 2019-2020. Results revealed that most of the agribusiness students are motivated in learning calculus. Female student is more likely motivated compared to male students. Females are more focusing on their studies while males are affected by online games. The study revealed that learning attitude and health are significant factors of motivation in learning. Also, students’ experience in calculus makes them creative in the classroom which positively contributes to their interest. Perhaps, this students makes or invents new ideas in the learning environment. Results documented that problems encountered in the classroom does not affects their interest in learning. Furthermore, results showed that their perception to their calculus teacher is relatively high which is a significant factor to their motivation. In fact, their teachers are the most important factor that contributes to their level of achievement in Calculus, more important than any other school resources.
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Casinillo, Leomarich F., and Emily L. Casinillo. "Econometric Modelling on Happiness in Learning Mathematics: The Case of Senior High Students." Indonesian Journal of Curriculum and Educational Technology Studies 8, no. 1 (April 29, 2020): 22–31. http://dx.doi.org/10.15294/ijcets.v8i1.38031.
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This study developed econometric models on the students’ happiness in learning mathematics to identify its influencing factors. A complete enumeration of 115 grade 11 students in the Visayas State University were employed as participants. Results showed that about 61% of the students considered themselves as moderately happy in learning. Their expected happiness is approximately the same with their actual happiness, which is one of the significant determinants in the models. STEM students among other strands in senior high school are more likely happy learners. Household income, allowance, and mental health condition show a small influence on their happiness in learning. Students who spend more time in the library, and those living in rural places tend to be happy in learning. Furthermore, physical health condition shows an inverse effect on students’ well-being in learning mathematics, while social relationships and the distance from home to school do not contribute to their happiness.
Abstrak
Penelitian ini mengembangkan model ekonometrik pada kebahagiaan siswa dalam pembelajaran matematika untuk mengidentifikasi faktor-faktor yang memengaruhinya. Data diambil dari 115 mahasiswa kelas 11 di Universitas Negeri Visayas. Hasilnya menunjukkan bahwa sekitar 61% mahasiswa menilai diri mereka relatif bahagia. Tingkat kebahagiaan yang mereka harapkan relatif sama dengan yang mereka tunjukkan secara aktual dan ini merupakan salah satu model yang signifikan buktinya. Siswa yang mengikuti program STEM dan sejenisnya pada jenjang sekolah menengah atas (SMA) tampak lebih bahagia. Pendapatan di rumah, tunjangan, dan kesehatan mental tampak menunjukkan pengaruh yang tidak seberapa pada kebahagiaan belajar. Sementara itu siswa yang menghabiskan banyak waktu di perpustakaan dan tinggal di perdesaan cenderung bahagia dalam belajar. Lebih lanjut, kondisi kesehatan fisik tampak berbanding terbalik dengan kesejahteraan siswa dalam belajar matematika, sementara itu relasi sosial dan jarak rumah ke sekolah tidak berkontribusi pada kebahagiaan siswa.
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Sarkodie, Samuel Asumadu, Emmanuel Ackom, Festus Victor Bekun, and Phebe Asantewaa Owusu. "Energy–Climate–Economy–Population Nexus: An Empirical Analysis in Kenya, Senegal, and Eswatini." Sustainability 12, no. 15 (July 31, 2020): 6202. http://dx.doi.org/10.3390/su12156202.
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Motivated by the Sustainable Development Goals (SDGs) and its impact by 2030, this study examines the relationship between energy consumption (SDG 7), climate (SDG 13), economic growth and population in Kenya, Senegal and Eswatini. We employ a Kernel Regularized Least Squares (KRLS) machine learning technique and econometric methods such as Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS) regression, the Mean-Group (MG) and Pooled Mean-Group (PMG) estimation models. The econometric techniques confirm the Environmental Kuznets Curve (EKC) hypothesis between income level and CO2 emissions while the machine learning method confirms the scale effect hypothesis. We find that while CO2 emissions, population and income level spur energy demand and utilization, economic development is driven by energy use and population dynamics. This demonstrates that income, population growth, energy and CO2 emissions are inseparable, but require a collective participative decision in the achievement of the SDGs.
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Duan, Lingyun, Ziyuan Liu, Wen Yu, Wei Chen, Dongyan Jin, Denghua Li, Suhua Sun, and Ruixi Dai. "Modeling Analysis and Comparision of Neural Network Simulation Based on ECM and LSTM." Journal of Physics: Conference Series 2068, no. 1 (October 1, 2021): 012041. http://dx.doi.org/10.1088/1742-6596/2068/1/012041.
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Abstract Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive.
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Zhang, Junhao, and Yifei Lei. "Deep Reinforcement Learning for Stock Prediction." Scientific Programming 2022 (April 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5812546.
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Investors are frequently concerned with the potential return from changes in a company’s stock price. However, stock price fluctuations are frequently highly nonlinear and nonstationary, rendering them to be uncontrollable and the primary reason why the majority of investors earn low long-term returns. Historically, people have always simulated and predicted using classic econometric models and simple machine learning models. In recent years, an increasing amount of research has been conducted using more complex machine learning and deep learning methods to forecast stock prices, and their research reports also indicate that their prediction accuracy is gradually improving. While the prediction results and accuracy of these models improve over time, their adaptability in a volatile market environment is questioned. Highly optimized machine learning algorithms include the following: FNN and the RNN are incapable of predicting the stock price of random walks and their results are frequently not consistent with stock price movements. The purpose of this article is to increase the accuracy and speed of stock price volatility prediction by incorporating the PG method’s deep reinforcement learning model. Finally, our tests demonstrate that the new algorithm’s prediction accuracy and reward convergence speed are significantly higher than those of the traditional DRL algorithm. As a result, the new algorithm is more adaptable to fluctuating market conditions.
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Casinillo, Emily L., Eusebio R. Lina, Jr., Leomarich F. Casinillo, Paulo G. Batidor, and Meralyn R. Lebante. "Econometric Evidence on Statistical Anxiety of Engineering Students during the New Normal Setup." Philippine Social Science Journal 5, no. 4 (December 15, 2022): 9–17. http://dx.doi.org/10.52006/main.v5i4.564.
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Students' anxiety is one of the hindrances to good academic achievement due to its adverse impact on cognitive attitudes. This study aimed to ascertain the impact of socio-economic profile and learning experiences that are affected by the COVID-19 pandemic concerning students' anxiety in learning statistics. The study used descriptive measures and econometric models to elucidate the anxiety level and its predictors of engineering students in a state university. Result reveals that the mean students' perception score for their anxiety level is 34.19 (SD=4.94) and is classified as "anxious". This implies that students are experiencing uncomfortable moments and distressing learning behavior due to the adverse impact of the pandemic on the educational system. The econometric model reveals that older and female students are more anxious about learning statistics online. Findings showed that the level of difficulty and less creativity in statistics lessons contributes to the anxiety level of students. In conclusion, instructors/professors must motivate their students and build their interest in learning statistics by giving them realistic and enjoyable activities that suit online education. Furthermore, instructors/professors must undergo training that develops and improves their teaching strategies in statistics to become competitive educators in online learning amid the pandemic.
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Nymoen, Ragnar. "Economic Covid-19 effects analysed by macro econometric models—the case of Norway." National Accounting Review 5, no. 1 (2023): 1–22. http://dx.doi.org/10.3934/nar.2023001.
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<abstract><p>Counterfactual analysis of the impact of Covid-19 can be based on a solution of a macroeconomic model for a scenario without the coronavirus interfering with the macroeconomic system. Two measures of impact are introduced with the aid of a simple theoretical model, and then used in the empirical analysis: (Ⅰ) The difference between the counterfactual without Covid-19 and a baseline model solution. (Ⅱ) The difference between the counterfactual and the actual development of the economy. In order to analyze the impact on GDP we use two model categories. First, empirical final form model equations, which were purpose-built with the aid of a machine learning algorithm. Second, an operational multiple-equation model of the Norwegian macroeconomic system. Empirically, we find a significant impact of Covid-19 on the GDP Mainland Norway in 2020. For some of the estimator/model combinations, the impacts are also significant in the two first quarters of 2021. Using the multiple-equation model, the assessment is extended to the impact of Covid-19 on value added in four Mainland Norway industries, on imports and exports, and on final consumption expenditure and gross capital formation.</p></abstract>
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Celbiş, Mehmet Güney, Pui-Hang Wong, Karima Kourtit, and Peter Nijkamp. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach." Sustainability 13, no. 23 (December 3, 2021): 13426. http://dx.doi.org/10.3390/su132313426.
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This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.
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Menculini, Lorenzo, Andrea Marini, Massimiliano Proietti, Alberto Garinei, Alessio Bozza, Cecilia Moretti, and Marcello Marconi. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices." Forecasting 3, no. 3 (September 15, 2021): 644–62. http://dx.doi.org/10.3390/forecast3030040.
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Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate.
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Sultana, Abira, and Murshida Khanam. "Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models." Dhaka University Journal of Science 68, no. 2 (October 29, 2020): 143–47. http://dx.doi.org/10.3329/dujs.v68i2.54612.
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Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh.
Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)
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Sheng, Yankai, and Ding Ma. "Stock Index Spot–Futures Arbitrage Prediction Using Machine Learning Models." Entropy 24, no. 10 (October 13, 2022): 1462. http://dx.doi.org/10.3390/e24101462.
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With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R2). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R2 of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.
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Varian, Hal R. "Big Data: New Tricks for Econometrics." Journal of Economic Perspectives 28, no. 2 (May 1, 2014): 3–28. http://dx.doi.org/10.1257/jep.28.2.3.
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Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.
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Adamopoulos, Panagiotis, Anindya Ghose, and Alexander Tuzhilin. "Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments." MIS Quarterly 46, no. 1 (February 15, 2022): 101–50. http://dx.doi.org/10.25300/misq/2021/15611.
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In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers’ utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs for the recommended alternatives outperform other recommendations. In addition, recommendation strategies combining social proofs with higher levels of induced awareness due to the prescribed temporal diversity have an even stronger effect on the mobile channel. We also examine the heterogeneity of the demand effect across items, users, and contextual settings, further verifying empirically the aforementioned information and persuasion mechanisms and generating rich insights. We also facilitate the estimation of causal effects in the presence of endogeneity using machine-learning methods. Specifically, we develop novel econometric instruments that capture product differentiation (isolation) based on deeplearning models of user-generated reviews. Our empirical findings extend the current knowledge regarding the heterogeneous impact of recommender systems, reconcile contradictory prior results in the related literature, and have significant business implications.
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Rosienkiewicz, Maria. "Artificial intelligence-based hybrid forecasting models for manufacturing systems." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (February 17, 2021): 263–77. http://dx.doi.org/10.17531/ein.2021.2.6.
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The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.
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Nieto, M. R., R. B. Carmona Benitez, and J. N. Martinez. "Comparing models to forecast cargo volume at port terminals." Journal of Applied Research and Technology 19, no. 3 (June 30, 2021): 238–49. http://dx.doi.org/10.22201/icat.24486736e.2021.19.3.1695.
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Economic growth has a direct link with the volume of cargo at port terminals. To encourage growth, investment decisions on infrastructure are required that can be performed by the development of econometric models. We compare three time-series models and one machine-learning model to estimate and forecast cargo volume. We apply an ARIMA+GARCH+Bootstrap, a multiplicative Holt-Winters, a support vector regression model, and a time-series model with explanatory variables ARIMAX. The models forecast cargo through the ports of San Pedro using data from 2008 to 2016. The database contains imports and exports of bulk, container, reefer, and ro-ro cargo. Results show that the multiplicative Holt-Winters model is the best method to forecast imports and exports of bulk cargo, while the support vector regression model is the best method to forecast imports and exports of container, reefer, and ro-ro cargo. The Diebold-Mariano Test, the RMSE metric, and the MAPE metric validate the results.
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chougale, Jeevan, Abhishek Shinde, Ninad Deshmukh, Dhananjay Sawant, and Vaishali Latke. "House Price Prediction using Machine learning and Image Processing." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 18, 2021): 961–65. http://dx.doi.org/10.51201/jusst/21/05280.
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We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.
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Khoa, Bui Thanh, and Tran Trong Huynh. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6625. http://dx.doi.org/10.11591/ijece.v12i6.pp6625-6634.
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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>
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Shankar, Venky. "Big Data and Analytics in Retailing." NIM Marketing Intelligence Review 11, no. 1 (May 1, 2019): 36–40. http://dx.doi.org/10.2478/nimmir-2019-0006.
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AbstractBig data are taking center stage for decision-making in many retail organizations. Customer data on attitudes and behavior across channels, touchpoints, devices and platforms are often readily available and constantly recorded. These data are integrated from multiple sources and stored or warehoused, often in a cloud-based environment. Statistical, econometric and data science models are developed for enabling appropriate decisions. Computer algorithms and programs are created for these models. Machine learning based models, are particularly useful for learning from the data and making predictive decisions. These machine learning models form the backbone for the generation and development of AI-assisted decisions. In many cases, such decisions are automated using systems such as chatbots and robots.Of special interest are issues such as omnichannel shopping behavior, resource allocation across channels, the effects of the mobile channel and mobile apps on shopper behavior, dynamic pricing, data privacy and security. Research on these issues reveals several interesting insights on which retailers can build. To fully leverage big data in today’s retailing environment, CRM strategies must be location specific, time specific and channel specific in addition to being customer specific.
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Chen, Mary, Matthew DeHaven, Isabel Kitschelt, Seung Jung Lee, and Martin J. Sicilian. "Identifying Financial Crises Using Machine Learning on Textual Data." Journal of Risk and Financial Management 16, no. 3 (March 1, 2023): 161. http://dx.doi.org/10.3390/jrfm16030161.
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We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with traditional econometric techniques and using readily available market data, we find that textual data helps in reducing false positives and false negatives in out-of-sample testing of such models, especially when the crises are considered more severe. Building a framework that is consistent across countries and in real time can benefit policymakers around the world, especially when international coordination is required across different government policies.
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Ampomah, Ernest Kwame, Zhiguang Qin, and Gabriel Nyame. "Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement." Information 11, no. 6 (June 20, 2020): 332. http://dx.doi.org/10.3390/info11060332.
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Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.
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Ji, Xuan, Jiachen Wang, and Zhijun Yan. "A stock price prediction method based on deep learning technology." International Journal of Crowd Science 5, no. 1 (March 5, 2021): 55–72. http://dx.doi.org/10.1108/ijcs-05-2020-0012.
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Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.
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Jacob, Daniel. "CATE meets ML." Digital Finance 3, no. 2 (June 2021): 99–148. http://dx.doi.org/10.1007/s42521-021-00033-7.
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AbstractFor treatment effects—one of the core issues in modern econometric analysis—prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only the average but a personalized treatment effect—the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains meta-learners, like the doubly-robust, R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and 401(k) example, we find a positive treatment effect for all observations but conflicting evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.
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Ghosh, Indranil, Rabin K. Jana, and Manas K. Sanyal. "Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms." Applied Soft Computing 82 (September 2019): 105553. http://dx.doi.org/10.1016/j.asoc.2019.105553.
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Geraldo-Campos, Luis Alberto, Juan J. Soria, and Tamara Pando-Ezcurra. "Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models." Economies 10, no. 8 (July 30, 2022): 188. http://dx.doi.org/10.3390/economies10080188.
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COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the credit risk of companies under the Reactiva Peru Program because of COVID-19. A multivariate regression analysis was applied with four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level), with a population of 501,298 companies benefiting from the program, under the CRISP-DM methodology oriented especially for data mining projects, with artificial intelligence techniques under the machine learning Lasso and Ridge regression models, with econometric algebraic mathematical verification to compare and validate the predictive models using SPSS, Jamovi, R Studio, and MATLAB software. The results revealed a better Lasso regression model (λ60 = 0.00038; RMSE = 0.3573685) that optimally predicted the level of risk compared to the Ridge regression model (λ100 = 0.00910; RMSE = 0.3573812) and the least squares model with algebraic mathematics, which corroborates that the Lasso regression model is the best predictive model to detect the level of credit risk of the Reactiva Peru Program. The best predictive model for detecting the level of corporate credit risk is the Lasso regression model.
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Agbenyega, Diana Ayorkor, John Andoh, Samuel Iddi, and Louis Asiedu. "Modelling Customs Revenue in Ghana Using Novel Time Series Methods." Applied Computational Intelligence and Soft Computing 2022 (April 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/2111587.
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Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future.