Risk Investment Research Articles

A study on portfolio management strategies to maximize investment returns of listed companies

Listed companies can solve the problem of capital redundancy after financing through venture capital, but due to the risky nature, it becomes increasingly difficult to get a good capital return, therefore, it is an effective way for listed companies to utilize their capital by diversifying and reducing risks through portfolio investment to obtain high returns. In this paper, based on the characteristics of listed companies, we analyze the risk investment methods and risk factors of listed companies, and then construct a log-optimal portfolio model under VaR and CVaR risk control and verify it, so as to provide scientific basis for the portfolio investment decision management of listed companies.

Theoretical and practical research on mathematical modeling of economy and finance based on artificial intelligence

Abstract In the context of the rapid development of big data technology, developing an artificial intelligence economy is beneficial to China’s economic transformation, industrial upgrading, and enhancing international competitiveness. This paper first constructs a financial mathematical model of the AI economy based on the theoretical foundation of financial mathematics. The structure of the financial mathematical model mainly consists of three parts: portfolio model, capital asset pricing model, and financial derivatives pricing model. To address the problem of the unstable development of China’s AI economy, the theoretical framework of investment risk management is constructed, which can help investors intelligently implement investment risk management plans and implement investment risk management countermeasures and measures. Finally, to verify the performance of the risk investment optimization model and intelligent algorithm proposed in this chapter, three investment scales will verify the investment risk management capability based on the portfolio model. The results show that the convergence performance of the portfolio model is 0.2025, 0.4021, and 0.4391 for three investment sizes of 2, 4, and 9, and the penalty coefficients are 1.5, 28, and 0.2, respectively. At the beginning of the iteration, the investment risk is high, and the convergence speed of the portfolio model is higher than the capital asset pricing model and the financial derivatives pricing model. This study applies it to solve financial optimization problems, including portfolio, stock forecasting, risk management, and many other fields, and provides a better solution to the optimization decision problem of the portfolio.

The Optimal Allocation of Securities for Portfolios

Portfolio optimization assists in the selection of the optimum portfolio to meet certain goals. The most often usedportfolio optimization model is the Markowitz Model (MM). The approach highlights the need to select assetscomplementing one another to reduce risk for investors. It compares the risks and returns of multiple equities to discoverwhich asset offers the best returns while posing the fewest hazards. To simplify the Markowitz Model, the Index Model(IM) employs a single element, the market index, which impacts all investment returns. Using the MM and IM, thisstudy analyzes permitted portfolio areas for ten stocks and one broad equity index. The ten businesses were chosenfrom various industry areas. SPX, NVDA (Technology), CSCO (Technology), INTC (Technology), The Goldman SachsGroup (Financial Services), US Bancorp (Financial Services), TD CN (Financial Services), Allstate (Financial Services),Procter & Gamble Company (Personal Care Products), Johnson & Johnson (Pharmacy), Colgate-Palmolive (PersonalCare Products).The firms from several industrial sectors are chosento guarantee the risk-diversified final portfolio. As aconsequence, the efficient portfolio employs the weightsthat yield the highest returns for a given risk level orthe lowest risk for a given projected return level whendetermined using the MM or IM. We concluded from thedata that the IM optimization model well approximatesthe MM optimization model by minimizing the number ofestimations necessary for model estimation.

Entrepreneurial Management & Risk Investment

Introduction. Most of the new ventures are immersed in a risk or failure situation due to the lack of knowledge about a management process structured specifically for them, and to the ignorance of new terminology used in risk investment for raising capital. Objective. The purpose of this research is to describe the terminology currently used on risk investment for ventures and managerial actions that guarantee their sustainability, based on a bibliometric analysis of the literature on the research topic, considering the practices of innovative entrepreneurship worldwide with managerial actions that allowed us to recognize gaps, challenges, and opportunities. Analysis. The bibliometric analysis began with a search equation on entrepreneurship management from 1979. Data was processed with VOSviewer and the content analysis was performed with ATLAS.ti. Results. As a result, co-occurring terms emerged in the literature; business management, innovation, risk management, investments, and risk assessment stand out. There are other innovative terms that are used in the current dynamics of venture investment for startups. They are one of the contributions of this research. Conclusions. The conclusions present the best management practices that guarantee the sustainability of startups, as well as findings that reveal the new terminology in risk investment. They become facilitators for the permanence of incipient startups over time.

Exploiting Long Short-term Memory Neural Network for Stock Price Prediction

Stock as a high yield, high risk investment has been favored by the public. In order to increase the return on investing in stocks, investors need to predict stock prices. In the past, investors used traditional mathematical methods to make predictions. Now, neural networks are used by investors to predict stocks, which can improve the accuracy of stock forecasting. To further verify the effectiveness of these methods, this work discusses the effects of different network structures and hyperparameters on stock prediction models using short-term memory (LSTM) neural networks. The results show that deeper network layer can get better training effect, but it needs more training time, resulting in a lot of time waste. In addition, this experiment tests the prediction effect under different dropout parameters. The results show that the dropout function should not be too large or too small. Multiple experiments are needed to find an appropriate dropout value.

A Novel Method of Blockchain Cryptocurrency Price Prediction Using Fractional Grey Model

Cryptocurrency prices have the characteristic of high volatility, which has a specific resistance to cryptocurrency price prediction. Therefore, the appropriate cryptocurrency price predictive method can help reduce the investment risk of investors. In this study, we proposed a novel prediction method using a fractional grey model (FGM (1,1)) to predict the price of blockchain cryptocurrency. Specifically, this study established the FGM (1,1) through the closing price of three representative blockchain cryptocurrencies (Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC)). It adopted the PSO algorithm to optimize and obtain the optimal order of the model, thereby conducting prediction research on the price of blockchain cryptocurrency. To verify the predictive precision of the FGM (1,1), we mainly took MAPE, MAE, and RMSE as the judging criteria and compared the model’s predictive precision with the GM (1,1) through experiments. The research results indicate that within the data range studied, the predictive accuracy of the FGM (1,1) in the closing price of BTC, ETH, and LTC has reached a “highly accurate” level. Moreover, in contrast to the GM (1,1), the FGM (1,1) outperforms predictive capability in the experiments. This study provides a feasible new method for the price prediction of blockchain cryptocurrency. It has specific references and enlightenment for government departments, investors, and researchers in theory and practice.

Do financial markets predict macroeconomic performance? US evidence from risk‐based measures

AbstractFinancial markets are expected to predict macroeconomic conditions as movement in the former depends upon expectations of future performance for the latter. However, existing evidence is mixed. We argue that this arises because the stock return and term structure series typically used in studies, fail to sufficiently capture investor risk preferences. For US data, we use the variance risk premium (VRP) and default yield (DFY) to better capture such a risk measure and demonstrate that these variables exhibit greater evidence of predictive power for key macroeconomic series. In addition to VRP and DFY, we include further variables that may also capture market risk. Given similar dynamics between different risk measures and the potential for multicollinearity in estimation, we consider variable combinations. Using results obtained through predictive regressions, out‐of‐sample forecasting and a probit model designed to capture periods of expansion and contraction, we show that these combination variables can predict future movements in macroeconomic conditions as well as results using individual variables. Of key interest, combinations that include the VRP and DFY are preferred across all macro‐series.

Counterfeit Luxury Businesses in China: The Impacts of Counterfeit Consumption on Global Economy

Today, counterfeiting is growing prevalent all over the world due to enormous returns on investment and low levels of risk. Despite of all the intellectual and ethical issues proposed by criminologists and policy makers, the issue of counterfeit products has been addressed in the economic sector. It is conspicuous that global businesses are raising awareness in problems caused by counterfeit products. The purpose of this study is to investigate Chinese consumer’s purchasing behaviours in counterfeit luxury products and discuss how they will potentially influence the global luxury market.

On the Exchange Rate Dynamics of the Norwegian Krone

Global energy production is undergoing a transition from fossils to renewables. At the same time, the Norwegian Oil Fund has grown exponentially in size and is now a major global investor. These events in combination are likely to impact the dynamics of the Norwegian krone. Concurrently, the persistent weakening of the Norwegian krone (NOK), hitting record low exchange rates against the major currencies, is sparking national and international interest. Using updated data, we find that oil prices and global asset prices are both important drivers of EURNOK returns. However, we find that the relative importance changed following the 2015 oil price decline, whereafter asset prices became more significant. Furthermore, we observe an impact of investor risk aversion, suggesting that the krone is no longer a safe-haven currency.

Offsetable Derivatives and Investor Risk Assessment

U.S. generally accepted accounting principles (GAAP) allows banks to offset derivative assets against derivative liabilities with the same counterparty and report only the net amount on the balance sheet. Derivative offsetting under international financial reporting standards (IFRS) is much more restrictive, resulting in the single largest difference in balance sheet presentation between U.S. GAAP and IFRS. Two important factors dominate the standard-setting discussion on this issue: (1) whether these derivatives are informative about bank risk and (2) whether disclosing them substitutes for recognition on the balance sheet. Using a hand-collected global sample of banks, we first show that offsetable derivatives are positively associated with bank risk, based on multiple risk measures, suggesting that these derivatives are informative about bank risk. Next, exploiting the differential accounting treatment across U.S. GAAP and IFRS banks, we find that disclosure versus recognition of offsetable derivatives matters for the risk assessment of equity investors but not for that of (more sophisticated) credit default swap investors. Additional tests corroborate the inference that investor sophistication helps explain the differential investor assessment of recognized versus disclosed offsetable derivatives. Collectively, our findings suggest that offsetable derivatives convey information about bank risk and that, for less sophisticated investors, disclosing them may not substitute for recognizing them. This paper was accepted by Eric So, accounting. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4831 .

Performance of ESG-integrated smart beta strategies in Asia-Pacific stock markets

Environmental, Social, and Governance (ESG) investing is about ethical investing. While ESG investing has garnered heightened attention, the research has not settled on whether ESG investing can “do well while doing good”. Using a proprietary ESG rating database of monthly firm-specific data, we examine the performance of ESG-incorporated investing strategies in Australia, Mainland China, Hong Kong, Malaysia, and Singapore. Specifically, we combine positive screening and the smart beta approach to evaluate the performance of ESG-based and non-ESG-based (traditional) equity portfolios. Our key findings reveal that high-ESG-based portfolios do not offer superior risk-adjusted returns compared to the low-rated portfolios. While a high-ESG-rated portfolio generally outperforms the market index, the ESG and traditional smart beta alphas differ little. Our results also indicate that the minimum-volatility portfolio achieves the best performance of all the factors. We use data from Refinitiv ESG and Bloomberg ESG to substantiate and support the results. Our findings add to the growing ESG literature that answers whether investors risk sacrificing returns while investing ethically.

ESG Disclosure and Sustainability Transition: A New Metric and Emerging Trends in Responsible Investments

Abstract Environmental sustainability, social engagement and robust governance gained growing attention from consumers and investors alike, leading to what we call ‘ESG finance’. ESG criteria are now shaping the behaviour and choices of enterprises, investors and consumers. Indeed laudable, the increased importance of ESG finance could raise concerns about the robustness underneath this new set of financial products. Moreover, the reliability of ESG-related data and information shared by companies may also be challenged due to the ability of those indicators to shape the public profile of companies and their attractiveness for investors. A new breed of ESG rankings and ratings is widening the metrics that consumers and investors use to make informed decisions about their consumption and investment. Yet, such rankings and ratings hinge on the individual disclosure approaches of the interested companies. This article wishes to complement available data and information about specific emissions data released by companies with the ESG disclosure levels, in particular relating to the “environment” dimension. Based on these disclosure levels, the authors build a new metric with the purpose of reducing asymmetric information and promoting more responsible investment. Starting from ESG-related data and publicly available information, a new disclosure-adjusted pollution index (namely, the GHG Scope-1 DAdj index) is developed. The second part of the article puts forward an empirical analysis on the basis of this new index, suggesting that the rush to ESG finance could be poised to generate leeway for new types of asymmetries and possible distortions in investment decision-making, also providing grounds for potentially reckless speculative attitudes, especially in the domain of product development of financial instruments that may generate new forms of risk for investors. Using the GHG Scope-1 DAdj index makes a few companies less environmentally friendly and interesting for investors who are seeking responsible and sustainable investment options. The innovative index and the empirical analysis lead the authors to suggest to “split the domains of ESG” to better gauge the relation between impact and compliance costs for companies as the individual components of environment, social engagement and governance are considered separately.

Can one hear the shape of a target zone?

We develop an exchange rate target zone model with finite exit time and non-Gaussian tails. We show how the tails are a consequence of time-varying investor risk aversion, which generates mean-preserving spreads in the fundamental distribution. We solve explicitly for stationary and non-stationary exchange rate paths, and show how both depend continuously on the distance to the exit time and the target zone bands. This enables us to show how central bank intervention is endogenous to both the distance of the fundamental to the band and the underlying risk. We discuss how the feasibility of the target zone is shaped by the set horizon and the degree of underlying risk, and we determine a minimum time at which the required parity can be reached. We prove that increases in risk beyond a certain threshold can yield endogenous regime shifts where the “honeymoon effects” vanish and the target zone cannot be feasibly maintained. None of these results can be obtained by means of the standard Gaussian or affine models. Numerical simulations allow us to recover all the exchange rate densities established in the target zone literature. The generality of our framework has important policy implications for modern target zone arrangements.

Risk‐sensitive large‐population linear‐quadratic‐Gaussian Games with major and minor agents

AbstractThis paper studies large‐population dynamic games involving a linear‐quadratic‐Gaussian (LQG) system with an exponential cost functional. The parameter in the cost functional can describe an investor's risk attitude. In the game, there are a major agent and a population of minor agents where is very large. The agents in the games are coupled via both their individual stochastic dynamics and their individual cost functions. The mean field methodology yields a set of decentralized controls, which are shown to be an ‐Nash equilibrium for a finite population system where . Numerical results are established to illustrate the impact of the population's collective behaviors and the consistency of the mean field estimation.

Systematic extreme correlation of Chinese stock market

ABSTRACT This article investigates the impact of systematic extreme correlations on the cross-section returns in the Chinese stock market. First, it extends the proposed extreme downside correlation (EDC) to the extreme upside correlation (EUC), and employs them to measure systematic tail risk. We find the asymmetry of systematic extreme correlations that EDC is always greater than EUC in the Chinese stock market. Then, we calculate the average returns of portfolios sorted by extreme measures and find that systematic extreme measures have a negative relationship with the Chinese stock returns. After controlling other potential variables such as size, lagged return, higher moments and higher co-moments, these two extreme correlation measures still have significantly negative relationship with stock returns. The results suggest that EDC and EUC help explain the cross-section returns of the Chinese stocks and manage tail risk for investors.

Sales Strategy, Risk Investment and Property Rights Allocation: Analysis Based on Endowment Effect

The endowment effect suggests that people value what they have now and the status quo, and thereby want to avoid losing what they have or just step out of the comfort zone. Several scholars have identified and demonstrated that people’s utility is reduced by a given quantity of loss more than it is increased by an equal amount of gain. Due to the endowment effect, the decision-making process is out of balance, and are more likely to avoid harm rather than pursue profits. This paper reviews the concepts, experiments and applications of endowment effect and loss aversion theory in behavioral economics. Furthermore, it explains endowment effect using three economic applications of sales strategy, risk investment and property rights allocation. In all the three economic applications, people’s economic behavior totally conforms to the endowment effect: people buy things that they don’t need, make the investment irrationally, or bring trade frictions. Due to its commonness, endowment effect has been widely tested and applied, attracting heated discussion among academia research and policy regulators. In this sense, this paper also provides us a good understanding of endowment effect and behavior economics.

Performance evaluation of possibilistic fuzzy portfolios with different investor risk attitudes based on DEA approach

By considering the stock market’s fuzzy uncertainty and investors’ psychological factors, this paper studies the portfolio performance evaluation problems with different risk attitudes (optimistic, pessimistic, and neutral) by the Data Envelopment Analysis (DEA) approach. In this work, the return rates of assets are characterized as trapezoidal fuzzy numbers, whose membership functions with risk attitude parameters are described by exponential expression. Firstly, these characteristics with risk attitude are strictly derived including the possibilistic mean, variance, semi-variance, and semi-absolute deviation based on possibility theory. Secondly, three portfolio models (mean-variance, mean-semi-variance, and mean-semi-absolute-deviation) with different risk attitudes are proposed. Thirdly, we prove the real frontiers determined by our models are concave functions through mathematical theoretical derivation. In addition, two novel indicators are defined by difference and ratio formulas to characterize the correlation between DEA efficiency and portfolio efficiency. Finally, numerical examples are given to verify the feasibility and effectiveness of our model. No matter what risk attitude an investor holds, the DEA can generate approximate real frontiers. Correlation analysis indicates that our proposed approach outperforms in evaluating portfolios with risk attitudes. At the same time, our model is an improvement of Tsaur’s work (2013) which did not study the different risk measures, and an extension of Chen et al.’s work (2018) which only considered risk-neutral attitude.

Pricing the Pandemic: Evidence from the Bond Market in China

ABSTRACT This study investigates whether and how the pandemic is priced in the bond market in China. Using the city-level COVID-19 cases on a daily basis, we find a significant positive relationship between the pandemic outbreak and corporate credit spreads, implying that investor risk perception on pandemic exposure attracts a premium. Consistent with the default risk channel, corporate financial resilience alleviates pandemic pricing. Information asymmetry and tail risk can amplify the pricing effect because of reduced investor risk-bearing capacity. These findings are robust in addressing endogeneity concerns. We contribute to the emerging literature on the pandemic effect on credit markets.

Optimization-based tail risk hedging of the S&P 500 index

In this paper, we present a mixed risk-return optimization framework for selecting long put option positions for hedging the tail risk of investments in the S&P 500 index. A tractable formulation is developed by constructing hypothetical portfolios that are constantly rolling put options. Variance and sample CVaR are used as risk measures. The models are tested against out-of-sample historical S&P 500 index values as well as the values of the index paired with long put options of varying strike prices. The optimized hedged portfolio could provide sufficient protection in market downturns while not losing significant return the long horizons. This is achieved by dynamically adjusting the put option compositions to market trends in a timely manner. Allocations to different put options are analyzed in various market trends and investor risk aversion levels. The strategy overcomes the traditional drawbacks of protective put strategies and outperforms both directly investing in the underlying asset and holding a constant long position in a particular put option.

USING CRYPTOCURRENCIES IN FINANCING SUSTAINABLE DEVELOPMENT GOAL PROJECTS: SWOT ANALYSIS

The interest and adoption of cryptocurrencies, such as Bitcoin and Ethereum, have significantly increased as a medium of payment and investment. At the same time, the international community has set a list of Sustainable Development Goals (SDGs) to be achieved by 2030, which includes eradicating poverty, protecting the planet, and ensuring peace and prosperity for everyone. One of the most significant challenges in achieving these SDGs is financing the necessary projects to implement them. Traditional financing sources, such as government budgets and private investments, may not be sufficient to cover the enormous costs associated with achieving the SDGs. Therefore, there is a need to explore alternative financing mechanisms to support the implementation of the SDGs. Cryptocurrencies represent a potential alternative source of funding for SDG projects. However, the lack of regulation and the high volatility of cryptocurrency prices may create uncertainty and risk for investors and project stakeholders. Examples of the use of cryptocurrencies in various SDG projects to enable fast and low-cost cross-border transactions are presented. The feasibility of using cryptocurrencies as a financing mechanism for SDG projects, a comprehensive SWOT analysis of the use of cryptocurrencies in financing SDG projects, potential problems and opportunities related to the use of cryptocurrencies for financing SDG projects, practical recommendations for policy makers, project developers and investors who are interested in using cryptocurrencies for financing SDG projects are discussed in the article. To address this issue, this article aims to conduct a SWOT analysis to evaluate the strengths, weaknesses, opportunities, and threats of using cryptocurrencies as a financing mechanism for SDG projects. The analysis will provide valuable insights into the potential benefits and drawbacks of utilizing cryptocurrencies for SDG project financing and identify strategies to mitigate risks and maximize opportunities. Ultimately, this research aims to contribute to the broader conversation around alternative financing mechanisms for sustainable development projects.