Traditional Investment Research Articles

GameStop or Game Just Started? Leveling the Playing Field for Social Media Meme Investors to Rebuild the Public’s Trust

Against the GameStop frenzy in 2021, this article defines meme investors as a new group of investors in financial markets while demonstrating meme investors’ regulatory and social implications. By comparing meme investors with traditional investors under the MiFID II regime, this article finds that meme investors are significantly less wealthy than traditional investors, trade via digital trading platforms, and rely on social media information for investment decision making. This article argues that the emergence of meme investors is an expression of the public’s desire for financial inclusion and their frustration with traditional financial institutions. Therefore, properly engaging with meme investors is crucial for rebuilding the public’s trust towards regulators. After illustrating meme investors’ exposure to default risks, legal uncertainty, and online misinformation, this article calls for regulators to engage with social media meme investors and improve financial literacy among the public.

Classifying responsible investors: Identifying clusters of Ontario investors

AbstractLittle empirical research has explored Canadian investors' interest in responsible investing. A variety of demographic and attitudinal characteristics can contribute to who makes ethical investments. Using a survey of 1,834 residents of Ontario, Canada three groups of investors are identified: those interested in responsible investments who actively avoid investing in some sectors, those interested in responsible investments who do not actively avoid any types of investments, and traditional investors that are not interested in responsible investing. Demographic and attitudinal variables that explain who falls into which group are explored.

Understanding public perceptions and intentions to adopt traditional versus emerging investment platforms: The effect of message framing and regulatory focus theory on the technology acceptance model

With the rise of blockchain technology, asset tokenisation – issuing blockchain tokens to represent asset ownership – is expected to become more prevalent. This study uniquely proposes that tokenised health data could serve as a digital currency for the purchase of tokenised financial assets, and draws upon technology acceptance model, framing, and gamification to examine the Singapore public's acceptance of such emerging platforms compared to traditional investment platforms. We test how message framing (through promotion-prevention message frames and gamification) influence perceived ease of use and perceived usefulness of traditional investment versus asset tokenisation platforms, and how regulatory fit and risk propensity moderates these main effects. Using a 2 (promotion-focused versus prevention-focused message frame) × 2 (gamification versus non-gamification) × 2 (traditional investment versus asset tokenisation platform) between-subject factorial design, results showed that when users’ regulatory orientation and the message frame matched, the investment platform's perceived risk was lowered. Risk-taking users perceived gamified investment platforms as easier to use and useful, and vice versa for risk-averse participants. Gamified platforms were perceived as easier to use. Prevention-frame messages led to higher perceived risk than promotion-frame. The findings provide future direction in tailoring more effective messaging to influence attitudes and intentions towards investment platforms.

Impacts of the Additive Manufacturing Process on the Roughness of Engine Scale Vanes and Cooling Channels

Abstract By leveraging the additive manufacturing (AM) platform, development time and costs for turbine component testing can be reduced relative to traditional investment casting. Surface roughness is a key characteristic of the additive manufacturing process that can impact flow, heat transfer, and mechanical integrity of printed components. There are multiple design and build considerations that result in variability in surface roughness, especially when additively fabricating complicated three-dimensional vanes and internal cooling passages. This study characterizes the surface roughness of internal cooling passages, vanes, and flat external surface samples made using additive manufacturing, specifically the direct metal laser sintering process. The samples were manufactured with various wall thicknesses, layer thicknesses, build locations, build directions, and on different AM machines. A combination of computed tomography scanning and optical profilometry was used to evaluate surface roughness levels. The data indicate that the dominant factors in roughness for a given layer thickness are a function of wall thickness, build location, and build direction.

Ethical and Conventional Stock Price Performance: An Empirical Investigation under CAPM-GARCH Models

The present paper aims to examine the financial performance of the Moroccan Ethical, Social and Governance index (ESG10) compared to its conventional counterpart the Moroccan All Shares Index MASI index. The ethical investment is a kind of investment that applies screenings criteria to include or exclude assets from a portfolio. This asset filtering strategy, however, may have additional financial costs compared with traditional investments. Therefore, we will examine the following issue: Does ethical investment impact positively/negatively on corporates’ stock market performances? The study covers the period 2019-2022. To this end, various performance ratios was used and a CAPM-GARCH model was estimated. Our findings indicate that the ethical index is not only more volatile than the conventional peer, but also shows a smaller return. This paper is one of the first studies to tackle comparative performances for the ESG 10 index, which was launched by the Casablanca Stock Exchange in September 2018.

A Study of Investors Awareness towards ETF in Comparison with Other Traditional Investment Options.

The present study is conducted with the objective of assessing the awareness level of the investors. The success of ETF depends upon the awareness and confidence level of the investors. The investment pattern varies with age, education, gender, occupation. The study was conducted in Karnataka the study is based on primary data with a sample size of 87 respondents the study observed that awareness about ETF among the investors. It also observed no significant difference in the awareness level the investors belonging to different education background and gender. There is no significant difference in the awareness level about ETF Investment with respect to age, gender, qualification & occupation.

Quantitative Investment Trading Model Based on Model Recognition Strategy with Deep Learning Method

With the acceleration of economic globalization, the frequent price fluctuations of gold and bitcoin and other currencies have attracted wide attention from the quantitative investment industry. For market traders, rational use of deep learning means to improve traditional investment trading strategies has become one of the main contents of current work. In this paper, the deep learning method is used to make a horizontal comparison of the benefit increase of the model recognition strategy with deep learning as the main means compared with the other two strategies, and a longitudinal comparison is made between the deep learning method and the traditional time series of fitting accuracy advantages. We grouped gold and bitcoin prices in the LSTM/GRU framework, trained the recursive dynamic neural network model on the daily data of each group, and used the dropout algorithm to reduce the overfitting effect of the model and retained 20% of the data for cross-checking. The results obtained by this method show that the benefit of the whole neural network model is more obvious when making decisions on the data of the day, and the fitting accuracy of the model is more than 73%, and the average absolute error is 14.040908, indicating a good fitting degree. Compared with model recognition strategies represented by LSTM/GRU, follow-the-winner and follow-the-loser have obvious disadvantages in terms of investment trading principle, and their returns are far lower than the $22,059.583248 obtained under the model recognition strategy. We compare the price trends of gold and bitcoin under ARIMA(2,1,1) and ARIMA(4,1,5) by comparing the LSTM/GRU method under the framework of model recognition with the time series method in model recognition and find that the mean square error is much greater than the fitting results of neural network. Therefore, it is concluded that the model recognition strategy integrating the deep learning model is the best fit and the best profit among the three conditions. Finally, we change the transaction cost of gold and bitcoin to 7% to simulate whether the transaction model in different countries is stable. The conclusion shows that when the transaction cost changes within 7%, the model still has high feasibility and stability and is relatively robust.

A Study on Formation of Candlestick Chart Patterns with Respect to the Indian Automobile Sector

The Indian economic scenario is changing and so are the spending and investment habits of Indian citizens. The investment choices of individual investors have switched from traditional physical assets such as gold, real estate, as well as bank deposits to stocks and other similar assets like bonds, debentures, and derivatives because of increased awareness of alternative investment options, improved technical analysis tools, ease of holding the investment, technological advancements in the form of user-friendly trading platforms, decreasing interest rates on traditional investment options, and the effect of a pandemic such as salary cuts, job losses, and an uncertain economic future, etc. According to a recent survey, the participation of retail investors in NIFTY alone grew to 45% in 2021 from 33% in 2016. The study intended to assist investors in the automobile sector by analyzing the success rate of some selected frequently occurring candlestick patterns and stating the patterns that have a high degree of reliability. The simple percentage analysis of the formations of the selected patterns over five years depicted that the bearish engulfing pattern and the bullish engulfing pattern were the most reliable patterns with a higher rate of success in comparison with the other selected patterns.

Benefits of investing in cryptocurrencies when liquidity is a factor

Previous studies find that cryptocurrencies offer diversification benefits when are allocated in a portfolio with traditional assets. However, they disregard the impact of liquidity and the fact, that investors dislike the illiquidity assets. The goal of this paper is to evaluate the cost of liquidity preferences for portfolios that combine traditional assets and cryptocurrencies. We find that, once liquidity is considered, those portfolios with the highest expected return become unavailable and the rest suffer losses in performance of 10–25% in the efficient frontier. If we focus on a traditional investor who limits exposure to cryptocurrencies, the benefits of adding cryptocurrencies to the investor’s portfolio are reduced by about 15%, depending on the liquidity measure analysed.

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

Can artificial intelligence beat the stock market?

PurposeAcademia and financial practitioners have mixed opinions about whether artificial intelligence (AI) can beat the stock market. The purpose of this paper is to investigate theoretically what would happen if AI has further evolved into a superior ability to predict the future more accurately than average investors.Design/methodology/approachA theoretical model in an endowment economy with two types of representative investors (traditional investors and AI investors) is proposed, and based on the model, a long-run survival analysis for both types of investors is implemented.FindingsThe model presented in this paper indicates that being equipped with a superior ability to predict the future more accurately than traditional investors cannot guarantee AI investors to always beat the stock market in the long run. Those investors may be extinct, all depending on the structure/parameters of the stock market.Originality/valueTo the best of the author’s knowledge, they are the first to set up a representative agent equilibrium model to explore the above question seriously.

Crowdfunding Sustainable Projects: Cross-country Heterogeneity in Environmental Support Systems

Past research has shown that ventures focused on addressing environmental sustainability face considerable difficulties in attracting funds from “traditional” sources of finance (i.e., external equity investors, banks), while crowdfunding could ease resource attraction for such ventures. Nevertheless, although crowdfunding is a global phenomenon—with both projects and backers located across the globe in countries with fundamentally different environmental support systems—our understanding of how this heterogeneity influences fundraising success remains limited. To examine this issue, we invoke an institutional logic perspective and study 290,126 Kickstarter campaigns located in 139 countries. Our findings are broadly in line with our hypotheses: in crowdfunding, where a community logic often prevails, fundraising success is greater for environmental sustainability projects, particularly when these projects are located in countries with weaker environmental support systems. Moreover, these projects are also more likely to tap funding from backers located in countries with stronger environmental support systems. We discuss the theoretical implications of these findings, which challenge commonly-held assumptions on institutions and cross-border money flows in entrepreneurial finance developed from examining traditional investors who generally hold a market logic.

Exploring the Basic Logic of the Enabling Effect of Government Data Governance on Investment Promotion

With the arrival of the era of big data, giving full play to the application of data elements in investment promotion has gradually become an important means for the government to break the traditional investment promotion model. This paper starts from the basic content of data governance, explains the concept, purpose and application of data governance, and analyzes how data governance can accelerate the transformation of government functions and drive the development of investment promotion work from three levels: value logic, objective logic and practical logic. It aims to explore how big data technology can effectively solve the problems existing in the current investment promotion work by promoting the innovation of government governance methods and means and governance system mechanism, and provide references for further improving the level of government investment attraction.

The reaction to CSR controversies by institutional investors

Despite the importance of Corporate Social Responsibility (CSR) firm controversies, little is known about their effect on institutional investors. We study the most important institutional investors worldwide: pension funds and mutual funds. The separation between fund management and ownership raises the need to examine how fund managers and fund participants react to investee-firms’ CSR controversies. Considering the conventional/Socially Responsible Investment (SRI-fund nature, we find that investee-firms’ controversies diversely affect fund performance, depending on the controversy type. Furthermore, participants and managers of SRI pension and SRI mutual funds display a passive behavior toward controversies. These attitudes are consistent with enduring behavior and continuity investment policies, such as amending/controlling CSR-firm controversies. In contrast, conventional pension-fund and conventional mutual-fund participants seem guided by traditional investment rules to deal with unsatisfactory situations and respond to controversies after managerial decisions regarding these events with negative reactions. Finally, firms developing CSR-engagement strategies may soften market and managerial reactions toward controversies. Nonetheless, symbolic CSR-engagement practices arouse participants’ responses. JEL CLASSIFICATION: G11, G23, M14

Practical Applications of The State of ESG Investing: A Portfolio Management Perspective

In <b><i>The State of ESG Investing: A Portfolio Management Perspective</i></b>, from the Summer 2022 issue of <b><i>The Journal of Impact and ESG Investing</i></b>, authors <b>Stephen M. Horan</b> (of the <b>University of North Carolina)</b>; <b>Elroy Dimson</b> (of <b>Cambridge University)</b>; and <b>Clive Emery</b>, <b>Kenneth Blay</b>, and <b>Glen Yelton</b> (all of <b>Invesco</b>) explain the main challenges facing ESG investors and fund managers. ESG investors differ from traditional investors in that they seek to pursue nonfinancial goals through investments. Those goals vary widely, depending on investors’ particular interest in different ESG topics, intensity of commitment to nonfinancial goals, and preferences among different investing strategies. Therefore, investors, financial advisors, and fund managers need relevant, reliable, and relatable information to help them choose investments that align with their ESG goals. Unfortunately, there is a lack of such information—especially standardized company information that fund managers can aggregate to demonstrate ESG portfolio metrics over time. The authors present a framework to help investors articulate the information they need, help companies report information that investors and fund managers can use, and help regulators create reporting standards to make such information aggregable at the fund level so investors can easily compare and choose ESG funds.¹

Investment dynamics and forecast: Mind the frequency

We analyze the in-sample fit and the out-of-sample forecast performance of the investment equation using different proxies for Tobin’s Q, controlling for cash flow, and using their frequency-decomposed components. We show that different frequencies of bond Q and cash flow significantly improve the empirical performance (both in-sample and out-of-sample) of the traditional investment equation. The key step is to filter out the noisy frequencies of the predictors and only retain those that have the greatest individual predictive power.

Forecasting the future of robo advisory: A three-stage Delphi study on economic, technological, and societal implications

Robo advisors represent a digital financial advice solution challenging traditional wealth and asset management, investment advice, retirement planning, and tax-loss harvesting. Based on algorithms, big data analysis, machine learning, and other technologies, these services minimize the necessity for human intervention. Based on an international three-stage Delphi study, we provide a plausible forecast of the development of the robo advisor industry, with regards to market development, competition, drivers of growth, customer segments, challenges, services, technologies, and societal change. The results suggest that the financial advice market will experience a further increase in the number of robo advisor services available. Existing and traditional financial advice players will be forced to adjust to the changing environment of the market. Due to low fees and ease of use, robo advisors will be made available to a broad cross section of society, and will cause significant market losses for traditional investment advice companies. Ten years from now, the predominant investment class will remain Exchange Traded Funds (ETFs). Even though degrees of human intervention are expected to vary considering the complexity of advice, automation will increase in significance when it comes to the development of robo advisors.

금융기관의 대체투자 중 부동산 Equity 투자 결정요인 분석

Interest rates, which have continued to fall since the 2008 financial crisis, have recently fallen to 0.5% of the Bank of Korea's benchmark interest rate, entering an ultra-low interest rate era. Investors in financial institutions are paying attention to alternative investment assets to cope with the weakening profitability of their investments, as it is difficult to expect profitability from bonds and stocks, which are traditional investment assets. Among them, real estate investment has steadily become an essential factor in institutional investors' portfolios, as real estate investments can be guaranteed high returns depending on location and supply and demand conditions, but are relatively less affected by financial market trends. In particular, financial institutions, which are actively expanding alternative investments due to falling operating returns due to the worsening economy, are focusing on profitable real estate equity investments. However, despite the growing demand for real estate Equity investment by financial institutions, there are few specific investment criteria for which investment impact factors are determined, in the case of domestic real estate investment decision-makers, few have focused on investment. The objective of this study is to empirically analyze and identify the investment impact factors on the investment satisfaction and willingness of financial institutions in the real estate equity investment market, and to specifically analyze the differences in the types of investment impact factors. This study first examined the preceding research to derive the influences of real estate equity investment, and conducted a survey on investors from financial institutions. In addition, investor characteristics used descriptive statistical analysis, and factor analysis was conducted to verify the validity of the survey variables. Based on this, a regression analysis was conducted to test the hypothesis, and additionally a descriptive statistical analysis was used to analyze differences in the importance of investment impact factors depending on the type of financial institution. The results of the study are summarized as follows. First, as a result of hypothesis tests on investment impact factors and investment satisfaction for investors in financial institutions, location characteristics, profitability, and stability were adopted, and location characteristics were the factors that affected investment satisfaction the most. A hypothesis test of investment impact factors and willingness to reinvest in financial institutions showed that profitability and stability had a positive impact on the willingness to reinvest. Second, after analyzing the difference in importance of investment impact factors according to the type of financial institution, bank investors value stability and capital investors value timeliness the most among the investment impact factors. Investor in insurance companies put the most importance on profitability. Overall, investors in financial institutions participating in real estate equity investments demonstrate similar relationships between investment impact factors, investment satisfaction, and reinvestment intentions, and empirically analyzed that there are also differences in investment impact factors. These findings suggest that in financial institutional investment markets, financial institutional investors make decisions based on profitability, stability and location characteristics, but consider differences in investment propensity for financial institutions.

The link between corporate sustainability and willingness to invest: new evidence from the field of ethical investments

In recent decades, academia has addressed a wide range of research topics in the field of ethical decision-making. Besides a great amount of research on ethical consumption, also the domain of ethical investments increasingly moves in the focus of scholars. While in this area most research focuses on whether socially or environmentally sustainable businesses outperform traditional investments financially or investigates the character traits as well as other socio-demographic factors of ethical investors, the impact of sustainable corporate conduct on the investment intentions of private investors still requires further research. Hence, we conducted two studies to shed more light on this highly relevant topic. After discussing the current state of research, in our first empirical study, we explore whether besides the traditional triad of risk, return, and liquidity, also sustainability exerts a significant impact on the willingness to invest. As hypothesized, we find that sustainability shows a clear and decisive impact in addition to the traditional factors. In a consecutive study, we investigate deeper into the sustainability-willingness to invest link. Here, our results show that improved sustainability might not pay off in terms of investment attractiveness, however and conversely, it certainly harms to conduct business in a non-sustainable manner, which cannot even be compensated by an increased return.

Investment myths and its effect on investment behaviour of young investors influencing socially responsible investment (Sri)

The investment trend gets changes day after day. It became the need of the day to make the investments more sustainable. The concept of Socially Responsible Investment (SRI) became popular in 2017’s. Investors of companies around the world believe and support the fact of the SRI investment strategies to attract national and global investments. This in turn made the inventors consider various non-financial disclosures apart from financial disclosures for investment portfolio creation that affect the investors' traditional investment strategies. This paper covers the current strategy of investment format and pattern in portfolio investment assumptions, beliefs of investment (myths), and its effect on the investment behaviour of young investors.