Unpacking the effects of scams in marketplace lending: investor confidence and attention

Figures & data

Figure 1. The number of marketplace lending platforms.

Figure 2. The number of active lenders and borrowers.

Table 1. Summary statistics.

	Mean	SD	Min	Max
Panel A: market-level characteristics
Net capital inflow (billion CNY)	8.889	25.088	−66.718	57.745
Abnormal ratio (%)	3.799	2.474	0.694	14.990
Transformational ratio (%)	1.947	1.437	0	6.424
Problematic ratio (%)	1.851	1.569	0.235	10.213
Interest rate (%)	12.162	3.456	9.210	21.630
Duration (months)	9.346	3.400	4.230	15.750
# of lenders (thousand)	2563.338	1384.381	165.300	4541.000
# of borrowers (thousand)	1858.241	1578.097	26.700	5207.700
Year 2015	0.176	0.384	0	1
Year 2016	0.176	0.384	0	1
Year 2017	0.176	0.384	0	1
Year 2018	0.176	0.384	0	1
Year 2019	0.118	0.325	0	1
# of observations	68
Panel B: daily Baidu search trends
Peer-to-peer collapse (thousand)	0.806	1.993	0	23.782
Peer-to-peer runaway (thousand)	1.197	1.010	0	10.271
Peer-to-peer financing (thousand)	6.817	4.594	1.086	36.075
Peer-to-peer lending (thousand)	7.913	3.094	2.687	22.142
WDZJ (thousand)	10.298	3.371	3.732	33.158
P2Peye (thousand)	6.422	2.510	2.294	23.656
# of observations	2069
Panel C: monthly Baidu search trends
Concerns of risk (thousand)	60.918	77.399	0.572	455.097
Concerns of industry (thousand)	956.939	262.427	433.921	1864.926
# of observations	68

Table 2. Scams and net capital inflow.

	(1)	(2)	(3)	(4)
	Net capital inflow
Abnormal ratio	−5.113***	−4.453***
	(0.708)	(0.977)
Transformational ratio			−3.331	−1.030
			(2.115)	(2.099)
Problematic ratio			−6.048***	−6.234***
			(1.037)	(0.928)
Interest rate	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes
Year FE	No	Yes	No	Yes
Observations	67	67	67	67
R^2	0.791	0.816	0.796	0.831

Notes: Results from estimating model (1) reveal the effects of scam conditions on the net capital inflow of the peer-to-peer lending market. In Columns (2) and (4), we incorporate year fixed effects as control variables. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 3. Scams and concerns of risk.

	(1)	(2)	(3)	(4)
	Concerns of risk
Abnormal ratio	22.739***	21.021***
	(4.311)	(3.920)
Transformational ratio			7.830	4.978
			(6.242)	(6.741)
Problematic ratio			30.583***	29.382***
			(7.051)	(5.782)
Interest rate	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes
Year FE	No	Yes	No	Yes
Observations	68	68	68	68
R^2	0.762	0.799	0.797	0.833

Notes: Results from estimating model (2) reveal the effects of scam conditions on lenders’ concerns on the peer-to-peer lending risk. In Columns (2) and (4), we incorporate year fixed effects as control variables. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 4. Scams and concerns of industry.

	(1)	(2)	(3)	(4)
	Concerns of industry
Abnormal ratio	64.179***	65.423***
	(10.722)	(13.604)
Transformational ratio			31.398	17.126
			(19.714)	(26.614)
Problematic ratio			81.424***	90.595***
			(11.739)	(11.937)
Interest rate	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes
Year FE	No	Yes	No	Yes
Observations	68	68	68	68
R^2	0.699	0.709	0.714	0.736

Notes: Results from estimating model (3) reveal the effects of scam conditions on lenders’ concerns on the peer-to-peer lending industry. In Columns (2) and (4), we incorporate year fixed effects as control variables. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Figure 3. Heatmaps of the feature importance.

Table 5. Scams and investor confidence and attention: dynamic estimate.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
L2.Transformational ratio	−1.454	0.288	2.751	0.268	−10.585	−32.422
	(2.086)	(2.049)	(3.816)	(4.322)	(22.637)	(27.708)
L1.Transformational ratio	−0.575	0.221	0.312	−0.787	−13.473	−22.288
	(2.055)	(1.792)	(4.676)	(4.133)	(26.718)	(27.189)
Transformational ratio	−2.465	−1.029	8.115	8.765	48.256*	35.754
	(2.402)	(2.371)	(5.070)	(5.391)	(26.326)	(32.130)
L2.Problematic ratio	−0.703	−2.076	−3.821	−2.977	−16.618	−4.385
	(1.239)	(1.397)	(4.448)	(5.375)	(16.412)	(19.576)
L1.Problematic ratio	−0.549	−0.643	24.053***	23.743***	33.980*	30.485
	(1.848)	(1.735)	(8.775)	(8.173)	(18.121)	(23.326)
Problematic ratio	−5.943***	−6.302***	18.639***	20.132***	61.609***	73.977***
	(1.772)	(1.617)	(5.087)	(5.217)	(15.404)	(16.216)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	No	Yes	No	Yes	No	Yes
Observations	66	66	66	66	66	66
R^2	0.803	0.840	0.885	0.906	0.705	0.749

Notes: The inclusion of lag terms for the transformational ratio and problematic ratio in the baseline models (1) to (3) reveals the dynamic effects of scam conditions on net capital inflow, concerns of risks, and concerns of industry in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. “L2.Transformational ratio” and “L2.Problematic ratio” are the transformational ratio and problematic ratio on the two months before the target month, respectively. “L1.Transformational ratio” and “L1.Problematic ratio” are the transformational ratio and problematic ratio on the one month before the target month, respectively. In Columns (2), (4) and (6), we incorporate year fixed effects as control variables. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 6. Scams and investor confidence and attention: heterogeneity across supervision stages.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
	Policy = 1	Policy = 0	Policy = 1	Policy = 0	Policy = 1	Policy = 0
Transformational ratio (TR)	−1.615	−8.407**	2.317	10.762***	2.546	172.211
	(2.125)	(3.536)	(8.058)	(3.310)	(19.055)	(114.035)
Problematic ratio (PR)	−7.363***	1.118	36.347***	0.894	77.259***	21.381
	(0.996)	(1.152)	(8.537)	(0.872)	(16.287)	(21.949)
Difference of TR between policy = 1 and policy = 0	6.792*		−8.445		−169.664
	(3.974)		(8.862)		(109.470)
Difference of PR between policy = 1 and policy = 0	−8.481***		35.454***		55.878**
	(1.490)		(8.824)		(26.657)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	No	No	No	No	No	No
Observations	44	23	44	24	44	24
R^2	0.819	0.867	0.817	0.928	0.752	0.828

Notes: By employing subsamples corresponding to different supervision stages, results from estimating models (1) to (3) reveal the heterogeneous effects of scam conditions on net capital inflow, concerns of risks, and industry concerns in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. The first regulatory policy of peer-to-peer lending was published in December 2015. The observations after December 2015 are classified as the “Policy = 1” group. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 7. Scams and investor confidence and attention: heterogeneity across regions.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
	1^st-tier regions	Other regions	1^st-tier regions	Other regions	1^st-tier regions	Other regions
Transformational ratio (TR)	−0.780	−0.184	1.959	1.720	5.685	−5.141
	(1.405)	(0.583)	(1.398)	(4.336)	(4.887)	(24.457)
Problematic ratio (PR)	−3.901***	−1.518**	6.542***	20.460***	22.753***	57.360***
	(0.525)	(0.621)	(1.391)	(6.042)	(2.864)	(15.636)
Difference of TR between 1^st-tier regions and other regions	−0.596		0.240		10.826
	(1.521)		(4.556)		(24.940)
Difference of PR between 1^st-tier regions and other regions	−2.383***		−13.919**		−34.607**
	(0.813)		(6.200)		(15.896)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	67	67	68	68	68	68
R^2	0.859	0.753	0.831	0.774	0.798	0.704

Notes: By employing subsamples corresponding to different regions, results from estimating models (1) to (3) reveal the heterogeneous effects of scam conditions on net capital inflow, concerns of risks, and industry concerns in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. About 41% of platforms are located in Beijing, Shanghai, and Guangdong. These three regions are classified as the “1^st-tier regions” group. Other provinces of mainland China are classified as the “Other regions” group. We incorporate year fixed effects as control variables in all columns. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 8. Robustness tests: baseline results.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
ATO	−3.809***		18.127***		55.985***
	(0.752)		(2.942)		(10.212)
TTO		−0.740		3.390		12.112
		(1.914)		(6.236)		(23.767)
PTO		−5.393***		25.748***		78.673***
		(0.773)		(5.135)		(9.861)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	67	67	68	68	68	68
R^2	0.818	0.832	0.806	0.839	0.713	0.738

Notes: Using alternative measurements for scam conditions, results from estimating models (1) to (3) reveal the effects of alternative scam conditions on net capital inflow, concerns of risks, and industry concerns in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. “ATO” is the abnormal platforms to operating platforms ratio, measuring the alternative total scam condition. “TTO” is the transformational platforms to operating platforms ratio, measuring the alternative moderate scam condition. “PTO” is the problematic platforms to operating platforms ratio, measuring the alternative severe scam condition. We incorporate year fixed effects as control variables in all columns. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 9. Robustness tests: dynamic estimate.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
L2.TTO	−1.234	0.349	2.570	0.297	−10.283	−29.707
	(1.881)	(1.831)	(3.207)	(3.674)	(20.112)	(25.187)
L1.TTO	−0.596	0.128	−0.065	−0.966	−12.125	−19.678
	(1.864)	(1.598)	(3.983)	(3.576)	(24.029)	(24.698)
TTO	−2.021	−0.693	7.006	7.427	41.532*	29.170
	(2.225)	(2.182)	(4.433)	(4.729)	(23.733)	(28.777)
L2.PTO	−0.587	−1.786	−3.177	−2.256	−14.063	−2.873
	(1.067)	(1.161)	(3.450)	(4.162)	(14.091)	(16.924)
L1.PTO	−0.543	−0.667	21.305***	21.150***	30.549**	28.252
	(1.549)	(1.465)	(6.646)	(6.193)	(15.081)	(19.383)
PTO	−5.118***	−5.430***	16.448***	17.715***	52.936***	63.518***
	(1.488)	(1.315)	(3.977)	(3.998)	(12.438)	(13.303)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	No	Yes	No	Yes	No	Yes
Observations	66	66	66	66	66	66
R^2	0.804	0.841	0.902	0.919	0.709	0.753

Notes: Using alternative measurements for scam conditions, results by adding lag terms for scam conditions in the baseline models (1) to (3) reveal the dynamic effects of alternative scam conditions on net capital inflow, concerns of risks, and concerns of industry in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. “L2.TTO” and “L2.PTO” are the transformational platforms to operating platforms ratio and problematic platforms to operating platforms ratio on the two months before the target month, respectively. “L1.TTO” and “L1.PTO” are the transformational platforms to operating platforms ratio and problematic platforms to operating platforms ratio on the one month before the target month, respectively. In Columns (2), (4) and (6), we incorporate year fixed effects as control variables. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 10. Robustness tests: heterogeneity across supervision stages.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
	Policy = 1	Policy = 0	Policy = 1	Policy = 0	Policy = 1	Policy = 0
TTO	−1.231	−8.175**	1.030	10.370***	0.189	169.011
	(1.965)	(3.288)	(7.363)	(3.024)	(17.101)	(106.106)
PTO	−6.268***	1.099	31.390***	0.816	67.849***	19.504
	(0.918)	(1.079)	(7.807)	(0.818)	(12.944)	(20.837)
Difference of TTO between policy = 1 and policy = 0	6.944*		−9.340		−168.822
	(3.689)		(8.097)		(101.744)
Difference of PTO between policy = 1 and policy = 0	−7.367***		30.574***		48.345**
	(1.386)		(8.070)		(23.763)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	No	No	No	No	No	No
Observations	44	23	44	24	44	24
R^2	0.817	0.869	0.819	0.929	0.763	0.832

Notes: By employing subsamples corresponding to different supervision stages, results from estimating models (1) to (3) reveal the heterogeneous effects of alternative scam conditions on net capital inflow, concerns of risks, and industry concerns in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. “TTO” is the transformational platforms to operating platforms ratio, measuring the alternative moderate scam condition. “PTO” is the problematic platforms to operating platforms ratio, measuring the alternative severe scam condition. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.

Table 11. Robustness tests: heterogeneity across regions.

	(1)	(2)	(3)	(4)	(5)	(6)
	Net capital inflow		Concerns of risks		Concerns of industry
	1^st-tier regions	Other regions	1^st-tier regions	Other regions	1^st-tier regions	Other regions
TTO	−0.541	−0.145	1.454	0.968	4.165	−6.774
	(1.270)	(0.545)	(1.308)	(4.059)	(4.293)	(22.181)
PTO	−3.355***	−1.378***	5.624***	18.629***	19.716***	52.367***
	(0.444)	(0.515)	(1.269)	(5.006)	(2.152)	(12.980)
Difference of TTO between 1^st-tier regions and other regions	−0.396		0.486		10.939
	(1.382)		(4.264)		(22.593)
Difference of PTO between 1^st-tier regions and other regions	−1.977***		−13.005**		−32.651**
	(0.681)		(5.164)		(13.158)
Interest rate	Yes	Yes	Yes	Yes	Yes	Yes
Duration	Yes	Yes	Yes	Yes	Yes	Yes
# of lenders	Yes	Yes	Yes	Yes	Yes	Yes
# of borrowers	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	67	67	68	68	68	68
R^2	0.859	0.758	0.833	0.784	0.803	0.709

Notes: By employing subsamples corresponding to different regions, results from estimating models (1) to (3) reveal the heterogeneous effects of alternative scam conditions on net capital inflow, concerns of risks, and industry concerns in Columns (1) and (2), (3) and (4), and (5) and (6), respectively. “TTO” is the transformational platforms to operating platforms ratio, measuring the alternative moderate scam condition. “PTO” is the problematic platforms to operating platforms ratio, measuring the alternative severe scam condition. We incorporate year fixed effects as control variables in all columns. Robust standard errors are in parentheses. *** Significant at 1%, ** significant at 5%, and * significant at 10%.