Simulation Research on Real Estate Financial Data Analysis Based on Improved Genetic Algorithm

With the advent of the information age, in order to get the information we need, people have higher and higher requirements for data processing in all aspects. The unique correlation between data can better help us analyze and make judgments so as to take appropriate actions. The fluctuation of real estate price has a wide impact on the real economy by changing the behavior of economic subjects. Specifically, the fluctuation of real estate has an impact on investment and residents' consumption behavior through the financial accelerator mechanism, and then affects the balance between total social demand and total supply. Based on this, on the basis of explaining the mechanism of economic financialization on real estate prices, this paper empirically studies the impact of economic financialization on real estate prices by combining the spatial panel data of 31 provinces in China. The single artificial neural network model (BPNN), principal component analysis and neural network combination model (PCA-BPNN), genetic algorithm and neural network combination model (GA-BPNN) and the improved genetic algorithm and neural network combination model (IGA-BPNN) in this paper were used for experiments. The results show that the proposed method can effectively reduce the number of variables while ensuring basically the same prediction accuracy.

I. Introduction

With the progress of modern technology, we are entering the information era of the information explosion, and people have increasingly high requirements for data processing in all aspects. Through the analysis of the unique correlation between data, we can finally get the information we need to help people make judgments so as to take appropriate actions [1]. However, financial data, including data of stocks, bonds and other products, have many characteristics different from other types of data. The dynamic trend of financial data is affected by many complex factors, not only by complex macroeconomic and political factors, but also by artificial short-term complex psychological factors, with great volatility [2]. Therefore, based on the large sample and high-dimensional characteristics of the current data set, the effect of financial analysis methods in the past is not ideal. How to deal with financial data more effectively and analyze it is a huge challenge. At the same time, it has also attracted wide attention and discussion of scholars from all aspects [3].