Investment Strategies Research Articles

Foreign Direct Investment and Its Effect on Agricultural Productivity Kenya

Kenya has historically been one of Africa's top destinations for foreign direct investment. Foreign direct investment is critical for Kenya's agricultural sector, offering capital, technology transfer, and skill development that enhance productivity and economic growth. Despite agriculture being a primary economic driver, challenges such as limited capital, outdated farming techniques, and insufficient infrastructure continue to hinder growth. The research explores the contribution of foreign direct investment on agricultural productivity in Kenya as well as to assess the effect of foreign direct investment on the productivity levels of small-scale and large-scale farmers in Kenya for a period 1993 to 2022. Neoclassical growth theory, Endogenous theory were key in guiding the study. This study adapted the causal research design employing time series data from World Bank spanning from years 1993 to 2022. Data was analyzed the data both descriptively and by inferential statistics. The findings of correlational analysis revealed a moderate positive relationships (r= 0.3213) between Foreign direct investment and agricultural productivity. Multiple regression analysis confirmed the significant positive effect of foreign direct investment on agricultural productivity with a coefficient of 0.3661 and p value 0.0014<0.05. This signifies that, in the short term, a 1% increase in foreign direct investment results in a 36.61% rise in the growth of Agricultural productivity, assuming all other variables remain constant. The findings implied that foreign direct investment inflow has the ability to create employment, technological development and improve access to international markets playing a pivotal role in accelerating the agricultural productivity. The Kenyan government should have more effective foreign direct investment strategies to facilitate technology transfer agreements between foreign investors and local agricultural enterprises, additionally, the government should pursue macroeconomic policies that attract foreign direct investment by providing an investor-friendly environment.

Fractal momentum investment strategies based on liquidity non-linear fluctuations in Chinese stock market

Fractal momentum investment strategies based on liquidity non-linear fluctuations in Chinese stock market

Mapping The Growth of ESG Research: A Bibliometric Analysis Using Bradford's, Lotka's, and Zipf's Laws

Objective: The objective of this study is to investigate the growth and development of research on Environmental, Social, and Governance (ESG) factors using bibliometric analysis. The aim is to understand the publication trends in ESG research and identify influential authors, key themes, and opportunities for future studies. Theoretical Framework: The research is grounded in key bibliometric laws such as Bradford's law (journal productivity), Lotka's law (scientific productivity of authors), and Zipf's law (word frequency analysis). These laws form the basis of the study and provide a solid framework for understanding the evolution of ESG research. Method: This study adopts a bibliometric analysis approach, using data from the Web of Science and Scopus databases. A total of 699 articles from 345 journals and 1,574 authors were analyzed. Techniques like citation analysis, co-citation analysis, bibliographic coupling, and network analysis were employed to assess publication patterns and collaborations. Data was processed using R software and Bibliometrix. Results and Discussion: The results reveal a significant increase in ESG-related publications, particularly after 2013, with annual growth of 39.97%. The study identifies influential authors, such as Hebbe and Buallay, and explores the themes of sustainability, corporate social responsibility, and governance. In the discussion, the results are contextualized within the broader ESG framework, highlighting the role of ESG in corporate behavior and investment strategies. Research Implications: The study provides insights into how ESG research can inform policy-making and corporate decision-making. The findings highlight the importance of ESG factors in sustainable development and suggest future research avenues in areas like digital transformation, entrepreneurship, and innovation. Originality/Value: This study contributes to the literature by offering a comprehensive overview of ESG research, utilizing innovative bibliometric techniques to track its evolution. The originality lies in the use of multiple bibliometric laws to analyze ESG trends, making it valuable for researchers and practitioners looking to understand current and future directions in ESG.

A Critical Study of Psychology of Retail Investors

This research critically examines the psychology of retail investors, focusing on the behavioural finance approach to investment decision-making. Behavioral finance challenges the traditional assumption that investors are always rational, proposing instead that psychological, social, and cultural factors influence financial behaviours. The study aims to explore how demographic factors such as gender, age, and investment experience, along with macroeconomic conditions like interest rates and inflation, shape retail investors' decision-making processes. By analysing both quantitative and qualitative data from surveys and questionnaires, the study seeks to uncover patterns in how emotions, cognitive biases, and social networks affect investment choices across different groups. The scope of this research extends to identifying significant variations in investment behaviour based on demographics and cultural influences. Moreover, the study will test hypotheses related to gender differences in the influence of peers and interest rate changes on investment decisions. Through a mixed-methods approach, this research aspires to provide valuable insights for financial institutions and individual investors, enhancing their understanding of the psychological drivers behind market behaviour and investment strategies.

Evaluating robo-advisors through behavioral finance: a critical review of technology potential, rationality, and investor expectations

The mini review assesses the value propositions of robo-advisors through the lens of behavioral finance. Despite their promise of data-driven, rational investment strategies, robo-advisors may not fully replicate the personalized service of human financial advisors or eliminate human biases in decision-making. A content analysis of 80 peer-reviewed articles and publications was conducted, focusing on the intersection of financial technology and behavioral finance. Literature was retrieved using The Chicago School University Library's OneSearch and the EBSCO host database, with key terms including “robo-advisor,” “investment behavior,” “risk tolerance,” “financial literacy,” and “affective trust.” The review identifies four key limitations of robo-advisors: (1) their inability to replicate the service-relationship of human advisors; (2) the presence of human bias in supposedly rational algorithms; (3) the inability to minimize market risk; and (4) their limited impact on improving users' financial literacy. Instead, robo-advisors temporarily compensate for a lack of financial knowledge through passive investment strategies. The findings suggest that integrating behavioral finance principles could enhance the predictive power of robo-advisors, though this would introduce additional complexities. The review calls for further research and regulatory measures to ensure that these technologies prioritize investor protection and financial literacy as they continue to evolve.

Investment and Financing Decisions for Fuel Consumption Reduction in a Vehicle Supply Chain

This paper models the financing and fuel reduction investment decisions of a supply chain under chain and chain competition to analyze firms’ pricing, product, and investment and financing strategies. In two competing supply chains, one of them is willing to invest in fuel consumption reduction to meet its carbon social responsibility and pursue a competitive. It can choose between two financing models, manufacturer financing and retailer financing, to address the lack of investment capital. Consumers are inclined to purchase low-fuel consumption vehicles due to the cost of use and carbon concerns. Results show that under the model of manufacturer financing, the utility of both the manufacturer and the retailer improves. However, in this model, the fuel consumption reduction is lower than that under the model of retailer financing. If it takes the model of retailer financing, the manufacturer can achieve higher utility than under the model of manufacturer financing, but the retailer’s profits fall rather than increase. So the retailer would not take it unless the manufacturer and government compensate it.

Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability.

Patterns of sperm swimming behaviour depend on male mating tactic and spawning environment in chinook salmon

Many species exhibit alternative mating tactics (ARTs), with larger socially dominant males competing for females and smaller males adopting “sneaker” strategies to exploit fertilisation opportunities without competition or courtship. Females typically prefer larger socially dominant males, but their ability to manipulate mating or fertilisation outcomes is largely unknown. Here, using chinook salmon Oncorhynchus tshawytscha, we examined whether the female’s ovarian fluid (OF) differentially influences the temporal patterns of sperm swimming traits in ejaculates from non-preferred sneaker (‘parr’) and preferred (dominant) males. Results demonstrate that OF improves sperm swimming speed and linearity compared to river water, regardless of male mating tactic. We report a novel tactic-specific difference in sperm linearity in which parr male sperm initially maintain straighter trajectories in river water, compared to dominant males, but then rapidly change to less linear and more circular paths over time. Intriguingly, we show that OF counteracts this change in sperm linearity in parr males so that patterns become indistinguishable from dominants when parr sperm swim in OF. Together, these results show that male chinook salmon exhibit differential sperm trait investment strategies depending on reproductive tactic.

Influence of Corporate Information Transparency on Foreign Institutional Investors' Shareholding Behavior: Evidence from Public Listed Companies in China

In 2003, China implemented the Qualified Foreign Institutional Investor (QFII) system, which has garnered considerable interest from international institutional investors in the mainland market. As global investors, QFIIs depend significantly on precise, thorough information to bolster their investing criteria. Consequently, the transparency of corporate information becomes a crucial factor for QFIIs. However, due to the relatively late development of the Chinese market, an imbalanced structure of listed companies and information asymmetry, the information disclosure of listed companies has been subject to certain degrees of distortion. Therefore, to better facilitate the development of China's securities market and to help QFIIs fully realize their advanced value investment strategies, this paper aims to study the impact of corporate information transparency on the stockholding behavior of foreign institutional investors. This paper begins by reviewing the literature on the factors influencing institutional investor holdings, corporate transparency, and the characteristics of foreign institutional investors' stockholding. It then defines the concepts of corporate information transparency and foreign institutional investors, analyzes the current state of information transparency in Chinese listed companies, and examines the development of the QFII system in China. The theoretical framework explores the mechanisms through which corporate information transparency influences the stockholding behavior of foreign institutional investors. In the empirical analysis, the paper plans to measure the information transparency of listed companies from three perspectives: earnings quality, information ratings by the Shenzhen Stock Exchange, and the number of analyst followings, to investigate how corporate information transparency influences QFII stockholding behavior.

Exploring the Impact of Employee Motivation on Investment Decisions: - A Study of Factors Influencing Mutual Fund Investment Among Corporate Employee

This study explores the impact of employee motivation on investment decisions, specifically focusing on the factors influencing mutual fund investments among corporate employees. As financial literacy and investment participation grow, understanding the underlying motivations that drive investment choices is crucial for both individuals and financial institutions. Utilizing a mixed-methods approach, we conducted surveys and in-depth interviews with corporate employees across various sectors. The findings reveal that intrinsic motivations, such as personal financial goals and career advancement, significantly influence mutual fund investment decisions. Additionally, extrinsic factors, including company-sponsored financial education programs and peer influence, play a critical role in shaping employees' investment behaviors. Our analysis indicates that motivated employees are more likely to engage in proactive financial planning and make informed investment choices. We also identified barriers such as risk aversion and lack of confidence in investment knowledge that hinder mutual fund participation. The implications of this study suggest that organizations should enhance employee motivation through targeted financial education initiatives and create a supportive investment culture. By doing so, companies can not only improve employee financial well-being but also foster a more engaged and motivated workforce. This research contributes to the existing literature on employee motivation and investment behavior, offering insights for corporate stakeholders and financial advisors aiming to enhance mutual fund investment strategies among employees.

A Global-in-Time Neural Network Approach to Dynamic Portfolio Optimization

ABSTRACT We discuss a neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The approach allows for objectives of a very general form encompassing both time-consistent and time-inconsistent objectives, as well as objectives requiring multi-level optimization. The number of parameters of the neural network remains independent of the number of portfolio rebalancing events. Compared to reinforcement learning, this technique avoids the computation of high-dimensional conditional expectations. The approach remains practical when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance, mean-semi-variance, and mean-conditional value-at-risk problems. Numerical experiments show that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. This method is agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.

Forecasting oil futures markets using machine learning and seasonal trend decomposition

ABSTRACT Can machine learning improve prediction for seasonal commodity prices? We explore the effectiveness of a combined method that integrates seasonal trend decomposition using LOESS (STL) with machine learning (ML), referred to as STL-ML. We apply Extreme Gradient Boosting and Random Forest to forecast oil futures price dynamics over a sample period from 2004 to 2023. Our STL-ML results indicate no significant improvement for crude oil futures but enhanced accuracy for heating oil futures, highlighting STL’s benefit for datasets with strong seasonality. We demonstrate the potential for machine learning performance enhancement with STL, emphasising the variability of predictive model effectiveness due to data characteristics and providing insights for refining investment strategies based on seasonality and trends.

The Influence of ESG Factors on Portfolio Performance Based on the Perspective of Markowitz Portfolio Theory

Abstract: In this paper, the inclusion of Environmental, Social, and Governance (ESG) factors in portfolios is investigated to determine their impact on portfolio performance and its mechanism. Based on the data collected by Bloomberg for 10 stocks from 2003-2023 and Markowitz's portfolio model, it is found that: (1) the inclusion of ESG constraints negatively affects portfolio performance; (2) the inclusion of ESG constraints shifts the GMVP to the right and reduces the convexity of the efficient frontier, thus lowering the portfolio's performance. This study enriches the literature on the factors affecting portfolio performance as well as responsible investment and ESG investment, and is of value to investors or organizations in making investment strategy choices.

The Impact of ESG Standards and Investment : Global Trends and Practices

In recent years, ESG (Environmental, Social, and Governance) investment has significantly integrated into investment strategies worldwide. The concept of ESG investing is not merely a fleeting trend but rather an essential tool for investors looking towards sustainable and responsible investment practices. With the evolution and adoption of ESG investments in the global market, a thorough examination of ESG investment practices reveals valuable insights. Nowadays, in economic landscape, the emphasis on ESG factors in investment decisions continues to grow, underscoring the importance of responsible investing practices in fostering long-term value creation and societal impact. This study offers noteworthy recommendations regarding ESG investment standards, aiming to drive improved ESG performance within enterprises. By adhering to robust ESG criteria, organizations can access enhanced financial prospects, paving the way for them to garner increased social recognition. Besides, this paper also put forward countermeasures and suggestions for China's ESG to improve China's ESG information disclosure system and the awareness of ESG.

SOCIAL MEDIA SENTIMENT AND STOCK MARKET VOLATILITY: EVIDENCE FROM THE US HI-TECH COMPANIES

Purpose: This study aims to investigate the effects of social media sentiment, measured by Twitter-based uncertainty index, on the stock market volatility of the US technology companies. Design/methodology/approach: Methodologically, we employ the quantile regression model. Our findings reveal that the volatility of Amazon, Apple, Google and IBM stocks are sensitive to the variations in twitter-based economic and market uncertainties (i.e., TEU and TMU indexes). Findings: We observe significant influences at both lower and upper quantiles. Thus, for both high and low volatility regimes, the information on twitter-based uncertainty indexes can be used to predict the market volatility of these leading hi-tech companies. Moreover, TEU and TMU indexes exert positive effects on the stock price implied volatility implying that the variance of these technology firms experiences an upward trend as the social media uncertainty rises. Originality/value: While numerous studies have focused on the influence of social media (e.g., Facebook, twitter etc.) on investment strategies, the impact of twitter sentiments on the risk linked to hi-tech firms remains understudied. Hence, investors participating in the technology sectors could use our findings for managing portfolio risk.

Big data service investment strategy for low-carbon supply chains with reference effect

Big data service investment strategy for low-carbon supply chains with reference effect

Non-diversified portfolios with subjective expected utility

Diversification is the typical investment strategy of risk-averse agents. However, non-diversified positions that allocate all resources to a single asset, state of the world or revenue stream are common too. We show that whenever finitely many non-diversified demands under uncertainty are compatible with risk-averse subjective expected utility maximization under strictly positive beliefs, they are also rationalizable under the same beliefs by many qualitatively distinct risk-averse as well as risk-neutral and risk-seeking preferences.

Do metaverse implementation announcements enhance firms’ stock market value in China? A signaling theory perspective

PurposeThe metaverse has garnered increasing attention from researchers and practitioners, yet numerous firms remain hesitant to invest in it due to ongoing debates about its potential financial benefits. Therefore, it is crucial to analyze how the implementation of metaverse initiatives affects firms’ stock market value – an area that remains underexplored in the existing literature. Additionally, there is a significant lack of research on the contingency factors that shape the stock market reaction, leaving a noticeable gap in managerial guidance on the timing and benefits of investments in the metaverse. To narrow these gaps, we examine whether and when the implementation of metaverse initiatives enhances firms’ stock market value.Design/methodology/approachBased on 73 metaverse implementation announcements disclosed by Chinese listed firms during January 2021–August 2023, we employ an event study approach to test the hypotheses.FindingsWe find that metaverse implementation announcements elicit a positive stock market reaction. Moreover, the stock market reaction is stronger for technology-focused announcements and smaller firms, or when public attention to the metaverse is higher. Nevertheless, firms’ growth prospects do not significantly alter the stock market reaction.Originality/valueThis study extends the nascent literature on the metaverse by applying signaling theory to offer novel insights into the signaling effect of metaverse implementation announcements on stock market value and the boundary conditions under which the effectiveness of the signal varies. Besides, it provides managers with important implications regarding how to tailor the investment and information disclosure strategies of the metaverse to more effectively enhance firms’ stock market value.

Case Study on Transforming Financial Decision-Making with Big Data and Advanced Analytics

This case study examines the transformative impact of big data, artificial intelligence (AI), and machine learning (ML) on financial decision-making. These technologies enable more informed, data-driven investment strategies and improved risk management by providing real-time insights and predictive analytics. While they enhance key performance metrics such as return on investment (ROI) and portfolio stability, challenges remain. Issues like data quality, skill gaps, and regulatory compliance hinder seamless integration. Addressing these challenges is essential to fully unlocking the potential of advanced analytics in the finance sector.

Environmental Policies and Tax Incentives in the Pillar Two Era: A Comparison of the EU and US Approaches

This article explores the interaction between global corporate taxes, specifically under the OECD’s Pillar Two, as implemented by the EU Minimum Taxation Directive (2022/2523) in the European Union and by the Corporate Minimum Tax (CAMT) in the United States, and fiscal incentives supporting environmental tax policies under the European Union’s Green Deal and the US Inflation Reduction Act (IRA). The article compares the EU and US approaches and reveals the impact on the investment strategies of MNEs, with a focus on the location of intangibles related to renewable energy technologies, as well as State aid considerations. The article proposes that, instead of marginalizing the green tax credits, they should be reshaped and made subject to a benchmark that supports sustainable innovation within the global tax framework.