Traditional Investment Research Articles

Exploring Investment Processes between Venture Capital Investors and Sustainable Start-ups

Venture capital is an important funding source for sustainable entrepreneurship and, thus, drives sustainable development. However, sustainable startups have trouble acquiring venture capital. Hence, existing literature focuses on dedicated impact investors or aims to understand why more traditional investors avoid sustainable investment opportunities. In contrast, this study investigates investment processes from venture capitalists without a dedicated “green” focus on sustainable startups. We investigate 166 documents, containing business plans and decision documents of sustainable and non-sustainable new ventures. We explore whether sustainable entrepreneurs present their business plans in a different style than non-sustainable entrepreneurs and whether investors react differently to sustainable business models. Using NLP and topic modeling, we reveal that traditional venture capitalists do not justify an investment by writing about the topic sustainability. However, investigating writing styles based on sentiment and word usage shows that both, entrepreneurs and investors, argue more emotionally when writing about a sustainable business case.

Non-pricing drivers of underwriters’ market shares in corporate bond markets

Banks' role as underwriters has gained relevance, fostering competition for market share in this traditional investment banking activity. This paper explores the effects of competitive pricing and non-pricing factors on banks’ market share as underwriters. Using a panel of active-underwriters in the European corporate bond markets during a highly competitive period (2007–2013), we find that pricing factors are of second-order importance in explaining changes in market shares. Providing joint lending and underwriting services, hiring star-analysts, and being the leader in placing bonds within an industry positively affect market share. These results explain how banks have developed their underwriting businesses.

Negative interest rate: Way to tackle inflationary housing prices

While housing affordability is on drop by the rising prices, specially in large cities, the young generation suffers from the pending independence as a result. Despite the significantly good industrial performance in the construction sector in Australia, reduced housing prices is still under burden by the middle mans and real estate brokers. Several socio-economic factors are indeed involved in the rising prices such as unequal wealth distribution, banking strategies, wages growth rate, mortgage interest rate, population growth, etc. which are being discussed in this article. Data had been collected from the Australian Bureau of Statistics in order to compare and analyze the effectiveness of negative interest rates on housing affordability. New financial asset classes such as cryptocurrencies had also been introduced in order to propose alternatives to the traditional investment banking which also ensures profit earning for bankers.

Adaptive trading system based on LSTM neural network

With the continuous development of statistics and computer technology, the methods of technical analysis are also constantly expanding. A large number of statistical and mathematical methods have been applied to the analysis of stocks, which greatly expands the tools available for stock analysis. With the rapid development of deep learning and the great progress of computer hardware technology, statistical learning algorithms have been widely used in big data processing. Including convolutional neural network model and cyclic neural network model, have shown great advantages in the processing of time series such as images, speech and text. In today’s financial market, due to the existence of a large number of assets and fast information flow, people are more and more inclined to use quantitative investment. Quantitative investment as a means of financial sector, the New Deal, which is mainly composed of fundamental analysis and technical analysis from the traditional investment method, its core is to use mathematical model set up trading strategy, and with the help of computer technology on the financial data for quantitative analysis, and judgment, found that financial market rules, eliminating dependence on the experience of the people in the course of investment and emotional impact, so as to guide investment decisions. In order to improve the prediction ability of LSTM neural network model, this paper chooses the stack LSTM neural network model, and combines the stack LSTM neural network model with ADAM algorithm to achieve better prediction results. The empirical results show that the prediction effect of ADAM-based stack LSTM neural network is better than that of ADAM-based LSTM neural network.

Optimal Seasonal Heat Storage in a District Heating System with Waste Incineration

European Union climate goals aim to increase waste incineration instead of landfills. Incineration of waste increases the mismatch between heat production and consumption since waste is generated constantly but energy demand varies significantly between seasons. Seasonal energy storage is suggested to alleviate this mismatch. However, traditional seasonal storage options have not been cost-effective investments for energy companies. This paper explores the feasibility of a large cavern thermal energy storage in a large district heating system with waste incineration. First, 62 one-year optimisations for seasonal storage with varying size and power were conducted to determine the economic performance of the system. Second, the annual system emissions were estimated. The results show that even small capacity seasonal storage reduces system emissions significantly. Return on investment for the most profitable storage with a capacity of 90 GWh and power of 200 MW range between 3.6% and 9.4%, and the investment varies between EUR 43–112 M depending on costs. Seasonal energy storages are still not as profitable as traditional energy investments. This might change due to growing waste heat recovery and the rising cost of carbon emissions. Further research is needed into new business models for implementing large seasonal storages.

Investment casting with FFF (fused filament fabrication)-printed appliances: the intermediate step.

Some modifications of orthodontic appliances such as the rapid maxillary expansion (RME) device with a Hyrax screw or Herbst are fabricated using traditional investment casting (lost-wax casting). This is precise but very labor-intensive. New technologies enable us today to use direct selective laser sintering (SLS) to produce freeform metallic structures. These machines are very expensive and only available in specialized laboratories. The aim of this investigation was to combine fused filament fabrication (FFF) 3D printing with wax-based filaments to produce orthodontic appliances via investment casting. For demonstration purposes, a lingual arch, a palatal arch, and an RME appliance were digitally designed based on an intraoral scan. The *.stl files were sliced and printed with a dual-nozzle FFF printer. The object was printed with a wax-based filament especially suited for investment casting, and support structures were printed with water-soluble polyvinyl-alcohol (PVA) filament. The printed objects were cast in metal and finished. All appliances were successfully cast and polished. They were provisionally placed intraorally. The fit was clinically very good and comparable to traditionally crafted appliances. The printing and handling of the parts made of these special filaments is challenging. With this experiment, the successful production of investment casting using FFF printing was shown for the first time.

Switch and defer option in renewable energy projects: Evidences from Brazil

The global electricity sector is responsible for a large share of greenhouse gas. Renewable energy (RE) is a feasible alternative to maintain sustainable growth and economic development, as it appears to be a means to reduce emissions and decarbonize economies. Renewable energy projects present some uncertainties and flexibilities that are ignored by traditional methods of evaluation, such as the net present value (NPV), which can lead to undervaluation. This paper aims to assess the switch-output option and defer option for RE projects according to their features in the Brazilian electricity market. The real options approach (ROA) for RE projects seeks to capture the value of uncertainties and flexibilities that all projects have from an investor's point of view. Then, when we adopted a more sophisticated approach that values flexibilities and uncertainties for RE projects, it was possible to conclude that there was a clear increase in value when compared to traditional investment analysis methods like the NPV.

ESG/Green Investment and Allocation of Portfolio Assets

This article examines the current portfolio allocation in ESG and Green projects. Traditional investments focus on rates of return and risks associated with investment. Environmental, Social and Governance (ESG) or Green factors are additional components that investors have to pay attention to. Environmental protection is very important. However, as we see the current different definitions of ESG or Green factors lead to distorted allocations in portfolio investments. In order to bring portfolio allocations to a desirable direction, global taxation on pollution or creation of an accurate Green credit rating based on emissions of various pollutants are recommended.

Переваги та ризики краудфандингу як моделі фінансування стартапів і проектів громадського розвитку

Crowdfunding as a tool for alternative financing has emerged relatively recently and is of limited use in Ukraine today. At the same time, it has significant potential, which can contribute to the implementation of a wide range of projects that for various reasons are not of interest to traditional lenders or investors. The aim of the article is to determine the benefits of crowdfunding for its participants, the peculiarities of the implementation of certain types of crowdfunding and identify risks that may be generated by them, as well as develop practical recommendations for crowdfunding campaigns by entrepreneurs and authors of community development projects. The article identifies the benefits of crowdfunding for project authors (low cost of capital, access to information and potential investors) and investors (clarity, low risks, access to new products, the ability to support creative ideas), substantiates the role of crowdfunding platforms in realizing the benefits of crowdfunding. The advantages and disadvantages of using certain types of crowdfunding are described. Recommendations for planning and implementation of the main stages of crowdfunding campaigns - idea development, target audience determination, research, communication, project budgeting, reward system development, campaign schedule development – are developed. The factors of choosing a crowdfunding platform for hosting the project are determined. The possibility of using crowdfunding for collective financing of socio-cultural projects within the public budgets of the united territorial communities is shown. The risks of using crowdfunding for project authors and potential investors are identified. Those risks are primarily related to realistic expectations and proper preparation for the fundraising campaign by project authors, as well as the lack of guarantees for investors in the event of problems or bankruptcy of the crowdfunding platform. The author highlights that the growth of public awareness about the possibilities of implementing social or business initiatives through crowdfunding platforms will contribute to the development of platforms, improvement of technological equipment, and expansion of their range of services.

An Investment Strategy Based on Impact

Impact investing is based on using the ESG framework as a tool to evaluate firms that engage in generating positive impact. Most impact investors and fund managers now integrate the ESG framework in their investment and stock-picking process. However, due to lack of standardisation of ESG reporting, it remains a challenge for investors and the public to identify the truly sustainable companies. We propose an additional measure of tax avoidance to identify firms that are socially responsible. When firms indulge in excessive tax avoidance behaviour, it may be viewed as unethical or socially irresponsible. We integrate the empirical association between corporate social responsibility (CSR) and tax avoidance into an investment strategy based on impact. We adopt an investment strategy based on firm‐level ESG ratings and tax avoidance practices. In a ‘pure’ impact investment strategy based on ESG and tax avoidance, we find that investing in high‐ESG rated firms and low tax avoidance firms yield a buy and hold abnormal return of 2.6% per annum and 14.3% in a three-year investment horizon. Next, if impact investors were to combine traditional investment strategies based on risk with impact measures, we find that portfolios of high‐ESG and high price‐to‐book‐ratio firms earn a buy and hold abnormal return of 25.5%, while a portfolio of low tax avoidance and high price-to-book portfolios earn 33.1% in the long run. Collectively, our results suggest that whilst impact investing do provide investors a return, it does not necessarily outperform traditional investment strategies. Our results are robust to other risk factors and the sector of the firm. Keywords: impact investing; corporate social responsibility; tax avoidance; abnormal returns JEL Codes: G11, G15, G30

Are clean energy assets a safe haven for international equity markets?

While clean energy assets have become new investment vehicles for market participants, their potential for diversification of traditional investments has not been fully explored in previous studies. To fill this void, this paper investigates whether diversifying into clean energy stocks or green bonds could reduce portfolio downside risk for investors holding dirty energy stocks or international equity indices. Specifically, the risks of equity portfolios are compared before and after they are blended with clean energy assets. The incremental impact on the portfolio risk profile is evaluated through variable weightings for clean energy assets. The persistence of diversification effects is then explored by changing the assessment period. Results show that both green bonds and clean energy stocks provide risk diversification benefits for investors with dirty energy stocks. However, green bonds reduce risk while clean energy stocks generally increase the risk of the international equity index portfolio. Further analysis indicates the robustness of the results for the recent COVID-19 period. The findings broaden the limited understanding of the risk reduction capacity of clean energy assets to conventional equities and provide useful implications for investors seeking to decarbonize their equities and portfolio managers designing optimal portfolios to hedge against downside risks.

Computer simulation of investment efficiency function model based on GMM method and artificial intelligence

Traditional investment efficiency evaluation methods mostly focus on single-stage investment portfolio evaluation, and there is no comprehensive theoretical analysis method in multi-stage investment evaluation. Therefore, the construction of a scientific and reasonable multi-stage investment portfolio evaluation system is an urgent need for the theoretical and practical industries. In order to improve the efficiency of investment efficiency model evaluation, based on the GMM method and machine learning algorithm, this paper constructs an investment efficiency model based on the GMM method, and uses the characteristics of multi-scale Gaussian convolution to establish the MGCGMM model corresponding to the GMM model. The experimental results provided in this paper show that the MGC algorithm and the MGC-EM algorithm have higher estimation accuracy than the traditional algorithm EM algorithm. The intuitive meaning of this model can be understood as the mathematical expectation of the random variable function induced by the kernel function, which provides a way to directly estimate the GMM model using sample data. According to the case analysis, the model constructed in this paper has certain effects.

Banks, IPO underwriting, and allocation in Japan

We investigate how universal banking groups utilized information advantage at the initial public offerings (IPOs) in Japan over the period 2002-2012. We focus on the IPO issues of universal bank clients allocated by underwriters from the issuers’ main bank groups, competing commercial bank groups, and traditional investment bank groups. We find that the main bank underwriters do not advantage their affiliated mutual funds in terms of the initial and aftermarket returns; neither do the traditional investment bank underwriters. We interpret the former finding as indicating that the information advantage generated by universal banking had not been utilized to increase the benefits of their investment customers. On the other hand, the less informed and thus less monitored commercial bank underwriters allocate IPO issues with low risk-adjusted aftermarket returns to their affiliated mutual funds, suggesting conflicts of interest or an inefficient outcome.

Evaluation of multi-asset investment strategies with digital assets

The drastic growth of the cryptocurrencies market capitalization boosts investigation of their diversification benefits in portfolio construction. In this paper with a set of classical and modern measurement tools, we assess the out-of-sample performance of eight portfolio allocation strategies relative to the naive 1/N rule applied to traditional and crypto-assets investment universe. Evaluated strategies include a range from classical Markowitz rule to the recently introduced LIBRO approach (Trimborn et al. in Journal of Financial Econometrics 1–27, 2019). Furthermore, we also compare three extensions for strategies with respect to input estimators applied. The results show that in the presence of alternative assets, such as cryptocurrencies, mean–variance strategies underperform the benchmark portfolio. In contrast, CVaR optimization tends to outperform the benchmark as well as geometric optimization, although we find a strong dependence of the former’s success on trading costs. Furthermore, we find evidence that liquidity-bounded strategies tend to perform very well. Thus, our findings underscore the non-normal distribution of returns and the necessity to control for liquidity constraints at alternative asset markets.

The Light Assets Trend of Multinational Corporation OFDI and Its Enlightenment to China

The asset-lighting of foreign direct investment is a new phenomenon in the development of multinational corporations. There are four major characteristics: the increase of non-equity investment, the low proportion of fixed capital, the high proportion of investment in the service industry, and the decline of productive investment. Although the current global foreign direct investment There has been a decline for four consecutive years, but due to the impact of COVID-19, the trend of traditional international investment patterns has further emerged. The overseas investment of multinational companies has entered a new stage, which is mainly affected by the strengthening of restrictions on foreign investment by various countries, the digitization of global value chains, and the further decline of manufacturing. Facing the continuous decline in global foreign direct investment and the new trend of asset-light assets of multinational OFDI, China needs to embrace the digital transformation and shift its focus from a traditional investment model to a digital investment model. In the process of foreign investment, we should combine the differentiation of non-equity models to expand overseas markets, build a regional industrial chain, promote regional production specialization and industrial diversification, and use new methods to solve new problems of anti-globalization.

THE COINTEGRATION RELATIONSHIP BETWEEN BITCOIN AND THE STOCK INDICES OF TURKEY AND BRICS COUNTRIES

Bitcoin, the most important of all cryptocurrencies, is a currency that is not included in the central monetary system and has a digital format. With the rapid increase in the value of Bitcoin in recent years, it has attracted the attention of investors. Bitcoin, as an alternative to traditional investment tools, has sparked a lot of controversies. In this study, the Bitcoin price relationship between stock market indices and the BRICS countries belonging to Turkey is intended to be detected. In this direction of Bitcoin 01.01.2013-31.12.2019 period to test the relationship between Turkey and the BRICS countries, stock index monthly data are used. After determining the basic statistical properties of the series, cointegration and causality test was performed to determine the financial relationship. ADF (Augmented Dickey-Fuller) unit root tests and stationarity analysis are applied, and then the existence of long-term relationships between stock exchanges is explained with the Johansen cointegration test. The Vector Error Correction Model (VECM) was used to analyze whether the long-term relationship is in equilibrium. Also, short-term relationships were determined by Granger causality analysis. The analysis performed between variables were identified as a result of a long-term relationship, Russia (MOEX) and Turkey (BIST100) was found to be the cause of the stock market index of Bitcoin. It has been determined that Bitcoin is the cause of China (SHANGAI) exchange. In these exchanges, it has been observed that the change in Bitcoin prices in the short term affects investment decisions.

Social responsibility portfolio optimization incorporating ESG criteria

Social responsibility investment (SRI) has attracted worldwide attention for its potential in promoting investment sustainability and stability. We developed a three-step framework by incorporating environmental, social, and governance (ESG) performance into portfolio optimization. In comparison to studies using weighted ESG rating scores, we constructed a data envelopment analysis (DEA) model with quadratic and cubic terms to enhance the evidence of two or more aspects, as well as the interaction between the environmental, social, and governance attributes. We then combined the ESG scores with financial indicators to select assets based on a cross-efficiency analysis. The portfolio optimization model incorporating ESG scores with selected assets was constructed to obtain a social responsibility investment strategy. To illustrate the effectiveness of the proposed approach, we applied it in the United States industrial stock market from 2005 to 2017. The empirical results show that the obtained SRI portfolio may be superior to traditional investment strategies in many aspects and may simultaneously achieve the consistency of investment and social values.

Search behavior and decision confidence in equity crowdfunding: An information search process model perspective

ABSTRACT Equity crowdfunding allows entrepreneurs to raise much needed capital by selling shares of ownership to individuals while avoiding the challenges associated with an initial public offering. However, the process by which crowdfunders choose to engage in equity crowdfunding has received limited attention in the academic literature. This gap is noteworthy because funders can withdraw committed funds without penalty and without notice if their feelings about the funding decision change before the funding round closes. This dynamic places considerable importance on understanding funding decision confidence—that is, the belief an individual has made a wise funding decision. We draw on Kuhlthau’s information search process model to guide our theorizing of how funding decision confidence evolves as individuals search for and ultimately act upon information. To isolate key decision processes, we conduct a policy capturing study using a novel process tracing method and find that information search increases funder decision confidence only to a point, after which confidence then decreases. Overall, our findings challenge the belief that equity crowdfunders are like traditional equities investors who primarily care about earning a return on their investment and suggest that entrepreneurs engaged in crowdfunding should consider funder motivation and actively manage the structure of information presented to potential crowdfunders.

SOCIAL ENTREPRENEURSHIP: ISSUES OF LEGAL REGULATION

Social entrepreneurship, as an established concept, has entered economic life relatively recently. At the same time, this concept has already been reflected in the current legislation. The author analyzes the current legislation on social investment. On the basis of the study, a list of persons who, from the point of view of the state, belong to the least protected is identified and a comparison is made with respect to persons subject to social protection during the USSR. The most acute contradictions of social entrepreneurship are identified. The most acute problem of social entrepreneurship is the lack of funding. The article attempts to classify investments in social entrepreneurship by the ratio of the expected economic benefit and the socially directed effect from traditional investments to charity. The paper notes that the main areas of financial support for social entrepreneurship in Russia can be such well-established financial institutions in developed countries as crowdfunding, social and green bonds, and meson financing. Another important issue related to social entrepreneurship, according to the author, is the place of non-profit organizations in the system of social entrepreneurship. This issue is particularly relevant now, since the current Russian legislation defines social entrepreneurship as commercial enterprises. In conclusion, it is concluded that despite the fact that the trends of social entrepreneurship in recent decades have appeared in developed Western countries, our country has its own rich experience in solving social problems, which can and should be used in the new socio-economic conditions.

When Early Adopters Learn From the Followers: The Cryptocurrency Return Predictability of GBTC Discount and Premium

We show that change in Grayscale Bitcoin Trust premium is the single most significant predictor of Bitcoin daily return. This sentiment measure is similar to the closed-end fund discount measure as in Baker and Wurgler (2006), but more likely to reflect the excess demand from traditional investors than from blockchain specialists. Although there is a substantial variation in Bitcoin price quotes worldwide, this Grayscale premium and discount predict Bitcoin daily return for the most liquid Bitcoin exchanges. Using K-means clustering and LDA analysis, we find that this predictability is especially significant when there is a large variation in bullish and bearish market sentiment, innovation regarding CBDC, regulations on crypto exchanges, but not when there is innovation regarding blockchain technology or bitcoin mining. A simple long and short strategy based on this signal generates a daily alpha of 40 bps. These findings suggest that Bitcoin prices react with a delay to the information contained in the sentiment of traditional investors and investors who are constrained from directly holding Bitcoin.