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基于金融物理学中著名的对数周期幂律模型(log-periodic power law model, LPPL)来预警2015年6月份中国上证综合指数、创业板指数的崩盘. 鉴于已有采用LPPL模型预警市场崩盘的研究均只考虑市场历史交易数据. 本文将投资者情绪因素纳入到LPPL模型建模过程, 以改进LPPL模型的预警效果. 采用文本挖掘技术结合语义分析方法对抓取的财经媒体的股评报道进行词频统计, 以构建媒体情绪指数. 进一步修改LPPL模型中的崩溃概率函数表达式, 将其表示为关于历史交易数据及媒体情绪的函数, 构建LPPL-MS组合模型预警股市崩盘. 实证结果表明, 本文所构建的LPPL-MS组合模型相比LPPL模型具有更高的预警精度, 其预测的大盘见顶的临界时点与上证指数、创业板指数真实的见顶时点更为接近, 并且其拟合结果通过了相关检验.This paper is based on the famous log-periodic power law model (LPPL) in financial physics to warn of the collapse of China's Shanghai Composite Index and GEM Index in June 2015. In view of the existing research using the LPPL model to warn of market crash, only the historical trading data of the market are considered. For the first time, investor sentiment factors are incorporated into the modeling process of LPPL model to improve the early warning effect of LPPL model. Using the text mining technology combined with semantic analysis methods to grasp the financial media's stock evaluation report for word frequency statistics, in order to build the medium sentiment index. The further modified expression of the crash probability function in the LPPL model is represented as a function of historical trading data and medium sentiment, and thus constructing an LPPL-MS combination model to warn of stock market crash. The empirical results show that the LPPL-MS combination model constructed in this paper has higher warning accuracy than the LPL model, and its prediction crash time is closer to the actual crash time of the Shanghai Index and GEM Index, and its fitting results have passed the relevant test.
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Keywords:
- LPPL-MS model/
- medium sentiment/
- stock market crash/
- warning
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序号 积极词汇 消极词汇 1 突破 拖累 2 增长 不涨反跌 3 高涨 分化 4 火爆 沉重打击 5 赚钱 恐慌 6 上升 下降 7 一枝独秀 狂跌 8 蒸蒸日上 跌停板 9 活跃 退市 10 提速 暴涨暴跌 
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MSE A B C m ω ϕ λ $ {t_{\text{c}}} $ LPPL 0.997 10.131 –0.864 –0.017 0.188 12.098 1.748 — 369.022 1.078 10.161 –0.828 –0.015 0.197 11.991 1.840 — 379.036 1.112 9.949 –0.805 0.014 0.188 10.368 2.029 — 361.668 LPPL-MS 0.948 9.639 –0.562 –0.014 0.226 12.398 6.686 0.008 362.729 0.991 9.597 –0.509 –0.013 0.240 12.911 3.717 0.007 365.669 1.038 9.552 –0.508 –0.012 0.237 12.953 3.671 0.010 360.949 下载:
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MSE A B C m ω ϕ λ $ {t_{\text{c}}} $ LPPL 2.429 8.444 –0.334 0.006 0.239 13.988 2.241 — 335.923 2.443 8.654 –0.436 –0.015 0.217 13.009 3.989 — 341.826 2.477 8.517 –0.355 –0.011 0.236 13.039 3.998 — 340.435 LPPL-MS 2.703 8.617 –0.395 0.011 0.229 15.242 0.288 0.015 344.122 2.819 9.218 –0.766 0.016 0.174 16.445 5.320 0.010 352.216 2.904 8.922 –0.559 0.010 0.200 21.717 0.256 0.010 353.759 下载:
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最低绝
对误差最高绝
对误差平均绝
对误差LPPL 上证指数 9 27 18 创业板指 4 10 7 LPPL-MS 上证指数 8* 13* 11* 创业板指 1* 8* 6* 注: *表明预测的见顶时点最接近真实值. 下载:
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拟合情形1 拟合情形2 拟合情形3 LPPL 上证指数 0.014** 0.021** 0.013** 创业板指 0.0428** 0.073* 0.0959* LPPL-MS 上证指数 0.001*** 0.001*** 0.012** 创业板指 0.019** 0.041** 0.074* 注: *表明在10%水平下显著; **表明在5%水平下显著; ***表明在1%水平下显著. 下载:
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