摘要翻译:
本文构建了一个综合预警系统(EWS)来识别和预测股票市场的动荡。基于切换ARCH(SWARCH)滤波高波动率区间的概率,本文提出的EWS首先根据一个指标函数对股市危机进行分类,该指标函数通过双峰法动态选取阈值。然后在一个长短时记忆(LSTM)网络的框架内开发了一个混合算法来进行警告动乱的日常预测。在基于10年中国股票数据的实证评价中,本文提出的EWS模型得到了令人满意的结果,其测试集精度为96.6%$,预警期平均为2.4$天。通过交叉验证和反向测试,证明了该模型的稳定性和实时决策的实用价值。
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英文标题:
《An Integrated Early Warning System for Stock Market Turbulence》
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作者:
Peiwan Wang, Lu Zong and Ye Ma
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最新提交年份:
2019
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Quantitative Finance 数量金融学
二级分类:Mathematical Finance 数学金融学
分类描述:Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法,包括随机、概率和泛函分析、代数、几何和其他方法
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英文摘要:
This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. A hybrid algorithm is then developed in the framework of a long short-term memory (LSTM) network to make daily predictions that alert turmoils. In the empirical evaluation based on ten-year Chinese stock data, the proposed EWS yields satisfying results with the test-set accuracy of $96.6\%$ and on average $2.4$ days of the forewarned period. The model's stability and practical value in real-time decision-making are also proven by the cross-validation and back-testing.
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PDF链接:
https://arxiv.org/pdf/1911.12596