Mean–variance combining rules that outperform naïve diversification

The mean–variance framework developed by Markowitz (1952). Portfolio selection, The Journal of Finance, 7(1), 77–91 is still the major model used nowadays in asset allocation and active portfolio management. However, the estimated mean–variance rules often fail to deliver superior performance compared with the simple naïve rule (the equally weighted portfolio) due to the problem of estimation errors. In this paper, I propose a portfolio construction method that is effective in dealing with estimation errors in the optimization process. Particularly, I specify the portfolio weights as an optimal combination of the equally weighted portfolio and a sample zero-investment portfolio. I show analytically that the proposed method alleviates the problem of estimation errors and dominates naïve diversification. I suggest two implementable versions of the combining method and show, empirically, their good performances relative to the naïve rule. The newly developed rules work well, particularly, for portfolios with a medium and high number of assets. Moreover, the outperformance persists generally even in the presence of transaction costs. Since the combinations are theory-based, my study may be interpreted as reaffirming the usefulness of the Markowitz portfolio theory in practice.

QUADRATIC PROGRAMMING: AN OPTIMIZATION TOOL FOR BUILDING GLOBAL MINIMUM VARIANCE PORTFOLIO WITH NO SHORT SALE

Quantitative method in portfolio selection is a fascinating issue to make a decision in investment. Portfolio optimization is a very important to manage investment risk. There are many papers dealing with the Markowitz portfolio model, but not all of the papers studied about positive weight portfolio or no short sale constrained portfolio. Positive weight portfolio describes that short sale is allowed for the investor. While, short sale is banned in a certain economic condition due to its ability in decreasing stock market index. Besides, Islamic capital market does not allow speculative transaction such as short selling. Hence, portfolio with no short sale constraint is needed. This study aims to build Global Minimum Variance Portfolio (GMVP) with no short sale constraint. The GMVP with positive asset allocation based on Markowitz model can be built by using quadratic programming with interior point method. The main theory applied in this research is Markowitz portfolio optimization model. Mean and variance of stocks closing price are two things that should be considered in this model. The result shows that the positive weight of GMVP includes 0% of ADRO shares; 2, 65% of ANTM shares; 0% of CTRA shares; 30,27% of EXCL shares; 37,21% of ICBP shares; 3,37% of INCO shares; 13,89% of KLBF shares; 0% of PGAS shares; and 12,61% of PTBA shares.

Machine learning-based allocation of renewable power production

<p>The integration of renewable energy sources into the power grid is of the utmost importance for achieving the goal of zero carbon emission. Although there are feasibility studies showing that renewable energy might be able to cover 2050 global energy demand using less than 1 % of the world's land for footprint and spacing, see Jacobson and Delucchi (2011), nowadays renewable energy production is known to be highly intermittent due to substantial uncertainties in the weather conditions. One possibility to reduce such uncertainty (besides storage and employing hydrogen technologies) is spatiotemporally diversified allocation of renewable power capacities which (alongside with the transmission infrastructure) should guarantee that the power demand is met at any given time with a certain (high) probability. We treat the question of spatiotemporal diversification of renewable capacities as a Markowitz portfolio problem with the difference that instead of n = 1, …, N stocks we have geographical locations each with a certain expected level of renewable power production (instead of expected returns for stocks) and the corresponding variance. Another difference to a classical Markowitz portfolio problem is that we require additionally that at each given time point t = 1, …, T, we can reach a predetermined level of renewable power production with a certain probability, i. e. we solve so called chance-constrained problem. Finally, instead of solving one-step problem as it is the case with a Markowitz portfolio we reformulate our problem in the optimal control framework in continuous time and solve it with a reinforcement learning algorithm as suggested in Lillicrap et al. (2019). The advantage of this approach is that the optimal capacities (control) are updated continuously as a response to changing weather conditions (state). We exemplify our approach with the data from ERA5 data, see Hersbach et al. (2020), and suggest possible allocation of renewable energy sources across the European Union.</p>

Winner Strategies in Crisis

Research background: Since the publication of Markowitz’ Portfolio Selection Theory, researchers and practitioners have been searching for the optimal structure of investment portfolios. An unlimited number of portfolio-based investment strategies have been created since 1952. However, none of these strategies seem to continuously generate overperformance over a long time period. This may also be due to the strong dynamics of economic development and other external factors. Purpose of the article: The aim of this article is to analyze which strategies are successful in generating winning portfolios in times of crisis. Three types of crises are considered: first, the bursting of the dot-com bubble in 2001, second, the financial crisis of 2008, and finally, the performance impact of the corona crisis. Methods: The data of the S&P 500 and STOXX Europe 600 companies are analyzed. The first step is the statistical review of the performance of companies in different periods with the focus on the analysis of the crisis years. Subsequently, the formation of portfolios is carried out according to known key figures such as high-low PE ratio, high-low market-to-book ratio, and others. In the form of a regression analysis, selected fundamental data are used to statistically check their relevance for performance. Findings & Value added: The results shows that all crises have similarities in certain factors. However, they also show that companies with a digital business model are able to manage crises better than those without a digital business model.

Multi-Process Analysis and Portfolio Optimization Based on Quantum Mechanics (QM) Under Risk Management in ASEAN Exchanges

This research attempts to classify, predict, and manage the financial time-series trends of the large stock prices of significant companies in the development of e-commerce and e-business in the ASEAN countries. Moreover, the Markowitz portfolio optimization analysis based on quantum mechanics was utilized to find out the direction of e-commerce and e-business in the future. Data collection for this study consists of Maybank, PPB Group Berhad, Golden Agri-Resource, SingTel, and Global Logistic Properties. And the stock prices of those companies were carried out to this study from 2004 to 2018 by daily data. Interestingly, the empirical results would provide a possible solution and efficiently suggest a beneficial for the development of both e-commerce and e-business in the ASEAN countries. The commerce and business based on electronics in ASEAN, especially agribusiness, energy business, and telecommunication business, still play a major important role in the economy of ASEAN countries.

Testing of portfolio optimization through investments in stock market indices and Bitcoin

This paper presents an empirical verification of the effectiveness and usefulness of investment diversification using the main stock exchange indices and Bitcoin. The objective is to determine the effects applying the Markowitz portfolio optimization theory, i.e., the advantages of applying the modern portfolio theory for institutional investors. The research offers an answer to the following question: what are the advantages and disadvantages of using Bitcoin in portfolio optimization? The paper contributes to the representation of the reach and limitations of the modern portfolio theory for institutional investors. The conclusion is that rational behaviour of institutional investors requires consideration of portfolio optimization using the Markowitz model, because it is possible to create portfolios which, on the basis of historical returns, provide desired returns alongside certain risks. The methodology includes the analysis of high frequency data, i.e., daily trading data were used. The results indicate that the use of the Markowitz portfolio selection method, with all its limitations, is desirable, possible and applicable, but that it entails serious flaws in the sense of neglecting transaction costs, foreign exchange differences and the real value in the stock market. The results of the research show that Bitcoin is a good source of diversification in a portfolio that contains traditional financial instruments both for the risk-averse investor as well as for those investors who have a greater appetite for risk. The conclusion is that rational behavior of institutional investors requires consideration of investing in Bitcoin using the Markowitz model. However, given the high degree of volatility, investors should be very careful when making decisions about including Bitcoin in the portfolio.

Multi-Process Analysis and Portfolio Optimization Based on Quantum Mechanics (QM) Under Risk Management in ASEAN Exchanges

This research attempts to classify, predict, and manage the financial time-series trends of the large stock prices of significant companies in the development of e-commerce and e-business in the ASEAN countries. Moreover, the Markowitz portfolio optimization analysis based on quantum mechanics was utilized to find out the direction of e-commerce and e-business in the future. Data collection for this study consists of Maybank, PPB Group Berhad, Golden Agri-Resource, SingTel, and Global Logistic Properties. And the stock prices of those companies were carried out to this study from 2004 to 2018 by daily data. Interestingly, the empirical results would provide a possible solution and efficiently suggest a beneficial for the development of both e-commerce and e-business in the ASEAN countries. The commerce and business based on electronics in ASEAN, especially agribusiness, energy business, and telecommunication business, still play a major important role in the economy of ASEAN countries.