政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141032

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141032

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题名:	學習型決策支援系統用以預測美股牛熊市的轉折點 A learning-based decision support system for predicting the turning points of the bull and bear market
作者:	簡琬玲 Chien, Wan-Ling
贡献者:	蔡瑞煌盧敬植 Tsaih, Rua-Huan Lu, Ching-Chih 簡琬玲 Chien, Wan-Ling
关键词:	學習型決策支援系統牛熊市預測轉折點預測落差時間統計單隱藏層前饋神經網路概念飄移移動窗口 Learning-based decision support system Bull/ bear markets predictions Turning points predictions Lag time statistics Single-hidden layer feedforward neural network Concept drift Moving window
日期:	2022
上传时间:	2022-08-01 17:21:30 (UTC+8)
摘要:	股票市場預測一直是投資者和市場分析師感興趣的研究議題之一，而其中如何預測牛熊市轉折點也漸漸成為關注重點。然而，學術界以及實務界尚未有一完善的決策支援系統能反應股市長期趨勢的轉折變化，此外，這個決策支援系統仍有許多困難待克服，例如大多數使用的預測模型皆仰賴歷史資料，而當資料屬於動態數據流時，將面臨概念飄移問題，使得模型的準確率下降。為解決上述痛點，本研究設計了一個學習型決策支援系統(LDSS)，包括自創一套自變數(含總體經濟以及股市歷史資料變數)以及自行研發ISMCR機制和推論機制，來預測美股牛熊市的轉折點。本研究也加入移動窗口機制以因應概念飄移問題，使預測模型能在概念飄移環境中有效學習。此LDSS會依據推論機制預測牛熊市與轉折點候選者，並提供實際轉折點與預測轉折點(TTP_PTP)的落差時間統計給決策者做參考。為了驗證LDSS預測牛熊市與轉折點候選者的有效性，本研究使用S&P 500歷史資料進行實驗，並選擇Logit模型做為比較工具。實驗結果證實ISMCR機制是有效的，而LDSS在「牛熊市預測」的整體平均預測準確度可達0.959，同時也證實LDSS在「牛熊市預測」和「轉折點候選者預測」上的整體平均表現皆比Logit好，而且TTP_PTP的平均落差時間也比Logit短。 There is no good decision support system (DSS) that can reflect the changes in the long-term trend of the stock market (for example, the turning points (TPs) of the bull and bear markets) due to many difficulties to be overcome. For example, most predictive models rely on historical data, and when the data is a dynamic data stream, they need to cope with the concept drift issue. To address the aforementioned challenges, this study proposes a Learning-based Decision Support System (LDSS) through creating a set of independent variables regarding the U.S. stock market (including the macroeconomic and stock market historical data variables) and developing the ISMCR mechanism and the inferencing mechanism to predict the TPs of bull/bear markets. This study also adds the moving window mechanism to deal with the concept drift issue so that the predictive model can learn effectively in the concept drift environment. The inferencing mechanism of the proposed LDSS releases the bull/bear market information and turning point candidates (TPC), as well as provides the lag time between the theoretical turning point (TTP) and the predicted turning point (PTP) for decision support. To verify the effectiveness of LDSS, this study uses S&P 500 historical data to conduct experiments, and selects the Logit model as the benchmark. The experiment results provide evidence for the effectiveness of the ISMCR mechanism, and the overall average accuracy in the bull/bear markets’ predictions is 95.9%. The results also confirm that the overall average performance of LDSS is better than that of Logit and the average lag time is also shorter in LDSS.
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描述:	碩士國立政治大學資訊管理學系 109356011
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109356011
数据类型:	thesis
DOI:	10.6814/NCCU202200850
显示于类别:	[資訊管理學系] 學位論文

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