Target Return Research Articles

Portfolio Selection with Hierarchical Isomorphic Risk Aversion

Researchers usually specify risk aversion coefficients from 1 (lowest) to M (highest) for a portfolio to indicate active or passive approaches. How effective is this practice? Recent studies suggest that the global minimum variance portfolio (GMVP) is statistically equivalent to portfolios with extensive risk aversion coefficients (the GMVP-equivalent). Expressing the risk aversion coefficient as a Taylor series of the target return and efficient set constants, we generalize the previous result to the non-GMVP-equivalents and segment mean-variance portfolios according to a hierarchy of risk aversion coefficients. In this paper, we show that hierarchical risk aversion coefficients are superior to isometric attributes.

Comparison of the efficacy of two preoxygenation techniques using oxygen reserve index.

Preoxygenation is very important to protect the patient from hypoxia before intubation. However, pulse oximetry has some limitations in detecting hypoxia. We aimed to compare the effectiveness of 2 preoxygenation techniques based on oxygen reserve index (ORI) levels. Twenty healthy male volunteers were included in the study. They inhaled 100% FiO2 oxygen administered at 5 L/min as the 1st technique (M1) with a ventilation mask as much as their tidal volumes for 3 min. The 2nd technique (M2) applied 100% FiO2 oxygen at 10 L/min flow using the same mask and 8 deep inspiratory volumes, which was aimed to be completed within 1 min. Maximum ORI levels, duration to reach that level, and time needed to reach the target ORI level (0.35) and return back to the "0" were measured. In the M1 group, ORI levels were significantly higher during and after 60 s, according to post hoc tests. In the M2 groups, ORI levels were significantly higher during and after the 4th inspiration, according to post hoc tests. Oxygen reserve index values at the 60th 2nd (M1) and 8th inspiration (M2) were compared as the 8th inspiration corresponded to the 60th second. The maximum ORI values were significantly lower in the M1 group compared to the M2 group (p < 0.001 and p = 0.006, respectively). Seven volunteers (36.8%) in the M1 group and 2 volunteers (10.5%) in the M2 group could not reach the target ORI (McNemar's test, test statistic 3.2, degrees of freedom (df) = 1, p = 0.063). The time to reach the target ORI value and to reach maximum ORI values was significantly longer in the M1 group than in the M2 group (p = 0.008 and p < 0.001, respectively). We observed that the 8-deep breath technique is more effective in preoxygenation compared to the 3-min tidal volume technique.

Practice Briefing: The implications of a move towards explicit discounted cash flow (DCF) models for property investment valuations

PurposeThe primary purpose of this Practice Briefing is to examine the implications of the recommendations of the Royal Institution of Chartered Surveyors (RICS) Review of Investment Valuations to shift towards (explicit) discounted cash flow (DCF) valuations on valuers, clients, risk management and market data.Design/methodology/approachThis Practice Briefing will develop a conceptual framework to articulate the impact of the shift towards explicit modelling assumptions in valuations. A comparative analysis is conducted to contrast traditional valuation approaches with an explicit DCF approach.FindingsThe proposed shift to DCF valuations in the real estate industry carries both subtle and profound implications. While on the surface, the move appears to bring minimal change since current valuations are already DCF-based, a deeper exploration reveals a potential transformation to the understanding, analysis and measurement of pricing and risks in commercial real estate.Practical implicationsThe traditional techniques adopted in valuations have become pervasive in the industry, hindering more sophisticated modelling and analysis of individual assets and portfolios. Explicitly modelling assumptions in both the initial years of cash flow and perpetuity calculations would unveil fundamental relationships and rationales that have previously been buried within, and perhaps ignored by, an All Risks Yield (ARY).Originality/valueThis briefing identifies the cause of confusion when interpreting ARY differentials on implied target returns and risk and demonstrates how a change to explicit assumptions, such as growth and letting periods, would enhance transparency, enabling more informed client–valuer discussions, which in-turn will boost confidence in valuation accuracy, lead to more sophisticated asset modelling, market data, forecasting and market analysis.

A refined classification method of heat customers to improve inter-household thermal balance intelligent control and regulation

Hydraulic imbalance in district heating systems (DHSs) especially in the secondary pipe network hinders the improvement of system energy efficiency. To improve inter-household thermal balance on the secondary side, this paper proposes a refined classification and intelligent regulation method for heat customers. Firstly, an intelligent balancing system is proposed with the smart valves installed at heat customer's thermal entrance. Then, the refined method classifies the heat customers based on room comprehensive thermal characteristic coefficient. The regulation periods and target return temperature predictive models are determined for each customer type to achieve balance regulation. Finally, the proposed intelligent balance system was applied to a practical DHS secondary pipe network to verify its balance effect. After the regulation, indoor temperature non-uniformity coefficient for each type of heat customer decreased by 15 %; while the return water temperature dispersion reduced by 8 %–18 %. This led to a significant improvement in hydraulic and thermal balance among heat customers, resulting in a 6.2 % heat-saving rate. The application provides a reference for inter-household thermal balance regulation in scenarios where indoor temperature loggers are limited within similar secondary pipe network of DHSs.

A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection

AbstractThis study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out‐of‐sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018–2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

Optimal Portfolio Analysis of Listed Companies in IDX 30

Investors face the dual considerations of return and risk when making investment decisions. Therefore, proper analysis is crucial, especially during the COVID-19 crisis, to achieve maximum returns while minimizing risk. This research used three portfolio optimization models, the Mean-Variance Model, the Mean-Absolute Deviation Model and the Value-at-Risk Model, to construct a stock portfolio. The findings indicated that the Mean-Variance Model can yield an expected return of 16.55% and a portfolio risk of 258.66%. The result from the Mean-Absolute Deviation Model was that the target return is 16%, along with a portfolio risk of 282.43%. Keywords: Portfolio Optimization, Mean-Variance, Mean Absolute Deviation, Value at Risk, R Language, IDX30

Critic-Guided Decision Transformer for Offline Reinforcement Learning

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the action distribution based on target returns for each state in a supervised manner. However, prevailing RCSL methods largely focus on deterministic trajectory modeling, disregarding stochastic state transitions and the diversity of future trajectory distributions. A fundamental challenge arises from the inconsistency between the sampled returns within individual trajectories and the expected returns across multiple trajectories. Fortunately, value-based methods offer a solution by leveraging a value function to approximate the expected returns, thereby addressing the inconsistency effectively. Building upon these insights, we propose a novel approach, termed the Critic-Guided Decision Transformer (CGDT), which combines the predictability of long-term returns from value-based methods with the trajectory modeling capability of the Decision Transformer. By incorporating a learned value function, known as the critic, CGDT ensures a direct alignment between the specified target returns and the expected returns of actions. This integration bridges the gap between the deterministic nature of RCSL and the probabilistic characteristics of value-based methods. Empirical evaluations on stochastic environments and D4RL benchmark datasets demonstrate the superiority of CGDT over traditional RCSL methods. These results highlight the potential of CGDT to advance the state of the art in offline RL and extend the applicability of RCSL to a wide range of RL tasks.

Transportation networks and competition in the market for corporate control

AbstractResearch SummaryWhile competition in the market for corporate control determines firms' ability to capture value from acquisitions, there is limited evidence of factors that influence such competition. This study explores whether airline routes intensify competition in this market and affect the target's returns. Targets can become better connected to distant latent acquirers (DLAs), which can increase the targets' bargaining power. Similarly, better connectivity can allow acquirers to reach distant latent targets (DLTs) and therefore increase the acquirers' bargaining power. Examining acquisitions between US public companies during 1980–2018, I find that lower travel time between the target and its DLAs increases the target's returns and the number of competing bids. Instead, the travel time between the acquirer and its DLTs does not play a role.Managerial SummaryIn corporate acquisitions, the competitive threat posed by potential alternative acquirers of the target generally forces the focal acquirer to offer high returns to the target to gain its control. Indeed, these transactions are often not profitable for acquirers. Yet, we know little about what determines the competitive threat faced by acquirers in the market for corporate control. This study shows that the presence of airline routes that reduce the travel time between the target and its latent acquirers increases the target's returns from the deal and the number of competing bids. The study also tests whether lower travel time between the acquirer and its latent targets gives the acquirer more bargaining power vis‐à‐vis the focal target. However, there is no evidence of this effect.

Small target detection in sea clutter using dominant clutter tree based on anomaly detection framework

It is difficult for maritime high-resolution radars to realize the small target detection in sea clutter, due to weak target returns and complicated clutter characteristics. Cooperation of multiple features is a recognized way to distinguish target returns from clutter. Therefore, it becomes crucial to build a detector, a special classifier, with unbalanced training samples, i.e., ergodic clutter versus non-ergodic target samples. In this paper, a preferential decision tree (pre-decision tree) with the oblique stopping criterion is proposed, where a preferential Gini index (pre-Gini index) is defined to replace the Gini index and considers rigorous false alarm rates and tolerable missed probabilities for radar target detection. Then, an improved pruning is added to the pre-decision tree to generate a dominant clutter tree, which can accurately control the false alarm rate. The two-step decision is based on the anomaly detection framework and solves the unbalance of the training samples. The proposed method can work in the high-dimensional space directly, and its decision only involves linear operations. The experimental results on the recognized IPIX and CSIR databases illustrate that the proposed method performs well among the available feature-based detectors.

Revenue Strategies that Encourage Graduates’ Funding Initiatives in South African Public Universities

As a result of the increasing unemployment rate amongst youth in South Africa, this study sought to investigate revenue generating strategies aimed at sustaining universities financially in the long run whilst focusing primarily on placing graduates in employment opportunities emanating from business start-ups or business partnerships led by universities.Under the theme of developing university revenue strategies that create graduates’ work opportunities, two research questions aimed to address the role of universities in supporting graduates’ business start-ups. The study examined how university-led graduates’ initiatives could serve as a significant contributor to the university’s third income strategy, whilst simultaneously reducing youth unemployment, which is currently one of the biggest challenges in the South African economy. The focus was on all twenty-six South African public universities using secondary data that was already publicly available. A positivist epistemology, using a quantitative research method, was used when statistically testing the first key research question which was conducted by demonstrating the strength of statistical association between two variables through applying the Pearson’s correlation test. Thereafter, in the second research question forecasting technique determined how universities can realize benefits aimed at generating extra revenue from investment contributions made towards graduates’ investment initiatives through forecasting anticipated targeted Returns on Investments. Key findings demonstrated that there was a significant association between each university’s net profit and the graduates produced, and the funding subsidy from the South African government in higher education also confirms this practice. The forecasting approach using the Excel Solver equation was possible to apply in addressing the target return on investment. This implied providing positive guidance in solidifying executive decisions to strategize allocating a university profit proportion as the basis of financing capital resources supporting university-led graduates’ initiatives for creating work opportunities. Keywords: Revenue, Funding, South Africa, Publica Universities.

Çoklu Yansıma Ortamlarında Geniş Menzilli Hedeflerin Uyarlanabilir Radar Tespiti

This paper discusses the adaptive detection of extended radar targets buried in Gaussian clutter, assuming a diffuse multipath environment. The target return signal from each range cell is modeled as the sum of a deterministic data vector, which includes an unknown scaling factor representing the direct path component, and a randomly distributed data vector in a Gaussian distribution with unknown covariance matrix representing multipath echoes. During the design phase, it is assumed that the primary data covariance matrix falls within the vicinity of a sample covariance matrix that is devised from the secondary data set. The paper proposes a constraint Generalized Likelihood Ratio Test (GLRT) for the adaptive detection problem of extended radar targets in diffuse multipath environments, and conducts a performance analysis comparing the developed algorithm with well-known adaptive detectors in the literature. The results and performance analysis demonstrate that the proposed approach enhances the detection performance of extended radar targets in environments with diffuse multipath. Overall, this article provides valuable insights for improving the adaptive detection of extended targets in challenging environments, with potential applications in radar and sensing technologies.

Long-time adaptive coherent detection of small targets in sea clutter by fast inversion algorithm of block tridiagonal speckle covariance matrices

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. The proposed detector is verified by an X-band high-resolution island-based radar data with an unmanned aerial vehicle (UAV) as test target. The experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors.

Improved computational methods for probabilistic liquefaction hazard analysis

Current procedures for analysis of and design against liquefaction hazards focus primarily on the use of probabilistic ground motions at a single ground-shaking hazard level, with the cyclic loading represented by a peak ground acceleration (PGA) corresponding to a target return period and a single representative moment magnitude Mw. These parameters are typically used in conjunction with deterministic simplified procedures for estimating liquefaction triggering and consequences that largely neglect the considerable uncertainties inherent to liquefaction problems. Motivated by these limitations and the resulting inconsistencies in liquefaction design levels, early methods for probabilistic liquefaction hazard analysis (PLHA) were proposed that incorporate the full ground motion hazard space, integrated with probabilistic liquefaction triggering models. Such methods provide liquefaction factor of safety (FSL) hazard curves for standard penetration test (SPT) data. Recognizing the increased use of higher-resolution cone penetrometer test (CPT) data in engineering analysis and design and the potential computational challenges it presents, an expanded suite of probabilistic triggering models, and wider availability of more detailed seismic hazard data, an improved PLHA computational methodology is presented in this study. The methods described utilize the U.S. Geological Survey National Seismic Hazard Model directly through web services to obtain PGA hazard and disaggregation calculations for any site and average shear wave velocity in the upper 30 m of the site (Vs30) in the conterminous United States, to reconstruct the PGA hazard space for use in PLHA calculations, and to employ array calculations for efficient liquefaction hazard curve estimates for the thousands of CPT measurements in a given profile. The framework presented here is modular in nature, and discussion on the use of alternative models and extension to probabilistic liquefaction consequence evaluation is presented. The importance of appropriate representation of probabilistic liquefaction model uncertainties is also highlighted, along with the impacts of different levels of uncertainty on the PLHA calculation. Finally, a potential roadmap for incorporation of the PLHA framework in seismic provisions is presented, with an illustration of how it can address current limitations and impacts in liquefaction hazard analysis and design.

Portfolio Optimization Using 0-1 Knapsack Quadratic Programming Model: A Case Study

Portfolio management is critical to selecting the right mix of investments which produces the best of results for any business entity. Using the 0-1 Knapsack quadratic model together with the mean-variance approach, this study sought to determine the optimal asset mix for TCF Microfinance bank. Five asset types were evaluated at a 70% target return. After three iterations, an optimal portfolio mix constituting of three out of the five assets was achieved, which exceeded the predetermined benchmark by 49.3% and at a risk value of less than 5%. This optimal investment can easily be practically applied.

The Rating Scale Paradox: An Application to the Solvency 2 Framework

This work aims to identify the optimal rating scale for the rating system used by a credit insurance company subjected to the Solvency 2 regulatory framework. To do so, we apply and further develop a previously published result concerning the rating scale properties. The partition underlying a given rating scale must satisfy two needs of the rating model user: efficient information synthesis and stable semantics. Those needs cannot be addressed together in general. Nonetheless, it is possible to specify the partition as a linear combination of the two choices that meet one requirement each. We numerically show that, in general, the optimal combination is nontrivial under realistic assumptions and is mainly driven by the target return fixed by the company’s stakeholders and the debtors’ probability of default distribution.

System reliability analysis of mooring system for floating offshore wind turbine based on environmental contour approach

The system reliability of the mooring system for floating offshore wind turbines (FOWT) is analyzed in this paper. Various failure modes such as mooring line breaking, fatigue and floater offset are included and solved as a series system. This work employed a new mooring breaking assessment algorithm, wherein the effect of fatigue damage on breaking strength is simulated by introducing functional dependences into the coupled dynamic analysis. In this research, the extreme design loads are estimated using the environmental contour (EC) approach and a logarithmic function is proposed to describe the relationship between the extreme response and the target return period to simplify the full long-term analysis. The time-domain coupling analysis of the FOWT is performed considering all one-year sea states, the long-term fatigue damage was approximated based on linear criterion. The fatigue damage can contribute significantly to the breaking strength of mooring lines. The results indicate that ignoring the dependence between the residual strength and cumulative fatigue damage would cause under-estimated results. The calculated failure probability of the floating system is larger than that of any single failure mode, while it is mainly dominated by the main single failure mode with the largest failure probability.

Learning Pessimism for Reinforcement Learning

Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected target returns. In this work, we propose Generalized Pessimism Learning (GPL), a strategy employing a novel learnable penalty to enact such pessimism. In particular, we propose to learn this penalty alongside the critic with dual TD-learning, a new procedure to estimate and minimize the magnitude of the target returns bias with trivial computational cost. GPL enables us to accurately counteract overestimation bias throughout training without incurring the downsides of overly pessimistic targets. By integrating GPL with popular off-policy algorithms, we achieve state-of-the-art results in both competitive proprioceptive and pixel-based benchmarks.

Bayesian Detectors for Wideband Radar Target With Range Walking in Compound Gaussian Clutter

In this paper, the problem of the wideband radar target detection with the range walking in compound Gaussian clutter without the secondary data is addressed. The wideband radar target return with the range walking is represented as a canonical linear model in the multi-rank subspace, and the clutter is modeled as a two-dimensional random process with separable complex inverse Wishart distributed random covariance matrices in the pulse and range-frequency domains. The generalized likelihood ratio test (GLRT), the Wald test and the Rao test based on the above target and clutter models are designed under the Bayesian framework. The Laplace approximation is adopted to compute the second-kind confluent hypergeometric function of matrix argument (SKCHFMA) in the proposed detectors. Finally, the performance of the detectors is validated by the simulations.

Antenna Beampattern Matched Optimum Coherent Detection in High-Resolution Mechanically Scanning Maritime Surveillance Radars

Mechanically scanning radars are still widely used in ocean guard and surveillance areas nowadays, owing to simple structure, light weight and low cost. The antenna rotation causes amplitude modulation of target returns along pulses by the antenna azimuth beampattern. The traditional coherent detection that ignores this modulation suffers from some performance loss. In this paper, an antenna beampattern matched optimum coherent detector and a dual-threshold detection scheme are proposed for high-resolution mechanically scanning maritime surveillance radars. Firstly, it is shown that the modulation of target returns is determined by the antenna azimuth beampattern, scan rate, and pulse repetition interval (PRI) and the antenna scanning also affect the temporal correlation of sea clutter. Secondly, the antenna beampattern matched optimum coherent detector is given in compound-Gaussian sea clutter with inverse Gamma distributed texture and the pulse scanning mode is used to deal with unknown target azimuth angle in the matched filter. Thirdly, to mitigate the high computational cost of the pulse scanning mode, a dual-threshold scheme is given, which searches all pulse-range cells by short-time integration and further confirms suspected cells whose test statistics are between the two lower and higher thresholds in the search by long-time integration. Moreover, two fast recursive clutter covariance matrix inversion algorithms along pulses and range cells are presented, which bring a great reduction in computational complexity. Finally, real measured radar data is used to verify the proposed detector and scheme and the experimental results show that it behaves better in detection of small targets than the traditional detectors.

An intelligent control and regulation strategy aiming at building level heating balance in district heating system

In district heating system (DHS), hydraulic imbalance has hindered further energy saving, which results in high energy consumption and meaningless heat loss. This issue is particularly serious in the building side whose scale and structure are more complex than that of heat exchange station (HES) side. Focusing on the building side balance, this paper proposed a building level intelligent balancing system and corresponding control strategy. The balancing system featuring smart valves installed at the building entrance, adjust the valves opening according to target return temperature to accommodate the heat distribution among buildings. A dynamic calculation model coupling a linear correction is established to decide the target return temperature of each smart valves considering buildings’ thermal characteristics. The proposed intelligent balance system is applied to a practical district heating system. The unregulated indoor temperature ranges from 20.9 °C–25.2 °C, while a smaller range of 22.6–24.0 °C after regulation. The results indicated that the hydraulic and thermal imbalance among buildings were significantly improved with regulation. Moreover, the heat consumption index was reduced and an energy saving rate of 3.73% was achieved. This study provides a reference for similar building side pipe network balance regulation.