Selective transfer learning with adversarial training for stock movement prediction

Figures & data

Figure 1. (Better viewed in colour) The overview of our proposed architecture STLAT. The target batch in (b) represents the validated samples from target stock and the target batch in (c) represents the training samples from target stock.

Table 1. Technical features used in the model and their calculation. ma: moving average. roc: rate of change.

Features	Calculation
c_open, c_high, c_low	c_open=${\mathsf{o}\mathsf{p}\mathsf{e}\mathsf{n}}_t/{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_t-1$
5-ma, 10-ma, 15-ma,	n-ma=$\frac{{\sum }_{i=0}^{n-1}\mathsf{a}\mathsf{d}\mathsf{j}\mathrm{\_}{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_{t-i}}{\mathsf{a}\mathsf{d}\mathsf{j}\mathrm{\_}{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_t}-1$
20-ma, 25-ma, 30-ma
1-roc, 5-roc, 10-roc,	n-roc=$\frac{{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_t}{{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_{t-n}}-1$
15-roc, 20-roc, 25-roc
1-roc', 5-roc', 10-roc',	n-roc'=$\frac{\mathsf{a}\mathsf{d}\mathsf{j}\mathrm{\_}{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_t}{\mathsf{a}\mathsf{d}\mathsf{j}\mathrm{\_}{\mathsf{c}\mathsf{l}\mathsf{o}\mathsf{s}\mathsf{e}}_{t-n}}-1$
15-roc', 20-roc', 25-roc'

Table 2. Experimental results (Acc and MCC) comparison of our model and baselines on three stock benchmarks.

	ACL18		KDD17		CN50
Model	ACC(%)	MCC	ACC(%)	MCC	ACC(%)	MCC
RAND	49.72	−0.0056	50.09	0.0018	49.88	−0.0023
ARIMA (Brown, 2004)	51.03	−0.0021	50.28	−0.0053	50.83	0.0437
RF (Kumar & Thenmozhi, 2006)	52.13	0.0112	51.18	0.0208	51.73	0.0410
LSTM (Hochreiter & Schmidhuber, 1997)	53.19	0.0136	51.62	0.0183	51.16	0.0393
DA-RNN (Qin et al., 2017)	54.90	0.1043	51.94	0.0261	51.80	0.0433
SFM (Zhang et al., 2017)	56.12	0.1242	52.10	0.0277	52.11	0.0506
S-TF (He et al., 2019)	56.68	0.1323	52.71	0.0289	52.14	0.0512
Adv-ALSTM (Feng, Chen et al., 2019)	57.20	0.1483	53.05	0.0523	52.16	0.0515
MAN-SF (Sawhney et al., 2020)	60.80	0.1950	54.15	0.0621	53.44	0.0860
STLAT-AT	60.61	0.2240	55.73	0.1110	56.01	0.1178
STLAT-TL	58.78	0.1698	54.57	0.0864	54.86	0.0914
STLAT-S	61.44	0.2394	56.08	0.1233	56.78	0.1363
STLAT	64.56	0.2967	58.27	0.1585	58.33	0.1636

Table 3. Performance with different metrics on ACL18 dataset.

Model	Specificity (%)	Precision (%)	Sensitivity (%)	F1-score (%)
Adv-ALSTM (Feng, Chen et al., 2019)	59.41	56.22	60.10	58.09
MAN-SF (Sawhney et al., 2020)	58.11	61.44	58.16	59.75
STLAT	63.14	65.15	63.19	64.15

Figure 2. Impact of the perturbation scale.

Figure 3. Impact of the time-step lag size.

Table 4. Performance in Acc / MCC is reported with four strategies.

Model	ACC (%)	MCC
$S_1$ / $S_1$+DS	58.45 / 60.16	0.1672 / 0.2284
$S_2$ / $S_2$+DS	57.61 / 59.38	0.1438 / 0.2026
$S_3$ / $S_3$+DS	62.78 / 63.44	0.2631 / 0.2845
$S_4$ / $S_4$+DS	63.14 / 64.87	0.2710 / 0.3041

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