We propose an asset pricing factor model constructed with semi-parametric characteristics based mispricing and factor loading functions. This model captures common movements of stock excess returns and includes a two-layer network of arbitrage returns interconnected by securityspecific characteristics. We approximate the unknown functions by B-splines where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-spline coefficients. Meanwhile, the strengthened power approaches to one asymptotically. And the dynamic networks are explored through Hierarchical K-Means Clusterings. We apply our methodology to CRSP monthly data for the US stock market with one-year rolling windows during 1967-2017. This empirical study shows the presence of mispricing functions in certain time blocks and a dynamic network structure of arbitrage returns through groups of some characteristics.

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Background: Depression is a common and potentially life-threatening mental illness, and currently, there is a lack of effective treatment. It has been reported that dehydrocorydaline (DHC) can inhibit monoamine transporter uptake in depressed CUMS mice, but more possible mechanisms of action remain to be further studied.Methods: C57BL/6 mice were exposed to chronic unpredictable mild stress (CUMS) for five consecutive weeks. The mice were administrated with dehydrocorydaline or fluoxetine (FLU) for four consecutive weeks. Behavioral tests including sucrose preference test (SPT), tail suspension test (TST), and forced swimming test (FST) were applied. In parallel, hematoxylin and eosin (H&E) staining and Nissl staining were used to explore the effect of DHC on pathological changes in the hippocampus. The concentrations of depression-related factors (5-HT and DA) and inflammatory factors (TNF-α, IL-6, and IL-1β) in the hippocampus and serum were assessed by ELISA assay. NLRP3 inflammasome pathway-related proteins (NLRP3, IL-18, IL-1 IL-1α, and caspase-1) were detected by western blot. The activation of microglia and astrocytes was subjected to immunofluorescent staining. Additionally, microglia were treated with DHC (100 mg/L) for 24 h following incubation with 100 ng/ml LPS for 12 h. ov-NC or ov-NLRP3 plasmid was transfected into microglia 6 h before LPS induction for exploring the effect of NLRP3 overexpression on DHC-inhibited microglia activation. Then, conditioned media of microglia were collected from each group, followed by intervention of astrocytes for 24 h to explore the effect of NLRP3 overexpression of microglia on astrocyte activation.Results:In vivo administration of DHC was found to ameliorate depressive-like behaviors and attenuate neuron damage of CUMS mice. DHC increased neurotransmitter concentration, reduced the proinflammatory factor levels, attenuated NLRP3 inflammasome activation, and decreased A1 astrocyte and microglia activation in the hippocampus of CUMS mice. Furthermore, in vivo results showed that activated microglia induced activation of A1 astrocytes but not A2 astrocytes.Conclusion: Taken together, we provided evidence that DHC exhibited antidepressive effects on CUMS mice possibly via NLRP3 inflammasome-mediated astrocyte activation.

We propose an asset pricing factor model constructed with semi-parametric characteristics-based mispricing and factor loading functions. This model captures common movements of stock excess returns and includes a two-layer network of arbitrage returns interconnected by security-specific characteristics. We approximate the unknown functions by B-splines where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-spline coefficients. Meanwhile, the strengthened power approaches to one asymptotically. And the dynamic networks are explored through Hierarchical K-Means Clusterings from detected mispricing functions. We apply our methodology to CRSP monthly data for the US stock market with one-year rolling windows during 1967-2017. This empirical study shows the presence of mispricing functions in certain time blocks and a dynamic network structure of arbitrage returns through groups of some characteristics.