Investment Risk Appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another approach to modelling the market environment

Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: a dynamic forward approach

Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV) in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF) is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.Peer Reviewe

A Comparative Analysis of an Interior-point Method and a Sequential Quadratic Programming Method for the Markowitz Portfolio Management Problem

In this paper, I give a brief introduction of the general optimization problem as well as the convex optimization problem. The portfolio selection problem, as a typical type of convex optimization problem, can be easily solved in polynomial time. However, when the number of available stocks in the portfolio becomes large, there might be a significant difference in the running time of different polynomial-time solving methods. In this paper, I perform a comparative analysis of two different solving methods and discuss the characteristics and differences

A Comparative Analysis of an Interior-point Method and a Sequential Quadratic Programming Method for the Markowitz Portfolio Management Problem

In this paper, I give a brief introduction of the general optimization problem as well as the convex optimization problem. The portfolio selection problem, as a typical type of convex optimization problem, can be easily solved in polynomial time. However, when the number of available stocks in the portfolio becomes large, there might be a significant difference in the running time of different polynomial-time solving methods. In this paper, I perform a comparative analysis of two different solving methods and discuss the characteristics and differences

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Fuzzy portfolio optimization with tax, transaction cost and investment amount: a developing country case

The socioeconomic or political structures of countries and investment costs play a crucial role in investor decisions, especially in developing countries where the environment is unstable. In this regard, fuzzy models that consider the investment amount and cost may enable making more realistic decisions rather than the deterministic models used in portfolio optimization (PO). Hence, the objective of this paper is to examine the effects of the environment, investment amount and cost on PO in a politically, socially and economically unstable environment. Konno-Yamazaki PO model was fuzzified by adopting fuzzy linear programming (FLP) approaches of Verdegay and Werners for this purpose. Afterward, extended models were created. To do that, investment amount, tax and transaction costs were integrated into the return constraint of the fuzzified models. Mean-Variance Model (MVM) of Markowitz was also used for comparatively interpreting the results of the optimization. Results show that the fuzzified models based on Verdegay and Werners FLP approaches can be suggested as a decision-making tool, respectively for risk-averse and risk-taker investors. The extended models provide much better results compared to the fuzzified models. On the other hand, they are not more successful than the MVM in an unstable environment but the stable environment. The main contributions are onsidering political, social and economic events in the optimization, comparatively analyzing fuzzified Konno-Yamazaki model with its extended versions and the MVM, investigating the relationship between optimization models and investor types.</p

Mean–Variance portfolio selection in presence of infrequently traded stocks

This paper deals with a mean-variance optimal portfolio selection problem in presence of risky assets characterized by low frequency of trading and, therefore, low liquidity. To model the dynamics of illiquid assets, we introduce pure-jump processes. This leads to the development of a portfolio selection model in a mixed discrete/continuous time setting. In this paper, we pursue the twofold scope of analyzing and comparing either long-term investment strategies as well as short-term trading rules. The theoretical model is analyzed by applying extensive Monte Carlo experiments, in order to provide useful insights from a Önancial perspectiv