International Journal of Financial Engineering

The purpose of this paper is to discuss the general risk assessment under the Hologram framework for the enterprise 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 (here we mainly focus on SMEs as they are one of the very important classes for enterprises with less information available from financial accounting report and associated assets. Indeed, the approach discussed here is applicable to general enterprises).

The key idea of our new approach is to introduce and use 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 analyze (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, etc. 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.

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” are based on the network generated from an SMEs’ related-party 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, etc. which heavily depend on financial accounting and associated assets used by financial risk analysis in practice. Using big data and FinTech Hologram method discussed in this paper utilizes the related-party information (in term of investment and management) of each SME 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, one always has the related-party information in terms of either investment, or management dynamically, which is indeed also an explanation for the reason why the new approach proposed only comes true only until the era of big data’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 paper’ conclusion will address the performance of the “Holo Credit Loan”.

We develop the real option signaling game models of equity financing of a risky project under asymmetric information, where the firm quality is known to the firm management but not outside investors. Unlike the usual assumption of perpetuity of investment, we assume that the time window of the investment opportunity has a finite time horizon. The firm chooses the optimal time to issue equity to raise capital for the investment project. The number of shares of equity issued to fund the project depends on the outside investors’ belief on the firm quality. The low-type firm has the incentive to sell overpriced securities through mimicking the investment strategy of the high-type firm in terms of investment timing and number of equity shares. On the other hand, the high-type firm may adopt the separating strategy by imposing mimicking costs on the low-type firm. We examine the incentive compatibility constraints faced by the firm under different quality types and discuss characterization of the separating and pooling equilibriums. We also explore how the separating and pooling equilibriums evolve over the time span of the investment opportunity. The information costs and abnormal returns exhibit interesting time dependent behaviors, in particular, at time close to expiry of the investment opportunity.

There is possible to develop plans and programs of firms in future via increasing R&D programs and policies and this issue can have effect on financial performance and market value of firms. Based on the argument, the present research tries to study the moderating role of ownership structure on R&D expenditure policies on accounting performance and market value of firms. For this purpose, two approaches including investmentizing R&D expenditures and R&D intensity were used to measure R&D policy. The research hypotheses were analyzed using a sample with 73 firms listed on Tehran Stock Exchange from 2011 to 2015 using multivariate regression model based on panel data technique. The results of the research indicated that there is a positive significant relationship between investmentizing R&D expenditures with performance and market value of firms and also there is a positive relationship between R&D intensity with performance and market value of firms. Finally, it was determined that institutional ownership of firms can the effect of R&D intensity on performance and market value of firms sustain negatively.

This paper studies an optimal dynamic proportional reinsurance in a risk model with two dependent classes of insurance business. Under the criterion of maximizing the mean–variance utility of the terminal wealth with state-dependent risk aversion, we formulate the time-inconsistent problem within a game theoretic framework. By the technique of stochastic control theory, explicit expressions of the optimal results are derived not only for diffusion risk model but also for compound Poisson risk model. Furthermore, the similar problem with constant risk aversion is studied as well. Finally, some numerical examples are presented to show the impact of model parameters on the optimal strategies for both compound Poisson and diffusion cases.

We study a stochastic control approach to managed futures portfolios. Building on the (Schwartz, 1997) stochastic convenience yield model for commodity prices, we formulate a utility maximization problem for dynamically trading a single-maturity futures or multiple futures contracts over a finite horizon. By analyzing the associated Hamilton–Jacobi–Bellman (HJB) equation, we solve the investor’s utility maximization problem explicitly and derive the optimal dynamic trading strategies in closed form. We provide numerical examples and illustrate the optimal trading strategies using WTI crude oil futures data.

We have developed a statistical technique to test the model assumption of binary regime switching extension of the geometric Brownian motion (GBM) model by proposing a new discriminating statistics. Given a time series data, we have identified an admissible class of the regime switching candidate models for the statistical inference. By performing several systematic experiments, we have successfully shown that the sampling distribution of the test statistics differs drastically, if the model assumption changes from GBM to Markov modulated GBM, or to semi-Markov modulated GBM. Furthermore, we have implemented this statistics for testing the regime switching hypothesis with Indian sectoral indices.

In this paper, financial networks are applied to develop new measures of systemic risk. Using the CoVaR as a well-defined systemic risk measure, risk spillovers among different firms are estimated in Tehran Stock Exchange. Networks of systemic risk exposures are represented across time and two indices of vulnerability and systemic importance are introduced to define the most effective and vulnerable firms. The results show that the proposed network-based indices have a good performance to identify the vulnerable and systemically important firms. The results also show that when the market is in periods of financial downturn/crisis, the network interconnectedness was maximized.

This paper focuses on the impact of the executive compensation of state-owned enterprises and executive power on Real Activity Earnings Management (REM). Furthermore, this paper discusses the differences of impacts mentioned above between the central government holding enterprises and the local government holding enterprises, in order to test the impact of the government compensation regulation and the executive compensation of different types of enterprises on the enterprise accounting behavior. The empirical research shows that executive compensation can induce accountings to implement REM, which is more notable in local state-owned enterprises than in central enterprises.

This paper examines the main drivers of the returns of gold miner stocks and ETFs during 2006–2017. We solve a combined optimal control and stopping problem to demonstrate that gold miner equities behave like real options on gold. Inspired by our proposed model, we construct a method to dynamically replicate gold miner stocks using two factors: the spot gold ETF and market equity portfolio. Furthermore, through each firm’s factor loadings on the replicating portfolio, we dynamically infer the firm’s implied leverage parameters of our model using the Kalman Filter. We find that our approach can explain a significant portion of the drivers of firm implied gold leverage. We posit that gold miner companies hold additional real options which help mitigate firm downside volatility, but these real options contribute to lower returns relative to the replicating portfolio when gold returns are positive.

Investors prefer positively skewed portfolio returns, while value portfolios have substantial negative skewness in their returns. We use a Power-Log utility optimization algorithm and a put, or call option overlay to reverse the negative skewness of the Russell 1,000 Value index return, and produce portfolios with far better risk and return characteristics than the index itself, using historical monthly returns for the index with VIX-based standard deviation for forecasting. All the optimal portfolios containing the call have positively skewed returns, smaller maximum drawdowns except for the very riskiest portfolios, and higher Sortino ratios than the index, and also have better characteristics than those containing the put.