Investment Scenarios Research Articles

Energy Flexibility in Aluminium Smelting: A Long-Term Feasibility Study Based on the Prospects of Electricity Load and Photovoltaic Production

This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia’s case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.

UCB and Thompson Sampling Algorithms in Long-Term Investing

In the rapidly evolving financial markets, traditional investment strategies are increasingly being replaced by advanced algorithms. Among these, the Upper Confidence Bound (UCB) and TS algorithms, which originate from the multi-armed bandit framework, have demonstrated significant potential across various sectors. This study utilizes historical stock data from companies during the period from 2019 to 2024 to evaluate the performance of these algorithms in long-term investment strategies. The research aims to identify how these algorithms manage investment risks and maximize returns under varying market conditions. The results reveal distinct behaviors of UCB and TS in the financial markets: UCB performs better in markets with lower volatility, while TS exhibits stronger adaptability in high-volatility environments, thereby enhancing potential returns. Through a detailed comparison of UCB and TS in long-term investment scenarios, this study provides valuable insights into the strategic application of these algorithms. It offers investment strategy guidance for investors and financial strategists and supplies effective information for economic decision-making in relevant fields.

Investing in Netflix: Accounting for Content Assets*

ABSTRACTThis case describes an equity investment scenario in the context of the COVID‐19 pandemic. A fictional decision‐maker, Amandeep Singh, is considering whether or not to invest in Netflix, Inc. (Netflix). Netflix has benefited from changing habits during the pandemic, but Singh questions whether those benefits will persist post‐pandemic. To make his decision, Singh needs to perform a qualitative analysis of the video streaming industry and Netflix itself before moving to a quantitative analysis of Netflix's performance. To properly assess performance, Singh needs to fully understand Netflix's accounting methods and estimates for content assets—especially original content. The case is ideally suited to an MBA or Executive MBA audience. Students can easily engage with the case since they will be familiar with the company and industry. This familiarity allows for a general discussion about the industry's future and key success factors. The case provides an opportunity to cover core financial statement analysis concepts and drives home the importance of understanding accounting judgments when assessing firm performance.

Do we get along well? A dyadic study of how and when congruence in specific investments influence innovation

Using a matched survey of 209 buyer-supplier pairs, this study adopts a dyadic approach to investigate the impact of congruence in specific investments on innovation performance. It explores the mediating role of knowledge acquisition and moderating role of digital collaboration capability. The study focuses on the outcomes differentiated by levels of investment congruence (high-high vs. low-low congruence) and scenarios (congruence vs. in congruence), including the direction of incongruence (buyer-higher vs. supplier-higher incongruence). The findings indicate that investment congruence correlates with increased knowledge acquisition compared with incongruence, particularly for high-high specific investment congruence. In cases of incongruence, scenarios with supplier-higher incongruence led to more effective knowledge acquisition compared with buyer-higher incongruence. The research also reveals that congruence in specific investments indirectly enhances buyer innovation performance through knowledge acquisition, and the effect is enhanced by digital collaboration capabilities. This study thereby contributes to the business-to-business literature by revealing how and when congruence in specific investments influence innovation performance.

‘The assisting hand’ or ‘the grabbing hand’: experimental evidence from PPP investments in the implementation of official rotation systems

In the context of uneven and insufficient social development, examining the relationship between the official rotation system and the quality of government services offers an effective guide to addressing government misconduct and reducing wealth disparities. This study employs a public goods game experiment to simulate the investment scenario of Public-Private Partnership (PPP) projects, focusing on three pivotal questions: Firstly, do the ‘assisting hand’ and the ‘grabbing hand’ of officials differ between developed and underdeveloped regions? Secondly, can the interregional rotation system enhance the ‘assisting hand’ and weaken the ‘grabbing hand’ in both areas? Lastly, can political tournaments more effectively alleviate the issue of ‘government misconduct’? The experimental results reveal: Firstly, low economic growth potential limits the ‘assisting hand’ of officials while fostering the ‘grabbing hand’; secondly, the interregional rotation system strengthens the ‘assisting hand’ in underdeveloped areas but weakens it in developed regions; finally, compared to the interregional rotation system, the tournament system reinforces the ‘assisting hand’ in developed regions. These findings provide micro-level insights into the relationship between official behavior and regional characteristics, and offer significant implications for the reform of governance policies.

Inova Lab Una - Economic Radar: Mobile Application to Enhance Investment Attractiveness in Small Cities

The process of investing in cities and their businesses is an extremely complex issue that directly impacts the daily lives of citizens. However, agile and consolidated information is not always available so that people with less market knowledge can assess the possibleattractiveness of investments in interior cities. Information often exists, but it is very widespread and fragmented. The general objective of this paper is to present a central platform to gather and make available fundamental information about the municipality and its surrounding region. The proposal is to create a robust and objective virtual environment, facilitating the analysis of companies that are considering possible investments in the area. Furthermore, the initiative seeks to promote the visibility of small cities, which are often neglected in the investment scenario. By providing detailed and updated information, our platform aims to highlight the economic, cultural and social potential of these locations, encouraging investors' interest in contributing to the sustainable development of these communities. The first experiment of this platform focuses on the city of Bom Despacho, a city with more than 50 thousand inhabitants, to provide accurate and relevant data, to not only attract investments, but also promote a more equitable distribution of economic opportunities, contributing to the construction of more resilient and prosperous communities. A Mobile application with interaction with public databases related to investments, with the ability to receive data and search in data available for access, was developed for experiments that can be used in other cities with the same profile.

Driving factors on corporate green investments behaviors: From the strategic intersection of governments regulation and public participation

As the global community confronts the challenges of climate change, businesses face increasing pressure to adopt sustainable practices. This study develops a tripartite game model to investigate the impact of green investments on corporate performance, considering the dynamic interplay between governments regulations and public participation in shaping strategic initiatives. First, the evolutionary stability strategy (ESS) is identified by solving replicator dynamic equations and performing stability analysis of equilibrium points. Next, the practicability and rationality of the evolutionary game model are assessed by analyzing ESSs under various corporate green investment scenarios. Finally, a case-based example is provided to validate the theoretical findings and support the following arguments: there are eight equilibrium points and four potential ESSs in the game model; the selection of each ESS is primarily determined by the trade-off between costs and revenues for each stakeholder; increased governmental regulatory costs prompt a strategic shift, incentivizing corporations to enhance green investments; while rising penalties drive a preference for green options; and corporations recognizing compensatory responsibilities are steered towards sustainable pathways.

Strategic investment and program effectiveness: a roadmap to ending AIDS in Bangladesh by 2030

ABSTRACT The journey towards ending AIDS epidemic in Bangladesh by 2030 is ambitious yet achievable. Although Bangladesh has always had a low rate of HIV among its general population, it remains one of seven countries in Asia and the Pacific where new HIV cases are rising. This study evaluates the effectiveness of HIV programmatic strategies and investment scenarios using the AIDS Epidemic Model (AEM) from 2023 to 2030, focusing on optimizing resource allocation and interventions. The findings indicate that without improved program effectiveness, new HIV infections will increase to 1,382 by 2030, failing to meet the targets of the Global AIDS Strategy 2021–2026. If Bangladesh improves its HIV program effectiveness according to the Global AIDS Strategy 2021–2026, the NSP and Global AIDS Strategy targets could significantly lower new infections and AIDS-related deaths and increase treatment coverage to meet Ending AIDS targets. The NSP targets could reduce new HIV infections to under 275 annually and achieve treatment goals by 2030. The study reveals that NSP targets are the most cost-effective, offering the highest benefit-cost ratio, highlighting the urgent need to enhance HIV prevention program effectiveness, particularly among key populations, to achieve both public health and economic benefits.

Parental Investment, Status, and Child Gender: Some Evidence for the Trivers–Willard Hypothesis from a Survey Experiment

This study critically evaluates and empirically tests the Trivers–Willard (TW) hypothesis, which proposes a relationship between parental socioeconomic status and sex: Parents with higher status are expected to be more likely to have male offspring and to preferentially invest in male offspring, whereas parents with lower status are expected to be more likely to have female offspring and to preferentially invest in daughters. Although the TW hypothesis has been explored in terms of offspring sex ratio and parental investment, findings in modern developed societies generally show null results, with notable exceptions in the domain of parental investment in their children’s education. Previous studies have often not explicitly addressed the potential underlying mechanisms of the TW effect. This includes the authors of the original hypothesis (Trivers and Willard 1973), who discussed some potential mechanisms but ultimately left the question of mechanisms unanswered. Building on Matthews’s (2011) proposition to explore psychological underpinnings, this paper posits that the TW effect, if present, may be rooted in general parental preferences. To investigate this, a factorial survey experiment was designed to measure respondents’ preferences in parental investment while minimizing social desirability bias. The study specifically examines the extent to which respondents’ assessments of favorability and fairness in various parental investment scenarios depend on child characteristics believed to influence differential parental behavior. The findings reveal patterns that are somewhat in line with the TW hypothesis but are minor and lack statistical significance. The article concludes by proposing three future research directions aimed at further unraveling the intricacies of the TW effect.

Multi-Scale analysis of the impacts of soil and water conservation practices and landscape on grain yield and return on investment in the sub-humid ethiopian highlands

Ethiopia's sub-humid highlands face a critical challenge in balancing agricultural productivity with land degradation. This study explores the effectiveness of soil and water conservation practices (SWCPs) in addressing this challenge. We investigated the interaction effects of types of SWCPs, landscape positions, and location on Teff (Eragrostis teff) and wheat (Triticum aestivum) yield. In addition, we assessed the economic viability of SWCPs using cost-benefit analysis with farmer-funded and cost-sharing scenarios. The results indicated that yield was significantly affected by the interactions between factors like SWCP type and landscape position. Soil bunds consistently increased crop yield across diverse locations and landscapes, indicating superior erosion control benefits. Lower landscape positions on foot slopes benefited most from SWCP implementation. Teff yield increased by 188 % and wheat yield by 181 % under soil bunds. The cost-benefit analysis confirmed the financial viability of SWCPs, particularly for Teff (NPV = 4499.35 USD, IRR = 50 %, and BCR = 1.51) and wheat (NPV = 544.35 USD, IRR = 16 %, and BCR = 1.06) grown on lower landscapes with farmer-funded investment scenarios. Positive return on investment was observed in both scenarios, with cost-sharing offering greater economic benefits for farmers. These findings highlight the importance of an integrated approach to SWC implementation for achieving multiple Sustainable Development Goals (SDGs) by enhancing food security, improving farmer incomes, and promoting sustainable and productive landscape management practices. Future research should explore the long-term sustainability of SWCPs, their adaptation across diverse agroecological zones and landscapes, the incorporation of various crops, the broader socioeconomic impacts, and the development of effective extension programs for wider adoption by farmers.

Real-GPT: Efficiently Tailoring LLMs for Informed Decision-Making in the Real Estate Industry

Lately, large language models (LLMs) such as ChatGPT and Llama have gained significant attention. These models demonstrate a remarkable capability to solve complex tasks, drawing knowledge primarily from a generalized database rather than niche subject areas. Consequently, there has been a growing demand for domain-specific LLMs tailored to social and natural sciences, such as BloombergGPT or BioGPT. In this study, we present a domain-specific LLM focused on real estate: Real-GPT. The model is based on the parameter-efficient fine-tuning technique known as quantized low-rank adaptation (QLoRA) applied to Mistral 7B. To create a comprehensive fine-tuning dataset, we compiled a curated 21,000 self-instruction dataset sourced from 670 scientific papers, market research, scholarly articles, and real estate books. To assess the efficacy of Real-GPT, we devised a set of 1046 multiple-choice questions to gauge the real estate knowledge of the models. Furthermore, we tested Real-GPT in a real investment scenario. Despite its compact size, our model significantly outperforms GPT 3.5 and Mistral 7B. Hence, the model not only showcases superior performance but also illustrates its capacity to facilitate investment decisions or interpret current market data. This development showcases the potential of LLMs to revolutionize real estate analysis and decision-making.

Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

Optimal Pricing and Retailing Strategy for an Assembled Product Manufacturing–Remanufacturing Process under Carbon Emission Regulations and Autonomation

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by 18.12%, whereas autonomated inspection increased retailer profit by 10.27%.

Comparative Analysis of SVR and LSTM in Stock Price Forecasting Across Market Cycles

This study investigates the predictive capabilities of Support Vector Regression and Long Short-Term Memory networks on stock price trends across different market conditionsbear, bumpy, and bull markets. With the ongoing evolution of machine learning technologies, their application in financial forecasting has shown substantial potential for capturing complex patterns in vast datasets, which traditional models often fail to process efficiently. This study particularly focuses on the performance of these models in forecasting stock prices from the S&P 500 index, evaluated through the lens of Modern Portfolio Theory (MPT). The models are assessed based on their ability to forecast trends and their implications when applied to constructing investment portfolios, evaluating key financial metrics such as expected returns, standard deviation, Sharpe ratio, and maximum drawdown. The findings indicate that while both SVR and LSTM exhibit competence in trend prediction, especially in bull markets, their predictions diverge from actual market performance when applied to portfolio construction under MPT. This discrepancy underscores the need for further refinement in modeling approaches to enhance accuracy and reliability in real-world investment scenarios. This research contributes to the empirical literature by demonstrating the practical implications of deploying advanced machine learning and deep learning models in dynamic market environments and suggests directions for future enhancements.

Application of Heterogeneous Data Analysis based on SEA Grid in User Investment Analysis

To solve the problem of yield calculation in complex investment scenarios, a time-cost double-weighted rate of return calculation method based on SEA grid is proposed, and its effectiveness is verified by comparing with traditional methods and researching the quantitative evaluation and analysis method of user investment based on structured data. To solve the above problems, a data-heterogeneous federated learning method based on user investment analysis FedPSG is proposed, which changes the data form transmitted from the client to the server from model parameters to model scores, and only a small number of clients need to upload model parameters to the server in each round of training, thereby reducing communication costs. At the same time, a model retraining strategy is proposed, which uses server data to train the global model for second iteration, and further improves the model performance by alleviating the impact of data heterogeneity on federated learning. The method of event dimension analysis of user investment is designed, and a credibility index is proposed to evaluate the analysis results. Experiments show that by combining event data, it can effectively provide users with event factors in the fluctuation of investment profit and loss, and help users better analyze their own investments.

Neural network approach to portfolio optimization with leverage constraints: a case study on high inflation investment

Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. We formulate an optimal control problem to outperform a benchmark portfolio throughout the investment horizon. To obtain strategies under the bounded leverage constraint among other realistic constraints, we propose a novel leverage-feasible neural network (LFNN) to represent the control, which converts the original constrained optimization problem into an unconstrained optimization problem that is computationally feasible with gradient descent, without dynamic programming. We establish mathematically that the LFNN approximation can yield a solution that is arbitrarily close to the solution of the original optimal control problem with bounded leverage. We further validate the performance of the LFNN empirically by deriving a closed-form solution under jump-diffusion asset price models and show that a shallow LFNN model achieves comparable results on synthetic data. In the case study, we apply the LFNN approach to a four-asset investment scenario with bootstrap-resampled asset returns from the filtered high inflation regimes. The LFNN strategy is shown to consistently outperform the passive benchmark strategy by about 200 bps (median annualized return), with a greater than 90% probability of outperforming the benchmark at the end of the investment horizon.

The Best Investment Scenario for Hajj Fund Management in Indonesia

The Hajj Fund Management Agency (Badan Pengelola Keuangan Haji [BPKH]) is an institution responsible for managing Hajj funds in Indonesia. Through the investment mechanism, the BPKH’s benefit value can reduce the burden of prospective pilgrims in covering the cost of organizing the Hajj pilgrimage (Biaya Penyelanggaran Ibadah Haji [BPIH]). This study examined the combination of optimal portfolio construction in two situations, namely, without using regulatory restrictions and with government regulatory restrictions. The results of a linear programming approach with the Markowitz method revealed that investment allocation without regulatory restrictions forms an optimal portfolio to maximize Sharpe with a moderate scenario. Likewise, investment allocation based on regulations forms a portfolio with the same goal, namely maximizing the Sharpe ratio. This is in accordance with BPKH’s investment characteristics, which consider acceptable levels of return and risk, as well as BPKH’s risk profile, namely low to moderate. This research is expected to be useful for BPKH, especially in compiling investment portfolios to produce the optimal value of benefits and create sustainability in Hajj finance.

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market.

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors.

RESEARCH PAPER FOR USING MARKOWITZ MODEL AND INDEX MODEL UNDER REALISTIC ADDITIONAL CONSTRAINTS

The Markowitz and index models are widely used in portfolio optimization to achieve optimal asset allocation and maximize returns while controlling risk. However, in practical investment scenarios, additional constraints often need to be considered to ensure the feasibility and effectiveness of the portfolio strategy. This research focuses on incorporating realistic additional constraints into the Markowitz and index models for portfolio optimization. The study utilizes historical financial data and statistical techniques to estimate expected returns, variances, and covariance matrices. The Markowitz model is modified to incorporate the additional constraints, allowing for more realistic and accurate portfolio optimization outcomes. The index model is also adapted to consider the impact of these constraints on the construction and performance of passive investment portfolios.

Investment in vehicle-to-grid and distributed energy resources: Distributor versus prosumer perspectives and the impact of rate structures

Photovoltaic panels, electric vehicles, and vehicle-to-grid technologies are becoming more common and hold significant promises to improve the grid and foster the energy transition. However, significant questions remain unanswered with respect to who should invest in this equipment and what tariff should be used. This paper examines whether the distribution company or prosumer should invest in and manage Distributed Energy Resources (DER), the ideal combination of DER to utilize, and the appropriate tariff to implement. Central to this analysis is the assessment of different stakeholder objectives, particularly from the investor's perspective, where net present value is used as the primary criterion for evaluating the different investment scenarios. Additionally, the impact of these scenarios on the annual system cost is calculated. A mathematical scenario analysis model is developed to simulate the operation of DER and energy management systems. This model utilizes the Vermont electricity grid's real-world consumption, generation data, and cost structures. The results underscore the significance of incorporating vehicle-to-grid technology to enhance the profitability of DER investments. This inclusion of specific data sources and stakeholder criteria aims to provide insight into the complex dynamics of smart-home deployment.