Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

mean-variance portfolio optimization

Portfolio optimization is the process of determining the best combination of securities and proportions with the aim of having less risk and obtaining more profit in an investment. Utilizing covariance as a risk measure, mean-variance portfolio optimization model has brought a revolutionary approach to quantitative finance. Since then, along with the advancements in computational power and algorithmic enhancements, a lot of efforts have been made on improving this model by considering real-life conditions and solving model variants with various methodologies tested on various data and performance measures. A comprehensive literature review of recent and novel papers is crucial to establish a pattern of the past, and to pave the way on future directions. In this paper, a total of 175 papers published in the last two decades are selected within the scope of operations research community and reviewed in detail. Thus, a comprehensive survey on the deterministic models and applications suggested for mean-variance portfolio optimization in which several variants of this model as well as additional real-life constraints are studied. The review classifies the approaches according to exact and approximate attempts and analyzes the proposed algorithms based on various data and performance indicators in depth. Areas of future research are outlined. (C) 2019 Elsevier Ltd. All rights reserved.C1 [Kalayci, Can B.; Ertenlice, Okkes; Akbay, Mehmet Anil] Pamukkale Univ, Fac Engn, Dept Ind Engn, TR-20160 Kinikli, Denizli, Turkey

portfolio optimization

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean-variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology. (C) 2017 Elsevier Ltd. All rights reserved