Application of Big Data Complexity Analysis Hedging Operation of Derivative Financial Products

Cheng Chung Wu,Menglin Yang,Tiantong Yuan,Ya Ju Tsai,Qionghui Fu,Wei Wang

doi:10.1155/2021/6618873

Abstract

This study is based on the situation of Taiwan listed companies as derivative financial products from 2015 to 2017, analyzing the relationship between the hedging of derivative financial products and characteristics of enterprises and the factors that affect the hedging decision-making of companies. It is found that even after the announcement of Taiwan’s No. 34 and No. 36 bulletins, there are still some problems that are needed to improve in the disclosure of derivative financial product investment information by Taiwan’s listed companies, at least in the disclosure of the reasons for this conduct which is still insufficient. In this study, two-stage regression analysis method is applied to empirical analysis, and it is found that hedging activities are related to corporate characteristics, such as expected financial crisis costs, corporate size, equity issues, growth investment opportunities, and information asymmetry. In the investment of derivative financial products, enterprises should evaluate their own financial characteristics as a reference for the risk avoidance decision. At the same time, it is necessary to investigate different natures of hedging tools used in appropriate risk categories, so as to fully achieve the hedging effect and maximize the hedging benefits. This study also found that companies with higher growth investment opportunities, larger size, and higher financial crisis costs will tend to use derivative financial products for hedging. As for the impact of other industries, it is found that the electronic and electrical machinery industries are more active than other industries in hedging behaviors of undertaking derivative financial products’ transaction.

Highlights

With the rapid development of economy, sound capital market development has become an important factor in economic development and in economic growth
When designing the hedging regression model of derivative financial products for listed companies that only estimate the sample of the listed company, the nonlisted companies are ignored. erefore, in the case where such data observations are truncated, if they are directly evaluated by the least squares (OLS) estimation model or logistic regression model, sample selection bias will occur due to ignoring the samples of nonlisted companies. erefore, this study uses Heckman’s [13] two-stage estimation method to correct the bias caused by sample selection bias
Research Conclusions. e main purpose of this study is to analyze the relationship between the hedging of derivative financial products and the characteristics of enterprises and the factors that affect the hedging decision-making based on the situation of Taiwan listed companies undertaking derivative financial products from 2015 to 2017

Summary

Introduction

With the rapid development of economy, sound capital market development has become an important factor in economic development and in economic growth. The least square method (ordinary least squares, OLS) is used to join inverse Mills ratio to explore the determinants of the adoption of different types of derivative financial products in the business community, so as to solve the problem of sample selection bias, and to explore the types of derivative financial products and operational strategies of enterprises after the implementation of No 34 and No 36 bulletins, so as to understand the hedging decisions of Taiwan enterprises in the application of derivative financial products. Erefore, this study uses Heckman’s two-stage regression to explore the determinants of the adoption of derivative financial products, so as to solve the problem of sample bias If only the data of listed companies are collected for analysis, the results are likely to produce sample bias. erefore, this study uses Heckman’s two-stage regression to explore the determinants of the adoption of derivative financial products, so as to solve the problem of sample bias

Research Data and Design

Empirical Analysis

Conclusions and Recommendations

Findings

Research Recommendations