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This paper introduces a new initialization method of individuals for genetic algorithm (GA) in portfolio optimization problems. In our approach, first a set of assets, variables, composing the portfolio is selected, and then combination of real-valued weights of the portfolio is optimized by GA. In the asset selection, a pairwise asset selection which is an iterative greedy scheme based on the bordered...
We consider the problem of replicating the returns of a financial index as accurately as possible by selecting a subset of the assets that constitute the index and determining the portfolio weight of each selected asset subject to various constraints that are relevant in practice, including the UCITS III (Undertakings for Collective Investments in Transferable Securities) 5/10/40 concentration rule...
In the previous approach, by grouping genetic algorithm, an approach has been proposed for mining diverse group stock portfolio which can be used to generate various stock portfolios. However, a parameter, number of groups, should be given in advanced of that approach, and it is hard to set appropriate value. Hence, the intent of this research is to design an algorithm that can find appropriate number...
Analytic Hierarchy Process is a popular decision tool that ranks the aggregated results from prioritizing the pairwise reciprocal matrices judged by the users. Saaty's Eigen method for prioritizing pairwise reciprocal matrix leads to the possibility of rank reversal, and is still one of the unsettled issues, although many applications of this method have been made. This study proposes the genetic...
In recent years, lots of portfolio analysis methods been proposed one after another. The investors care about how much money they can earn, and the companies need to know how much performance they gain. In this paper, a new stock portfolio construction strategy using Investment Satisfied Capability Index (ISCI) and Interactive Artificial Bee Colony (IABC) is proposed. Two-year daily Return on Investment...
The Portfolio Optimization problem is an example of a resource allocation problem with money as the resource to be allocated to assets. We first have to select the assets from a pool of them available in the market and then assign proper weights to them to maximize the return and minimize the risk associated with the Portfolio. In our work, we have introduced a new “greedy coordinate ascent mutation...
How to allocate the weights of stocks is an interesting technology in stock index optimized replicate. This paper proposed a hybrid algorithm of adaptive genetic algorithm and pattern search (AGA-PS) to find the optimal portfolio weights. In AGA-PS, the crossover probability and mutation probability are adjusted adaptively. The weight from adaptive genetic algorithm is as the search start point of...
ACO is a new distributed intelligent biologically-inspired algorithm simulated evolution, and is widely used for solving various combinatorial optimization problems. The simulation results showed that this method is better than genetic algorithm in the iterations and results. So ant system algorithm with variance as the risky measure indexes is better than basic ACO and genetic algorithm in real estate...
This research is mainly based on the index funds constructing methods of Oh, et al (2005) and Chang and Lai's (2006) Adjusted-GA model. Then, a new model is constructed based on the Taiwan market environment and its characteristics. The fixed-rate deposit offered at the Taiwan was added to the modified model to achieve optimal portfolio performance. The results show that the modified stock selection...
The portfolio optimization/rebalancing problem is to determine a proportion-weighted combination in a portfolio in order to achieve certain investment targets. For this problem, many researchers have used various evolutionary methods and models such as genetic algorithms and simulated annealing. On the other hand, the portfolio optimization/rebalancing problem can be viewed as a multi-dimensional...
This paper describes a decision system based on rules for the management of a stock portfolio using a mechanism of dynamic learning to select the stocks to be incorporated. This system simulates the intelligent behavior of an investor, carrying out the buying and selling of stocks, such that during each day the best stocks will be selected to be incorporated in the portfolio by reinforcement learning...
This paper proposes a new strategy β-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta β efficiently measures the volatility relative to the benchmark index or the capital market, β is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The...
Portfolio optimization is an important research field in modern finance. The most important characteristic within this optimization problem is the risk and the returns. Modern portfolio theory provides a well-developed paradigm to form a portfolio with the highest expected return for a given level of risk tolerance. Multi objective portfolio optimization problem is the portfolio selection process...
In this paper, Robust Genetic Network Programming (R-GNP) for generating trading rules on stocks is described. R-GNP is a new evolutionary computation, which represents its solutions using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid stock trading model, R-GNP is applied to generating stock trading rules using variance of...
Financial risk has evolved from simple variability of returns in stock trading activities toward interconnected uncertainty factors in our economic systems. In this context, building global portfolios provides a natural mechanism to manage diversified risk between asset classes. This paper proposes a novel framework for the asset selection and allocation under global diversification principles using...
Searching for portfolios co-integrated with an index offers new opportunities in designing robust investment strategies. The problem of finding optimal index co-integrated portfolios that are maximally stationary is combinatorial. Indeed, given a basket of equities, the portfolio/index co-integration cannot be simply expressed in terms of equity/index co-integration. In this paper we investigate the...
Portfolio optimization plays a critical role in determining portfolio strategies for investors and it is intrinsically a discrete multiobjective optimization problem whose decision criteria conflict with each other. This paper extends a novel numerical multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the portfolio optimization problem. The proposed...
In this paper, we present a decision-making process that uses our proposed quasi-oppositional comprehensive learning particle swarm optimizers (QCLPSO) to solve multi-period portfolio problem. Multi-stage stochastic financial optimization takes order with portfolio in ever-changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization...
There are several studies extended classification system (XCS) in past years, the model can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation the experiment to evolve XCS for global asset allocation in the country-specific Exchanged Traded Funds (ETFs). Since international stock price trend is influenced by unknown and unpredictable...
This research adduced process capability indices of quality management to develop a new performance appreciation method. It constructs the capability of a larger-the-better process with process capability index. Not only do investment satisfied capability index (ISCI) and investment satisfied degree evaluates efficiently when construct lower limit of confidence by investment performance indices, but...
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