【摘要】：隨著市場經(jīng)濟(jì)的不斷發(fā)展,企業(yè)在行業(yè)內(nèi)的競爭愈發(fā)激烈,許多企業(yè)的財(cái)務(wù)狀況頻頻陷入困境,財(cái)務(wù)風(fēng)險(xiǎn)使得企業(yè)的平穩(wěn)發(fā)展面臨著越來越嚴(yán)重的挑戰(zhàn)。本文聯(lián)合應(yīng)用主成分分析與機(jī)器學(xué)習(xí)算法,開發(fā)了企業(yè)財(cái)務(wù)風(fēng)險(xiǎn)分析系統(tǒng)。旨在提高企業(yè)的財(cái)務(wù)管理水平,把企業(yè)潛在的財(cái)務(wù)風(fēng)險(xiǎn)揭示出來,從源頭阻止企業(yè)財(cái)務(wù)危機(jī)的發(fā)生,解決當(dāng)下亟待的熱點(diǎn)問題。本文通過分析前人研究和閱讀大量文獻(xiàn)資料,總結(jié)風(fēng)險(xiǎn)分析領(lǐng)域研究成果的優(yōu)點(diǎn)和不足,在此基礎(chǔ)上對SVM算法先做PCA降維,然后應(yīng)用決策樹和SVM完成財(cái)務(wù)風(fēng)險(xiǎn)分析,并從財(cái)務(wù)管理和財(cái)務(wù)風(fēng)險(xiǎn)分析兩大方面完成財(cái)務(wù)風(fēng)險(xiǎn)分析系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)。本文的研究內(nèi)容主要分為六個(gè)部分。第一部分緒論,首先介紹本研究的提出背景和現(xiàn)實(shí)意義,然后介紹當(dāng)前國內(nèi)外研究現(xiàn)狀以及本文主要研究內(nèi)容和結(jié)構(gòu),第二部分相關(guān)技術(shù),主要介紹財(cái)務(wù)風(fēng)險(xiǎn)指標(biāo)體系的構(gòu)建和本文用于風(fēng)險(xiǎn)分析研究的相關(guān)技術(shù):主成分分析、支持向量機(jī)、決策樹等,在Eclipse平臺(tái)上用Java語言編程實(shí)現(xiàn)該系統(tǒng)。第三部分系統(tǒng)需求分析,從功能和非功能兩方面對本系統(tǒng)進(jìn)行需求分析。第四部分系統(tǒng)設(shè)計(jì),介紹了本文系統(tǒng)的總體設(shè)計(jì)和詳細(xì)設(shè)計(jì),包括系統(tǒng)總體設(shè)計(jì)、模塊設(shè)計(jì)及財(cái)務(wù)管理數(shù)據(jù)庫設(shè)計(jì)等。第五部分系統(tǒng)實(shí)現(xiàn)與測試,根據(jù)前面的設(shè)計(jì)完成企業(yè)財(cái)務(wù)風(fēng)險(xiǎn)分析系統(tǒng)實(shí)現(xiàn),然后對系統(tǒng)做了簡單的測試,通過實(shí)例測試驗(yàn)證各模塊及風(fēng)險(xiǎn)分析性能。第六部分總結(jié)和展望,從工作總結(jié)和研究展望兩方面對財(cái)務(wù)風(fēng)險(xiǎn)分析研究給出工作建議及下一步研究方向。
[Abstract]:With the continuous development of the market economy, the competition of enterprises in the industry is becoming more and more intense, and the financial situation of many enterprises is in a difficult position. The financial risk makes the smooth development of the enterprise facing more and more serious challenges. This paper has developed the enterprise financial risk analysis system with the application of principal component analysis and machine learning algorithm. The financial management level of the high enterprise reveals the potential financial risks of the enterprise, prevents the occurrence of the financial crisis from the source and solves the hot issues urgently needed at the moment. This paper analyzes the advantages and disadvantages of the research results in the field of risk analysis by analyzing the predecessors and reading a large number of documents, and on this basis, the SVM algorithm is made PC first. A reduces the dimension, then uses the decision tree and SVM to complete the financial risk analysis, and completes the design and implementation of the financial risk analysis system from two aspects of financial management and financial risk analysis. The research content of this paper is divided into six parts. First part of the introduction, first introduces the background and practical significance of the research, and then introduces the current country. The present situation of internal and external research and the main research content and structure of this paper, second parts related technology, mainly introduce the construction of the financial risk index system and the related technologies used in the research of risk analysis: principal component analysis, support vector machine, decision tree and so on. The system is realized by Java language programming on the Eclipse platform. The third parts of the system need to be implemented. Analyze the system from two aspects of function and non function. Fourth parts of the system design, introduce the overall design and detailed design of this system, including the overall system design, module design and financial management database design. Fifth parts of the system implementation and testing, according to the previous design to complete the financial risk of the enterprise The system is implemented, and then the system is tested, and the performance of each module and risk analysis is verified through the case test. The sixth part summarizes and looks forward to the two aspects of the work summary and research prospect, and gives the suggestions and the next research direction of the financial risk analysis.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52

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