Profitable Investment Research Articles

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

Reinforcement learning when your life depends on it: A neuro-economic theory of learning.

Synaptic plasticity enables animals to adapt to their environment, but memory formation can require a substantial amount of metabolic energy, potentially impairing survival. Hence, a neuro-economic dilemma arises whether learning is a profitable investment or not, and the brain must therefore judiciously regulate learning. Indeed, in experiments it was observed that during starvation, Drosophila suppress formation of energy-intensive aversive memories. Here we include energy considerations in a reinforcement learning framework. Simulated flies learned to avoid noxious stimuli through synaptic plasticity in either the energy expensive long-term memory (LTM) pathway, or the decaying anesthesia-resistant memory (ARM) pathway. The objective of the flies is to maximize their lifespan, which is calculated with a hazard function. We find that strategies that switch between the LTM and ARM pathways, based on energy reserve and reward prediction error, prolong lifespan. Our study highlights the significance of energy-regulation of memory pathways and dopaminergic control for adaptive learning and survival. It might also benefit engineering applications of reinforcement learning under resources constraints.

Raspberry Production Opportunity to Develop an Agricultural Business in the Context of the Circular Economy: Case Study in South-West Romania

This paper presents a study on the establishment and the capitalization of a remontant red raspberry crop, the Polka variety, on a privately agricultural land area of 0.2 ha in a crop with a support system in V using a geotextile membrane for soil mulching and the method of micro-irrigation by drip. It has been shown that the annual gross profit is advantageous for diversifying the population incomes of rural areas, and the red raspberry is economically profitable regarding cultivation because the recovery of the invested sum is achieved in a maximum of 5 years after the establishment of the culture. The aim of this paper is to explore the growth and commercialization of red raspberry cultivation on privately owned arable land in rural Romania, emphasizing its potential for productivity and sustainability in the context of the circular economy. This initiative not only delivers substantial profits for investors but also fosters rural development and boosts local income levels. The study demonstrates that this cultivation method of red raspberry, aligned with the principles of the circular economy, enhances sustainability by reducing waste, optimizing resource use, and involving local communities in production cycles.

Цифровые финансовые активы как технология фиксации имущественных требований и обмена данными о них

The authors explore the phenomenon of digital financial assets from the perspective of innovative investment technology. The dualism of legislative provisions interpreting the legal nature of these assets is criticized. The conclusion is substantiated that digital financial assets do not belong to objects of civil rights previously unknown to the Russian legal system, but represent property rights that are individualized in circulation by unique information codes. Being negotiable investment assets, they have a commodity-value nature; data on their ownership and transfer of rights to them are transmitted via Internet communications. Through digital financial assets, information technology has been implemented to record the property claims of participants in civil turnover and exchange data about them; they can submit mandatory requirements that are not legally prohibited from alienating them within the framework of assignable structures. Due to this innovative technology, participants in civil turnover receive the legal equivalent of a certain mandatory requirement in digital form, including a requirement fixed in equity securities, a requirement to transfer such securities, as well as corporate rights determined by participation in the capital of a non-public joint-stock company. The use of digital financial assets provides an opportunity for individuals and legal entities to build investment relationships with minimal intermediary participation, conduct financial transactions in a transparent manner, increase investment profitability, and simplify the search for counterparties.

Shareholder Loans: Fact or Fiction

Shareholder loans are often used as an alternative to traditional third-party loans or equity especially for private companies in various jurisdictions, including South Africa, to finance their business activities. These loans provide companies with greater flexibility to meet their financing needs, i.e., there is no need to seek external financing while offering shareholders a potentially profitable investment opportunity. However, the legal nature of shareholder loans could pose complex legal questions and this form of loans may not necessarily be as straightforward as it first appears. This article explores the legal framework and practical considerations surrounding shareholder loans in South Africa in small private companies, with a focus on developments in case law and their implications for companies and shareholders. Amongst other issues, the application of the principle of arbitrium boni viri to the interpretation and enforcement of these agreements will be discussed. The article aims to provide a critical analysis of the legal questions associated with shareholder loans in South Africa.

Hybrid offshore wind projects. Social desirability vs. incentives to invest

To unlock the full potential of offshore wind requires investment in a more integrated offshore grid infrastructure combining generation and interconnection between countries in so-called hybrid projects. Taking the perspective of a social planner, these projects may result in more efficient allocation of scarce resources. Alas, the distribution of this welfare gain may result in disagreement on what grid design is the most attractive, what markets should be connected and whether and when to invest. To examine these dilemmas, we apply real options theory to investments projects with different grid design and market characteristics. We compare the incentives to invest in a) only the wind farm and b) both the wind farm and the grid infrastructure, assuming the project value of the last alternative can serve as a proxy for social welfare. Solutions are derived using a simulation approach called least squares Monte Carlo method. We find: 1) Non-stationary stochastic prices and/or decreasing costs may make it socially optimal as well as commercially beneficial to postpone even profitable investments. 2) Connecting markets is socially desirable but may reduce the offshore wind investor's project value. 3) Connecting markets with different characteristics is socially desirable but reduce the offshore wind investors' project value.

Techno-economic analysis of biocrude, biogas, and fertilizer production from microalgae Coelastrella striolata cultivated in agroindustrial wastewater

Coelastrella microalgae have gained attention due to their robustness, high lipid content, effective bioremediation agent, and ability to thrive in diverse environmental conditions. The objective of this study was to evaluate the economic feasibility of an integrated biorefinery of the unique original Coelastrella striolata var. multistriata strain 047 applied in agro-industrial wastewater, flue gases, and excess energy on a 1.2 ha open raceway pond (ORP). This study consists of three scenarios: (1) biocrude only (BO); (2) biocrude and biogas (BB); and (3) biocrude, biogas, and fertilizer (BBF). The findings indicated that the BB and BBF scenarios enhanced the financial feasibility and potential profitability of investment. This is reflected in the economic feasibility indicators for the BB scenario over a project lifetime of 20 years, which include a net present value (NPV) of $4,757,082, an internal rate of return (IRR) of 25.6%, a profitability index (PI) of 2.77, and a payback period of 3.75 years. Similarly, in the BBF scenario, financial viability is demonstrated with an NPV of $4,714,418, an IRR of 25.2%, a PI of 2.73, and a payback period of 3.79 years. Operational expenditures were identified as the most sensitive aspect of this study, particularly in the hydrothermal liquefaction (HTL) process for biocrude production. The utilization of effluent and excess energy from palm oil mill (POM) reduced the operational costs for energy and nutrients by up to 69% compared to the baseline, where palm oil mill effluent (POME) and excess energy were not utilized. Additionally, to achieve optimal results in the BO scenario, increasing the concentration of Coelastrella striolata var. multistriata strain 047 in POME proved to be the most effective approach for increasing biocrude oil production, reducing costs, and increasing economic sustainability.

Dynamic evolution of causal relationships among cryptocurrencies: an analysis via Bayesian networks

AbstractUnderstanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Feasibility study of floating solar photovoltaic systems using techno-economic assessment and multi-criteria decision-making method: A case study of Bangladesh

Many developing countries with abundant solar resources and rich history of agriculture, face overpopulation and land shortage issues. Floating solar photovoltaics (FSPVs) do not compete with agriculture for land and offer higher energy potential than land-based photovoltaics (LBPVs). For feasibility and site suitability evaluation of FSPVs, this study proposes an integrated approach of techno-economic assessment (TEA) and multi-criteria decision making (MCDM), based on a case study of Bangladesh. The system advisor model (SAM) was used to conduct TEA, evaluating the potential of nine FSPV sites on a regional scale using ten criteria. Combined compromise solution (CoCoSo) was implemented to determine the optimum site. Bergobindopur lake was ranked top, with the highest energy yield and lowest cost of energy of 1564 kWh/kW and $0.055/kWh respectively. Case study of Bergobindopur revealed buy-all-sell-all electricity tariff was more profitable than net billing and net metering. Energy generation was found to be influenced by tilt angle and soiling. Sensitivity analyses demonstrated the superiority of FSPVs over LBPVs for varying electricity prices, degradation and discount rates, project durations, and investor's equities. Longer project durations and lower equities resulted in higher profits for investors. The proposed approach contributes to the expansion of FSPV in land-constrained areas.

The Dynamic Relationship Between Exchange Rate Volatility and Stock Prices in the Egyptian Real Estate Market and the Moderating Effect of Interest Rates

Among different channels of the financial market, the exchange rates and stock markets play a vital role in the domestic and international markets. This study aims to explore the dynamic relationship between the exchange rate (USD/EGP) and the real estate sector listed companies of the Egyptian Stock Exchange (EGX) from 2013 to 2023. Additionally, the study examines the moderating effect of interest rates on the relationship. To achieve this, the study employs several econometric methods such as Augmented Dicky Fuller (ADF) test, Granger Causality test to determine the direction of the relationship, Vector Autoregressive (VAR) model, and more. The results demonstrate that a long-run relationship exists between the variables. Furthermore, there is a significant unidirectional causality that runs from the exchange rate to real estate stock prices; thus, fluctuations in the exchange rate can significantly affect the profitability of investments in real estate stocks. Therefore, investors should closely monitor the movements in the exchange rate level to predict changes in real estate stock prices. When including the interest rate as a moderator, the outcome changes to bidirectional causality, meaning that the exchange rate and real estate stock price movements affect each other at a 1% significance level. Hence, interest rate could be used as an instrument to control demand in both the stock market and foreign exchange market. Finally, it is recommended that policymakers use the monetary policy while considering this bidirectional relationship in the presence of interest rate as a moderating variable.

Harnessing machine learning algorithms to unveil energy efficiency investment archetypes

Increasing transparency about the performance of different projects is crucial to reducing the heterogeneity in the energy efficiency services market, thereby upscaling investments. In this context, machine learning algorithms could assist in identifying and analyzing energy efficiency project archetypes, although this field has so far been explored with a limited view in the literature. This paper aims to address this gap by identifying energy efficiency investment families and the determinant factors of the classification scheme, using machine learning. In this effort, it hinges on a wide range of indicators from implemented projects around Europe and the USA, including investment profitability, initial investment, risk of failure, intervention type, life measure, region of implementation and building type. The analysis employs two clustering approaches, namely Partitioning Around Medoids (PAM) and K-means, determining the number of clusters based on the Silhouette index and total within–cluster sum of squares. The results indicate that energy efficiency investments can be classified into three categories: (i) “junk investments”, characterized by low–profitability (IRR∼10%), moderate risk, and extended horizons; (ii) “safe profitability”, distinguished by high profitability (IRR∼30%) and minimal risk; and (iii) “high stakes”, described by exceptionally high profitability (IRR∼40%), coupled with a substantial risk. Next to profitability and risk of failure, also energy efficiency intervention and building type (sector) emerge among the most influential factors in the classification scheme. Feature importance shows a significant sensitivity to the chosen classification model.

Evaluating ESG Investment Profitability: From the Perspective of Sophistication in Investment Decision-Making

Environmental, Social, and Governance (ESG) investments are widely discussed in the stock market. Further, the debate on ESG investment profitability has garnered significant scholarly attention. This study examines the relationship between ESG investments and equity investment return (EIR) and whether this relationship is affected by investors' sophistication. The results suggest that while ESG investors generally gain relatively lower profits than others, sophisticated investors, characterized by greater financial knowledge, diversified investments, contrarian investment strategies, and not being influenced by familiar biases, can mitigate these lower returns. These findings enhance our understanding of ESG investments and offer valuable insights to guide investors in making sophisticated ESG investment decisions.

Capital is not a factor of production but organizes the allocation and distribution of resources in capitalism

AbstractEconomists usually define capital as a factor of production—roughly speaking, as physical equipment. This paper demonstrates the limits of this common approach and develops a meaningful alternative. In actual business life, capital refers to the monetary value of business assets, regardless of what the assets consist of, and as such, it is an important aspect of economic calculations that helps to guide entrepreneurial activities. In this sense, capital is pervasive in capitalist societies. Virtually all goods and services are produced by profit-oriented enterprises. Natural resources, intermediate goods, and finished products all appear on the balance sheets of enterprises and are consequently part of business capital. Likewise, the majority of people are employees of enterprises. They receive their pay because the employing firms consider this to be a profitable investment of their capital. Despite its extreme practical importance, this business notion of capital is ignored by nearly the entire economics profession. The paper shows how economists could profit from adopting the business notion of capital, irrespective of their ideological backgrounds.

The Genesis of the Crypto-economy: Application of the Institutional Theory

This study investigates the institutionalization of the crypto-economy from an institutional theory perspective and also examines the role of existing institutional pillars in its development. The path analysis using global institutional indicators reveals that the development of the crypto-economy is significantly affected by regulatory, cognitive, and normative factors of a country. Further, the regulative pillar fully mediates the relationship between the social pillar and the crypto-economy. While we find convincing evidence that a country’s social pillar endorses crypto-economy, its indirect effect is more prominent and significant than the direct effect.

SYARIAH STOCKS: A NORMATIVE ANALYSIS OF ISLAMIC LAW AND INVESTMENT PRACTICES IN INDONESIA

Syariah stocks have recently gained attention in the Islamic community, which relies on Shariah principles to guide investment decisions. Islam emphasizes the importance of profitable investments that benefit all parties involved, while prohibiting investments that involve uncertainty or exploitation. This article provides an overview of the position of stock shares in Islamic law. Using a normative legal research method, this study analyzed the relevant Islamic texts, including the Quran, Hadith, and Fatwas issued by scholars. Syariah stocks are specifically designed to meet the requirements of Islamic law, which emphasizes transparency, fairness, and ethics in financial transactions. The National Sharia Council of the Indonesian Ulama Assembly (DSN MUI) issued a Fatwa No. 80/DSN-MUI/III of 2011, outlining the implementation of Islamic principles in equity securities trading. The Fatwa stipulates that the means of payment must be known in terms of amount and form, whether in the form of money or goods, and that there should be no payment in the form of debt. Additionally, the time and place of delivery must be clearly specified based on the agreement between parties. This type of contract involves multiple parties, including banking institutions, and must be conducted in accordance with Islamic principles. The Fatwa has contributed to clarifying the terms of stock trading under Islamic law, providing a framework for Syariah stocks to operate within the boundaries of Islamic law.

An insight into the signaling role of Sharia status: a case from an emerging IPO market

Purpose This study aims to examine the potential of Sharia status as ex ante information to signal the quality of an issuing firm by improving the decision-making process of potential investors when assessing initial public offerings (IPOs) in an environment where information asymmetry is pronounced. Potential investors face challenges in evaluating and determining the true value of IPO issues, which inherently influences their decision-making. Consequently, this results in pronounced price fluctuations in IPO shares, leading to higher underpricing. Design/methodology/approach This study uses a sample of 350 IPOs listed on the Kuala Lumpur Stock Exchange (KLSE) between 2004 and 2021 to examine the signaling role of Sharia-compliance status. A three-model approach is used to ensure that the study's objectives are met. The first model investigates the effect of Sharia status on underpricing to determine whether the main beneficiary of such a signal is the investor or the issuer. The second model examines the effect of Sharia status on investor demand to determine if such a signal influences prospective investors' investment decision-making processes. The third model inspects the effect of Sharia status on investor divergence of beliefs to measure the signal's ability to reduce information asymmetry within the Malaysian IPO market. Findings The Malaysian IPO market relies heavily on the fixed-price mechanism, which exacerbates high information asymmetry, affecting potential investors' behavior, asset price formation and return generation on the first day of listing. The study results indicate that Sharia status does not have any signaling role in the Malaysian IPO market. This is because investors in the Malaysian market are driven by ex ante information that helps unveil relevant information that leads to capital gains. Furthermore, most new issues in the Malaysian IPO market fall under Sharia status, diluting the relevance of such information for prospective investors in determining profitable investments. Practical implications The findings highlight the challenges faced by issuing firms in estimating market demand due to limited premarket insights and the difficulties prospective investors face in identifying the quality of issuing firms. Efforts to provide more information on investor demand can reduce uncertainty and facilitate more informed decision-making. Originality/value This research stands as one of the pioneering efforts to provide an empirical explanation of the potential signaling influence of Sharia status in an emerging IPO market.

Global reversal strategy: equilibrium of endogenous trading?

PurposeAfter considering the price reversal among countries' indices as a global, coordinated and generalized phenomenon, this paper aims to examine the profitability of the reversal strategy internationally and find an economically essential and predictive reversal effect. Indices' portfolios form based on the prior 48 months; prior losers outperform prior winners by 8.86% per year during the subsequent 48 months. Interestingly, the reversal effect is substantially stronger for emerging countries, yielding 14.04% annually. It remains profitable post-globalization, countering the concern of whether the integration of equity markets synchronized the price reversal worldwide. Returns' differences consistent with portfolio formation approaches are also observed.Design/methodology/approachThis study follows the methodology De Bondt and Thaler (1985) set out and uses the same methodological framework Wouassom et al. (2022) put forward. Nevertheless, this study does not focus on stocks. Still, it employs global equity indices from the viewpoint of an international investor who can switch between worldwide equity indices using a contrarian trading strategy.FindingsMy findings indicate that reversal strategies with overlapping portfolios are profitable over the entire sample period and every formation and holding period. These returns are highly statistically significant and vary considerably from one horizon to another. More importantly, the reversal strategies remain, on average, profitable and significant in the period post-1994 but are not particularly distinctive, which implies that the reversal effect survives the globalization impact and indicates that the integration of equity markets together with the international correlation among markets do not synchronize the prices reversal effect around the world given that.Research limitations/implicationsFurther work would be recommended to study a more extended period dating back to the nineteenth century or the Victorian Era, characterised by rapid economic development in almost every domain, to verify if reversal is historically compensation for carrying risks exclusively during contraction.Practical implicationsMy analysis takes on particular significance given the association between lagged market movement in share prices and investors’ optimism that appears among traders, generating an increasing reversal effect (Siganos and Chelley-Steley, 2006) and has direct implications for predicting and controlling trading costs associated with asset allocation strategies.Social implicationsThe difficulty with using the reversal strategy to uncover the long-term return reversal effects in the equity markets today resides in the fact that the globalization of the economy has fuelled the concentration of assets within institutional investors. The critical insight is that the concentration of equity in the hands of institutional investors activated international equity trading. These institutional investors seek to maximize their shareholder value from the opportunity by simultaneously dealing in many markets while constructing and holding portfolios that include assets from various countries using highly profitable investment strategies such as reversal.Originality/valueTo the best of the authors’ knowledge, this is the first paper to show an easily implemented contrarian strategy that switches back and forth between country indices and generates extraordinarily high abnormal returns of more than 8.86% per annum. We also show that these returns compensate for global risks and for investors ready to take them during contraction.

Indonesian Stock Price Prediction Using Neural Basis Expansion Analysis for Interpretable Time Series Method

The stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investors.

Harnessing Artificial Neural Networks for Financial Analysis of Investments in a Shower Heat Exchanger

This study focused on assessing the financial efficiency of investing in a horizontal shower heat exchanger. The analysis was based on net present value (NPV). The research also examined the possibility of using artificial neural networks and SHapley Additive exPlanation (SHAP) analysis to assess the profitability of the investment and the significance of individual parameters affecting the NPV of the project related to installing the heat exchanger in buildings. Comprehensive research was conducted, considering a wide range of input parameters. As a result, 1,215,000 NPV values were obtained, ranging from EUR −1996.40 to EUR 36,933.83. Based on these values, artificial neural network models were generated, and the one exhibiting the highest accuracy in prediction was selected (R2 ≈ 0.999, RMSE ≈ 57). SHAP analysis identified total daily shower length and initial energy price as key factors influencing the profitability of the shower heat exchanger. The least influential parameter was found to be the efficiency of the hot water heater. The research results can contribute to improving systems for assessing the profitability of investments in shower heat exchangers. The application of the developed model can also help in selecting appropriate technical parameters of the system to achieve maximum financial benefits.

A novel integrated optimization method of micrositing and cable routing for offshore wind farms

In traditional wind farm planning, the design of wind turbine locations and cable layouts is usually undertaken sequentially. However, this approach may potentially result in suboptimal solutions. While the increased spacing between wind turbines enhances power output by reducing wake losses, it also imposes a negative impact by raising cable costs. Addressing this challenge, we propose a novel multi-objective optimization model that simultaneously considers the micrositing of wind turbines and cable routing. A joint framework of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Mixed-Integer Linear Programming (MILP) is established to optimize the layout of wind turbine locations, points of connection, and cable paths. The results indicate that, compared to the traditional sequential optimization, our integrated optimization exhibits significant economic advantages since improved the balance between micro siting and cable routing. By strategically sacrificing a portion of power generation to reduce cable costs, the overall investment profitability can be remarkably improved, with a maximum gain equivalent to 10.02 % of the cable costs.