Volume 118

Published on October 2024

Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis

Conference website: https://2024.icftba.org/
ISBN:978-1-83558-659-4(Print) / 978-1-83558-660-0(Online)
Conference date: 4 December 2024
Editor:Ursula Faura-Martínez
Research Article
Published on 25 October 2024 DOI: 10.54254/2754-1169/118/20242043
Haiyi Zhang
DOI: 10.54254/2754-1169/118/20242043

Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.

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Zhang,H. (2024).Predicting Housing Prices Using Supervised Machine Learning Models.Advances in Economics, Management and Political Sciences,118,1-7.
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Research Article
Published on 25 October 2024 DOI: 10.54254/2754-1169/118/20242011
Jiawen Tan
DOI: 10.54254/2754-1169/118/20242011

Abstract: This paper selects the financial statement data of 18 listed companies in the field of education and selects 9 financial indicators from the four aspects of solvency, profitability, development ability and operation ability that can reflect the comprehensive strength of listed companies. Finally, the stocks of 18 listed companies were divided into three categories: high-value stocks, potential stocks and low-value stocks according to the comprehensive strength of the companies. The results show that: (1) From the comparison between the rankings of the selected 18 stocks combined with the traditional methods by combining principal component analysis and entropy weight method, it can be seen that for the comprehensive multi-index evaluation analysis in the past five years, the ranking agreement between the two methods can reach 80% or more. Due to the unstable fluctuations of the stock market, the consistency between the analysis results and investment recommendations obtained in this paper and the actual investment situation of the stock market can also exceed 60%. (2) From the results of cluster analysis, Chuanzhi Education, Entrepreneurship Dark Horse, Guoxin Culture, China Hi-Tech and Action Education are all high-value stocks in the education category, while Meijim, Qinshang Co., Ltd., and Kaiwen Education are potential education stocks.

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Tan,J. (2024).Research on Statistical Assessment Methods of Stock Investment Value-Taking Education Industry as Example.Advances in Economics, Management and Political Sciences,118,8-18.
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Research Article
Published on 25 October 2024 DOI: 10.54254/2754-1169/118/20242013
Yanru Wang
DOI: 10.54254/2754-1169/118/20242013

Abstract: Innovation is also playing an increasingly important role in the process of transitioning to high-quality economic development. Therefore, it is imperative that enterprises, as micro-entrepreneurs improving the quality of economic growth, pay attention to the enhancement of their R&D capabilities. So how can positive ESG performance by firms alleviate financing constraints and thus promote R&D investment? Therefore, this paper investigates the impact of ESG performance and financing constraints on R&D investment, which is of great significance in promoting enterprises to pay attention to ESG management, alleviating financing constraints, and promoting the enhancement of R&D capability. The results of this study show that corporate ESG performance has a positive effect on R&D investment; corporate ESG performance has a negative effect on financing constraints; financing constraints play a partly intermediary role in the process of corporate ESG performance affecting R&D investment.

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Wang,Y. (2024).Impact of ESG Performance on Enterprise Innovation Investment —Evidence from China Listed Enterprises.Advances in Economics, Management and Political Sciences,118,19-27.
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Research Article
Published on 25 October 2024 DOI: 10.54254/2754-1169/118/20242000
Yixuan Yu
DOI: 10.54254/2754-1169/118/20242000

Abstract: Stock price prediction has always been an important topic in financial research. Accurate price prediction can not only help investors make informed investment decisions but also enhance market stability and reduce systemic risk. In recent years, with advancements in computing technology and data science, machine learning methods have been increasingly applied in the financial field. Compared to traditional methods, machine learning methods can better handle high-dimensional, nonlinear, and large datasets, thus demonstrating higher prediction accuracy and applicability in stock price prediction.This paper reviews relevant literature and selects five models for empirical research: Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), LightGBM, a combination of LSTM and LightGBM, and Convolutional Neural Network (CNN). The effectiveness of these models in predicting the stock price of Meituan-W (3690) was analyzed and compared in detail. The experimental results show that the LightGBM model performs best in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), proving its significant advantages in handling large-scale, high-dimensional, and nonlinear data. By comparing the prediction results of different models, this paper explores the strengths and weaknesses of each model and their feasibility and effectiveness in practical applications. Machine learning methods have significant potential in stock price prediction, model selection needs to comprehensively consider data characteristics, computational resources, and practical application scenarios.

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Yu,Y. (2024).Research on the Feasibility of Machine Learning Methods in Stock Price Prediction.Advances in Economics, Management and Political Sciences,118,28-38.
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