Investment Situations Research Articles

Causal model of factors influencing the fundraising in Thai sports industry: Investment ecosystem evaluation for scale-up to innovation-driven enterprises

This pioneering research aimed to verify the causal model of factors influencing fundraising in Thai sports industry. Investment ecosystem and theory of planned behavior were employed to obtain understanding of investment situations in this industry. A mixed-method research approach was also utilized. In qualitative, seven experts were interviewed to drawing the structure of the causal model. In quantitative, the top 798 sport investors responded to modified rating-scale questionnaires for confirming indicators of underlying factors in the causal model. Analysis based on grounded theory revealed in the causal model that policy, market, culture, finance, supports, and human capital are antecedent factors that induce attitude, perceived value, and behavioral intention. Fundraising behavior was directly affected by behavioral intention. Confirmatory factor analysis supported that all indicators were appropriately loaded into 10 factors. Structural equation modelling of the causal model indicated that finance, human capital, and attitudes played a vital role in fundraising. The causal model showed that policymakers and entrepreneurs in Thailand will benefit from scaling the industry to innovation-driven enterprises.

Herd Behaviour Manifestation in Share Market: A Gender Based Study

Investment in shares is growing rapidly in India either directly or indirectly. Although the number of investors has risen, the knowledge and expertise needed for making share investment decisions is lacking among most of the investors. Investment process is also influenced by various psychological factors. These psychological factors are critical in the investment decision making process. Herd behaviour is an investment bias which investors in share market adopt mostly during complex investment situations. Behavioural finance identified this bias as one of the causes for bubble formation in the share market, which creates a deviation in the market price from its fundamental value. This paper has studied the differences in the level of herd behaviour between males and females in respect to share investment decisions. The study found no significant difference between herd behaviour manifestation between males and females.

Male and overconfident groups overinvest due to inflated perceived ability to beat the odds

Organizational decisions are often made by groups rather than individuals. Depending on the group composition, each member's characteristics—like gender and motivated beliefs—can influence the final group investment decision. To capture this, we design two types of investment situations in a randomized controlled laboratory experiment—one with fixed chances of success and one with performance-dependent chances of success. This novel design entails the perceived ability to “beat the odds” of the investment and thus models real-life investment situations more accurately than standard lottery choice. Our results demonstrate the benefits of mixed group composition in terms of both gender and overconfidence: Groups with all men and/or all overconfident group members consistently overinvest when a possibility to “beat the odds” is present, but not in standard situations. We explore several channels for our results and find that (i) individual probability perception, (ii) leader responsibility allocation and (iii) spillover effects from priming show significant effects.

Investment strategies and coordination for green food supply chain: a further research considering the inputs of the blockchain-based traceability system

PurposeTo study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.Design/methodology/approachWith the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.Findings(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.Originality/value(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.

The Impact of Geographic Distance between Venture Capital and Start-ups on Operating Performance

China has a vast territory, and the development scale of venture capital varies significantly among different regions. The geographical distribution of venture capital is unbalanced, which makes it difficult for venture capital to choose to invest in local enterprises or cross-regional investment. Many studies have shown that geographical distance is related to the formation of social networks, the strength of information asymmetry and the amount of transaction costs. Therefore, depending on geographical distance, venture capital will face different investment situations, investment costs and investment returns, and entrepreneurial enterprises will therefore receive different levels of super-vision, value-added and post-investment management services. Therefore, this paper analyses the impact of geographical distance between VCs and start-ups on the operating performance of start-ups from a unique geographical distance perspective, and takes relevant data of listed start-ups from 2009 to 2019 as samples for empirical test. The study finds that the closer the geographical distance between VCs and start-ups, the higher the short-term operating performance of start-ups. Geographical distance has an insignificant effect on the long-term performance of startups. In addition, poor internal control of start-ups will weaken the positive impact of geographical proximity on operating performance of start-ups.

Riding the wave of fashion rental: The role of power structures and green advertising

In recent years, the rise in the collaborative consumption of fashion products has propelled many fashion brands to tap into rental markets and offer rental services. Although many studies have investigated consumer perceptions and attitudes toward fashion rental services, analytical models that help businesses make more informed supply chain decisions are scarce. Consequently, we develop game-theoretical models to study the fashion market with two firms, i.e., a fashion manufacturer and its rental platform partner. The models consider four green-advertising investment situations: no firm invests, only the fashion manufacturer invests, only the rental platform invests, or both firms invest in green advertising for fashion rental services. We compare pricing and green advertising decisions under different power structures between the two firms across the four green-advertising investment situations. Our results demonstrate when and how the power structure affects the key decision variables. We reveal that it is least profitable for both firms if neither contributes to green advertising, while both firms obtain the highest profits when they both invest in green advertising. In the two situations when only one firm invests in green advertising, we note that it is not necessarily unfavorable for firms to accept the green-advertising cost proactively. Specifically, the Stackelberg follower benefits from investing in green advertising. Furthermore, in these two situations, whether or not the Stackelberg leader covers the green-advertising cost is more important than their identity. Our results and analysis reveal important managerial implications that can assist firms in excelling in the fashion rental market.

Greedy Strategies with Multiobjective Optimization for Investment Portfolio Problem Modeling.

The ultimate purpose of portfolio investment is to reduce investment risk and improve total return on the premise of ensuring reasonable allocation of capital. In this paper, we build a quantitative model to advise on trading based on the price movement of Bitcoin and gold between 2016 and 2021; our goal is to maximize profit while minimizing risk. We mainly use greedy strategies with multiobjective optimization models. For the purpose of obtaining the correct price trend, some popular trend indicator strategies are referred to predict the future price trend in the medium and long term. In addition, we also consider people with different trading preferences and divided them into aggressive, advanced, balanced, and cautious and provided trading strategies for each of these four groups. This gives our model scalability. Finally, we analyze the sensitivity of the model and discuss the impact of trading commission costs on the model results. The model can be applicable to various investment situations.

Acting as a Business Angel to Become a Better Entrepreneur: A Learning Innovation

TV shows such as “Dragons’ Den” and “Shark Tank” have become common teaching materials in entrepreneurial finance classrooms because they offer a window into the process of pitching to investors. TV exposure to pitch communication and business angels’ feedback is certainly a well-documented source of vicarious learning. It enables students to observe varying investment situations and learn from other entrepreneurs’ success as well as failure. Observation, however, does not replace direct experience. We combined vicarious learning and direct experience in order to enable students to learn how to take initial investment decisions, in practice. We originally proposed this training session in an entrepreneurship graduate program in France. This learning activity invites participants to become observers and actors in a real-life experience with entrepreneurs and angel investors. Students participate in a pitch session within a Business Angels Network (BAN). The BAN chooses entrepreneurs based on their applications and an evaluation meeting for the first round of a live pitch in their usual deal flow. Students are first spectators to the real-life pitch session, in which entrepreneurs face a team of five to 10 business angels (BAs). Then, students play the role of an active participant, making their own investment decisions in a report on each entrepreneurial project, as if they were BAs themselves.

A Decision-Making Model of Technological-Focused Government Agency Selection of Technological Start-Up Businesses

In developing countries, the government has played an important role in supporting startup businesses in various aspects, primarily through tech-focused government agencies. With a limited budget, the government agencies are critical to select plenty of tech startups for funding, leaving only promising tech startups. Consequently, government agencies inevitably face decision-making problems under uncertain circumstances, like private equity investment situations. Reviewing the relevant decision-making frameworks has identified that a classical multiple criteria decision-making (MCDM) approach is currently used, assuming decision-makers acquire complete information that is not realistic. Moreover, both qualitative and quantitative criteria used in evaluating startup businesses cannot represent the uncertainty which is the fundamental nature of the decision-making circumstance. Thus, this article presents a decision-making framework of tech-focused government agencies for selecting startup businesses based on a fuzzy MCDM of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Besides, it identifies selection criteria with mixed research methodologies and determines weights of importance criteria by the Delphi method. Finally, the proposed framework results are fairness, transparency, and eliminating bias in decision-making, including more efficiency when the framework’s ranking orders significantly correspond with actual performances. HIGHLIGHTS Criteria for selecting start-up businesses in technological-focused government agencies A decision-making framework of tech-focused government agencies for selecting startup businesses based on a fuzzy MCDM of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) The performance of the decision-making framework in selecting startup businesses to acquire high potential tech startups to drive the national economy GRAPHICAL ABSTRACT

A framework for modelling cash flow lags

Traditional models of irreversible investment problems assume that the investment starts generating cash flows immediately, i.e., at the same time as the investment is undertaken. Real-world investment situations are characterized by time-to-build or investment lags, which means that there is a time difference between when the investment is made and when the investment starts generating cash flows. We combine two existing models of investment lags to obtain a flexible, yet simple, way of modelling and analyzing the effects of investment lags. Both traditional models, and models that incorporate the effects of time-to-build, typically assume that the expected future cash flows generated by an investment are represented by a single cash flow that reflects the size of the market value of an investment. To reflect real-world cases where investments generate cash flows in several time periods, we present a framework in which cash flows are explicitly allowed to be spread out in time. Our model can be used to incorporate cases where an investment is partially sold in different time periods. Using an irreversible optimal investment timing problem case study, we show how our framework makes it possible to easily compare the effect of different cash flow timings. In this case, the value and the timing of the investment depend on a constant that in a natural way can be decomposed into three parts, thereby showing the influence of the value and timing from the respective parts of the framework.

An Economic Perspective on Language Management in Iraqi Kurdistan

This paper is about ‘An Economic Perspective on Language Management in Iraqi Kurdistan’. In many languages the relationship between the economy and the language is seriously taken into consideration. Unfortunately, Kurdish language is in a critical situation in this respect. This paper tries to show the linguistic situation of Kurdish language from the economic perspective through studying the related concepts such as language economics, language investment, language industry and its relation to language planning and language policy. Aiming at drawing some of the possible aspects through which there could be a space for investment in the Kurdish language, this study also searches for coming across some current cases of linguistic investment in different parts of the world such as Britain, France and Canada. Meanwhile, it focuses on most outstanding obstacles that stand against the process of investment in Kurdish which includes the lack of awareness, the non-stimulating regulatory besides investment environment, and the unstable economic and linguistic situations in the region. The study comes to conclude that many countries had invested in language considerably to improve this aspect to pave the way for acquiring huge, direct and indirect economic revenues. As much as investors care about English, they need to care about Kurdish. Hereby, there is a crucial need to support experimental studies to reveal the potentials of investing in Kurdish to indicate the existing deficiencies regarding the effective language investment situations.

Goal-directed emotions nudge R&D investment decisions

R&D investment is a risk-taking decision for innovations. However, a trade-off relationship exists between short-term profit and R&D expenditure (present vs. future), because of the uncertainty involved. Choosing to invest in R&D is thus seen as a risk-taking propensity of the decision-makers, and how to perceive the risk is susceptible of their cognitive system. This study included three empirical studies involving R&D investment situations. It looked at how goal-directed emotion worked for the decision-maker's risk-taking propensity, and how this motivated to construct her/his goal-directed efforts in R&D investment. Study 1 showed that the affect heuristic confirmed the decision-maker's risk-taking propensity, and the electrodermal activity (EDA) measure in Study 2 revealed that the affect heuristic contributed to the goal-directed emotion. The last experiment (Study 3) revealed how one's risk-taking propensity, motivated by the decision-maker's goal-directed emotion, modulated the goal-directed efforts. Finally, the implications and suggestions for the decision-makers with regard to the R&D investment were discussed.

Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms

Many studies have been proposed to prove that technical analysis can help investors make trading decisions. The moving average (MA) is a widely used technical indicator that plays an important role in this field since it directly reflects stock fluctuations. However, most studies ignore the parameter settings of the MA, which leads to underestimation of the potential of the MA. Therefore, this paper is the first attempt to remove all restrictions and extend the limits of the MA to take advantage of the MA’s capability well. It also uses different kinds of MA, such as the weighted moving average (WMA) and the exponential moving average (EMA), to compose trading strategies. Our system proposes the global best-guided quantum-inspired tabu search algorithm (GQTS), which is better at searching than traditional algorithms, to optimize trading strategies based on the MA. Furthermore, an innovative 2-phase sliding window is invented to consider more investment situations in changeable stock markets. In summary, this paper intended to investigate the ability of MA and proposed dynamic and intelligent trading strategies based on MA, GQTS, and 2-phase sliding window to assist investors to make trading decisions. The experiments show that the proposed system flexibly discovers better trading points. Our system outperforms traditional methods and beats the buy-and-hold strategy to yield significant profits both in developed and emerging stock markets.

Goal-directed emotions nudge R&D investment decisions

R&D investment is a risk-taking decision for innovations. However, a trade-off relationship exists between short-term profit and R&D expenditure (present vs. future), because of the uncertainty involved. Choosing to invest in R&D is thus seen as a risk-taking propensity of the decision-makers, and how to perceive the risk is susceptible of their cognitive system. This study included three empirical studies involving R&D investment situations. It looked at how goal-directed emotion worked for the decision-maker's risk-taking propensity, and how this motivated to construct her/his goal-directed efforts in R&D investment. Study 1 showed that the affect heuristic confirmed the decision-maker's risk-taking propensity, and the electrodermal activity (EDA) measure in Study 2 revealed that the affect heuristic contributed to the goal-directed emotion. The last experiment (Study 3) revealed how one's risk-taking propensity, motivated by the decision-maker's goal-directed emotion, modulated the goal-directed efforts. Finally, the implications and suggestions for the decision-makers with regard to the R&D investment were discussed.

Sharpe Ratios, Target Ratios, and Return Goals

Some form of success estimation is present in virtually all decision-making processes. In most cases, estimations are implicit and judgmental. However, in certain data-rich areas, success prospects can be sharpened into probabilities. Although funds may settle for an expected return that equals some fixed target return, that match results in only a 50% probability of success. However, important goals may require a higher success probability, such as 60%. In this article, the authors present an approach that facilitates calculation of success probabilities for many common investment situations. The key success factor turns out to be the target ratio (T–ratio), a generalization of the standard Sharpe ratio. In addition to fixed return targets, the T-ratio can be applied to a wide range of market-dependent targets such as policy portfolios, benchmark indexes, and/or peer group percentiles. Moreover, within the typically relevant range, a simple approximation can directly map T-ratio values into success probabilities. The structure of the T-ratio underscores the importance of more tightly integrating risk control considerations and success probabilities into the return-seeking process. TOPICS:Performance measurement, risk management, volatility measures Key Findings • The common practice of matching the expected portfolio return to some fixed target return may prove insufficient for critically important goals that require a higher than 50% probability of success. • To obtain a success probability above 50%, a fund’s risk–return structure must provide a sufficiently high T-ratio, a generalization of the Sharpe ratio. A simple formula, based on this T-ratio, can be applied to estimate success probability. • To preserve a fund’s return advantage and desired probability of success, the fund also must achieve a level of risk control that results in the T-ratio value associated with that probability.

Demographic Variations in the Perception of the Investment Services Offered by Financial Advisors

With varying levels of complexity, diversity, and differing needs among individuals’ investment situations, it can be challenging for Financial Advisors (FAs) to know how the investment services that they offer are perceived by prospective and existing clients. Utilizing data from the National Financial Capability Study (NFCS), this study examines the demographic perceptions of utilizing FAs for specific investment services. The findings, generally, show females, non-whites, higher levels of education and net worth are associated with valuing the investment services offered by FAs.

Time-consistent strategies for multi-period mean-variance portfolio optimization with the serially correlated returns

In this paper, we discuss several different styles of multi-period mean-variance portfolio optimization problems under the serially correlated returns. We derive the time-consistent strategies for the classical multi-period mean-variance optimization with and without risk-free asset using a backward induction approach. We also propose an alternative multi-period mean-variance model, and the corresponding time-consistent strategies are derived. Whereafter, we provide some portfolio evaluation indexes and perform extensive empirical studies based on real data, aiming to provide useful advice for investors. To a large extent, the empirical results answer one important and practical question: in actual investment situations, which strategy is preferred by different investors?

Challenging investment decision-making in pension funds

PurposeThe purpose of this study was to gain a better understanding of pension fund managers investment thinking when confronted with challenging investment decisions. The study focuses on the theoretical question of how dual thinking processes in experts’ investment decision-making emerge. This question has attracted interest in economic psychology but has not yet been answered. Here, it is explored in the context of pension funds.Design/methodology/approachThe sample included 22 pension fund managers. The authors explored their decision-making by applying the critical incident interview technique, which entailed collecting investment decisions that fund managers retrieved from recent memory (Flanagan, 1954). Questions concerned the investment situation, the decision-making process and the challenges and uncertainties the fund managers faced.FindingsMany of the 61 critical incidents examined concerned challenging (mostly stock) investments based on extensive analysis (e.g. reliance on external analysts for advice; analysis of massive amounts of hard company and stock market information; scrutiny of company reports and personal meetings with CEOs). However, fund managers to a high degree based their decisions on soft information judgments such as experience and qualitative judgements of teams. The authors found heuristics, intuitive thinking, biases (sunk cost effects) and social influences in investment decision-making.Research limitations/implicationsThe sample is small and not randomly selected.Practical implicationsThe authors suggest anti-bias training and better acquaintance with human forecasting limitations for pension fund managers.Originality/valuePension fund managers’ investment thinking has not previously been investigated. The authors show the types of investment situations in which analytical and intuitive thinking and biases occur.

The influence of functional and relational proximity on business angel investments

Business angels are a vital source of capital for innovative start-up firms. However, many potentially attractive investment opportunities are rejected during the angel's decision-making process. Information asymmetry, risk and distrust in the relationship between the investor and the entrepreneur result in investment barriers. The concept of proximity has been proposed as a conceptual foundation to understand how the relationship between angel and entrepreneur hinders or benefits the investment decision. Specifically, researchers distinguish between the functional (geographic) and relational dimensions of proximity. This paper uses structural equation modelling to examine the influence of proximity on angel investments based on data from 226 investment situations gathered in fall 2014 from 56 business angels and 87 entrepreneurs in Sweden. We find that the relationship between geographic proximity and the likelihood of a positive investment decision is indeed positive and significant, supporting prior research; however, it is fully mediated by relational proximity.

A Response to Professor Paul A. Samuelson’s Objections to Kelly Capital Growth Investing

The Kelly capital growth investment strategy maximizes the expected utility of final wealth with a Bernoulli logarithmic utility function. In 1956, Kelly showed that static expected log maximization yields the maximum asymptotic long-run growth. Good properties include minimizing the time to large asymptotic goals, maximizing the median, and being ahead (on average) after the first period. Bad properties include extremely large bets for short-term favorable investment situations because the Arrow-Pratt risk aversion index is essentially zero. Paul Samuelson was a critic of this approach, and his criticism is partially responsible for the fact that most finance academics and investment professionals do not recommend Kelly strategies. Samuelson’s points are theoretically correct and sharpen the theory, cautioning Kelly investors to understand the strategy’s true characteristics, including ways to lower investment exposure. His objections help us better understand the theory without detracting from its numerous valuable applications.