The Dynamical Mechanism for SMEs Evolution Under the Hologram Approach

Abstract

The purpose of this paper is to discuss enterprise risk assessment under the Hologram framework based on big data language; and to illustrate the Hologram as a new tool for establishing a mechanism to evaluate SMEs growth and change in financial technology dynamically, but mainly we focus on SMEs (but the approach is applying to general enterprises), as this is one kind of very important enterprises with less financial accounting report and associated assets information available.

The key idea of our new approach is to introduce and use of the “ Hologram ” (similar to, “holographic portrait” used in portrait holography), a platform for data fusion dynamically, as a tool and mechanism to describe the dynamic evolution of SMEs based on their business dynamic behavior. Through processing structured and/or unstructured data in terms of “related-party” information sets which analyzes (1) “investment” and (2) “management” information provided by SMEs business behavior, and extracting “Risk Genes” from complex financial network structures in the business ecosystem, we can establish a “good” or “bad ” rating for SMEs by using data fusion dynamically and financial technology. This method to assess SMEs is a new approach to evaluating SMEs development dynamically based on the network structure information of enterprise and business behavior. The framework introduced in this paper for the dynamic mechanism of SMEs’ development and evolution allows us to assess the risk of any SMEs (in particular to evaluate SMEs’loan applications) even not available for critical data required in traditional finance analysis including information such as financial accounting and associated assets, et al. This new “Hologram” approach for SMEs assessment is a pioneering innovation that incorporates big data and Financial technology for inclusive financial services in practical application. Ultimately, the Hologram approach offers a new theoretical solution for the long-standing problem of credit risk assessment for SMEs and individuals in practice.

More precisely, the framework for SMEs risk assessment under the newly established Hologram method relies on the following principles:

First, the use of the “capital-product” paradigm is fundamental. This papers new approach to evaluating SMEs is based on the idea of the “capital-product paradigm for enterprises in general. To explain the cyclical forces associated with the “capital-product transition effect, we use the stochastic resonance (SR) theory for “two-states”. By taking into consideration the capital-product paradigm, the random fluctuations caused by internal and external environments including SMEs capital and business risk factors, and corresponding competitive and cooperative driving forces and response forces, we can better describe the evolution of SMEs development dynamically.

Second, the use of the two key concepts: (1) Spectral Amplification Index (SAI)”, and (2) “Risk Return Rate (URR)” is crucial to risk assessment measurement. According to the principle that maximum utility for SMEs transforms between two locally stable states in terms of capital and product, then the coexistence of an optimal risk level and coordination within cyclical forces drives a good enterprise (the SME) with periodic development changes to approach the maximum value of the output; this is achieved through the concepts of “spectral amplification index (SAI)” and “risk return rate (URR)”. We use these two concepts generated under one unit to reflect the systemic risk level as a fundamental measurement for SMEs evolution dynamically.

Since the information embedded in SMEs’ business behavior reveals the competition and cooperation mechanism that drives its stochastic resonance (SR) behavior which is associated with successful SMEs development, the two concepts of SAI and URR under the Hologram approach to risk assessment that identifies if an SME is “good” based on the network generated from an SMEs related-parties information in terms of “investment” and “management” dynamically, along with other available information such as related investment capital and risk control. Significantly, the Hologram approach to risk assessment for SMEs does not require critical data of traditional financial account and related assets, et al which heavily depend on financial accounting and associated assets used by financial risk analysis in practice. Using big data and FinTech, the Hologram method discussed in this paper utilizes the related-party information (in term of investment and management) of each SMEs which exists in an embedded business network to overcome the situation for SMEs which always have not or have not enough in providing accounting and associated asset information in the practice.

By the feature of each Hologram for a given SME always has the related-party information in term of either investment, or management dynamically, is indeed also an explanation for the reason why the new approach proposed only comes true only until the era of bigdata’s occurring by using ideas from financial technology today.

Furthermore, this paper explores the implementation of the “Holo Credit Loan”, a pure credit loan without any collateral and guarantee launched in 2016, as practical applications of the Hologram approach. We illustrate the framework of SMEs risk assessment under the Holograms new theoretical basis for solving the long-standing problem of credit risk assessment for SMEs (and individuals). Moreover, this papers conclusion will address the performance of the “Holo Credit Loan”.

Keywords: SMEs, Hologram, Data fusion, Dynamical evolution mechanism, Technology-Capital paradigm, Related-party, Shareholder structure, Board of director, Risk gene, Credit score, Stochastic resonance, business behavior network, Systemic risk, Spectral amplification index (SAI), Unit risk-return (URR), U-sha

JEL Classification: B20, B53, C18, C24, C44, C54, C58, C63, C73, D01, D53, D91, E14, G17, G21, G30, G38, L22, L26, M13,