Decision Horizon Research Articles

CEO decision horizon and corporate environmental protection investment: evidence from China

ABSTRACT This paper explores the relationship between the chief executive officer (CEO) decision horizon represented by age and firms’ environmental protection investments. We find that there is an inverted U-shaped relationship between CEO age and firms’ environmental protection investments. Managerial environmental protection awareness plays a mediating role in such an inverted U-shaped relationship. Furthermore, this inverted U-shaped relationship is more pronounced when firms face more severe environmental pressures.

Managerial decision horizon and corporate greenwashing: Evidence from China

Managerial decision horizon and corporate greenwashing: Evidence from China

Public debt stabilization via a stochastic differential game paradigm

In this paper, we examine a public debt stabilization in a country under inflation targeting by arguing that bond yields are sensitive to inflation risks. Endogenously defining the convenience premium function, we focus on fiscal and monetary strategic interactions within an optimal control setting. The level of the debt-to-gross domestic produced (GDP) ratio and current inflation rate of the country are considered as the state variables in the game. We then focus on the convenience risk premium and rate of inflation as market-based disciplining devices, to articulate the Nash noncooperative equilibria, cooperative equilibria and Stackelberg equilibria via a feedback control strategy. We show that the outcome of the game depends on the parameters of the game, the type of equilibrium one considers and the decision horizon. When the current inflation rate follows an uncontrolled Ornstein–Uhlenbeck dynamic process, we highlight its effects on the growth rate of the debt ratio. Hence, our model provides a novel framework in which monetary and fiscal authorities can simultaneously achieve their individual interests, while achieving an optimal debt stabilization policy in the presence of inflation. We also highlight the need for governments to adopt policy-approaches which maintain the debt-to-GDP ratio under an inflation-dependent ceiling.

Forecast Horizon of Dynamic Lot Sizing Problem with Perishable Inventory and Multiple Chain Stores: Shipping and Stockout Cost

Perishable products are very common, but managing inventory of perishable products can be very challenging for firms, especially in distribution systems, including multiple chain stores. In this environment, we consider a dynamic lot-sizing problem faced by a distribution center that dis-patches a single perishable product to multiple chain stores. Demand cannot be backlogged, but it does not have to be satisfied; unsatisfied demand means stockout (lost sale). The first step is to transform the total profit function into a special total cost function. Our next step is to explore the properties of the optimal solution and use them to formulate a dynamic programming algorithm to solve the problem. Furthermore, we establish forecast and decision horizon results, which help the operation manager to decide the precise forecast horizon in a rolling decision-making process. Based on the model setting and the methods of dynamic programming, we obtained two interesting findings: (1) the maximized profit objective function is equivalent to the minimized cost objective function, and (2) the famous zero inventory property conditionally holds in the inventory management of perishable products. On an extensive test bed, useful insights were obtained on the impact of the lifetime of the product and cost parameters on the total cost and length of the forecast horizon. Thus, the contributions of this study are as follows: (1) we explore two structure policies in an optimal solution to devise efficient algorithms to reduce computational complexity; (2) we provide a sufficient condition for forecasting and decision horizons; and (3) we determine that, for a given fixed cost, the median forecast horizon first increases with the lifetime of the product and stockout cost and then remains invariable when it reaches a certain level.

Systemic sustainability assessment: Analyzing environmental and social impacts of actions on sustainable development

An improved sustainability performance of one system does not automatically lead to an improved sustainability performance of larger systems (society and nature). A reliable sustainability assessment must be carried out to improve environmental and social sustainability performance for whole systems. It is necessary to show how a system of interest at any scale should (and can) improve sustainability performance. An examination of the literature on sustainability assessment highlights a lack of a systems perspective in most sustainability assessment frameworks. The aim of this paper is, therefore, to provide a framework that enables an assessment of environmental and social sustainability performance, taking systemic aspects into account. This assessment framework is based on a definition of first- and second-order sustainability performance. The former focuses on narrow efficiency issues, while second-order sustainability performance focuses on systemic effectiveness and covers sustainability impacts on society and nature. Improving the first-order level (narrow perspective) will not automatically lead to an improved second-order sustainability performance (systemic perspective). Thus, systemic effectiveness is not automatically increased in the case of first-order sustainability performance. Therefore, three essential dimensions have been identified and combined in an assessment framework, i.e., the dimension of scale, the decision horizon, and sustainability principles. The conceptualized assessment framework allows to analyze whether an action of a system of interest (e.g., corporate action, any process, project, or policy) contributes to global sustainable development.

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

AbstractAn adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger‐based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed‐integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real‐field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models.

Forecast and decision horizons in a commodity trading model

Forecast and decision horizons in a commodity trading model

Decision Making under Uncertainty for Reconfigurable Manufacturing Systems: a framework for uncertainty representation

Traditional manufacturing paradigms cannot deal with the current pace of uncertain events in demand, supply and beyond. Reconfigurable Manufacturing Systems (RMS) are designed to adapt to these challenges in a rapid and cost-effective way. In order to decide when and how to reconfigure an RMS, it is necessary to identify the external events which trigger change in the system. This paper proposes a framework for uncertainty representation in RMS based on three levels of uncertainty and decision horizons. An illustrative example shows how such framework can be used by researchers and practitioners to better understand RMS and its context.

Cooperative model predictive control for ship formation tracking with communication delays

Communication delays are crucial in ship formation control. This paper proposed a cooperative control scheme for ship formation tracking with communication delays based on Model Predictive Control. An MPC controller is designed for trajectory tracking for each ship. Secondly, a predictive observer is designed using the predictive state of the leader ship in each decision horizon. With the predictive observer, the followers can online estimate the current motion states of the leader depending on the received delayed motion information of the leader. The reference trajectories of the followers are determined based on the estimated states, and the control strategies are decided consequently. In the end, comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller.

Determinants of CEO Investment Horizon. A Literature Review

This paper conducts a comprehensive literature review of the factors influencing the emergence of the CEO investment horizon problem – a preference for short-term investments over long-term ones. The root cause of this CEO issue, as in-dicated in existing literature, is often attributed to the CEO's personal risk attitude, shaped by factors like age, tenure, and cultural background.Numerous sources contributing to the short-term investment problem in public companies are described in the current academic literature. Prominent among these determinants are the challenges of quarterly reporting, the association of corporate performance with short-term metrics, market pressures, and the company's specific risk profile. A study by McKinsey & Company, focused on the short horizon problem, demonstrates that companies inclined toward short-term investments exhibit weaker fundamentals and performance. The consulting firm Ernst & Young has introduced the Long-term Orientation Index, offering a basis for cross-country comparison of decision horizons. In 2010, Antia and colleagues introduced a metric for measuring CEO decision horizons, which relies on CEO personal characteristics. Despite these efforts, a comprehensive literature review addressing the specificity of the CEO investment horizon problem and its dis-tinctions from the broader corporate investment horizon problem has been absent.This paper not only investigates the initial empirical exploration of the short investment horizon problem but also raises questions about its cross-country manifestations, its potential correlation with economic crises, and the relevant personal traits of CEOs for its study. Finally, the paper proposes various strategies to mitigate the CEO investment horizon problem within companies.

Executives' horizon and trade credit

AbstractUsing executives' decision horizon as a measure of internal governance, this study examines the association between customer's internal governance and supplier's extension of trade credit. Suppliers may extend more trade credit to customers with strong internal governance because of their lower operational risk, higher firm performance, and better information environment. However, firms with better internal governance may have easier access to other sources of financing, and thus may need less trade credit. Using a sample of US listed firms between 1992 and 2021, we find that suppliers extend more trade credit to customers with strong internal governance. We also find that the association between internal governance and trade credit is more pronounced for financially, and informationally constrained firms. Our results are robust to alternative measures and specifications. Incremental to prior studies, we show that effectiveness of customers' internal governance affects suppliers' lending decisions.

Partial centralization in a durable‐good supply chain

There has been extensive research on the strategic choice between supply chain centralization and decentralization. However, most research assumes complete centralization or complete decentralization but omits the commonly adopted supply chain structure of partial centralization. With partial centralization, a firm owns a portion, but not all, of its partner. To help fill this research gap, in this paper, we make a major contribution by explicitly analyzing partial centralization in a supply chain where a durable‐good manufacturer owns a portion of its downstream retailer. We start with a two‐period model and derive analytical equilibrium outcomes of the supply chain and its members under complete centralization, complete decentralization, and partial centralization. First, our analysis reveals that partial centralization with an appropriate portion of ownership can yield the desirable product sales pattern over periods and alleviate the time‐inconsistency problem in selling durable goods. As a result, partial centralization can become the equilibrium structure for a durable‐good supply chain. Second, the manufacturer's optimal ownership level in the retailer decreases in the product durability and decision horizon length, implying that complete decentralization is more likely to be the supply chain structure in equilibrium for higher product durability and longer decision horizon. Third, our extended analyses demonstrate the robustness of the main results with backward partial centralization. Moreover, partial centralization outperforms conventional strategies such as two‐part tariffs that can coordinate a supply chain but not fully solve the time‐inconsistency problem associated with durable goods.

The generalist CEO pay premium and CEO risk aversion

AbstractWe investigate whether and how managerial risk aversion influences the structuring of the generalist pay premium. We use three candidate variables for managerial risk aversion: the CEO's equity portfolio vega, decision horizon, and wealth. We find that equity‐based pay differs between generalist CEOs (who have broader experience spanning several industries) and non‐generalist CEOs (who tend to have narrower, specialized industry‐specific experience) based on their risk appetite. Further, how the generalist pay premium is structured depends on the risk aversion measure. Because generalist CEOs tend to be better paid than their counterparts, understanding their risk aversion level will help companies improve the structure of these CEOs’ compensation packages, and ultimately those of all CEOs. When vega is the risk aversion measure, CEOs get compensated for bearing risk. However, generalist CEOs with high vegas suffer a “risk discount” in their compensation. These generalist CEOs are associated with lower cash, equity, and total pay. CEOs with longer decision horizons get more equity and less cash, but this structuring does not extend to the generalist CEOs. Finally, wealthier CEOs get more cash and total pay. However, generalist CEOs who are wealthier get less cash pay.

Application of Multiobjective Optimization to Provide Operational Guidance for Allocating Supply among Multiple Sources

This study is motivated by multipleobjective optimization in short-term water management for a regional water utility. Although an increasing application of multi-objective evolutionary algorithm (MOEA) has been reported in the literature, we are not aware of its use for short-term water management by water utilities with diverse supply sources. This study presents an innovative practice for determining monthly resource allocation from multiple water supply sources that consider multiple objectives, including deviation from budgeted production, under or overutilization of a given portfolio of resources, and total cost of water production. This method is comprised of a simulation model, namely a production allocation model (PAM) and a MOEA. The decision variables of the MOEA optimization problem are monthly groundwater production from two groundwater wellfields. TheMOEA is used to search for Pareto optimal solutions across different objectives and the PAM uses MOEA output and considers operational constraints to determine water production from the other four supply sources in the decision horizon. Stochastic demand and supply realizations were generated to capture a wide range of uncertainties which were then sampled by a Latin Hyper Cube to make the computation tractable. A parallel computing environment was used to implement this near real-time decision support tool, providing timely guidance for water resources managers. One major difference between this study and many reported in the literature is that the MOEA was used to find Pareto solutions for each demand-supply realization rather than the entire ensemble. This setup allows water resources managers to explicitly explore Pareto solutions based on different supply and demand outlooks. The application of the innovative practice is demonstrated for a regional wholesale water supply utility, Tampa Bay Water, on the west coast of Florida in the United States. One additional advantage of MOEA-assisted planning is that it allows water managers to combine expert judgments and institutional knowledge in identifying solutions. A comparison between MOEA-assisted monthly production planning and heuristic planning reveals that the potential impact of short-term operations, e.g., deviation from budgeted production, is fully considered in a systematic approach. The proposed method can be applied to other regions with similar challenges in water resources management.

Proposing a kinematic wave‐based adaptive transit signal priority control using genetic algorithm

AbstractThis paper proposes a kinematic wave‐based adaptive transit signal priority control (KATC) aiming to minimize average passenger delay at intersection level. The passenger delay minimization problem is formulated as a mixed‐integer non‐linear program (MINLP) with two decision variables of green time and phase sequence. The adoption of the phase sequence in the optimization and not involving the common simplifying assumptions in the delay models are the main contributions of the current study. General traffic and public transportation (PT) vehicle delay are estimated using kinematic wave theory. Genetic algorithm is utilized to solve the MINLP problem at 1‐cycle intervals and for a decision horizon of 3 consecutive cycles. The performance of KATC is evaluated against SYNCHRO and the KATC model without phase sequence optimization, using VISSIM. The results of the experiments indicate superior performance of KATC over the two other models in terms of both average passenger delay and PT passenger delay, especially at low congestion levels. Furthermore, increasing PT passenger occupancy can effectively contribute to higher passenger delay improvements. The adverse impacts on passenger vehicles are restricted to a 3.4% and 2.7% increase in general traffic delay compared to SYNCHRO and the KATC model without phase sequence optimization, respectively.

Green transition, investment horizon, and dynamic portfolio decisions

This paper analyzes the implications of investors’ short-term oriented asset holding and portfolio decisions (or short-termism), and its consequences on green investments. We adopt a dynamic portfolio model, which contrary to conventional static mean-variance models, allows us to study optimal portfolios for different decision horizons. Our baseline model contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. The asset with fluctuating returns can arise from fossil-fuel based sectors or from clean energy related sectors. We consider different drivers of short-termism: the discount rate, the nature of discounting (exponential vs. hyperbolic), and the decision horizon of investors itself. We study first the implications of these determinants of short-termism on the portfolio wealth dynamics of the baseline model. We find that portfolio wealth declines faster with a higher discount rate, with hyperbolic discounting, and with shorter decision horizon. We extend our model to include a portfolio of two assets with fluctuating returns. For both model variants, we explore the cases where innovation efforts are spent on fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests and may provide guidance to some online financial decision making.

Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From Data

Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management, namely for strategic, pre-tactical, tactical and post-operations. We predict missed flight connections in an airline’s hub airport using historical data on flights and passengers, and analyse the factors that contribute additively to the predicted outcome for each decision horizon. Our data is high-dimensional, heterogeneous, imbalanced and noisy, and does not inform about passenger arrival/departure transit time. We employ probabilistic encoding of categorical classes, data balancing with Gaussian Mixture Models, and boosting. For all planning horizons, our models attain an area under the curve (AUC) of the receiver operating characteristic (ROC) higher than 0.93. SHAP value explanations of our models indicate that scheduled/perceived connection times contribute the most to the prediction, followed by passenger age and whether border controls are required.

Subordinate executives' horizon and firm policies

Motivated by the internal governance theory, we investigate the links between subordinate executives' horizon and firm policies. Using the number of years to retirement to capture subordinate executives' horizon inside the firm, we find that subordinates' horizon is positively associated with firm's risk-taking, long-term investments growth, and research and development productivity, but negatively related to the dividend decision and the payout ratio. We also find a positive relationship between subordinates' horizon and firm value. Our results are robust to controlling for alternative explanations including the pay gap between CEO and subordinate executives, executives' overconfidence, CEO's decision horizon, and other governance mechanisms. The results are also robust to alternative measures of subordinates' horizon, and after addressing potential endogeneity concerns.

Financial institution shareholding and corporate innovation: Evidence from China

Existing literature on bank-firm ties mainly focuses on the financial decisions of non-financial firms when they are fully or partially owned or controlled by financial institutions (e.g., commercial banks, investment banks, insurance companies). However, there is an alternative pattern of tie between financial institutions and firms; that is, a non-financial firm invests a significant amount in banks or other financial institutions, which we refer to as financial institution shareholding (FIS). Little is known about how the investment decisions of non-financial firms are shaped by this FIS. We fill this gap by investigating the impact of FIS on corporate innovation in China. Our empirical results show that FIS impedes corporate innovation. Using a mediation model, we find that the negative effect of FIS on corporate innovation is brought about through managerial agency conflict. We also show that this negative impact is more pronounced among CEOs with shorter decision horizons, firms with greater analyst coverage, and firms whose stocks are more liquid. Furthermore, we find that FIS simultaneously results in the under-hiring of the highly educated workers critical to corporate innovation. Overall, our findings indicate that FIS causes firms to lose growth potential by exacerbating managerial agency conflict.

Advertising patterns in a dynamic oligopolistic growing market with decay

A finite-horizon Lanchester model of a (continuous-time) differential game of oligopolistic advertising is considered, and the analytical form of the unique closed-loop Nash equilibrium derived and analyzed. In contrast to previous research, the finite-horizon Lanchester model is modified to include two novel factors. First, a growing market allows us to analyze the competition for a potential market via generic advertising, with the latter giving the otherwise zero-sum-like game a public good dimension. Second, it is assumed that each firm’s market share declines in the absence of advertising efforts. The analysis investigates the Markovian (closed-loop) equilibrium calling for firms operating in a competitive growing market to invest in offensive, defensive or generic advertising in all or part of the decision horizon. In the most novel part of the paper, the non-cooperative outcome is compared to the Pareto-optimal or cooperative solution. The conclusions derived reveal significant differences in the resulting patterns of co-existence of the different dimensions of advertising over time, relative to the existing literature.