Portfolio Optimization with Risk Decomposition

2017 ◽  
Vol 2017 (5) ◽  
pp. 61-85
Author(s):  
Konstantin Asaturov
Keyword(s):  
Portfolio Optimization ◽  
Performance Indicators ◽  
Equity Market ◽  
Optimization Approach ◽  
Risk Decomposition ◽  
Market Index ◽  
Specific Risk ◽  
Capm Model

The paper offers the modification of traditional portfolio optimization approach to construct the portfolio with possibility to control both systematic and specific risk (portfolio with risk decomposition). Built on modern econometric tools, the author estimates and forecasts the dynamics of alphas and betas of stocks in the frame of CAPM model, which are further applied for portfolio optimization. The closing weekly prices of 10 Australian stocks and ASX Index as the market index during the period from July 2000 to July 2016 were used. Within the sample there is no evidence of arbitrage on the Australian equity market employing neutral beta portfolio. The study confirms that portfolios with risk decomposition outperform Markowitz’s one according to various performance indicators.

scholarly journals INVESTMENT PORTFOLIO OPTIMIZATION BY APPLYING A GENETIC ALGORITHM-BASED APPROACH

Ekonomika ◽  
2017 ◽  
Vol 96 (2) ◽  
pp. 66-78 ◽  
Author(s):  
Petras Dubinskas ◽  
Laimutė Urbšienė
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Optimization Approach ◽  
Investment Portfolio ◽  
Market Index ◽  
Key Issues ◽  
Genetic Algorithm Approach ◽  
One Year ◽  
Investment Portfolio Optimization

The investment portfolio optimization issues have been widely discussed by scholars for more than 60 years. One of the key issues that emerge for researchers is to clarify which optimization approach helps to build the most efficient portfolio (in this case, the efficiency refers to the minimization of the investment risk and the maximization of the return). The objective of the study is to assess the fitness of a genetic algorithm approach in optimizing the investment portfolio. The paper analyzes the theoretical aspects of applying a genetic algorithm-based approach, then it adapts them to practical research. To build an investment portfolio, four Lithuanian enterprises listed on the OMX Baltics Stock Exchange Official List were selected in accordance with the chosen criteria. Then, by applying a genetic algorithm-based approach and using MatLab software, the optimum investment portfolio was constructed from the selected enterprises. The research results showed that the genetic algorithm-based portfolio in 2013 reached a better risk-return ratio than the portfolio optimized by the deterministic and stochastic programing methods. Also, better outcomes were achieved in comparison with the OMX Baltic Market Index. As a result, the hypothesis of the superiority of a portfolio, optimized on the basis of a genetic algorithm, is not rejected. However, it should be noted that in seeking for more reliable conclusions, further research should include more trial periods as the current study examined a period of one year. In this context, the operation of the approach in the context of a market downturn could be of particular interest.

scholarly journals International portfolio diversification: United States and south Asian equity markets

2014 ◽  
Vol 61 (2) ◽  
pp. 241-252 ◽  
Author(s):  
Rizwan Mushtaq ◽  
Zulfiqar Shah
Keyword(s):  
Stock Market ◽  
South Asian ◽  
Equity Markets ◽  
Equity Market ◽  
Causality Test ◽  
Market Index ◽  
International Portfolio Diversification ◽  
The Us ◽  
Short And Long Term

This paper explores the dynamic liaison between US and three developing South Asian equity markets in short and long term. To gauge the long-term relationship, we applied Johansen co-integration procedure as all the representative indices are found to be non-stationary at level. The findings illustrate that the US equity market index exhibits a reasonably different movement over time in contrast to the three developing equity markets under consideration. However, the Granger-causality test divulge that the direction of causality scamper from US equity market to the three South Asian markets. It further indicates that within the three developing equity markets the direction of causality emanates from Bombay stock market to Karachi and Colombo. Overall, the results of the study suggest that the American investors can get higher returns through international diversification into developing equity markets, while the US stock market would also be a gainful upshot for South Asian investors.

scholarly journals Performance-Risk Nexus of Global Low-Rated ETFs During the QE-Tapering Period

2020 ◽  
Vol 15 (1) ◽  
pp. 194-211
Author(s):  
Panagiotis Anastasiadis ◽  
Efthimios Katsaros ◽  
Anastasios-Taxiarchis Koutsioukis ◽  
Athanasios Pandazis
Keyword(s):  
Abnormal Returns ◽  
Expected Returns ◽  
Market Index ◽  
Performance Risk ◽  
The Us ◽  
Alpha Beta ◽  
Capm Model

AbstractThis study investigates the performance of 50 global, one star (based on Morningstar rankings), ETFs during the US QE-tapering period starting in October 2014 up to September 2018, using the S&P500 as the market index. The methodology employed is based on the CAPM model. We adopt the Jensen’s Alpha, Beta, a / b, Sharpe and Treynor ratios measures in order to examine whether those ETFs have achieved abnormal returns. We conclude that managers of most ETFs do not exhibit selectivity skills and only six of these ETFs achieve higher returns than the market by showing bullish behavior. At the same time, most ETFs have positive Sharpe and Treynor ratios due to high expected returns during the period under scrutiny.

scholarly journals Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach

2020 ◽  
Vol 13 (11) ◽  
pp. 285
Author(s):  
Jiayang Yu ◽  
Kuo-Chu Chang
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Value At Risk ◽  
Machine Learning Algorithms ◽  
Conditional Value At Risk ◽  
Portfolio Performance ◽  
Optimization Approach ◽  
Macroeconomic Factors ◽  
Simulation Based ◽  
Portfolio Managers

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR.

scholarly journals Balancing energy security priorities: portfolio optimization approach to oil imports

2020 ◽  
Vol 53 (5) ◽  
pp. 555-574
Author(s):  
Simona Bigerna ◽  
Carlo Andrea Bollino ◽  
Philipp Galkin
Keyword(s):  
Portfolio Optimization ◽  
Energy Security ◽  
Optimization Approach ◽  
Balancing Energy

scholarly journals Company and asset specific risk factors: Short overview on high yield bonds

2016 ◽  
Vol 14 (1) ◽  
pp. 443-448
Author(s):  
Aki Lappalainen
Keyword(s):  
Risk Factors ◽  
Market Value ◽  
Equity Market ◽  
Expected Value ◽  
High Yield ◽  
Quality Factors ◽  
Factors Affecting ◽  
Short Overview ◽  
Specific Risk ◽  
A Company

This paper discusses the theory that risk factors divide to the company specific and asset specific risk factors. The first group affects to the expected value of an equity of a company whereas the second only to the positive cash outflows for a specific asset. I find that equity market, value, and quality factors are indeed possible company specific risk factors with influence on an expected equity of a company and dividend and volatility factors are possible stock specific risk factors affecting positively to dividends and other cash payments from a company to shareholders. These results are statistically significant and important for our understanding of risk factors and their characteristics.

scholarly journals Effect of Global Economic Crisis on the Price Behavior of Realty Sector Index Stocks of NSE

2019 ◽  
Vol 8 (4) ◽  
pp. 5854-5862
Keyword(s):  
Economic Crisis ◽  
Negative Impact ◽  
Asset Price ◽  
Equity Market ◽  
Housing Sector ◽  
Price Behavior ◽  
Asymmetric Volatility ◽  
Market Index ◽  
Global Economic Crisis ◽  
Significant Negative Impact

This study has been conducted to understand whether the global economic crisis triggered by sub - prime crisis, which was happened due to burst in asset price of housing sector in United States have made any significant negative impact on the price behavior of the housing sector stocks of Indian equity market ? and if yes, whether this negative impact has brought volatility, more specifically asymmetric volatility (Leverage effect) to the Indian equity market?. In the process finding out the answer to these questions, we have collected data relating to the price behavior of the constituent stocks of Realty sector index of NSE for the period from 9th January 2008 to 5th November 2010, the period where the Indian equity market index -NIFTY has travelled from its peak 6287 mark to 2573 mark and bounced smartly from this low to again its previous high (around 6300) and the price behavior data were collected from the official web site of NSE. The GARCH family models viz. GARCH (1,1) and EGARCH (1,1) were employed to understand the symmetric and asymmetric volatility and found that realty sector stocks were negatively influenced by the sub-prime crisis and has triggered the volatility of both symmetric and asymmetric volatility

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