A Method Of Quantitative Investment Analysis Based On BSC Theory With IT Implementation

Quantitative investment strategy began in the 1970s.With the enhancement of computing power of computer systems,it became popular in the 1990s.In the international market,especially since 2006,funds with quantitative models as the core investment strategy have been increasing,and quantitative investment has gradually become a mainstream investment method.This paper selects the Mainland China mutual funds market as the entry point for quantitative investment,and starts with fetching relevant data from the Internet by Python spider,then builds the data warehouse needed for analysis.This paper uses the balanced scorecard as the modeling framework,and uses a variety of analysis indicators in the financial field for modeling analysis.Some important dimensions are based on the CAPM theory.After that,a testing dataset is used for validating the effectiveness of the model.A11 the above processes are implemented using Python.This article describes the entire analysis processes in six chapters:Chapter 1 Outlines the background,themes,meaning,and basic ideas of the paper's research objectives.The second chapter briefly describes the theoretical models used in the paper and the tools used in the IT implementation process.The third chapter describes the main data sources of the model and the process of data acquisition and preprocessing.It divides the data into training sets and testing sets and performs descriptive statistics.The fourth chapter is the core chapter of the dissertation.It elaborates the analysis flow of selecting funds using the framework of the balanced scorecard,and details the processing of the data.It is actually implemented in Python in the process,and uses training set data to screen out excellent funds.In the fifth chapter,the previous chapter uses the data generated by the training set and uses the test set data to perform empirical verification.The results show that the model is effective for stock,hybrid and bond funds.The sixth chapter summarizes this thesis,and discusses the feasibility of the transformation of results,and makes a prospect for the transformation of related derivative applications.The purpose of writing this paper is not just about doing a test on a model,but more about doing a kind of exploration.The significance of this exploration is to prove through a real hands-on process:in the age of the Internet information explosion,IT engineering begins with the acquisition of open data and builds an application based on data model analysis that has its value.