The study on systemic risk of rural finance based on macro–micro big data and machine learning

Figures & data

Table 1. Descriptive statistics of macro data indicators.

Variables	Mean	Std. dev.	Skewness	Kurtosis	Variables	Mean	Std. dev.	Skewness	Kurtosis
RHITB	0.073	0.053	−0.735	6.605	RNLR	0.159	0.181	0.919	1.963
RHETB	0.121	0.045	0.789	3.818	RIER	1.486	0.208	0.868	3.333
RITB	0.209	0.303	−0.347	3.585	PBIER	0.891	0.068	−0.342	2.547
MCI	1.001	0.039	0.390	7.195	RFSTB	0.196	0.142	0.881	2.617
RLTB	0.268	0.749	5.877	43.004	RFSR	0.082	0.019	−0.778	1.876
RICHB	0.007	0.133	0.007	2.591	RELR	0.407	0.377	1.808	6.187
RDLR	1.229	0.109	1.396	5.598

Table 2. Descriptive statistics of micro data indicators.

Variables	Mean	Std. dev.	Skewness	Kurtosis	Variables	Mean	Std. dev.	Skewness	Kurtosis
OITB	0.158	0.143	0.367	2.889	ALRHB	0.004	0.033	−0.155	3.203
OIHB	0.369	0.720	−0.547	2.039	CR	1.171	0.142	−0.258	2.200
OCHB	0.392	0.762	−0.242	2.547	CRHB	−0.002	0.040	−0.148	2.776
OCR	0.848	0.034	0.798	5.557	QR	0.725	0.128	0.252	2.470
NPTB	0.100	0.970	0.974	6.652	QRHB	−0.005	0.065	−0.356	3.024
NPHB	0.321	1.282	−1.416	11.100	TAT	0.515	0.280	0.411	2.132
PTTB	0.483	1.393	2.626	11.886	AHB	0.042	0.057	1.430	6.808
PTHB	0.207	2.030	−6.217	50.510	ATB	0.073	0.071	1.348	6.231
ER	3.825	4.152	1.006	5.396	DHB	0.046	0.071	0.595	4.422
TAR	2.030	2.130	1.308	5.650	DTB	0.205	0.185	1.388	5.470
ALR	0.510	0.051	0.067	1.854

Table 3. KMO test and Bartlett test results of macro-micro indicators.

Related test standards		Macro factors (FH)	Micro factors (FW)
KMO Test value		0.657	0.623
Bartlett Sphericity test	Approximate cardinality	524.220	2044.933
	Degree of freedom	78	210
	p-value	0.000***	0.000***

(Note: ***, **, * represent 1%, 5%, 10% significance levels, respectively.)

Table 4. Eigenvalues and variance contribution of each principal component of macro-micro indicators.

	Macro indicators			Micro indicators
PC	Eigenvalue	Variance contribution rate	Cumulative variance contribution rate	Eigenvalue	Variance contribution rate	Cumulative variance contribution rate
1	3.677	28.287%	28.287%	5.432	25.868%	25.868%
2	2.557	19.671%	47.958%	3.781	18.005%	43.874%
3	1.373	10.562%	58.520%	3.014	14.355%	58.228%
4	1.106	8.509%	67.029%	2.037	9.702%	67.930%
5	0.976	7.511%	74.540%	1.483	7.062%	74.993%
6	0.868	6.679%	81.218%	1.366	6.506%	81.499%
7*	0.612	4.705%	85.923%	1.000	4.761%	86.260%
8	0.583	4.483%	90.406%	0.865	4.118%	90.378%
9	0.452	3.476%	93.882%	0.509	2.423%	92.801%
10	0.344	2.643%	96.524%	0.446	2.121%	94.922%

Figure 1. Heat map of factor loading matrix.

Table 5. Static and dynamic CAPM with the macro-micro big data sets.

Model	$\alpha$	${\text{β}}_0$	${\text{β}}_1$	${\text{β}}_2$	$R^2$	F-value
OLS-CAPM	−0.013 (0.070)*	0.874 (0.000)***			0.548	97.764***
OLS-DCAPM	−0.012 (0.098)*	0.084 (0.880)	1.578 (0.149)	0.076 (0.718)	0.560	32.684***
RR-DCAPM	−0.013 (0.090)*	0.344 (0.000)***	0.834 (0.000)***	0.145 (0.293)	0.553	31.795***

(Note: Parentheses represent significant p values;***, **, * represent 1%, 5%, 10% significance levels, respectively.)

Figure 2. Fitting diagram of the ridge regression model.

Figure 3. Feature importance histogram of decision tree and random forest.

Table 6. Comparison of regression fit superiority between decision tree and random forest.

	Decision trees model		Random forest model
Judgment criteria	Training set	Test set	Training set	Test set
Mean Square Error (MSE)	0.000	0.010	0.001	0.002
Root Mean Squared Error (RMSE)	0.003	0.098	0.027	0.046
Mean Absolute Error (MAE)	0.002	0.081	0.020	0.033
Mean Absolute Percentage Error (MAPE)	3.811	378.302	97.559	297.984
Square of Decidable Coefficient (R^2)	0.999	0.294	0.929	0.559

Figure 4. Time-series diagram of rural systemic risk beta value.

Table 7. Descriptive statistics of rural financial systemic risk β value.

Variable name	Maximum value	Minimum value	Mean value	Standard deviation	Skewness	Kurtosis	Coefficient of variation (CV)
β	1.026	0.424	0.781	0.099	−0.491	1.441	0.126