Milad Jasemi
University of Montevallo
29 Papers
48 Citations
Milad Jasemi is an academic researcher from University of Montevallo. The author has contributed to research in topics: Computer science & Portfolio optimization. The author has an hindex of 7, co-authored 22 publications. Previous affiliations of Milad Jasemi include State University of New York at Plattsburgh & K.N.Toosi University of Technology.
Chat about Author
Papers
New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic
Elham Ahmadi,Milad Jasemi,Leslie Monplaisir,Mohammad Amin Nabavi,Armin Mahmoodi,Pegah Amini Jam +5 more
TL;DR: Two hybrid models used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic show that SVM-ICA performance is better than SVM and most importantly the feed-forward static neural network of the literature as the standard one.
108
A New methodology for Multi-period Portfolio Selection based on the Risk Measure of Lower Partial Moments
TL;DR: The results show that in comparison to the regular method of computing LPM, the proposed method works better and improves the efficiency of portfolio optimization, especially in terms of the processing time.
35
Intellectual capital and knowledge management in the Iranian space industries
TL;DR: In this paper, the authors examined the concept of intellectual capital in the Iranian space industries and evaluated the mediating role of some of them in the relationship between the intangible assets and organizational performance.
26
A comparison on particle swarm optimization and genetic algorithm performances in deriving the efficient frontier of stocks portfolios based on a mean‐lower partial moment model
TL;DR: In this article, a portfolio optimization model on the basis of the risk measure of lower partial moment of the first order is discussed and two meta-heuristic methods of particle swarm optimization and genetic algorithm performances are applied and compared from different aspects to derive the stocks portfolios efficient frontier.
23
Measuring the effectiveness of AHP and fuzzy AHP models in environmental risk assessment of a gas power plant
TL;DR: Results of combining AHP and FAHP with EFM & EA and Melbourne methods show that risks with same RPN get different values, so the proposed hybrid approach seems effective and could help more scientific decision-making.
23