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摘要: 股票自动交易系统属于典型的复杂系统，其成功的关键是如何对股价进行有效的预测与决策.股价受多种信息的影响，但传统的自动交易模型多建立在历史交易数据的基础上.针对上述问题，本文综合利用新闻文本数据与股价技术指标数据，基于人工神经网络（Artificial neural netuorks，ANN）方法设计了一种多源数据驱动的股票自动交易决策模型.本文首先分析了各类财经新闻的特点及其对股价的影响，然后设计了相应模板抽取了中文文本中的财经新闻事件；在此基础上，设计了历史股价和新闻事件数据共同驱动的ANN-News模型，并利用实际数据验证了模型的有效性.实验发现，ANN-News模型比传统的机器学习类模型股价预测准确率提升约4%，收益率提升约7%.Abstract: Automatic trading systems are typical complex systems, and a successful automatic trading system should be excellent at prediction and decision. Stock prices are affected by the information from various sources, while traditional automatic trading systems only consider the historical trading data. For this issue, we design an automatic trading framework by considering the signals from stock prices and new information based on artificial neural networks. Specifically, we first analyze various kinds of financial events and their corresponding effects on stock prices, and then extract the financial events that have prominent effects on stock prices. Next, we design an automatic trading model driven by stock prices and financial event data. Experiments on real world datasets show that the proposed ANN-News model outperforms the conventional machine learning models by about 4% in prediction precision and 7% in return, respectively.
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Key words:
- Neural networks /
- data driven /
- automatic trading /
- decision model
1) 本文责任编委 赵勇 -
表 1 金融事件抽取模板
Table 1 Template of the extraction of financial events
类型 事件 触发词($T_r$) 方面词($A$) 依存路径(Path) 1 股价 股价上涨 上/涨 股票 $E_s/E_c \xrightarrow{\rm SBV} T_r$ 股价下跌 下/跌 股价 2 业绩 公司业绩上扬 上/扬 业绩 公司业绩下跌 下/跌 3 声誉 公司声誉提升 点赞 捐赠、公益 $E_s/E_c \xrightarrow{\rm ATT} A \xrightarrow{\rm SBV} T_r$ 公司声誉受损 下滑 口碑 4 利润 公司利润上升 上/升 利润 公司利润下滑 下/滑 5 负债 公司负债良好 良好 负债/债务 $E_s/E_c \xrightarrow{\rm ATT} T_r \xrightarrow{\rm ATT} C$ 公司负债堪忧 堪忧 6 高层 公司高层变动 变动/调整 董事长、CEO $E_c \xrightarrow{\rm ATT} A \xrightarrow{\rm SBV} T_r$ 7 业务 公司业务扩张 扩张 业务 公司合作 合作 公司($E_c^{(A)}, E_c^{(B)}$) $\{E_c^{(A)} + E_c^{(B)}\}\xrightarrow{\rm SBV} T_r$ 公司并购 并购 
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表 2 技术指标信号介绍
Table 2 Introduction of technical indicators
指标 公式 买卖信号 MACD EMA(12)-EMA(26) $+1:MACD_{t-1} \le 0~\&~MACD_t>0$
$-1:MACD_{t-1} \ge 0\&MACD_t<0$ROC $P_t-P_{t-20}$ $+1:ROC_{t-1} \le 1 ~\&~ ROC_{t} >1$
$-1:ROC_{t-1} \ge 1 ~\&~ ROC_{t} < 1$TRB $P_t^{\rm Max}={\rm Max}(P_{t-1}, P_{t-2}, \cdots, P_{t-20})$
$P_t^{\rm Min}={\rm Min}(P_{t-1}, P_{t-2}, \cdots, P_{t-20})$$+1:P_t>P_t^{\rm Max}$; $-1:P_t \ge P_t^{\rm Min}$ A/D $\frac{P^{High}_t-P_{t-1}}{P^{High}_t-P^{Low}_{t-1}}$ $+1:A/D_t\ge0;~-1:A/D_t<1$ Dis $P_t/MA(60)$ $+1:Dis_t \ge 1$; $-1:Dis_t<1$ VMA MA(20)-MA(60) $+1:VMA_{t-1}<0~\&~VMA_t>0$
$+1:VMA_{t-1}\ge 0~\&~VMA_t<0$}EMV $\frac{Mid_t-Mid_{t-1}}{BoxRatio_t}$, $Mid_{t}=\frac{P^{High}_t+P^{Low}_t}{2}$ $+1:EMV_{t-1}\le 0 ~\&~ EMV_{t} > 0$
$-1:EMV_{t-1}\ge 0EMV_t<1$CCI $\frac{M_t-SM_t}{0.015D_t}, $其中$M_t=\frac{P_t+P_t^{High}+P_t^{Low}}{3}$ $+1: CCI_t <-200~or~CCI_t >CCI_{t-1}$
$-1:CCI_t >200~or~CCI_t \le CCI_{t-1}$S/R $Sup(60)_t=MA(60)_t-2\sigma(60)_t$
$Res(60)_t=MA(60)_t+2\sigma(60)_t$$+1:P_t>Sup(60)_t$; $-1:P_t < Res(60)_t$ RSI $100-\frac{100}{1+(\sum^{n-1}_{i=0}Up_{t-i}/n)(\sum^{n-1}_{i=0}Dw_{i-1}/n)}$ $+1:RSI_{t}<30~or~RSI_{t}>RSI_{t-1}$
$-1:RSI_{t}>70~or~RSI_{t}\le RSI_{t-1}$注:其中, $P_t$表示$t$日的收盘价, $P^{High}_t$表示$t$日的最高价, $P^{Low}_t$表示$t$日的最低价. $MA(n)$为过去$n$天的简单移动平均计算为: $\sum^{n-1}_{i=0}p_{t-i}/n$, $EMA(n)$为过去$n$天($n=12/26$)的指数移动平均计算为: $P_t/n+(1-1/n)EMA(n)_{t-1}$, 其中$EMA(n)_0=P_1$, $\sigma(n)_t$为过去$n$天$(n=60)$收盘价的标准差计算为: $\sqrt{\sum^t_{i=t-n}(P_i-MA(n)_t)^2/n}$, $Up_t$是$t$日较上一期价格上涨的幅度(Upward-price-change), $Dw_t$是$t$日较上一期价格下降的幅度(Downward-price-change). $SM_t=\frac{\sum^n_{i=1}M_{t-i+1}}{n}$, $D_t=\frac{\sum^n_{i=1}|M_{t-i+1}-SM_t|}{n}$, $BoxRatio_t = Vol_t/1\, 000(P_t^{High}-P_t^{Low})$. $\pm1$代表买入/卖出信号, 其他情况表示持有.
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表 3 财经事件与股价收益统计分析
Table 3 Statistic on the relationship between financial events and stock returns
事件 I Freq. R0 (%) $d$ $p$ R1 (%) $d$ $p$ R2 (%) $d$ $p$ R5 (%) $d$ $p$ R10 (%) $d$ $p$ 股价上涨 2 1957 1.71 84 0.01 1.32 81 0.01 1.24 75 0.01 1.19 72 0.01 1.43 70 0.00 股价下跌 -2 2039 -0.92 82 0.00 -1.21 77 0.01 -1.21 69 0.00 -0.87 67 0.03 -0.70 67 0.00 公司业绩上扬 3 702 0.88 65 0.00 0.78 59 0.03 1.23 58 0.02 1.14 55 0.00 0.95 53 0.02 公司业绩下跌 -2 499 -0.59 54 0.04 -0.87 47 0.01 -0.75 51 0.30 -0.87 50 0.56 -0.71 48 0.42 公司声誉提升 2 415 0.41 63 0.05 0.44 59 0.27 0.38 56 0.37 0.29 57 0.54 0.23 49 0.15 公司声誉受损 -2 90 -0.26 51 0.05 -0.39 51 0.29 -0.15 50 0.05 -0.13 49 0.52 -0.16 47 0.04 公司利润上升 3 344 0.41 51 0.48 0.41 49 0.18 0.59 47 0.69 0.66 51 0.01 0.79 55 0.01 公司利润下滑 -3 200 -1.37 66 0.24 -0.88 64 0.10 -0.71 58 0.11 -0.49 53 0.04 -0.44 57 0.00 公司负债良好 1 189 0.13 54 0.15 0.34 51 0.47 -0.06 50 0.05 0.28 47 0.08 0.23 51 0.06 公司负债堪忧 -1 125 -0.15 56 0.01 -0.27 55 0.08 -0.66 57 0.01 -0.23 50 0.11 -0.25 59 0.04 公司高层变动 1 146 -0.13 51 0.19 0.08 60 0.45 -0.11 50 0.04 0.25 51 0.19 0.17 57 0.16 公司合作 1 137 0.16 49 0.00 0.19 50 0.36 0.05 50 0.11 0.09 56 0.32 0.14 51 0.04 公司业务扩张 1 147 0.24 51 0.12 0.58 49 0.15 -0.45 48 0.13 -0.16 49 0.24 0.46 49 0.31 公司并购 3 73 0.18 54 0.02 0.29 54 0.06 0.35 50 0.16 0.11 53 0.04 0.09 54 0.50 7 063 60 58 55 54 55
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表 4 预定义影响力和收益$R_x$的关系
Table 4 Relationship between predefined impact and $R_x$
$R_x$ $r$ $p$ $R_0$ 0.805 0.000 $R_1$ 0.861 0.000 $R_2$ 0.821 0.000 $R_5$ 0.813 0.000 $R_{10}$ 0.801 0.001
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表 5 数据集1技术信号与新闻信号收益统计
Table 5 Statistics of the returns generated by technical and news signals on Dataset 1
信号 买入(%) 卖出(%) $R_1$ $R_2$ $R_5$ $R_1$ $R_2$ $R_5$ MACD 0.146 0.123 0.339 -0.443 -0.510 -0.285 ROC 0.151 0.237 0.358 -0.062 -0.056 -0.097 A/D 0.103 0.187 0.350 -0.104 -0.024 0.309 Dis 0.057 0.129 0.199 -0.008 -0.025 0.015 VMA 0.330 0.628 1.156 -0.420 -0.415 -0.278 EMV 0.029 0.195 0.163 -0.024 -0.168 -0.169 TRB 0.345 0.459 0.925 -0.276 -0.247 -0.177 CCI 0.190 0.217 0.479 -0.054 0.063 0.118 SR 0.057 0.129 0.200 -0.005 -0.003 0.015 RSI 0.135 0.137 0.182 -0.017 0.105 0.029 新闻信号 1.019 0.847 0.515 -1.031 -0.614 -0.227
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表 6 表现最佳的3组ANN参数组合
Table 6 The best three combinations of ANN model
$n_1$ $n_2$ $mc$ 数据集1 (%) 数据集2 (%) 训练集 测试集 训练集 测试集 1 50 26 0.3 71.53 70.65 69.65 67.38 2 48 30 0.1 73.79 71.89 68.27 66.05 3 49 34 0.4 78.60 72.03 74.14 71.77
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表 7 ANN与ANN-News模型对比
Table 7 Comparison between ANN and ANN-News
$n_1$ $n_2$ $mc$ ANN (%) ANN-News (%) 训练集 测试集 训练集 测试集 1 50 26 0.3 71.53 70.65 69.65 67.38 2 48 30 0.1 73.79 71.89 68.27 66.05 3 49 34 0.4 78.60 72.03 74.14 71.77 4 49 34 0.7 69.14 65.17 78.08 72.58
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表 8 ANN模型与经典模型对比
Table 8 The comparison among ANN and other classical models
准确率 收益率 ANN 0.682 (±0.04) 1.306 (±0.06) ANN-News 0.739(±0.02) 1.403 (±0.03) SVM 0.653 (±0.04) 1.024 (±0.09) SVM-News 0.692 (±0.06) 1.067 (±0.12) Naïve Bayes 0.641 (±0.06) 1.102 (±0.05) Naïve Bayes-News 0.687 (±0.08) 1.281 (±0.08)
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