Fuzzy portfolio model with different investor risk attitudes

[[abstract]]We propose a fuzzy portfolio model designed for efficient portfolio selection with respect to uncertain or vague returns. Although many researchers have studied the fuzzy portfolio model, no researcher has yet attempted a behavioral analysis of the investor in the fuzzy portfolio model. To address this problem, we examined investor risk attitudes—risk-averse, risk-neutral, or risk-seeking behaviors—to discover an efficient method for fuzzy portfolio selection. In this study, we relied on the advantages of possibilistic mean–standard deviation models that we believed would fit the risk attitudes of investors. Thus, we developed a fuzzy portfolio model that focuses on different investor risk attitudes so that fuzzy portfolio selection for investors who possess different risk attitudes can be achieved more easily. Finally, we presented a numerical example of a portfolio selection problem to illustrate ways to address problems presented by a variety of investor risk attitudes.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

On-Line Portfolio Selection Strategy Based on Weighted Moving Average Asymmetric Mean Reversion

Mean reversion is an important property for constructing efficient on-line portfolio selection strategy. The existing strategies mostly suppose that the mean reversion is multi-period symmetric or single-period asymmetric. However, the mean reversion is multi-period and asymmetric in the real market. Taking this into account, on-line strategies based on multi-period asymmetric mean reversion is proposed. With designing multi-piecewise loss function and imitating passive aggressive algorithm, we propose a new on-line strategy WMAAMR. This strategy runs in linear time, and thus is suitable for large-scale trading applications. Empirical results on four real markets show that WMAAMR can achieve better results and bear higher transaction cost rate

Adjustable Security Proportions in the Fuzzy Portfolio Selection under Guaranteed Return Rates

[[abstract]]Based on the concept of high returns as the preference to low returns, this study discusses the adjustable security proportion for excess investment and shortage investment based on the selected guaranteed return rates in a fuzzy environment, in which the return rates for selected securities are characterized by fuzzy variables. We suppose some securities are for excess investment because their return rates are higher than the guaranteed return rates, and the other securities whose return rates are lower than the guaranteed return rates are considered for shortage investment. Then, we solve the proposed expected fuzzy returns by the concept of possibility theory, where fuzzy returns are quantified by possibilistic mean and risks are measured by possibilistic variance, and then we use linear programming model to maximize the expected value of a portfolio’s return under investment risk constraints. Finally, we illustrate two numerical examples to show that the expected return rate under a lower guaranteed return rate is better than a higher guaranteed return rates in different levels of investment risks. In shortage investments, the investment proportion for the selected securities are almost zero under higher investment risks, whereas the portfolio is constructed from those securities in excess investments.[[notice]]補正完

Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

[[abstract]]In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean–standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

A framework of distributionally robust possibilistic optimization

In this paper, an optimization problem with uncertain constraint coefficients is considered. Possibility theory is used to model the uncertainty. Namely, a joint possibility distribution in constraint coefficient realizations, called scenarios, is specified. This possibility distribution induces a necessity measure in scenario set, which in turn describes an ambiguity set of probability distributions in scenario set. The distributionally robust approach is then used to convert the imprecise constraints into deterministic equivalents. Namely, the left-hand side of an imprecise constraint is evaluated by using a risk measure with respect to the worst probability distribution that can occur. In this paper, the Conditional Value at Risk is used as the risk measure, which generalizes the strict robust and expected value approaches, commonly used in literature. A general framework for solving such a class of problems is described. Some cases which can be solved in polynomial time are identified

Another reason why the efficient market hypothesis is fuzzy

This paper makes use of the performance evaluation to test the validity of the efficient market hypothesis (EMH) in hedge fund universe. The paper develops a fuzzy set based performance analysis and portfolio optimisation and compares the results with those obtained with the traditional probability methods (frequentist and Bayesian models). We consider a data set of monthly investment strategy indices published by Hedge Fund Research group. The data set spans from January 1995 to June 2012. We divide this sample period into four overlapping sub-sample periods that contain different economic market trends. To investigate the presence of managerial skills among hedge fund managers we first distinguish between outperformance, selectivity and market timing skills. We thereafter employ three different econometric models: frequentist, Bayesian and fuzzy regression, in order to estimate outperformance, selectivity and market timing skills using both linear and quadratic CAPM models. Persistence in performance is carried out in three different fashions: contingence table, chi-square test and cross-sectional auto-regression technique. The findings obtained with probabilistic methods contradict the EMH and suggest that the “market is not always efficient,” it is possible to make abnormal rate of returns if one exploits mispricing in the market, and makes use of specific investment strategies. However, the results obtained with the fuzzy set based performance analysis support the appeal of the EMH according to which no economic agent can make risk-adjusted abnormal rate of return. The set of optimal invest strategies under fuzzy set theory results in a well-diversified portfolio of investment with an expected mean return equal to that of the efficient frontier portfolio under the Markowitz’ mean-variance

High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

trategy of investment is important tool enabling better investor's decisions in uncertain finance market. Rules of portfolio selection help investors balance accepting some risk for the expectation of higher returns. The aim of the research is to propose strategy of constructing investment portfolios based on the composition of distributions obtained by using high–low data. The ensemble of 176 Evolino recurrent neural networks (RNN) trained in parallel investigated as an artificial intelligence solution, which applied in forecasting of financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to the shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy

Critical Asset and Portfolio Risk Analysis for Homeland Security

Providing a defensible basis for allocating resources for critical infrastructure and key resource protection is an important and challenging problem. Investments can be made in countermeasures that improve the security and hardness of a potential target exposed to a security hazard, deterrence measures to decrease the likeliness of a security event, and capabilities to mitigate human, economic, and other types of losses following an incident. Multiple threat types must be considered, spanning everything from natural hazards, industrial accidents, and human-caused security threats. In addition, investment decisions can be made at multiple levels of abstraction and leadership, from tactical decisions for real-time protection of assets to operational and strategic decisions affecting individual assets and assets comprising a regions or sector. The objective of this research is to develop a probabilistic risk analysis methodology for critical asset protection, called Critical Asset and Portfolio Risk Analysis, or CAPRA, that supports operational and strategic resource allocation decisions at any level of leadership or system abstraction. The CAPRA methodology consists of six analysis phases: scenario identification, consequence and severity assessment, overall vulnerability assessment, threat probability assessment, actionable risk assessment, and benefit-cost analysis. The results from the first four phases of CAPRA combine in the fifth phase to produce actionable risk information that informs decision makers on where to focus attention for cost-effective risk reduction. If the risk is determined to be unacceptable and potentially mitigable, the sixth phase offers methods for conducting a probabilistic benefit-cost analysis of alternative risk mitigation strategies. Several case studies are provided to demonstrate the methodology, including an asset-level analysis that leverages systems reliability analysis techniques and a regional-level portfolio analysis that leverages techniques from approximate reasoning. The main achievements of this research are three-fold. First, this research develops methods for security risk analysis that specifically accommodates the dynamic behavior of intelligent adversaries, to include their tendency to shift attention toward attractive targets and to seek opportunities to exploit defender ignorance of plausible targets and attack modes to achieve surprise. Second, this research develops and employs an expanded definition of vulnerability that takes into account all system weaknesses from initiating event to consequence. That is, this research formally extends the meaning of vulnerability beyond security weaknesses to include target fragility, the intrinsic resistance to loss of the systems comprising the asset, and weaknesses in response and recovery capabilities. Third, this research demonstrates that useful actionable risk information can be produced even with limited information supporting precise estimates of model parameters