From Stream Flows to Cash Flows: Leveraging Evolutionary Multi‐Objective Direct Policy Search to Manage Hydrologic Financial Risks

2021 ◽  
Author(s):  
Andrew L. Hamilton ◽  
Gregory W. Characklis ◽  
Patrick M. Reed
Keyword(s):  
Cash Flows ◽  
Financial Risks ◽  
Stream Flows ◽  
Policy Search ◽  
Multi Objective

Globalization of Financial Risks and Evaluation of Common Stocks

2014 ◽  
pp. 231-240
Author(s):  
Dilaysu Cinar
Keyword(s):  
Financial Markets ◽  
Interest Rates ◽  
Global Economy ◽  
Cash Flows ◽  
Financial Risks ◽  
Treasury Bills ◽  
Government Bonds ◽  
Common Stocks ◽  
Management Policies ◽  
Globalization Strategy

Risk can be defined as uncertainty about the events that will occur in the future. Risks are encountered in all areas of life, and become more important when it comes to financial markets. Risk in financial markets is defined as investment securities. If the investment vehicle is government bonds or treasury bills, they are considered to be free of risk. Because of the sudden changes in exchange rates in the process of globalization or fluctuations in interest rates influencing the cash flows of companies, most companies consider hedging as a viable part of the globalization strategy. Risk management policies to ease problems and disasters, which may arise from the use of instruments. The stock market serves as a bridge between economic activity and finance under favor of functions such as reducing the risk of investment, and it meets the capital needs for companies. For this reason, the development of stock markets plays an important role for the global economy and finance. Thus, the aim of this chapter is to introduce financial risks and their effect on common stocks.

Financial risks management of heat exchanger networks under uncertain utility costs via multi-objective optimization

Energy ◽  
2017 ◽  
Vol 139 ◽  
pp. 98-117 ◽  
Author(s):  
Leandro V. Pavão ◽  
Carlos Pozo ◽  
Caliane B.B. Costa ◽  
Mauro A.S.S. Ravagnani ◽  
Laureano Jiménez
Keyword(s):  
Heat Exchanger ◽  
Financial Risks ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Heat Exchanger Networks

scholarly journals Tools for modeling the capital dynamics of financial organizations

2021 ◽  
Vol 16 (95) ◽  
pp. 48-65
Author(s):  
Olga I. Yarkova ◽  
◽  
Olga S. Chudinova ◽  
Keyword(s):  
Financial Stability ◽  
Financial Institution ◽  
Software Tool ◽  
Simulation Models ◽  
Cash Flows ◽  
Descriptive Statistics ◽  
Financial Resources ◽  
Financial Risks ◽  
Risky Assets ◽  
State Pension

The issues of ensuring the financial stability of financial institutions, which is understood as the sufficiency of assets to meet obligations, are of paramount importance both for clients and the management of a financial institution, and for the country's economy as a whole. Most often, the inability to fulfill obligations is associated with a lack of funds, therefore it is important to monitor the dynamics of the monetary capital of organizations, to assess their financial risks, including in the conditions of investment. Purpose of the study: development of tools for assessing the risks of financial organizations. Statement of the problem: to develop a simulation model that allows one to study the dynamics of the capital of an organization, whose financial resources are formed due to heterogeneous flows of inflow and outflow of funds and investment, including in risky assets, in an inflationary environment. The paper proposes a modeling algorithm that allows to collect a descriptive statistics on the distribution of financial resources, to estimate the dynamics of the money capital of financial organizations and investigate the "sufficiency" of the company's funds to meet financial obligations basing on data of cash flows for various types of contracts and returns (growth rates) of assets, presented in the form of statistical data and/or characteristics of time series models. The description of the software tool is given. A computational experiment was based on data of the inflow and outflow of funds of a non-state pension Fund under the program of non-state pension provision. Descriptive statistics are given for the distributions of the size of organization's funds constructed as a result of modeling. The probability of organization’s downfall in dynamics and the risk of entering the zone of financial insecurity are assessed. The proposed tools have scientific novelty in the field of designing simulation models and decision support systems for analyzing the activities of financial organizations and determining effective directions for their development.

Seasonal streamflow forecasts fostering hydro power cascade operation applying the adaptive policy search framework

2020 ◽  
Author(s):  
Christoph Libisch-Lehner ◽  
Harald Kling ◽  
Martin Fuchs ◽  
Hans-Peter Nachtnebel
Keyword(s):  
Power Plants ◽  
Energy Generation ◽  
Water Levels ◽  
Hydro Power ◽  
Policy Search ◽  
Multi Objective ◽  
Adaptive Policy ◽  
The Future ◽  
Hydro Power Plants ◽  
Seasonal Streamflow

<p>Hydro power assets contribute a valuable share of carbon-free energy generation worldwide. Large reservoirs are able to store energy and, combined with pump-storage capacities, they will play an important role in the future’s energy mix. In the future, the stronger integration of volatile energy sources, like solar and wind energy demands the flexibility of hydro power plants. In general, the operation of hydro power plants is a multi-stakeholder and multi-objective dynamic problem related to critical infrastructure. This requires flexible and robust reservoir operation policies, defined as closed-loop release functions where the system state is the input and turbine flows are the response of the function. Recently, Evolutionary-Multi-Objective-Direct-Policy-Search (EMODPS) yielded promising control policies for water resources systems. EMODPS is a kind of machine learning approach that relies on long records, or stochastic streamflow replicates capturing a wide range of possible conditions. A stochastic streamflow generator should actually cover all possible conditions related to the state-action-space and inflates the optimization process. Furthermore, the search procedure can implicitly identify the "most representative" states of the system and tends to approximate a better solution for these states. States that are very rarely explored but can be very important for a reliable operation have little effect on the optimized policy. In addition, artificial neuronal networks (ANN) derived from EMODPS suffer under the curse of instable sections . This is because ANN's are good at interpolating, but bad at extrapolating actions from unobserved states in the training sequence. Thus, we extend the well-known EMODPS framework by an re-optimizing approach utilizing seasonal streamflow predictions. Periodically, the reservoir policies are re-optimized based on an ensemble of streamflow predictions and the actual reservoir water levels. This adaptive policy search (APS) approach is applied to a three reservoirs cascade under Mediterranean climate, where the energy market will play an important role in the future. First results show that the hydropower operation can be improved: energy generation can slightly be increased at clearly lower cost of flood risk compared to static robust policies.</p>

Dynamic retrieval of informative inputs for multi-sector reservoir policy design with diverse spatio-temporal objective scales

2020 ◽  
Author(s):  
Marta Zaniolo ◽  
Matteo Giuliani ◽  
Paul Block ◽  
Andrea Castelletti
Keyword(s):  
Water Availability ◽  
Large Scale ◽  
Policy Design ◽  
Spatial Scales ◽  
Water System ◽  
Forecast Model ◽  
Southern Ethiopia ◽  
Policy Search ◽  
Spatial And Temporal Scales ◽  
Multi Objective

<p>Advances in monitoring and forecasting water availability at various time and spatial scales offer a cost-effective opportunity to enhance water system flexibility and resilience. By enriching the basic information system traditionally used to design reservoir operating policies (i.e., time index and reservoir storage) with additional inputs regarding future water availability, operators can better anticipate and prepare for the onset of extreme hydrologic conditions (wet or dry years). Numerous candidate hydro-meteorological variables and forecasts may potentially be included in the operation design, however, and the best input set for a given problem is not always evident a priori. Additionally, for multi-purpose systems, the most appropriate information set and policy shape likely changes according to the objective tradeoff. <br>In this work, we contribute a novel Machine Learning approach to link an Input Variable Selection routine with a multi-objective Direct Policy Search framework in order to retrieve the best policy input set online (i.e., while learning the policy) and Pareto-dynamically. The selected policy search routine is the Neuro-Evolutionary Multi-Objective Direct Policy Search (NEMODPS) which generates flexible policy shapes adaptive to changes in the policy input set. This approach is demonstrated for the lower Omo River basin, in southern Ethiopia, where regulation of the recently constructed Gibe III megadam must strike a balance between hydroelectricity generation, large scale irrigation, and ecosystem services downstream.<br>We develop a dataset of candidate policy inputs comprising streamflow and precipitation forecasts at multiple spatial and temporal scales, from days to months ahead. Long term (season-ahead) forecasts are conditioned on well-recognized climatic oscillations in the region. Specifically, Artificial Intelligence tools are used to detect relevant anomalies in gridded global climatic datasets of sea-surface temperature, sea-level pressure and geopotential height, which are used as predictors for a multi-variate non-linear forecast model.  Moreover, we analyze how varying objectives – and tradeoffs therein – benefit from different information.<br>Results suggest that informing water system operations with appropriate information can reduce conflicts between water uses, especially in extreme years when a basic policy is particularly inefficient.</p>

Manifold-based multi-objective policy search with sample reuse

2017 ◽  
Vol 263 ◽  
pp. 3-14 ◽  
Author(s):  
S. Parisi ◽  
M. Pirotta ◽  
J. Peters
Keyword(s):  
Policy Search ◽  
Multi Objective
Sign in / Sign up

Export Citation Format

Share Document