An Analysis of Studying Model for Digital Finance in China

Digital finance belongs to the pioneering field of finance theory in the 21st century. It demonstrates unique characteristics and drives the traditional financial theory into financial digitalization. Some researchers in China have recently been engaging with this new field to discover a new era in finance, namely digital finance. The main purpose of this paper is to evaluate the model and characteristics of digital finance explored in China as of date based on econometric theory and methodologies. The principal results are problems derived from literature in digital finance. It would be of importance, significance and implication for the learning world and society to conclude that the technical characteristics of digital finance can be summarized as a centralized digital financial system developed based on blockchain technology; its core foundation must be fiat currency digitization like E-CNY. Furthermore, decentralized Bitcoin is impossible to be the cornerstone of digital finance, nothing more than just a driving force to the development of digital finance.

This is a comprehensive review paper on the current state of research about digital finance in China. Here the digital finance-related papers referenced by this paper largely covered the latest research status in this field. The existing ones summarized here are digital finance and the related problems derived from the references. It is in the future that research, the cornerstone of a series of follow-up work, will be conducted. This may be the academic value of the current work. In order to study the status of digital finance, there must be a holistic cognition of it. There will be seven main parts to be investigated in the rest of this paper, like digitalization of finance, the concept of digital finance, digital financial technology features, risk characteristics of digital finance, digital finance with the power in interpretation, digital finance research paradigm and its problems, as well as conclusion and discussion. Now, they are stated separately as follows.

The explanatory power of digital finance

According to the literature here, the explanatory power of digital finance is primarily measured by the test of the t-statistic of the parameter, through the empirical research done derived from the establishment of relevant models. The digital finance explains the distribution of topics, included but not limited to, financing constraints, enterprise Innovation, total factor productivity, business growth, green innovation, regional Innovation, region entrepreneurship, agriculture-related loans, agricultural innovation, rural economy, entity economy, financial assets, economic growth, economic inclusive growth, trade finance, banking risk, impoverished/ poverty. For these topics, a large number of scholars have adopted an empirical approach to study them. On the issue of financing constraints, many scholars from different perspectives gave a different portrayal. For example, to define the financing constraint problem based on three dimensions, in terms of financing costs, financing structure namely indirect financing ratio and financing efficiency [46]. There are also differences in financing behaviour in companies of different sizes. Maybe it's not a simple linear process in financing constraints for businesses. In fact, as a measurement of financing constraints for business, the enterprise size is a nonlinear representation of the logarithm [47]. The larger the enterprise, its operating costs can occur complex changes, namely, as the size of the enterprise grows, the high operating costs could decrease, and after reaching the lowest point, the costs will continue to expand as the size of the enterprise continues, successively show a rapid or even sharp rise, until to be bankruptcy. The operating costs of the business are reflected in the sensitivity to cash flows [48,49]. Corporate financing constraints seriously affect corporate innovation. A measure of enterprise innovation is not exactly unified. The general metrics are: the ratio of research or experimental development expenditures and GDP, the ratio of inputs to outputs in science and technology innovation, the number of patent applications, the ratio of number of patents granted and the investment in scientific and technological innovation, the invention creation yield [50,51]. No matter what kind of enterprise innovation is measured, including the breakthrough innovation and incremental innovation, or equity pledge, estimated by parameter t, the criterion of the t test shows that the developments in digital finance seem to be contributing significantly to it [52-54]. In order to ease financing constraints, in turn, to promote enterprise innovation, digital finance encourages companies to lower the threshold for corporate financing, reduces valuation approval costs and reduces information asymmetry [55]. The enterprise innovation has increased dramatically in total factor productivity. To improve total factor productivity must surely be going to accelerate the growth of enterprises, and to consolidate the development of a green economy in urban areas enables regional innovation, namely the ratio of the number of patent applications for invention to R&D and the green innovation [56,57]. Regional entrepreneurship is achieved through an increase in employment rates [58]. It is broad and deep for digital finance to impact on all walks of life in society, which significantly and positively impacts agriculture-related lending, significantly promotes agricultural innovation [59,60]. It is not significant for digital finance to improve the rural economy. The role played by digital finance to improve economic development is difficult to form a consistent decision based on existing research methods. As known by empirical examination, digital finance significantly improves energy efficiency, significantly promotes the development of the real economy, and at the same time, significantly inhibits the tendency to allocate financial assets, significantly contributes to economic growth, including inclusive economic growth, and having injected new impetus into the development of small and medium-sized enterprises [61,62]. The small and medium-sized enterprises play an important role in foreign trade, but the trade finance is an important initiative to make up for the development of small and medium-sized micro enterprises. Digital finance drives the probability of poverty. Has deepened the extent of multidimensional poverty, and does not have a statistically significant impact on consumption among poor households. However, it has a statistically significant impact on the consumption of non-poor households, of a Matthew effect [63]. The size of consumer funds and the efficiency with which they are used can be fed back to the bank's operational risk monitoring, because consumers' spending money basically comes from their balance of deposits in the bank. With the development of digital finance, the transfer of consumer funds is controllable, and the probability of the risk occurring will be significantly reduced. So, the development of digital finance will significantly strengthen the risk bearing capacity of banks, the level of economic development has increased instead has weakened significantly banks' risk-taking capabilities.

Digital finance research paradigm and its problems

Non-large data indicates that information disclosure is not sufficient. Digital finance, or trusted finance can be decomposed as, big data, model, algorithm, system, process, operation, management, design, pricing, trade, hedge, risk control, internal control, supervision, etc., where big data is trustworthy and a trusted model is a support. Quantitative finance based on data and models will be the backbone of finance. However, the model on which digital finance is based is not necessarily credible, that is, the best evidence is that its model has a low fit to the data. According to the literature listed here, there may be a big discrepancy from the actual observation for a lot of empirical conclusions, since researchers rarely do in-depth research on it, and properly explain the reasonable or unreasonable parts of it. The current research paradigm for digital finance can be summarized as follows?

This research paradigm is popular today, but the results of the proof are not ideal. The reasons are at least the following?

One: There is no rational logical analysis of the model, to borrow from or the traces of imitation are too obvious, so be it. Inadvertently, the logical relationship between variables is diluted, it seems a little more casual;

Two: The definition of variables is not precise enough, and compare rough. The reason may be multifaceted, one of the high probabilities is that variables are selected based on the ease of availability of the data. As for whether such a variable can better illustrate the problem itself under study, no one did mind whether subjectively or objectively;

Three: “Only see the trees but not the forest”, the results are generally interpreted in mode, regardless of whether the mode is set appropriately after passing the empirical test, regardless of whether the explanatory variable of interest is statistically significant. On the surface, it looks beyond reproach, but in practice it is really ineffective. The so-called; “the skin does not exist (the rationality of the model), the hair will be attached (explain the statistical significance of variables)”? The skin no longer has its proper function, what is the point of studying Mao? Regrettably, this is how many papers do the like;

Four: In addition to the so-called robustness test in form, the empirical conclusions drawn about the results, have not been explained from a theoretical level, but abruptly stopped. It is difficult to justify itself;

Five: Theoretical analysis lacks logical reasoning, and it is simply to cite the existing literature to elaborate. The problem is that the cited literature is inherently problematic, the discussion is very inadequate. In this case, there seems to be a point of “the effect of spreading false rumours”. This reason may be a good explanation, why so many writing styles of articles are the same or like! The theoretical basis for model building is insufficiently elaborated, the addition or subtraction of variables is arbitrary. This invisibly affects the fit of the model, and it is also difficult to avoid the risk of model interpretation. This is the case, why, basically, it reflects the current state of similar research. The risk is very high to draw conclusions based on models that are not robust. It's like gambling, in an irrational situation. This is precisely the use of seemingly rational scientific analysis methods, but to get a true portrayal of irrational conclusions. It's all irrational to make any policy recommendations based on such a conclusion, although the original intention was rational. The problem with model setup is mainly based on the simplification of subjective speculation, this results in the setup of the model or selection on, its objectivity is relatively low, the model's ability to interpret samples is very limited, or the predictive risk of the model is very large. This case is detached from the academic value and practical significance of using models to study practical problems, thus derived from that for the sake of the model and the form of the model is formatted. It is extremely serious for such academic thinking and research methodology to harm the entire academic community. It is almost always exercised in the name of academic research to spread false rumours, the degree of harm cannot be overemphasized.

1. Are the models on which most studies are based are necessary? If necessary, why don't most research papers care about the suitability of models? If not, why spend a lot of time building a model and do regression analysis on it? Maybe a meaningful explanation is true that those who are used by “me” are true. “Only see the trees and not see the forest” thinking mode weakens the ability to interpret the variables of the explanation, even the interpretive function that causes failure. The model's building argument is inadequate, basically, to borrow someone else's model or that the original does not move or to make small additions or subtractions to the original model, etc., to prove the authority of the applied model. At the same time, “tangerine planted in the south of Huai river is orange, but in the north of Huai river is ‘bitter orange’”, the results are predictable. Many studies are based on model sources derived from the same way. That's why a lot of research is similar, lacking the necessary academic value.

   According to the previous study, the following four main conclusions are summarized, respectively, the statements are as follows:

   The model lacks rationality for its settings. This is primarily based on the model's fit to the data, not enough to support the credibility of the model. It also shows that the establishment of the model lacks scientific and rationality

   This section is listed that empirical findings lack reliable arguments, their common problem is that the model fits very poorly, the resulting final result interpretation credibility is not enough to support their conclusions, see Table 3 in summary (Table 3).

   The model setup lacks logic, this leads to the occurrence of pseudo-regression

   The pseudo-regression due to improper model settings, in a wide variety of academic papers that have been published, is seemingly, not easy to spot. But, from the regression reports of those papers, the evidence is still difficult to hide. Here are some of the specious phenomena that appear in the literature, to be discussed below.

   1. Before the introduction of financial freedom, the bank concentration has had a positive and significant impact the expansion of financial inclusion; but when financial freedom was introduced, although the banking concentration and financial freedom are positive implications for financial inclusion expansion, and it's just that the effect doesn't work. In theory, it should be in the opposite direction for the banking concentration and financial freedom respectively to affect the financial inclusion in expansion. The development of digital finance will significantly strengthen banks' risk-taking capabilities, and the level of economic development has increased, instead, has weakened significantly banks' risk-taking capabilities. These empirical conclusions do theoretically not seem too reasonable, because that the banks' poor loan rates should be with the development of digital finance and the reduction is in line with the logic of digital finance. At the same time, the economic development will have a capital pooling effect, and also reduce the bank's unfavourable loan ratio, unless the bank's lending behaviour is irrational.
   2. The governmental financial support does not produce positive results for the economic development in rural areas. This justment is contrary to what has actually been observed. The likelihood of rural poverty is relatively large. Digital finance drives the probability of poverty occurring, and deepens the extent of multidimensional poverty. This empirical conclusion is very different from people's general observations. If the development of digital finance exacerbates the likelihood of poverty and the extent of poverty, according to the existing literature, 2013 was the first year of digital finance, and then, how to explain table 4? As can be seen from Table 4, basically, it is digital finance as defined today, China's "Poverty Alleviation" Battle occurs within the year in which it occurred. This fact also illustrates that digital finance has accelerated the process of poverty alleviation, if digital finance has an impact on poverty, not the opposite (Table 4).
   3. Government fiscal expenditure has a significant inhibitory effect on inclusive growth for the economy. As known, a big feature of governmental expenditure is to create a balance of opportunities for economic growth as much as possible, and close the income gap as much as possible. But, the empirical results do not support this original intention of the government’s goals in the public financial policy. If the empirical results are reliable, that is to say what the government spends for inclusive economic development is ineffective. However, the conclusion is inconsistent with the actual observations.
   4. Digital finance development significantly and positively impacts the high level of quality development. Look at the regression results, the population is becoming more educated and also significantly promotes high-quality development. But, it is inexplicable for us to know that fixed asset investment and infrastructure construction play little role in high-quality development. On the contrary, the GDP significantly affected by investment is an important driver of high-quality development. The industrial structure does not play a bigger role in promoting high quality development. So here’s the problem, what is the foundation of high quality development? Why is there no foundation to support the growth?  These illusions are obviously not caused by the facts themselves, the problem should be that it comes up on the scale, namely, the non-scientific nature of the model settings.
   5. It is of significant positive facilitation for the development of digital finance to support high-quality economic development. The ability to innovate has increased, but it has decreased the degree of high quality economic development. This result is contrary to the facts.
   6. The increased levels of innovation in agriculture have been significantly inhibited by the level of lending by financial institutions, and also, the development of the first industry and agricultural innovation have a significant negative correlation. The situations are contrary to the objective situation well known.

The rationality of the model structure lacks rigorous scientific arguments, the model reflects the poor objectivity of the sample

Those are explained from the settings of the following model. Settings for the baseline model, the explained variable, area technology innovation and the core explain variables, digital finance, they have a cross-generational relationship in time, but why is the relationship, between them, of the same generation missing? What is the theoretical basis for the settings? One of the reasons given in the text is “to consider that digital finance may affect the level of technological innovation with the hysteresis characteristics for the region, and to a certain extent, relieve endogenous problems of reverse causality” [64]. Such reasons are subjective and insufficient. In fact, technological innovation is a continuous process, not an intermittent process, and the missing values for the same period can be logically represented by 0. So, the scientific and rational nature is debatable for such a model’s setting. Given the reasons above, the arguments are not too robust for the conclusion of “digital finance significantly increased the regional level of technological innovation” in the article. According to empirical studies, other than that the digital financial upfront conditions have a significant positive impact on today's bank loans. In a word, during the setup of the model, why should digital finance lag behind one period? What role will digital finance play in the same period? For the key factors, if the article can be demonstrated from a theoretical level, or from an empirical point of view, characterized by statistical analysis, then, that would solidify the robustness of the empirical results, and circumvention should be nowhere in the occurrence of pseudo-regression.

Literature citations lack scientific rigor, this leads to citations only to my liking, and do not ask whether the cited text is reasonable and credible.

Not all published papers are logically correct, and all the conclusions obtained would be inevitable from logical reasoning. In fact, there is no shortage of rare ones for conclusion fallacy caused by research methodological errors, among the many published papers. Unfortunately, there are no fewer authors that have very serious behavioural biases in quoting in their papers. They just go to look for those related a few words as a point of view to support their own research, but don't explore that just a few words are reliable, scientific reasoning, or that the resulting arguments are supported by lack of factual evidence based on virtual assumptions? There is no doubt that there is a huge risk of error and omission for quoting without interpretation pointed out that the conclusions obtained by are inconsistent with those by for the same problem, but did not argue the difference between them, namely, why are they so? Why? All unreasonable or who is reasonable and who is unreasonable? These key elements are not clarified, but just a few listings [65]. What does this mean? This is not an isolated phenomenon, but it's the norm of academic writing for years. This model of research shows that everyone only says their own thing, convergence citation, if not, it should be charged. Such a research status, the beneficial communication for scholarship is negative, nor does it fit the scientific logic of the problem [66].

The author would like to thank Kangjin Wang for proofreading of the paper. Of course, the author would be only responsible for any mistakes of the paper if available.

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