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This study delves into the optimization of investment portfolios within the Unified Accumulative Pension Fund in Kazakhstan, employing the Quasi-Sharp model as a pivotal tool. Through a meticulous analysis of the UAPF's investment portfolio evolution, this research scrutinizes shifts in asset allocation and their ramifications on investment returns. The Quasi-Sharp model, a tailored adaptation of the Sharpe model, is harnessed to ascertain the optimal equilibrium between profitability and risk in pension asset management. The STATA program was used to calculate the indicators. Results divulge the imperative role of diversification across various asset classes in mitigating risk while maximizing returns. Optimal investment portfolios are meticulously crafted, considering an array of financial instruments such as stocks, bonds, and bank deposits. The findings underscore that while stocks proffer higher potential returns, bonds offer stability and lower risk. Additionally, bank deposits exhibit stable returns, albeit with limited growth prospects. The study revealed that the maximum expected return with a risk limit of less than 5% could be provided by a portfolio including shares of Halyk Savings Bank of Kazakhstan, Kcell, and KazTransOil in the ratio of 57%, 32%, and 11%, respectively. Key recommendations for investors encompass regular monitoring of market dynamics, diligent assessment of risk tolerance, and recourse to professional advice when warranted. This study furnishes invaluable insights for pension fund managers and investors endeavoring to augment portfolio performance while adeptly managing risk within the Kazakhstani market landscape.

Asset pricing theory suggests that the correct proxy for the market portfolio should contain both the debt and equity claims of the economy, whereas prevailing empirical studies fail to include the debt claim. Motived by the discrepancy between the theoretical and empirical models and the difficulty in constructing proxies, the study uses the Credit Default Swaps (CDS) market index as a proxy for the debt market and empirically tests its explanatory power in explaining stock return variations. Employing panel regression and Fama-MacBeth regression of all publicly traded U.S. companies from 2005 to 2020, the study finds a negative relationship between CDS index returns and stock returns. On average, a one standard deviation increase in CDS index return is associated with a 0.02% decrease in daily stock returns. Results of two-stage regressions show that the estimated systematic credit risk is positively priced in stock returns with similar economic magnitude as the well-documented beta risk. These results support asset pricing theories in the inclusion of debt claim and the risk-return tradeoff, while contradicting the credit risk puzzle documented in prior studies.

In the conventional capital asset pricing model (CAPM), standard deviation as a measure of risk penalizes both upward and downward movements. However, while downward movements in investments are risky, upward movements are favourable. Therefore, standard semideviation that treats only those negative differences in returns over the benchmark as risky was proposed as a measure of downside risk. Attempts have been made to obtain the formulation of beta in this context, called downside beta. In this work, CAPM in the mean‐semivariance framework (D‐CAPM), with semivariance measured in terms of negative deviation around expected return, is derived and established that the model proposed earlier with a version of downside beta with respect to this definition of semivaraince is invalid. The appropriate version of downside beta is obtained, and under the rectified formulation, the validity of the true D‐CAPM is empirically tested during July 2013–July 2018. The solution to the tangent point is also obtained. The proportion of investment in the stocks in the tangent portfolio and the downside beta for the stocks in the tangent portfolio are determined. We find that there is no evidence to disprove that D‐CAPM holds true considering either Nifty 50 or S&P BSE Sensex as market portfolio. [ABSTRACT FROM AUTHOR]

We analyze the relation between time-series predictability and factor investing. We use a large set of financial, macroeconomic, and technical variables to time-series-manage the market portfolio. A combination of the out-of-sample market excess return forecasts of all variables yields a managed market portfolio that generates alphas relative to cross-sectional factor models that exceed 5% per annum. More broadly, the relation between time-series evaluation measures and (multifactor) alphas is weakly positive but complex. The variables' predictability for future returns is more important than that for volatility. Finally, we document that managed market portfolios based on lagged factor realizations also perform well. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4459. [ABSTRACT FROM AUTHOR]

The two opposing investment strategies, diversification and concentration, have often been directly compared. While there is much less dispute regarding Markowitz's approach as the benchmark for diversification, the precise meaning of concentration in portfolio selection remains unclear. This paper offers a novel definition of concentration, along with an extreme value theory-based estimator for its implementation. When overlaying the performances derived from diversification (in Markowitz's sense) and concentration (in our definition), we find an implied risk threshold, at which the two polar investment strategies reconcile—diversification has a higher expected return in situations where risk is below the threshold, while concentration becomes the preferred strategy when the risk exceeds the threshold. Different from the conventional concave shape, the estimated frontier resembles the shape of a seagull, which is piecewise concave. Further, taking the equity premium puzzle as an example, we demonstrate how the family of frontiers nested inbetween the estimated curves can provide new perspectives for research involving market portfolios. [ABSTRACT FROM AUTHOR]

The traditional mean-variance portfolio optimization models in practice have suffered from complexity and heavy computation loads in the process of selecting the best assets for constructing a portfolio. If not, they are considerably departed from the theoretically optimized values. In this work, we develop the optimized portfolio investment strategy in which only one asset substitution occurs when re-balancing a portfolio. To do this, we briefly look into a quadratically constrained quadratic programming (QCQP), which has been well-studied for the non-negative solution. Based on the quadratic programming, an efficient scheme is presented for solving the large-scale inverse problem. We more precisely update the rank of an inverse matrix, so that the optimal solution can be easily and quickly obtained by our proposed scheme. Various numerical and practical experiments are presented to demonstrate the validity and reliability of our scheme. Our empirical application to the U.S. and South Korea stock markets is tested and highlighted. Moreover, comparisons of a random allocation strategy and our proposed scheme reveal the better performance in the lower risks and higher expected returns obtained by our scheme.

Most enterprises focus on product portfolio management (PPM) and exclude market portfolio management, and individual markets are selected solely based on financial performance which may not be appropriate because other factors may dictate the outcome of the market selection. This study proposes a market portfolio model that considers market share, market growth, market competition, market risk, and market cost to maximize overall profit. A three-stage approach is proposed to identify potential product markets using an AI algorithm. A set of the most promising market is then determined based on the results of Stage 1 using a fuzzy multi-objective mathematical programming model by concurrently maximizing the total market share and market growth, and minimizing the market competition and market risk. A single-objective mathematical programming model is then used to determine a market portfolio to maximize the total profit using the results for Stage 2. The single-objective model is tested using two sets of threshold constraints. The balance of the market portfolio is discussed and the results are compared. The novelty of the article: (1) the first study involves a product market portfolio, (2) the decision of market selection is scientific (using AI instead of personal judgment), holistic (factors include market share, growth, competition, risk, cost, and profit) and optimized (using mathematical optimization), and (3) the market portfolio maximizes total profit. (4) the proposed method produces a portfolio with a higher profit than the TOPSIS method. [ABSTRACT FROM AUTHOR]

We review and investigate the difficulties with CAPM as a foundation for asset pricing by analyzing the proof that CAPM is the necessary condition for optimizing the objective function of an optimal portfolio within the mean-variance framework and with the Kuhn-Tucker conditions. Though the market portfolio in CAPM is the unique solution of the necessary condition, the optimal portfolio of risky assets or the necessary condition provides no basis for individual expected rates of return for those assets. This is why CAPM is dead for asset pricing. In addition, the necessary condition is a simultaneous linear equation rather than a linear function, i.e., the presumed linearity between the beta and expected rate of return on assets does not exist. Therefore, all previous empirical findings based on CAPM are dubious at best, and should be reexamined by other methods. Empirical testing of CAPM does not establish its veracity, i.e., CAPM is vacuous as a pricing model. [ABSTRACT FROM AUTHOR]

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When looking at the Global economy and the quantification of the operative risk in the cost of capital, a necessary ingredient for a meaningful analysis is an assessment of the country`s integration into world capital markets. The international financial markets are progressively becoming one huge, integrated, global capital market, that is contributing to higher stock prices in developed as well as developing economies. If for example a market is segmented from the rest of the world, its exposure with a common world factor may have little or no ability to explain its expected return. Large companies visible enough to attract global investors, have a lower cost of capital and a greater equity value for two main reasons: first, because the risks of equity are shared among more investors with different portfolio exposures and hence a different “appetite” for bearing certain risks, equity market risk premiums should fall for all companies in countries with access to global markets. Second, when firms in countries with less-developed capital markets raise capital in the public markets of countries with highly developed markets, they get more than lower-cost capital; they also import at least aspects of the corporate governance systems that prevail in those markets. Besides reducing market risk premiums and improving corporate governance, globalization also affects the systematic risk, of individual companies. In global markets, the beta of a firm's equity depends on how the stock contributes to the volatility not of the home market portfolio, but of the world market portfolio. For companies with access to global capital markets whose profitability is tied more closely to the local than to the global economy, use of the traditional CAPM will overstate the cost of capital because risks that are not diversifiable within a national economy can be diversified by holding a global portfolio.

Purpose: The aim of the article is to compare the results of effective portfolios obtained after the initial selection using multi-criteria methods with the results of the market portfolio. Design/methodology/approach: When selecting a long-term portfolio, a fundamental analysis can be used to assess a company's economic and financial condition. This analysis is based on fundamental and market indicators. By treating selected indicators as evaluation criteria, the problem can be considered as a multi-criteria problem. In the analyses the TOPSIS methods were used (standard and fuzzy one), which enabled the approach to the issue in a non-standard way. Findings: Three effective portfolios were determined: two of them were obtained after the initial selection of companies using selected multi-criteria methods, the third was generated from the set of all considered companies. The results of these portfolios, estimated for the whole of 2018, were compared with the market portfolio represented by the WIG20 index. The analysis showed that including the fuzzy approach when selecting a portfolio, it is possible to construct more profitable portfolios compared to the market portfolio. Research limitations/implications: The problem requires further research to confirm the recommendations made. Practical implications: Using the proposed approach, we can methodically build more profitable portfolios than the market portfolio. Originality/value: The values of criterion assessments from selected years were treated as triangular fuzzy numbers – this enabled the use of fuzzy approach and the selection of portfolios more attractive than the market one. The study may be of interest to stock market investors. [ABSTRACT FROM AUTHOR]

This study examines the rationality of utilizing the capital asset pricing model in Pakistani banking sector. The estimated return on banking sector is used as a proxy for measuring the best possible estimate of the stock return on banking sector in Pakistan. For conducting this study, the data was collected from 20 banks in total covering the period of 2006-2017 for which the time series yearly based pooled was applied. The capital asset pricing model is applied for the size and book to market portfolios. The excessive return is taken as dependent variable reflects that the investors required excessive return over the above Rf in order to take any additional risk, while independent variable used in study is market risk premium. The study shows that capital asset pricing model reflects more precisely the expected return. [ABSTRACT FROM AUTHOR]

Students considering a masters in Finance Engineering or Artificial Intelligence in Finance are usually required to have an undergraduate background in science, technology, engineering, or mathematics (STEM). STEM students have a good capacity in mathematics and science, but they may not have studied financial theory. To facilitate the classroom teaching of the Capital Asset Pricing Model (CAPM) for STEM students, this paper seeks to expound on the essence of the theory starting at a two-asset framework. Adopting the concepts proposed by Merton (1972), this paper accomplishes the derivation by virtue of basic mathematical tools such as linear algebra, geometry, and statistics except for calculus. We show that the major aspects of Merton’s derivation of the CAPM for a universe of N assets may also be obtained in a two-asset world. Through the methods of this article, students will learn the in-depth theory of CAPM and its hands-on empirical tool. For example, students will realize that even if investors specify different threshold rewards, their different CAPMs will yield identical pricing for assets and portfolios.

This paper shows a comparison of the results of return, risk, and risk price simulation by a modified and classic Fama-French model. The modified model defines the new ICAPM state variable as a function of the structure of a company’s past financial results. The model tests are run on the basis of stocks listed on the Warsaw Stock Exchange. In light of the classic model the risk price, on the tested market, turned out univariate due to HML, however, in light of the modified model, risk price turned out to be threedimensional due to the proposed factors, and market portfolio. The factors of the modified model, compared with the HML and SMB, are widely perceived by portfolio managers, and the simulation results indicate a greater possibility to use this pricing application by large institutional investors.

This paper examines relative performance of alternative asset pricing models using individual security returns. The standard multivariate test used in studies comparing the performance of asset pricing models requires the number of stocks to be less than the number of time series observations, which requires grouping stocks into portfolios. This results in a loss of disaggregate stock information. We apply a different statistical test to overcome this problem and to investigate relative performance of alternative asset pricing models using individual security returns instead of portfolio returns. Our findings suggest that a parsimonious six-factor model that includes the momentum and orthogonal value factors outperforms all other models based on a number of measures as well as the average F -test. Unlike the standard multivariate test, we find that the average F -test has superior power to discriminate among competing models and does not reject all tested models. [ABSTRACT FROM AUTHOR]

When looking at the Global economy and the quantification of the operative risk in the cost of capital, a necessary ingredient for a meaningful analysis is an assessment of the country's integration into world capital markets. The international financial markets are progressively becoming one huge, integrated, global capital market, that is contributing to higher stock prices in developed as well as developing economies. If for example a market is segmented from the rest of the world, its exposure with a common world factor may have little or no ability to explain its expected return. Large companies visible enough to attract global investors, have a lower cost of capital and a greater equity value for two main reasons: first, because the risks of equity are shared among more investors with different portfolio exposures and hence a different "appetite" for bearing certain risks, equity market risk premiums should fall for all companies in countries with access to global markets. Second, when firms in countries with less-developed capital markets raise capital in the public markets of countries with highly developed markets, they get more than lower-cost capital; they also import at least aspects of the corporate governance systems that prevail in those markets. Besides reducing market risk premiums and improving corporate governance, globalization also affects the systematic risk, of individual companies. In global markets, the beta of a firm's equity depends on how the stock contributes to the volatility not of the home market portfolio, but of the world market portfolio. For companies with access to global capital markets whose profitability is tied more closely to the local than to the global economy, use of the traditional CAPM will overstate the cost of capital because risks that are not diversifiable within a national economy can be diversified by holding a global portfolio. [ABSTRACT FROM AUTHOR]