32,514 research outputs found
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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
Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal
The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented
Новая взвешенная гибридная система рекомендаций с использованием коэффициента Шарпа для прибыльного диверсифицированного инвестиционного портфеля
Identifying where to invest and how much to invest can be very challenging for common people who have limited knowledge in the domain. Portfolio managers are financial professionals who spend a lot of time and effort to help investors in investing funds and implementing investment strategies, but not all can afford to consult them. The study aims to develop a weighted hybrid recommendation system that recommends an optimized investment portfolio based on the investor’s preferences regarding risk and return. Generally, investors usually ask investment for advice from friends or relatives with similar risk preferences or if they are interested in a particular item, the investors ask for the experience of someone who already has invested in the same item. Therefore, the methodology considers the investor’s past behavior and the past behavior of the nearest neighbor investors with similar risk preferences. Using user-based collaborative filtering the number of stocks is recommended using Pearson correlation based on the investor’s income, then using another user-based collaborative filtering the number of stocks is recommended based on the investor’s age. Weights are assigned to the recommended number of stocks generated based on income and age and their weighted average is finally considered. Finally, the feasibility of the proposed system was assessed through various experiments. Based on the received results, the authors conclude that the proposed weighted hybrid approach is robust enough for implementation in the real world. The novelty of the paper lies in the fact that none of the existing approaches make use of more than one type of weighted recommendation algorithm. Additionally, the final results obtained this way have been never further fortified with the highest Sharpe ratio and minimum risk for the investor. This combination of hybrid and Sharpe ratios has never been explored before.Выбор того, куда и сколько инвестировать, может быть очень сложной задачей для обычных людей, которые имеют ограниченные знания в этой области. Портфолио-менеджеры — это финансовые профессионалы, которые тратят много времени и усилий, чтобы помочь инвесторам в размещении средств и реализации финансовых стратегий, но не все могут позволить себе обратиться к ним за консультацией. Цель исследования — разработать взвешенную систему гибридных рекомендаций оптимизированного инвестиционного портфеля на основе предпочтений инвестора относительно риска и доходности. Как правило, инвесторы спрашивают совета по инвестициям у друзей или родственников со схожими предпочтениями в отношении риска, или, если их интересует конкретный товар, у того, кто уже инвестировал в тот же товар. Поэтому методология учитывает прошлое поведение инвестора и его ближайших соседей-инвесторов со схожими предпочтениями риска. С помощью коллаборативной фильтрации на основе интересов пользователя авторы рекомендуют выбрать определенное количество акций, используя метод корреляции Пирсона на основе дохода инвестора. Затем с помощью другой коллаборативной фильтрации на основе интересов пользователя рекомендуется определенное количество акций на основе возраста инвестора. Рекомендованному количеству акций, сгенерированному на основе дохода и возраста инвестора, присваиваются веса, и в итоге считается их средневзвешенное значение. В заключение проведена оценка реализуемости предложенной системы с помощью различных экспериментов. На основании полученных результатов авторы делают вывод, что предложенный взвешенный гибридный подход достаточно надежен для реализации в реальных условиях. Новизна работы заключается в том, что ни один из существующих подходов не использует более одного типа алгоритма взвешенных рекомендаций. Кроме того, конечные результаты, полученные таким образом, также никогда не отличались максимальным коэффициентом Шарпа и минимальным риском для инвестора. Такая комбинация гибридной фильтрации и коэффициента Шарпа никогда ранее не исследовалась
Practical Deep Reinforcement Learning Approach for Stock Trading
Stock trading strategy plays a crucial role in investment companies. However,
it is challenging to obtain optimal strategy in the complex and dynamic stock
market. We explore the potential of deep reinforcement learning to optimize
stock trading strategy and thus maximize investment return. 30 stocks are
selected as our trading stocks and their daily prices are used as the training
and trading market environment. We train a deep reinforcement learning agent
and obtain an adaptive trading strategy. The agent's performance is evaluated
and compared with Dow Jones Industrial Average and the traditional min-variance
portfolio allocation strategy. The proposed deep reinforcement learning
approach is shown to outperform the two baselines in terms of both the Sharpe
ratio and cumulative returns
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
In this paper, a likelihood based evidence acquisition approach is proposed
to acquire evidence from experts'assessments as recorded in historical
datasets. Then a data-driven evidential reasoning rule based model is
introduced to R&D project selection process by combining multiple pieces of
evidence with different weights and reliabilities. As a result, the total
belief degrees and the overall performance can be generated for ranking and
selecting projects. Finally, a case study on the R&D project selection for the
National Science Foundation of China is conducted to show the effectiveness of
the proposed model. The data-driven evidential reasoning rule based model for
project evaluation and selection (1) utilizes experimental data to represent
experts' assessments by using belief distributions over the set of final
funding outcomes, and through this historic statistics it helps experts and
applicants to understand the funding probability to a given assessment grade,
(2) implies the mapping relationships between the evaluation grades and the
final funding outcomes by using historical data, and (3) provides a way to make
fair decisions by taking experts' reliabilities into account. In the
data-driven evidential reasoning rule based model, experts play different roles
in accordance with their reliabilities which are determined by their previous
review track records, and the selection process is made interpretable and
fairer. The newly proposed model reduces the time-consuming panel review work
for both managers and experts, and significantly improves the efficiency and
quality of project selection process. Although the model is demonstrated for
project selection in the NSFC, it can be generalized to other funding agencies
or industries.Comment: 20 pages, forthcoming in International Journal of Project Management
(2019
Ghana Market Assessment: Market-Based Provision of Water at the Community Level
This market assessment evaluates the potential for decentralized market-based approaches to sustainable safe water service, focusing on the poor in rural communities and small towns in Ghana that are not supplied or connected by municipal schemes to safe water. Insights drawn from desktop analyses, field-based research, financial modeling, and engagement of water sector stakeholders are used to identify key barriers and propose solutions
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Monitoring-Based Commissioning: Tracking the Evolution and Adoption of a Paradigm-Shifting Approach to Retro-Commissioning
Proceedings of the 2012 ACEEE Summer Study (Panel 4, Paper 1130). Monitoring-based commissioning (MBCx) emphasizes permanent energy performance metering and trending—for diagnosis of energy waste, for savings accounting, and to enable persistence of savings. Emphasis on monitoring represents a paradigm shift for the retro-commissioning (RCx) industry, which has traditionally relied upon test protocols and modeled savings estimates. Since 2004, a major monitoring-based commissioning program at twenty-five California university campuses has evolved to meet the changing needs of university and utility partners. More recently the monitoring-based approach has been adopted by third-party programs in California. We present information on the progression of program design and results for the multiple phases of the original program, along with a look at third-party and other programs adopting similar program features
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