A major challenge for stochastic optimization is the cost of updating model parameters especially when the number of parameters is large. Updating parameters frequently can prove to be computationally or monetarily expensive. In this paper, we introduce an efficient primal-dual based online algorithm that performs lazy updates to the parameter vector and show that its performance is competitive...

Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering limited resources. Project portfolio managers need methods and tools to find a good solution for complex project portfolios and multiobjective target criteria efficiently. However, software project portfolios are chall...

DeMiguel, Garlappi, and Uppal (Review of Financial Studies, 22 (2009), 1915–1953) showed that in the stock market, it is difficult for an optimized portfolio constructed using meanvariance analysis to outperform a simple equally-weighted portfolio because of estimation error. In this paper, we demonstrate that portfolio optimization can be made to work in currency markets. The key difference be...

Software cost estimation is a crucial task in software project portfolio decisions like start scheduling, resource allocation, or bidding. A variety of estimation methods have been proposed to support estimators. Especially the analogy-based approach—based on a project’s similarities with past projects—has been reported as both efficient and relatively transparent. However, its performance was ...

Reinforcement learning algorithm has been successfully used in prediction and decision making [5,11]. The main contribution of this paper is to provide decision making using reinforcement learning approach to allocate resources optimally in stochastic conditions in a well known example; in the portfolio selection. The modern theories of portfolio selection consider some presumptions. But if the...

5 We present efficient partial differential equation (PDE) methods for continuous time mean6 variance portfolio allocation problems when the underlying risky asset follows a jump-diffusion. 7 The standard formulation of mean-variance optimal portfolio allocation problems, where the 8 total wealth is the underlying stochastic process, gives rise to a one-dimensional (1-D) non-linear 9 Hamilton-J...

In this work we perform an automatic data survey to draw up an optimum portfolio, and to automate the one year forecast of a portfolio’s payoff and risk, showing the advantages of using formally grounded models in portfolio management and adopting a strategy that ensures, a high rate of return at a minimum risk. The use of neural networks provides an interesting alternative to the statistical c...

This paper concerns the continuous time mean-variance portfolio selection problem with a special nonlinear wealth equation. This nonlinear wealth equation has a nonsmooth coefficient and the dual method developed in [6] does not work. We invoke the HJB equation of this problem and give an explicit viscosity solution of the HJB equation. Furthermore, via this explicit viscosity solution, we obta...

This paper considers a portfolio selection problem with type-2 fuzzy future returns involving ambiguous and subjectivity. Since this proposed problem is not well-defined due to fuzziness, introducing the fuzzy goal for the total future return and the degree of possibility, the main problem is transformed into the standard fuzzy programming problem including the secondary fuzzy numbers. Furtherm...

Abstract—Modern Portfolio Theory (MPT) according to Markowitz states that investors form mean-variance efficient portfolios which maximizes their utility. Markowitz proposed the standard deviation as a simple measure for portfolio risk and the lower semi-variance as the only risk measure of interest to rational investors. This paper uses a third volatility estimator based on intraday data and c...