A Multiobjective-Based Group Trading Strategy Portfolio Optimization Technique

The primary goal of this work is to determine if an active portfolio optimization strategy utilizing a two staged optimization approach outperforms an ordinary optimization technique. Both portfolio optimization models are based on Markowitz’s Modern Portfolio Theory (MPT), which relies on assets’ mean, variance, and correlation to maximize returns at any given level of risk. For the two staged optimization approach the process of optimization is applied twice. In the first stage, it is used to select an optimal portfolio of industries, and in the second stage optimization is applied to determine an optimal portfolio consisting of stocks within each industry. Our research indicates that portfolios formed based on ordinary optimization outperforms two staged portfolios and Market indexes by 37% during a bear market (2002) and outperforms Dow Jones Industrial Average and S&P 500 by more than 13% during a bull market (2003). The performance of each model was determined by the capital gains and the dividend returns during the 2002 to 2003 time period.

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CVaR Robust Mean-CVaR Portfolio Optimization

ISRN Applied Mathematics ◽

10.1155/2013/570950 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Maziar Salahi ◽

Farshid Mehrdoust ◽

Farzaneh Piri

Keyword(s):

Portfolio Optimization ◽

Asset Allocation ◽

Risk Measure ◽

Optimization Technique ◽

Large Error ◽

Data Uncertainty ◽

Final Decision ◽

Estimation Risk ◽

Expected Return ◽

The Impact

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk and return are uncertain parameters in the suggested portfolio optimization models and should be estimated to solve the problem. However, the estimation might lead to large error in the final decision. One of the widely used and effective approaches for optimization with data uncertainty is robust optimization. In this paper, we present a new robust portfolio optimization technique for mean-CVaR portfolio selection problem under the estimation risk in mean return. We additionally use CVaR as risk measure, to measure the estimation risk in mean return. To solve the model efficiently, we use the smoothing technique of Alexander et al. (2006). We compare the performance of the CVaR robust mean-CVaR model with robust mean-CVaR models using interval and ellipsoidal uncertainty sets. It is observed that the CVaR robust mean-CVaR portfolios are more diversified. Moreover, we study the impact of the value of confidence level on the conservatism level of a portfolio and also on the value of the maximum expected return of the portfolio.

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GA optimization technique for portfolio optimization of electricity market participation

2016 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci.2016.7849858 ◽

2016 ◽

Cited By ~ 4

Author(s):

Ricardo Faia ◽

Tiago Pinto ◽

Zita Vale

Keyword(s):

Portfolio Optimization ◽

Electricity Market ◽

Optimization Technique ◽

Market Participation

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High-Frequency Cryptocurrency Trading Strategy using Tweet Sentiment Analysis

10.5121/csit.2021.111410 ◽

2021 ◽

Author(s):

Zhijun Chen

Keyword(s):

Prediction Model ◽

Sentiment Analysis ◽

Portfolio Optimization ◽

High Frequency ◽

Optimization Procedure ◽

Trading Strategy ◽

Portfolio Allocation ◽

Auxiliary Data ◽

Cart Model

Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price and sentiment prediction model generates the parameters for optimization procedure to make decision and re-allocate the portfolio in the further step. Moreover, after the process of prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and CART model for the prediction of Bitcoin and Ethereum respectively. During the process of portfolio optimization, this project is trying to use predictive prescription to robust the uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration of figure.

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