A pure probabilistic interpretation of possibilistic expected value, variance, covariance and correlation

In this work we shall give a pure probabilistic interpretation of possibilistic expected value, variance, covariance and correlation

A short survey of normative properties of possibility distributions

In 2001 Carlsson and Full´er [1] introduced the possibilistic mean value, variance and covariance of fuzzy numbers. In 2003 Full´er and Majlender [4] introduced the notations of crisp weighted possibilistic mean value, variance and covariance of fuzzy numbers, which are consistent with the extension principle. In 2003 Carlsson, Full´er and Majlender [2] proved the possibilisticCauc hy-Schwartz inequality. Drawing heavily on [1, 2, 3, 4, 5] we will summarize some normative properties of possibility distributions

Some applications of possibilistic mean value, variance, covariance and correlation

In 2001 we introduced the notions of possibilistic mean value and variance of fuzzy numbers. In this paper we list some works that use these notions. We shall mention some application areas as wel

Robust portfolio management with multiple financial analysts

Portfolio selection theory, developed by Markowitz (1952), is one of the best known and widely applied methods for allocating funds among possible investment choices, where investment decision making is a trade-off between the expected return and risk of the portfolio. Many portfolio selection models have been developed on the basis of Markowitz’s theory. Most of them assume that complete investment information is available and that it can be accurately extracted from the historical data. However, this complete information never exists in reality. There are many kinds of ambiguity and vagueness which cannot be dealt with in the historical data but still need to be considered in portfolio selection. For example, to address the issue of uncertainty caused by estimation errors, the robust counterpart approach of Ben-Tal and Nemirovski (1998) has been employed frequently in recent years. Robustification, however, often leads to a more conservative solution. As a consequence, one of the most common critiques against the robust counterpart approach is the excessively pessimistic character of the robust asset allocation. This thesis attempts to develop new approaches to improve on the respective performances of the robust counterpart approach by incorporating additional investment information sources, so that the optimal portfolio can be more reliable and, at the same time, achieve a greater return. [Continues.

Asset allocation with multiple analysts’ views: a robust approach

Retail investors often make decisions based on professional analysts’ investment recommendations. Although these recommendations contain up-to-date financial information, they are usually expressed in sophisticated but vague forms. In addition, the quality differs from analyst to analyst and recommendations may even be mutually conflicting. This paper addresses these issues by extending the Black–Litterman (BL) method and developing a multi-analyst portfolio selection method, balanced against any over-optimistic forecasts. Our methods accommodate analysts’ ambiguous investment recommendations and the heterogeneity of data from disparate sources. We prove the validity of our model, using an empirical analysis of around 1000 daily financial newsletters collected from two top 10 Taiwanese brokerage firms over a 2-year period. We conclude that analysts’ views contribute to the investment allocation process and enhance the portfolio performance. We confirm that the degree of investors’ confidence in these views influences the portfolio outcome, thus extending the idea of the BL model and improving the practicality of robust optimisation

Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods

The financial crisis impact on the composition of an optimal portfolio in the stock market: study applied to portuguese index PSI 20

In order to maximize their utility function, investors select some assets over others, choosing the portfolio that will allow them to maximize their wealth. Each asset is chosen considering the relationship between the risk of that particular investment (usually measured by variance) - and the profitability it can offer, as well as the risk between this and other assets (measured by covariance). The purpose of this study consisted of constructing the minimum variance portfolio, using data from the PSI-20 (2008-2016) representative asset quotation, where investors are risk reluctant and wish to minimize risk while maintaining the same level of profitability, or on the other hand, maintaining the same level of risk but maximizing expected profit. In order to do this, a comparison of the optimal portfolio in 2004-2017 was carried out, compared to the minimum variance portfolio after the financial crisis (2008-2016). The method used to estimate each asset’s expected profitability that makes up the PSI-20 consists of extracting the obtained historical quotations. The optimal portfolio composition, in the period after the financial crisis, shows that the energy sector has an optimal portfolio weight reduction of 39.15%, that the big distribution sector (23.85%) was introduced into the portfolio and by last, the industrial sector stands its ground in the composition of the optimal portfolio.info:eu-repo/semantics/publishedVersio

The Financial Crisis Impact on the Composition of an Optimal Portfolio in the Stock Market - Study Applied to Portuguese Index PSI 20

In order to maximize their utility function, investors select some assets over others, choosing the portfolio that will allow them to maximize their wealth. Each asset is chosen considering the relationship between the risk of that particular investment (usually measured by variance) - and the profitability it can offer, as well as the risk between this and other assets (measured by covariance). The purpose of this study consisted of constructing the minimum variance portfolio, using data from the PSI-20 (2008-2016) representative asset quotation, where investors are risk reluctant and wish to minimize risk while maintaining the same level of profitability, or on the other hand, maintaining the same level of risk but maximizing expected profit. In order to do this, a comparison of the optimal portfolio in 2004-2017 was carried out, compared to the minimum variance portfolio after the financial crisis (2008-2016). The method used to estimate each asset’s expected profitability that makes up the PSI-20 consists of extracting the obtained historical quotations. The optimal portfolio composition, in the period after the financial crisis, shows that the energy sector has an optimal portfolio weight reduction of 39.15%, that the big distribution sector (23.85%) was introduced into the portfolio and by last, the industrial sector stands its ground in the composition of the optimal portfolio.info:eu-repo/semantics/publishedVersio

A temporal analysis system for early detection of health changes

Abstract from public.pdf.To make it possible for elders to live independently at home and yet get help from health care providers when small changes in health conditions take place, smart home technologies are developed to enhance safety and monitor health conditions via noninvasive sensors and other devices. To better analyze the wealth of the activity information from various kinds of sensors to locate trends that correspond states of wellbeing, this thesis proposes a new system to build adaptive models for detecting health changes based on temporal analysis, including outlier detection, customization and adaption to new changes. Our hope is that by using more sophisticated temporal analysis method we can capture more predictive alerts and more customized alerts that can help us detect more meaningful health changes before they become big problems. Since we cannot have full access to all the embedded sensor data from TigerPlace at the moment, the system is tested using synthetic datasets which simulate gradual changes, sudden changes, changes of baseline health condition and system noise that might happen in the real-world data. Based on the experiments on the synthetic datasets, the system is proved to have the ability to adapt to gradual changes, find anomalies and spawn a new component for the GMM when there is an emerging new normal pattern. The system achieves our goals when tested on the synthetic datasets over extended period of time. We hope that by using the system in Tiger Place, it will help by detecting health changes before real health issue happens