Long-term Investment Decisions Research Articles

Cracking the Code: Hidden Choices and Visible Impacts Pattern Recognition in Corporate Finance

AbstractResearch in corporate finance suffers from bounded rationality due to static modeling. Adopting factor analysis, an unsupervised machine learning approach, and balance sheet information (accounts) over time, we find underlying dynamic latent corporate finance decisions. Our study identifies three latent corporate finance decisions adopted by executives in Pakistan, (1) long-term capital investment, (2) short-term debt credit, and (3) financial flexibility. The order of the decisions and the empirical tests highlight agency problems rooted in familial ownership concentration. We find that long-term capital investment and short-term debt credit decisions significantly reduce the firms’ present and future performance. Conversely, managers do not embrace financial flexibility, despite its ability to improve performance. The study highlights a contradiction, firms bounded by ownership concentration prefer control over performance and do not make decisions to optimize and protect minority shareholders’ wealth, depicting a moral hazard problem.

CAPITAL BUDGETING

The project talks about a capital budgeting and its methods including discounted cash flow, payback, and throughput analyses. The process involves analyzing a project’s cash inflows and outflows to determine whether the expected return meets a set benchmark, we are going to talk about how capital budgeting is also important for small and big companies to evaluate major projects and investments, such as new plants or equipment. In the present scenario the efficient allocation of capital resources is a most important function of project management. This function involves firm’s decision to invest its funds in long-term assets like plant, machinery land, building, equipment etc. These assets are extremely important to the firm because the organizational profits are derived from the use of its capital investment in assets which represent a long-term commitment of funds. The future development of an enterprise depends on the capital investment projects. These projects may be the replacement of existing capital assets which turns out to be less attractive to the firm or expansion of business for implementing new ideas and planning. Thus, long term investment decisions of an enterprise fall within the definition of project budgeting or capital expenditure decisions. These decisions are concerned with the acquisition of assets in which funds will be invested by an enterprise. The assets of business include long term assets and short-term assets. Long term assets will yield a return over a period of time whereas short term assets are those assets which are easily convertible into cash within one accounting period, normally a year. The long-term investment decision is known as project budgeting/capital budgeting and the short-term investment decision are identified as working capital management.

Tax Reforms and Tax Burden of Listed Consumer Goods Firms in Nigeria

The lack of clarity in tax laws, coupled with frequent amendments and revisions, has given rise to uncertainty, hindering strategic planning and impeding long-term investment decisions. As a consequence, the publicly traded consumer goods firms in Nigeria face the formidable task of not only understanding and complying with the intricate tax structures but also striving to maintain competitiveness in an environment where the tax burden is increasingly becoming a pivotal determinant of business success. Given the foregoing this study examined tax reforms and tax burden: a study of listed consumer goods companies in Nigeria. In order to achieve the goals of the study, a longitudinal panel research design was implemented. The study concentrated on twelve consumer goods corporations that are publicly traded on the stock market. The selection of these companies was based on their continuous publication of annual audited financial statements from 2013 to 2022. The data obtained from these reports were analysed using the panel data regression approach, with the assistance of statistical tools like E-view 10. The study's findings indicated that direct tax exerts insignificant effect on tax burden of consumer goods firms in Nigeria. While indirect tax on the other hand, has a negative significant effect on tax burden of consumer goods firms in Nigeria. The study thus recommends that consumer goods firms should consider engaging with policymakers and tax authorities to advocate for favorable indirect tax policies. Collaborative efforts with government agencies can contribute to creating a tax environment that aligns with business needs and minimizes the negative impact on the tax burden.

Ranking Performance of MARCOS Method for Location Selection Problem in the Presence of Conflicting Criteria

The selection of the best location for a facility is generally a complex process especially when considering multiple criteria in the selection process. The costs associated with purchasing the land and related constructions makes the location selection problem a long-term investment decision. Hence, the problem should be analysed carefully with a reliable approach to avoid the consequences that follows awkward decisions. Many multiple criteria decision-making (MCDM) methods are developed recently which made the selection of a suitable MCDM method has become a confusing decision. In this study, measurement alternatives and ranking according to compromise solution (MARCOS) method is used to solve a location selection problem with conflicting criteria. The ranking produced by MARCOS method is analysed by a comparison with other common MCDM methods and a sensitivity analysis. The aim of the comparison to show that the method agreed the choice of the best alternative while the sensitivity analysis is employed to check the robustness and efficiency of this method. The sensitivity analysis includes the sensitivity of weight test and rank reversal (RR) test. The comparison of ranking with different methods showed that MARCOS method has strong ranking correlation with other methods. Moreover, the sensitivity analysis showed the reliability and robustness of MARCOS method as it produced high consistency of ranking through the tests. Thus, this study validates the applicability of MARCOS method to handle the presence of conflicting criteria in location selection problems.

Heterogeneous Market Hypothesis in Major European Stock Exchanges

The aim of this study is to investigate heterogeneous market efficiency in European stock exchanges using Augmented HAR-RV model. According to the heterogeneous market efficiency hypothesis, investors create portfolios according to different time horizons and different market situations may arise in the reflection of information on price. We find evidence of the validity of the heterogeneous market efficiency model in European stock exchanges. Investors interpret information differently at different time horizons. Medium- and long-term investment decisions are a major influence. These results help explain the volatility that may occur in different time horizons. Portfolio diversification should also be made according to different investments in different horizons. Short-term global volatility shock has been effective on European stock markets.

Determinants of Financial Security of European Union Farms—A Factor Analysis Model Approach

The objective of this study was to assess the level of financial security of farms and identify its determinants based on factor analysis. The data used in this research were obtained from the European FADN (Farm Accountancy Data Network). Factor analysis (FA) was employed to reduce the number of variables that potentially determine the financial security of farms. The results indicate that the surveyed entities maintained financial security between 2014 and 2021. This study suggests that it is necessary to examine these factors separately for farms engaged in crop farming and animal production. The results obtained for all farms were less satisfactory than those that took into account the specifics of agricultural production. This study addresses a gap in the literature by including highly correlated variables in the analysis of the determinants of financial security. Factor analysis is used to reduce the number of variables without losing important information. Firstly, seventeen variables related to the financial security of all farms were assigned to six factors. These were income and self-financing of operations; area and subsidies; long-term investments and financial decisions consequences; economic size, taxes, and non-breeding livestocks; investment activity; and inputs, stock, short-term loans, and labor. Then, the determinants of the financial security of farms were examined, taking into account the specialization of activities. For crop-producing farms, six factors were identified, including three that were identical to those for all farms: income and self-financing of operations; long-term investment and financial decisions consequences; and investment activity. In addition, the following items were specified: area, subsidies, non-breeding livestocks, and taxes; economic size, inputs, and labor; and stock and short-term loans. The correlated variables in the case of livestock production combined into factors in a different way. In this case, four factors were distinguished: economic size, non-breeding livestocks, income, and self-financing of operations; operational activities of animal production; long-term investment and financial decisions consequences; and investment activity. Financial security is a complex matter that can be affected by a range of factors related to agricultural activities.

Econometric issues in the estimation of the natural rate of interest

The natural rate of interest is a key benchmark steady-state rate of return that is used by central banks to measure the stance of monetary policy. Asset managers rely on estimates of the natural rate to make long-term investment decisions for their clients. This paper outlines a number of important econometric issues in the estimation of the natural rate of interest from a widely used structural model. Using a simulation experiment as well as empirical data for the U.S., I show that one of the key parameters of the structural model is overestimated and leads to a spuriously amplified downward trend in the natural rate. The paper shows how the overestimation can be remedied and provides alternative estimates of the natural rate. Various other issues are discussed that policy makers should be aware of when utilizing this model’s natural rate estimates for policy decision.

A Study of Factors Influencing Investor Expectations in the Capital Market

This paper examines three key factors that influence investor expectations: company announcements, market seasonality and environment, and social and corporate governance (ESG). It shows that these factors individually and jointly have a significant impact on investor behavior and market dynamics. Corporate announcements serve as an important source of information that directly influences investor behavior and expectations, while market seasonality demonstrates cyclical patterns in investor expectations. Meanwhile, ESG investments reflect investors’ concerns about corporate performance in environmental protection, social responsibility, and governance. While research on these factors has been more extensive, further research is needed to combine these factors with other market dynamics and to quantify their impact on long-term investment decisions and market trends. This study aims to provide a comprehensive understanding of the diverse factors that influence investor expectations by analyzing these factors in an integrated manner. This not only provides insights for investors and market analysts, but also provides important information for policymakers in guiding the market and formulating policies.

Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature

Abstract Offshore wind turbines intend to take a rapidly growing share in the electric mix. The design, installation, and exploitation of these industrial assets are regulated by international standards, providing generic guidelines. Constantly, new projects reach unexploited wind resources, pushing back installation limits. Therefore, turbines are increasingly subject to uncertain environmental conditions, making long-term investment decisions riskier (at the design or end-of-life stage). Fortunately, numerical models of wind turbines enable to perform accurate multi-physics simulations of such systems when interacting with their environment. The challenge is then to propagate the input environmental uncertainties through these models and to analyze the distribution of output variables of interest. Since each call of such a numerical model can be costly, the estimation of statistical output quantities of interest (e.g., the mean value, the variance) has to be done with a restricted number of simulations. To do so, the present paper uses the kernel herding method as a sampling technique to perform Bayesian quadrature and estimate the fatigue damage. It is known from the literature that this method guarantees fast and accurate convergence together with providing relevant properties regarding subsampling and parallelization. Here, one numerically strengthens this fact by applying it to a real use case of an offshore wind turbine operating in Teesside, UK. Numerical comparison with crude and quasi-Monte Carlo sampling demonstrates the benefits one can expect from such a method. Finally, a new Python package has been developed and documented to provide quick open access to this uncertainty propagation method.

Performance Analysis of Small Finance Banks in India using CAMELS Model

Small Finance Banks (SFBs) are specialised banking institutions incorporated to serve the unbanked, small and underserved customers. Their primary motive is to improve financial access by providing access to essential financial services. This article examines the performance of ten SFBs in India over four years. The CAMELS model has been applied for performance analysis, which provides performance and ranking of various SFBs using the model’s six parameters: Capital adequacy, Asset quality, Management, Earning quality, Liquidity and Sensitivity. This study will help identify SFBs in financial distress, which will be helpful in timely action for their revival. Financial analysts and credit rating agencies can also use the comparative performance of these banks to frame their opinion or ratings. The common public, including customers and investors, can frame their short-term and long-term investment decisions based on the performance and ranking of these institutions. Furthermore, a proper assessment of these banks is needed as their good performance can ensure that formal credit availability reaches the grassroots level.

Quantification of Expected Return of Investment in Wood Processing Sectors in Slovakia

The study focuses on the selected aspects of investment measurement and management for the support of financial and economic decision-making of investors in wood-processing sectors. The aim of the study was to analyze the indicators for the structure and cost of capital of furniture and paper/forest branches in Slovakia, quantify the actual expected return on investment based on the selected methodology, and consequently find out the fundamental differences resulting from the specific conditions of given sectors. Methodologically, the study uses procedures for the weighted average cost of capital (WACC), capital asset pricing model (CAPM) for determining the cost of equity, and calculation of the beta coefficient considering the risk premium. The results of the study demonstrated a similar levered beta in both analyzed sectors (1.17 in furniture, 1.20 in paper/forest), but in each sector for a different reason. The expected rate of return is higher in furniture (7.84%) compared to paper/forest products at the level of 6.04%. The findings provide the possibility of comparing the required and expected rate of return on invested capital and making the appropriate long-term investment decisions.

INVESTMENT ATTRACTIVENESS OF UKRAINE'S ECONOMY AS A DOMINANT FACTOR IN ATTRACTING INTERNATIONAL CAPITAL: ANALYSIS, PROSPECTS, PRIORITIES

The article analyses and proves the expediency of assessing the investment attractiveness of the Ukrainian economy on the basis of international ratings. The main task of the scientific publication is the analysis of the current state of investment attractiveness of the national economy and the determination of priority directions of its further strategic development. The concretisation of methodological approaches to the assessment of the level of investment attractiveness of the national economy through the prism of rating assessments of various agencies becomes a prerequisite for the formation of modern methods, principles, forms and priority tools of state regulation of this process. The use of innovative methods and methods of conducting scientific research in the context of determining the level of investment attractiveness of the national economy is a fundamental element of the model of successful attraction of investment capital and implementation of long-term strategic investment decisions. The subject of the scientific article is a rating of the investment attractiveness of the national economy from the perspective of the post-war period. The methodology of scientific research is based on the need to determine the level of economic efficiency of investment capital in modern conditions of globalisation transformations in the economy of Ukraine. The paper reveals the main issues of the methodology of investment decision analysis and the definition of criteria of efficiency of investment capital attraction to the national economy. The criteria of selection, justification and adoption of balanced management decisions are detailed and analysed. The purpose of this study is a comprehensive analysis of the state's position in international rating assessments of investment attractiveness, identification of key factors influencing the formation of a high level of investment attractiveness of the Ukrainian economy and search for ways to increase it by attracting additional investment capital. The conclusion of the publication is the search for ways to ensure a high level of investment attractiveness of Ukraine's economy as a dominant factor in attracting international investment capital. The fundamental strategic priority of the state investment policy is to form a comprehensive mechanism for ensuring a high level of investment attractiveness of the economy by developing a long-term programme for ensuring a high level of investment attractiveness of the national economy.

Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices

Deep machine learning algorithms play an important role in facilitating the development of predictive models for the stock market. However, most studies focus on predicting next-day stock prices or movements, limiting the usability of the predictive model for investors. This study extensively explores the ability of deep learning models to predict out-of-sample the daily prices of global stock indices over a long term, up to a year. The performance of six models, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), are compared using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The models predict the long-term daily prices of five global stock indices, namely the Nifty, the Dow Jones Industrial Average (DJIA), the DAX performance index (DAX), the Nikkei 225 (NI225), and the Shanghai Stock Exchange composite Index (SSE). The results confirm the superiority of LSTM for predicting long-term daily prices. The Bi-LSTM does not improve the result of LSTM but performs better than other algorithms. CNN overfits the training data and poorly forecasts the long-term stock prices of global indices on the testing data. This research demonstrates the potential of deep learning models for long-term stock price forecasting, offering valuable insights for investors. Additionally, the patterns of predicted daily prices can be helpful in building trading and risk management decision systems.

Analysis of Corporate Long-Term Investment Decisions

The incorporation of long-term investment has consistently held a prominent position within the realm of business advancement, exerting a substantial influence on business circumstances and economic outcomes. Consequently, delving into the matter of effectively managing risk and attaining commensurate returns warrants thorough investigation. The company's experience is replete with examples of unsuccessful long-term investment choices. This paper discusses the implementation of long-term investment analysis in enterprises. It employs the methodology of literature analysis and review to examine the reasons behind enterprises engaging in long-term investments. Furthermore, it provides a detailed analysis of investment decision-making methods, identifies associated challenges, and proposes corresponding solutions. The goal of this paper is to assist enterprises in making informed decisions regarding long-term investments and ultimately selecting the most optimal choices.

Analyzing the factors influencing the wind energy adoption in Bangladesh: A pathway to sustainability for emerging economies

The future of energy security has become a prominent concern for emerging economies due to the inevitable depletion of fossil fuels and the ongoing disruptions in their supply. The crippling effect of complete dependence on expensive fossil fuel imports is magnified by the ineffective policy response to the enduring energy crisis, impeding progress across various sectors and thwarting efforts to meet the demands of population growth and industrialization amid acute electricity shortages. Amidst the economic growth of a prominent emerging economy, Bangladesh, wind energy emerges as a transformative solution to effectively tackle the mounting challenges of electricity demand, environmental pollution, greenhouse gas emissions, and the depleting reserves of fossil fuels. Therefore, this study utilizes an integrated multi-criteria decision-making (MCDM) approach combining the inter-valued type 2 intuitionistic fuzzy (IVT2IF) theory with the decision-making trial and evaluation laboratory (DEMATEL) method aiming to identify, prioritize, and investigate the relationships among the factors that impact the sustainable adoption and growth of wind energy in an emerging economy like Bangladesh. Initially, the factors were derived from reviewing existing literature. After subsequent expert validation, sixteen factors were selected for analysis using the IVT2IF DEMATEL method. The findings of the study indicate that "Fossil fuel supply disruption," "Stable financial investment and resource mobilization," and "Geographical region" are the most significant factors influencing the adoption of wind energy for national grid support with prominence value 4.415, 4.406 and 4.339 respectively. Moreover, "Fossil fuel supply disruption" is also the most significant causal factor with a causal weight of 1.274, which is followed by "Stable financial investment and resource mobilization" and "Geographical region" with a causal weight of 1.029 and 0.794. The study's findings have the potential to aid decision-makers and policymakers in formulating long-term strategies and investment decisions to improve the sustainability of the national grid and achieve carbon neutrality.

Research on investment incorporating both environmental performance and long (short) term financial performance of firms

Firstly, with the rapid popularisation of the ESG concept and the deepening of the conception of green and low-carbon development, the performance of a firm in protecting and improving the ecological environment and the level of green technology investment has gradually become the reference criteria for judging whether the firm has social value and sustainability in development. Secondly, to pursue short-term survival and long-term development, the firm will also vigorously strive for short-term profit creation and long-term capital accumulation. Thirdly, due to the existence of information asymmetry, the principal-agent parties in a firm may generate agency conflicts in the pursuit of maximising their own interests, which may lead to losses or even bankruptcy. Therefore, making rational short-term, long-term, and green technology investment decisions that consider both characteristics of firm and agency conflicts is essential for the survival and sustainable development of the firm. From the perspective of contract theory, this paper considers the design of an optimal dynamic financial contract that considers the environment's improvement and the achievement of the firm's long- and short-term financial performance while satisfying incentive compatibility. On this basis, we explore how the optimal investment strategies vary with firm characteristics. The results of the study suggest that the decisions of green technology investment, short-term investment, and long-term capital investment should be appropriately adjusted according to the level of financial slack and the effect of different market shocks to facilitate the achievement of the dual objectives of long- and short-term profitability and environmental improvement.

Unhedged risk in hybrid energy markets: Optimising the revenue mix of Australian solar

Australia is facing a historic investment challenge, with an estimated $1.2 to $1.5 trillion in newly invested capital required by 2030 in order to meet 2050 decarbonisation targets. In response policy makers have begun “re-entering” energy markets, which were deregulated and privatised in the 1990’s. Public auctions for State-backed ‘Contracts-for-Differences’ (CfDs) now act as a key policy tool to “prime” energy markets for new variable renewable energy (VRE) capacity. In a phenomenon recently coined “market hybridisation”, market participants form long-term investment decisions that are increasingly disconnected from short-run spot pricing dynamics and day-to-day operations. Historically, it has been common practice to underpin VRE projects with some form of power purchase agreement (PPA). However, increasing investor appetites for “semi-merchant” projects (i.e. projects partially exposed to spot prices) has become an emerging trend. This article examines the semi-merchant phenomenon, aiming to explain why the investment model is becoming popular for investors in VRE. Of particular interest are the foregone advantages attributable to highly contracted plant when securing bankable project finance. Results reveal that a revenue mix comprising 70%–80% PPA coverage and 20%–30% merchant exposure appears viable and tractable for investors in utility scale solar — whilst maintaining a typical project finance capital structure of c.60%–70% gearing. By implication, policymakers targeting 4000MW of new solar capacity via CfD auction may need only offer ∼3000MW of contracted capacity, thereby reducing taxpayer exposures and protecting scarce government balance sheet resources.

When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments

Globally, 1.5 billion people live off the grid, with their only access to electricity often limited to operationally expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and nonexistent at night. Thanks to recent technological advances, which have made large-scale electricity storage economically viable, a combination of solar generation and storage holds the promise of cheaper, greener, and more reliable off-grid power in the future. Still, it is not yet well understood how to jointly determine optimal capacity levels for renewable generation and storage. Our work aims to shed light on this question by developing a model of strategic capacity investment in both renewable generation and storage to match demand with supply in off-grid use cases while relying on fossil fuel as backup. Despite the complexity of the underlying model, we are able to extract two general results. First, we find that solar capacity and storage capacity are strategic complements, except in cases with very high investment in generation capacity, when they surprisingly turn into strategic substitutes with implications for long-term investment decisions. Second, we develop a simple heuristic to determine which storage technology, within a given portfolio, can turn a profit in the broadest set of market conditions and thus, is likely to be adopted first. We find that currently, low-efficiency, cheap technologies, such as thermal, can more easily turn a profit in off-grid applications than high-efficiency, expensive ones, such as lithium-ion batteries. We then develop two newsvendor-like approximations of the general model that are analytically tractable, yield precise values for the optimal investment decisions and profit in some cases, and provide bounds to the optimal investment decisions and profits in all other cases. To conclude, we calibrate our models to measure the accuracy of our solutions utilizing real-life data from three geographically-diverse islands, and then, we use our approximations to provide high-level insights on the role that large-scale storage will play in the years ahead as technology improves, carbon taxes are levied, and solar becomes cheaper. This paper was accepted by Beril Toktay, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Mack Institute for Innovation Management at the Wharton School as well as the Kleinman Center for Energy Policy at the University of Pennsylvania. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.04129 .

Headline-Driven Classification and Local Interpretation for Market Outperformance and Low-Risk Stock Prediction

Financial news is one of the most influential sources for making long-term investment decisions. The goal of this paper is to learn whether it is possible, based on texts from news headlines, to select stocks with low forecasted volatility which outrun the market represented by the Standard and Poor’s 500 Index. We solve several binary classification problems using a range of machine learning and deep learning principles. The best classifiers are interpreted with a model-agnostic technique, LIME, to extract key words having the greatest impact on the probabilities of market outrun and low volatility.

Flexibility-expansion planning of multi-energy systems by energy storage for participating in balancing-power markets

The growing need for balancing power combined with the shutdown of conventional power plants requires new balancing-power providers. In this context, industrial energy systems are particularly promising. However, the main task of industrial energy systems is to provide various energy forms. For this purpose, they operate interconnected units to maximize efficiency, but the interconnected operation also increases complexity, limiting flexibility due to the need to supply fixed demands. Energy storage can increase the flexibility of current and future industrial energy systems, thus enhancing the potential for sector coupling within the overall energy system at a low cost. To improve the flexibility of industrial energy systems, we propose a design optimization framework that accounts for investment in energy storage and for the provision of balancing power. Since the request of balancing power is uncertain, we present a stochastic program for the balancing-power market and propose two ways to model storage that both derive feasible storage operations while being computationally efficient. In a case study of a multi-energy system, cost savings between 6% and 17% can be achieved by increasing flexibility for participation in the balancing-power market with investment in heat storage. The sensitivity analysis identifies heat storage as particularly advantageous for heat-driven energy systems. Our method combines long-term investment decisions with short-term operational uncertainties to identify optimal investment decisions, which enhance the energy system’s flexibility for the provision of balancing power.