發(fā)布時(shí)間：2018-06-06 00:00

  本文選題：財(cái)務(wù)預(yù)警 + BP神經(jīng)網(wǎng)絡(luò)��； 參考：《河北大學(xué)》2014年碩士論文

【摘要】：隨著我國(guó)市場(chǎng)經(jīng)濟(jì)發(fā)展程度的提高，市場(chǎng)競(jìng)爭(zhēng)愈發(fā)激烈，企業(yè)遇到了更多危機(jī)與挑戰(zhàn)。對(duì)于為數(shù)眾多的制造業(yè)上市公司而言，財(cái)務(wù)問題尤為重要，一旦財(cái)務(wù)陷入困境，不但危機(jī)自身的生存與發(fā)展，也給投資者、債權(quán)人以及眾多利益相關(guān)者帶來(lái)重大損失。因此，對(duì)于財(cái)務(wù)狀況進(jìn)行預(yù)警研究與探索，具有重要意義。 國(guó)內(nèi)外的大量文獻(xiàn)已經(jīng)能夠證明，企業(yè)的財(cái)務(wù)指標(biāo)對(duì)企業(yè)財(cái)務(wù)發(fā)展?fàn)顩r有一定的預(yù)示作用，但是對(duì)于財(cái)務(wù)預(yù)警指標(biāo)體系的構(gòu)建是不完善的。在研究財(cái)務(wù)預(yù)警的大量模型中，，也存在許多不足和局限性。本文就是針對(duì)財(cái)務(wù)預(yù)警指標(biāo)體系的構(gòu)建和預(yù)警模型來(lái)進(jìn)行研究和探索。 通過分析前人的研究成果，總結(jié)出了28個(gè)對(duì)企業(yè)財(cái)務(wù)狀況影響較大的預(yù)警指標(biāo)。其中，包含了財(cái)務(wù)指標(biāo)和公司治理等非財(cái)務(wù)指標(biāo)。豐富了預(yù)警指標(biāo)體系，為財(cái)務(wù)預(yù)警模型研究提供了基礎(chǔ)。當(dāng)輸入變量過多，輸入變量之間不是相互獨(dú)立時(shí)，BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型容易出現(xiàn)過擬合現(xiàn)象，從而導(dǎo)致模型精度過低、建模時(shí)間長(zhǎng)等問題，因此本文選用遺傳算法對(duì)已經(jīng)選取的預(yù)警指標(biāo)進(jìn)行變量降維，并對(duì)BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型進(jìn)行優(yōu)化。 實(shí)證分析分為兩步，第一步先用BP神經(jīng)網(wǎng)絡(luò)做預(yù)警分析，第二步應(yīng)用遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)做預(yù)測(cè)。通過對(duì)比兩步的計(jì)算結(jié)果，發(fā)現(xiàn)用遺傳算法優(yōu)化過的BP神經(jīng)網(wǎng)絡(luò)對(duì)財(cái)務(wù)進(jìn)行預(yù)警有更明顯的效果。大大提高了預(yù)測(cè)精度，縮短了建模時(shí)間，為財(cái)務(wù)預(yù)警的理論和實(shí)踐研究提出了新的思路。
[Abstract]:With the development of the market economy in China, the market competition is becoming more and more intense, and the enterprises have encountered more crises and challenges. For a large number of manufacturing listed companies, financial problems are particularly important. Once the financial crisis is in trouble, not only the survival and development of the crisis itself, but also the investors, creditors and many stakeholders are also brought. Therefore, it is of great significance to conduct early-warning research and Exploration on the financial situation.
A large number of documents at home and abroad have proved that the financial indicators of enterprises have a certain predictive effect on the financial development of enterprises, but the construction of the financial early-warning index system is not perfect. There are also many shortcomings and limitations in the study of a large number of financial early-warning models. This paper is aimed at the structure of the financial early warning index system. Construction and early warning model for research and exploration.
Through the analysis of previous research results, 28 early warning indicators have been summarized, including financial indicators and corporate governance, which enrich the early warning index system, which provides a basis for the study of financial early warning models. When the input variables are too much, the input variables are not independent, BP The neural network early warning model is easy to appear over fitting phenomenon, which leads to the low precision of the model and the long modeling time. Therefore, this paper uses genetic algorithm to reduce the variable of the selected early warning index, and optimizes the BP neural network early warning model.
The empirical analysis is divided into two steps. First, the BP neural network is used to make early warning analysis, and the second step is to optimize the BP neural network by genetic algorithm. By comparing the results of the two steps, it is found that the BP neural network optimized by genetic algorithm has a more obvious effect on the financial early warning. It greatly improves the prediction accuracy and shortens the modeling time. A new train of thought is put forward for the theory and practice of financial early-warning.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;F406.72

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