6,545 research outputs found
Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları ĂŒzerine bir literatĂŒr araĆtırması
Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management. This model seeks an efficient frontier with the best trade-offs between two conflicting objectives of maximizing return and minimizing risk. The problem of determining an efficient frontier is known to be NP-hard. Due to the complexity of the problem, genetic algorithms have been widely employed by a growing number of researchers to solve this problem. In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature. Main specifications of the problems studied and the specifications of suggested genetic algorithms have been summarized
Adaptive Statistical Evaluation Tools for Equity Ranking Models
A major challenge in the investment management business is to identify which stocks are likely to outperform in the future, and which are likely to perform relatively poorly. To this end the strategy adopted by Genus is to identify factors (auxiliary information about the stock such as earnings-to-price ratio or dividend yield) that they believe are associated with future out-performance (i.e. factors that have predictive ability). The best of these factors are then combined (Genus use a weighted average) into a model which is used to rank the universe of stocks month-by-month. This ranking is then used to as the input to a trading strategy, resulting in a modified portfolio.
Genus had provided us with sample data, consisting of just over 12 years worth of monthly returns on a universe of 60 stocks, along with time series of 34 factors for each of the stocks. Using these data, the approach was to build software (MATLAB) models for:
1. ranking the stocks based on factor information;
2. implementing a trading strategy based on a stock ranking and assessing the performance of a given trading strategy by looking at measures such as hit ratio, information ratio and spread.
The IPSW team implemented a simplified trading strategy of selling the entire portfolio each month, and using the proceeds to invest equally in the top 20% of stocks as given by the computed ranking. They also implemented the following measures of portfolio performance: excess return, hit ratio and information ratio
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Updating, Upgrading, Refining, Calibration and Implementation of Trade-Off Analysis Methodology Developed for INDOT
As part of the ongoing evolution towards integrated highway asset management, the Indiana Department of Transportation (INDOT), through SPR studies in 2004 and 2010, sponsored research that developed an overall framework for asset management. This was intended to foster decision support for alternative investments across the program areas on the basis of a broad range of performance measures and against the background of the various alternative actions or spending amounts that could be applied to the several different asset types in the different program areas. The 2010 study also developed theoretical constructs for scaling and amalgamating the different performance measures, and for analyzing the different kinds of trade-offs. The research products from the present study include this technical report which shows how theoretical underpinnings of the methodology developed for INDOT in 2010 have been updated, upgraded, and refined. The report also includes a case study that shows how the trade-off analysis framework has been calibrated using available data. Supplemental to the report is Trade-IN Version 1.0, a set of flexible and easy-to-use spreadsheets that implement the tradeoff framework. With this framework and using data at the current time or in the future, INDOTâs asset managers are placed in a better position to quantify and comprehend the relationships between budget levels and system-wide performance, the relationships between different pairs of conflicting or non-conflicting performance measures under a given budget limit, and the consequences, in terms of system-wide performance, of funding shifts across the management systems or program areas
Portfolio Optimization Using Evolutionary Algorithms
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsPortfolio optimization is a widely studied field in modern finance. It involves finding
the optimal balance between two contradictory objectives, the risk and the return.
As the number of assets rises, the complexity in portfolios increases considerably,
making it a computational challenge. This report explores the application of the
Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and
Genetic Algorithm (GA) in the field of portfolio optimization.
MOEA/D and GA have proven to be effective at finding portfolios. However, it
remains unclear how they perform when compared to traditional approaches used
in finance. To achieve this, a framework for portfolio optimization is proposed, using
MOEA/D, and GA separately as optimization algorithms and Capital Asset Pricing
Model (CAPM) and Mean-Variance Model as methods to evaluate portfolios.
The proposed framework is able to produce weighted portfolios successfully. These
generated portfolios were evaluated using a simulation with subsequent (unseen)
prices of the assets included in the portfolio. The simulation was compared with
well known portfolios in the same market and other market benchmarks (Security
Market Line and Market Portfolio).
The results obtained in this investigation exceeded expectation by creating
portfolios that perform better than the market. CAPM and Mean-Variance Model,
although they fail to model all the variables that affect the stock market, provide a
simple valuation for assets and portfolios. MOEA/D using Differential Evolution
operators and the CAPM model produced the best portfolios in this research.
Work can still be done to accommodate more variables that can affect markets and
portfolios, such as taxes, investment horizon and costs for transactions
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A review of portfolio planning: Models and systems
In this chapter, we first provide an overview of a number of portfolio planning models
which have been proposed and investigated over the last forty years. We revisit the
mean-variance (M-V) model of Markowitz and the construction of the risk-return
efficient frontier. A piecewise linear approximation of the problem through a
reformulation involving diagonalisation of the quadratic form into a variable
separable function is also considered. A few other models, such as, the Mean
Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the
Minimax (MM) model which use alternative metrics for risk are also introduced,
compared and contrasted. Recently asymmetric measures of risk have gained in
importance; we consider a generic representation and a number of alternative
symmetric and asymmetric measures of risk which find use in the evaluation of
portfolios. There are a number of modelling and computational considerations which
have been introduced into practical portfolio planning problems. These include: (a)
buy-in thresholds for assets, (b) restriction on the number of assets (cardinality
constraints), (c) transaction roundlot restrictions. Practical portfolio models may also
include (d) dedication of cashflow streams, and, (e) immunization which involves
duration matching and convexity constraints. The modelling issues in respect of these
features are discussed. Many of these features lead to discrete restrictions involving
zero-one and general integer variables which make the resulting model a quadratic
mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the
algorithms and solution methods for this class of problems are also discussed. The
issues of preparing the analytic data (financial datamarts) for this family of portfolio
planning problems are examined. We finally present computational results which
provide some indication of the state-of-the-art in the solution of portfolio optimisation
problems
Using Column Generation to Solve Extensions to the Markowitz Model
We introduce a solution scheme for portfolio optimization problems with
cardinality constraints. Typical portfolio optimization problems are extensions
of the classical Markowitz mean-variance portfolio optimization model. We solve
such type of problems using a method similar to column generation. In this
scheme, the original problem is restricted to a subset of the assets resulting
in a master convex quadratic problem. Then the dual information of the master
problem is used in a sub-problem to propose more assets to consider. We also
consider other extensions to the Markowitz model to diversify the portfolio
selection within the given intervals for active weights.Comment: 16 pages, 3 figures, 2 tables, 1 pseudocod
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
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