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

PB-ADVISOR: A private banking multi-investment porfolio.

Private banking is a business area in which the investor requires tailor-made advice. Because of the current market situation, investors are requiring answers to difficult questions and looking for assurance from wealth managers. Private bankers need to have deep knowledge about an innumerable list of products and their characteristics as well as the suitability of each product for the client’s characteristics to be able to offer an optimal portfolio according to client expectations. Client and portfolio diversity calls for new recommendation and advice systems focused on their specific characteristics. This paper presents PB-ADVISOR, a system aimed at recommending investment portfolios based on fuzzy and semantic technologies to private bankers. The proposed system provides private bankers with a powerful tool to support their decision process and help deal with complex investment portfolios. The system has been evaluated in a real scenario obtaining promising results

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

Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Portfolio management using computational intelligence approaches. Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary Algorithms.

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN¿s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective

The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management

[No subject] This thesis explores the dynamics of the Johannesburg Stock Exchange returns to understand how they impact stock prices. The introductory chapter renders a brief overview of financial markets in general and the Johannesburg Securities Exchange (JSE) in particular. The second chapter employs the fractal analysis technique, a method for estimating the Hurst exponent, to examine the JSE indices. The results suggest that the JSE is fractal in nature, implying a long-term predictability property. The results also indicate a logical system of variation of the Hurst exponent by firm size, market characteristics and sector grouping. The third chapter investigates the economic and political events that affect different market sectors and how they are implicated in the structural dynamics of the JSE. It provides some insights into the degree of sensitivity of different market sectors to positive and negative news. The findings demonstrate transient episodes of nonlinearity that can be attributed to economic events and the state of the market. Chapter 4 looks at the evolution of risk measurement and the distribution of returns on the JSE. There is evidence of fat tails and that the Student t-distribution is a better fit for the JSE returns than the Normal distribution. The Gaussian based Value-at-Risk model also proved to be an ineffective risk measurement tool under high market volatility. In Chapter 5 simulations are used to investigate how different agent interactions affect market dynamics. The results show that it is possible for traders to switch between trading strategies and this evolutionary switching of strategies is dependent on the state of the market. Chapter 6 shows the extent to which endogeneity affects price formation. To explore this relationship, the Poisson Hawkes model, which combines exogenous influences with self-excited dynamics, is employed. Evidence suggests that the level of endogeneity has been increasing rapidly over the past decade. This implies that there is an increasing influence of internal dynamics on price formation. The findings also demonstrate that market crashes are caused by endogenous dynamics and exogenous shocks merely act as catalysts. Chapter 7 presents the hybrid adaptive intelligent model for financial time series prediction. Given evidence of non-linearity, heterogeneous agents and the fractal nature of the JSE market, neural networks, fuzzy logic and fractal theory are combined, to obtain a hybrid adaptive intelligent model. The proposed system outperformed traditional models

Predicting the Daily Return Direction of the Stock Market using Hybrid Machine Learning Algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks

\u3ci\u3eJournal of Actuarial Practice,\u3c/i\u3e Volume 5, No. 1, 1997

ARTICLES Providing Pensions for U.K. Employees with Varied Working Lives • Deborah R. Cooper Seeking the Profitability-Risk-Competitiveness Frontier Using a Genetic Algorithm • Ronnie Tan Fuzzy Underwriting: An Application of Fuzzy Logic to Medical Underwriting • Per-Johan Horgby, * Ralf Lohse, and Nicola-Alexander Sittaro Accelerated Death Benefits, Viatical Settlements, and Viatical Loans: Options for the Terminally III • Paula Schmidt The Right to Underwrite? An Actuarial Perspective With a Difference • Thomas A. Moultrie and R. Guy Thomast Discussion of T.A. Moultrie and R.G. Thomas\u27s \u27\u27The Right to Underwrite? An Actuarial Perspective With a Difference • Charles L. Trowbridge Editor - Colin Ramsay, University of Nebraska. Associate Editors: Robert Brown, University of Waterloo ○ Cecil Bykerk, Mutual of Omaha ○ Ruy Cardoso, Actuarial Frameworks ○ Samuel Cox, Georgia State University ○ David Cummins, University of Pennsylvania ○ Robert Finger, Retired ○ Charles Fuhrer, The Segal Company ○ Farrokh Guiahi, Hofstra University ○ Steven Haberman, City University ○ Merlin Jetton, Retired ○ Eric Klieber, Buck Consultants ○ Edward Mailander, WeIlpoint Health Networks ○ Charles McClenahan, Mercer Oliver Wyman ○ Robert Myers, Temple University ○ Norman Nodulman, Retired ○ François Outreville, United Nations ○ Timothy Pfeifer, Milliman USA ○ Esther Portnoy, University of Illinois ○ Robert Reitano, John Hancock Financial Services ○ Alice Rosenblatt, WeIlpoint Health Networks ○ Arnold Shapiro, Penn State University ○ Elias Shiu, University of Iowa ○ Michael Sze, Sze Associates Ltd. ○ Joseph Tan, National Actuarial Network ○ Ronnie Tan, Great Eastern Life ○ Richard Wendt, Tower Perrin; Margo Young, Technical Edito

The Structured Hedging of Financial Value: With Applications to Foreign Exchange Risk Management

The objective of the thesis is to develop a structured financial hedging framework that is empirically implementable and consistent with a corporate finance perspective. Value at risk provides a suitable framework for this purpose. The aversion implied in the value at risk and its generalised theory arises from a firm's concerns about contingent financial distress costs, which can be considered as the payoff of a put option written by stockholders of firms in favour of third parties. This enables the development of a hedging framework to explore how a firm's welfare might be enhanced by replacing natural exposures with hedged outcomes. An ideal hedging decision is to maximise the financial value in good times at minimal cost in terms of the generalised value at risk penalty function. In an efficient market, a fully hedged policy using forwards is generally the optimal decision, while alternatives should be taken into account where markets are not efficient. In such cases, the underlying empirical methodology should be able to detect inefficiencies and feed into the objective functions for maximising firm value. The empirical implementation is explored with a variety of econometric methodologies. These include the development of new semi-parametric or nonparametric techniques based upon wavelet analysis, as well as an incomplete forecasting algorithm. Such methods have been preferred to classical linear and stationary models, because they have broader application in an inefficient market where information is technically fuzzy and financial data may exhibit non-linearity or non-stationarity. Further decision dimensions concern exposure duration or path risk, in which individuals' perspectives of risk is time-dependent and linked to the evolution of value at risk through time. The proposed approaches find their main application in foreign exchange risk management, a topic of considerable importance and sensitivity in New Zealand. A statistically well-adapted hedge object for an exporter such as the dairy industry is the corporate terms of trade, which balances up output and expense prices as a single index related to the net profit margin. Further applications are to strategic fund management where the objective is to derive optimal foreign exchange forwards based hedges

Evaluating Capital Allocation Below Portfolio Level

This thesis explores the ability for retail banks to allocate economic capital below portfolio level. First, a discussion about capital requirements and risk measures to provide a sound basis for determining the economic capital of the bank. In general, economic capital is allocated to the banks portfolios but not on a more granular level, through a capital allocation method. This study discuss three dierent approaches for allocation of economic capital below portfolio level; game theory, nance and optimization. Both the game theory and nance approach reach the same conclusion, that the best allocation principle is the gradient of the risk measure. The optimization method allocates economic capital through minimization of a concept called risk residual, which conclude that the optimal allocation is derived from the marginal distribution of a customer. Capital allocation below portfolio level give the management a good overview of risks from dierent customers. In order to determine the performance of the portfolios in the bank a Risk-Adjusted-Return-On-Capital is used, with economic capital as input. The thesis include some comments about how the choice of capital allocation methods aect the performance measurement. The thesis concludes with an evaluation of the methods by simulations of a ctional bank conducted in the software R. Key Words: Risk Appetite, Economic Capital, Risk measure, Capital Allocation Methods, Allocation Below Portfolio level, Game theory, Optimization, Marginal Contributio