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Neural Computing and Applications

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16-06-2020 | S.I. : DPTA Conference 2019

Exploring the financial indicators to improve the pattern recognition of economic data based on machine learning

Authors: Xiaohui Wei, Wanling Chen, Xiao Li

Published in: Neural Computing and Applications | Issue 2/2021

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Abstract

Various economic data in the financial market need to be pattern-recognized to improve the efficiency of economic data pattern recognition, further improve the accuracy of economic-related decisions, and promote stable economic development. Based on machine learning technology, this study establishes a statistical model by establishing a multiple regression model to extract financial indicators that have significant effects on the financing trade of listed companies. Moreover, this study provides a preliminary empirical model for judging whether a company conducts financing trade based on some company’s financial indicators and uses data to verify the consistency of the model. In addition, this study conducts research and demonstration of the algorithm model of this research through empirical research. The research results show that the model shows high reliability and validity in accurately identifying whether the enterprise has the characteristics of conducting financing trade.

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Nyman R, Ormerod P (2017) Predicting economic recessions using machine learning algorithms. arXiv preprint arXiv:1701.01428

Raffinot T, Benoit S (2018) Investing through economic cycles with ensemble machine learning algorithms. SSRN 2785583

Lamperti F, Roventini A, Sani A (2018) Agent-based model calibration using machine learning surrogates. J Econ Dyn Control 90:366–389MathSciNetCrossRef

Katris C (2020) Prediction of unemployment rates with time series and machine learning techniques. Comput Econ 55(2):673–706CrossRef

Gu S, Kelly B, Xiu D (2020) Empirical asset pricing via machine learning. Rev Financ Stud 33(5):2223–2273CrossRef

Choubin B, Darabi H, Rahmati O et al (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281CrossRef

Souza JTD, Francisco ACD, Piekarski CM, Prado GFD (2019) Data mining and machine learning to promote smart cities: a systematic review from 2000 to 2018. Sustainability 11(4):1077CrossRef

Abu S (2016) Forecasting customer electricity load demand in the power trading agent competition using machine learning

Keita M (2017) Data Science sous Python: Algorithme, Statistique, DataViz, DataMining et Machine-Learning [Data Science with Python: Algorithm, Statistics, DataViz, DataMining and Machine-Learning] (No. 76653). University Library of Munich, Germany

10.

Freyman CA, Byrnes JJ, Jeffrey A (2016) Machine-learning-based classification of research grant award records. Res Eval 4:4

11.

Angrist J, Azoulay P, Ellison GD et al (2017) Economic research evolves: fields and styles. Am Econ Rev 107(5):293–297CrossRef

12.

Andini M, Ciani E, De Blasio G, D’Ignazio A, Salvestrini V (2017) Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy. Bank of Italy Temi di Discussione (Working Paper) No, 1158

13.

Scheidegger S, Bilionis I (2019) Machine learning for high-dimensional dynamic stochastic economies. J Comput Sci 33:68–82MathSciNetCrossRef

14.

Pierdzioch C, Risse M (2018) A machine-learning analysis of the rationality of aggregate stock market forecasts. Int J Finance Econ 23(4):642–654CrossRef

15.

Chowdhury R, Mahdy MRC, Alam TN, Al Quaderi GD, Rahman MA (2020) Predicting the stock price of frontier markets using modified Black-Scholes Option pricing model and machine learning. Physica A Stat Mech Appl 12:4444

16.

Hushani P (2018) Using autoregressive modelling and machine learning for stock market prediction and trading: ICICT 2018, London. Adv Intell Syst Comput 96:767–774

17.

Chou JS, Nguyen TK (2018) Forward forecast of stock price using sliding-window metaheuristic-optimized machine learning regression. IEEE Trans Industr Inf 14(7):3132–3142CrossRef

18.

Pyo S, Lee J, Cha M et al (2017) Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the Korean stock markets. PLoS ONE 12(11):e0188107CrossRef

19.

Park S, Lee J, Son Y (2016) Predicting market impact costs using nonparametric machine learning models. PLoS ONE 11(2):e0150243CrossRef

20.

Bin L, Lin Y, Tang W (2017) ML-TEA: a set of quantitative investment algorithms based on machine learning and technical analysis. Xitong Gongcheng Lilun yu Shijian 37(5):1089–1100

21.

Sen J, Chaudhuri TD (2017) A predictive analysis of the Indian FMCG sector using time series decomposition-based approach. SSRN Electron J 4(2):206–226

22.

Zhang XD, Li A, Pan R (2016) Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine. Appl Soft Comput 49:385–398CrossRef

23.

Ma X, Yang R, Zou D, Liu R (2020) Measuring extreme risk of sustainable financial system using GJR-GARCH model trading data-based. Int J Inf Manage 50:526–537CrossRef

Title: Exploring the financial indicators to improve the pattern recognition of economic data based on machine learning
Authors: Xiaohui Wei
Wanling Chen
Xiao Li
Publication date: 16-06-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05094-0

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