Market Portfolio Research Articles

Comparative Analysis on the Application of Markowitz Model and Index Model in Portfolios

This article presents a comparison between the Markowitz model and the index model, focusing on their applications in portfolio management. The Markowitz model, also known as the mean-variance model, revolutionized portfolio theory by introducing a quantitative method for balancing risk and return. This model constructs optimal portfolios that maximize expected return for a given level of risk or minimize risk for a given expected return, using a covariance matrix to assess relationships between asset returns. In contrast, index models popularized by William Sharpe offer a simplified approach focused on the relationship between individual asset returns and a common benchmark (usually a market portfolio). The article explores the theoretical foundations of the two models, highlighting their mathematical formulations and assumptions. It also discusses the computational methodologies involved in implementing these models and examines the challenges and advances in optimization techniques. It evaluates how these technological innovations have enhanced portfolio optimization, enabling the handling of larger datasets and more complex market dynamics. In addition to comparing these two models, the article delves deeper into the concepts of efficient frontier, inefficient frontier and minimum variance frontier. The efficient frontier is the portfolios that offer the highest expected return for target risk, while the inefficient frontier includes portfolios that do not offer the best return for the level of risk. Minimum variance frontier focuses on portfolios that minimize risk for target expected return, emphasizing the importance of risk management in investment decisions.

Equity Market Pricing and Central Bank Interventions: A Panel Data Approach

This paper analyzes the effects of central bank interventions via large-scale purchases of government debt securities on the pricing of stock market indices. This study examines the effects of changes in the size of the Federal Reserve’s balance sheet in three intervention scenarios: during the 2008–2013 period, the 2020–2022 period, and in the years between by using the instrumental variables three-stage least squares (3SLS) method for a time series approach, and calculates the effects of these interventions on each index in a fund of funds setup using the panel data strategy. This study confirms that large-scale purchases of government debt securities in response to the Great Recession and COVID-19 crises influenced the pricing of equity markets via their effect on the pricing of treasury bonds, with different degrees of sensitivity of each index to the effects on yields. Although the findings apply to the U.S. market, the results indicate that the pricing of small capitalization indices such as the Russell 2000 are less sensitive to changes in treasury yields caused by central bank interventions than large capitalization indices such as the DJIA. This research contributes to the understanding of financial asset pricing, particularly by identifying price distortions within equity market portfolios.

RESOURCE COMMITMENT AND PARTNER SELECTION IN ASYMMETRIC CORPORATE-STARTUP COLLABORATIONS

Startups and established firms collaborate in order to leverage complementary resources and innovative capabilities to sustain a competitive advantage. By forming alliances, startups are able to mitigate challenges connected to their smallness and lack of legitimacy. However, the underlying criteria by which startups screen and select potential collaboration partners in such asymmetric collaborations remain poorly understood. This paper examines the dynamics of these asymmetric collaborations, exploring the motives, selection criteria, and the willingness of startups to partner from a resource-based perspective. We conduct a conjoint experiment to examine the relationship between diverse resource commitment types and startups’ willingness to partner. The findings of the study confirm a strong influence of financial, technological, managerial, and physical resources in a startup’s decision-making process regarding partner selection. Notably, the attractiveness of a partner was found to diminish when they possess a similar market portfolio, potentially fostering future competition. Consequently, startups must implement effective control and protection mechanisms to prevent opportunistic behaviours from collaboration partners operating in the same market. To contribute to the existing literature, we adopt a resource-based theoretical lens that emphasises the pivotal role of capabilities in a startup’s decision-making process and demonstrates their influential role in forging successful collaborations with established partners. Ultimately, our findings enhance the understanding of the partner selection criteria, offering valuable insights for new ventures seeking beneficial partnerships at the early stages of a collaboration.

Intraday and daily dynamics of cryptocurrency

This paper examines and compares intraday and intraweek patterns in hourly and daily prices, returns, volumes and volatility of native cryptocurrencies, stablecoins and tokens traded on Bitstamp. We show that native cryptocurrencies and tokens share common intraday periodicity determined by the operating times of the NYSE, LSE and Hang Seng stock exchange markets. Periodic patterns are also documented in the returns on cryptocurrency market portfolio approximated by the PCA applied to intraday and intraweek cross-sectional correlation matrices of cryptocurrency returns. Stablecoins have distinct dynamics and their daily and hourly returns are uncorrelated with one another and with the returns on other cryptocurrencies. We introduce a functional CAPM to accommodate the periodic patterns and estimate it by regressing the functions of intraday and intraweek cryptocurrency returns on the market portfolio. We show that the return functions on Bitcoin, Ether, and Link satisfy affine relationships with the return functions of the market portfolio and their functional betas display periodic intraday and intraweek patterns.

Application of a Robust Maximum Diversified Portfolio to a Small Economy’s Stock Market: An Application to Fiji’s South Pacific Stock Exchange

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy’s stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji’s South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset’s beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered.

CAPM in Real World: Risk-Friendly Investments

The aim of this paper is to show how the existence of equilibrium in CAPM may be obtained when individuals/investors are risk friendly. This assumption is closer to the real world, since risk aversion is rare and the portfolios implying a greater payoff are the ones which increase the variance of the payoff itself. Specifically, we assume that the wage vectors of the individuals/investors do not lie in the market space, which is the usual assumption for CAPM, namely, the payoff of any portfolio is replicated by the wage of the individuals/investors. The interpretation of such an assumption is that the wealth of the individuals/investors may not invest in financial markets as a whole. The second main result is that the perception of risk by the objective probabilities for the states of the world does not affect the equilibrium existence. This is a consequence of the No-Arbitrage assumption. This condition may be stated in terms of the existence of the objective probabilities for the states of the world. Since CAPM is usually considered as a way to compare the return of the so-called “market portfolio” and the return of any other portfolio, in the final section of this paper, we do provide the regression form of this altered form of CAPM.

Estimation of expected return integrating real-time asset prices implied information and historical data

In this paper, we develop a novel estimation for expected stock returns combining forward-looking information implied by real-time asset prices and backward-looking information implied by historical data. Considering a general heterogeneous market composed of both informed investors and noise investors, we investigate the market equilibrium characterized by the expected returns, risk-neutral moments and market portfolio. To mitigate the negative impact of the market noise on the forward-looking information implied in market equilibrium, we then incorporate historical data and propose the combined estimation for expected return within a Bayesian framework. The combined estimation is adaptive to the market composition and adjustable to changes in market states. Monte Carlo simulations and empirical studies are performed to validate the merits of the proposed approach.

ARE FUND MANAGERS SKILLED OR IS IT SIMPLY LUCK? A CASE OF ENERGY MUTUAL FUNDS

Fund managers often tout that the performance of the funds they manage is due to their wisdom and management skills. The success in convincing the investors to this notion enables them to claim a higher salary and perks for themselves and charge a hefty or premium fee from the investors for the company they work for. However, changes in government policies, as well as in domestic and international market conditions, often influence the performance of the funds they manage. Suppose investors are not aware of this phenomenon. In that case, they may end up unnecessarily paying a hefty fee to the fund managers and investing in the wrong stock or mutual fund thereby suffering a loss in terms of the return on investment and capital loss. So, the investors must know whether the performance of any funds is due to the wisdom or skills of the fund managers or simply due to some exogenous factors beyond their control, which, in this study, we call luck. In this study, we apply the so-called Fama and French's bootstrap simulation method to determine whether the observed value of alpha – a measure of the fund’s performance – is due to fund managers’ skill or simply luck using data on monthly returns of the fifteen Equity Energy mutual funds from January 2024 through June 2019. This method involves computing a distribution of t(alpha) estimates of actual fund returns and comparing it to the performance of a cloned population of lucky funds with their true alphas set to zero. If actual funds produce higher values of t (alpha)s than the cloned population of lucky funds, then fund managers do have the skill. This distinction between skill and luck is crucial, as funds can do well or poorly by luck. Our study shows that funds can produce extreme alpha values just by luck, even if the fund's true alpha is zero. This implies that long-term investors are better off holding market portfolio ETFs and earning market return rather than paying more in fees and overall costs in attempts to beat the market. JEL classification: G1

Earnings optimism in green stocks

ABSTRACT In this study, we systematically examine earnings expectations, proxied by consensus analyst earnings forecast errors (EFEs), about stocks in green energy and clean technology sectors. We find that the EFEs of green stocks are generally positive from 1992 to 2016, indicating a predominance of earnings optimism. However, the earnings optimism of green stocks is significantly less strong than that of the market portfolio. In addition, we observe a segment-varying earnings optimism pattern in green stocks. Moreover, it is observed that investors become more and more optimistic when forecasting earnings of green stocks. In the period from 2008 to 2016, while investors exhibit slightly less optimistic expectations about market-level earnings performance after experiencing the 2008 Global Financial Crisis, they tend to be clearly more confident about the earnings prospects of green stocks in the same period as the first and second commitment periods of the Kyoto Protocol take effect.

Transaction Costs and Capacity of Systematic Corporate Bond Strategies

Can systematic corporate bond investments generate attractive returns net of costs? To answer this question, we apply the principle of market microstructure invariance and obtain bond transaction costs increasing in trade size. As the size of the bond fund increases, the market impact reduces net returns to zero. High-turnover strategies hit capacity constraints fast. Low-turnover credit risk–focused strategies have much higher capacities that can be further increased by constraining portfolio rebalancing in realistic ways. Transaction costs do not absorb the corporate bond risk premium even in the largest possible market portfolios.

Количественный анализ финансовых инструментов на основе портфельной теории и теории игр

The article focuses on modern methods of quantitative analysis of financial instruments based on the Markowitz portfolio theory, which allows designing optimal portfolios of financial instruments under the assumption of extreme rationality of investors and game theory, which allows taking into account the factor of player interaction between those participating in financial markets. The relevance of the research topic concerns the need to improve the quality of financial decisions made in conditions of increased volatility of financial markets. An initial set of financial instruments was built, consisting of fifty alternative options for placing funds. A preliminary analysis of the initial set of financial instruments allowed us to narrow it down to ten elements that are most preferable for placing funds. The classic Markowitz portfolio model, which assumes maximizing the expected return on a portfolio at a given level of risk, is complemented by auxiliary constraints that allow taking into account individual investor preferences. The presented six modifications of Markowitz portfolios help to identify the dynamics of the quantitative characteristics of portfolios depending on the individual preferences of investors. Expected profitability and expected risk, estimated from real financial data, are accepted as quantitative characteristics of portfolios. The game model is constructed in the form of a game with nature, which allows taking into account the complex nature of player interaction, which in most cases is not characterized by antagonism. The game model is designed to select the optimal net strategy of the investor, which takes into account various financial market conditions expressed by a market index (market portfolio). The study of the game model is implemented on the basis of the integrated Hodge-Lehman criterion regarding profitability and risk. Its use made it possible to take into account the individual level of investor confidence in the available financial information. In the process of practical implementation of these techniques, results were obtained that allow us to conclude about the degree of sensitivity of optimal investment strategies to individual preferences and perceptions of investors. The constructed models can be used to update the content of professional training of students in the higher economic school system, as well as to set up courses of additional professional education.

The Influence of Gold, Black Gold, and Digital Gold on Stock Market Portfolios

This research paper aims to study the influence of Bitcoin (BTC), Crude Palm Oil (CPO), Crude Oil (WTI), the Dollar Rate (USD), and Gold (XAU), on Stock Market Portfolios (KLSE). Time series data for this investigation was obtained from Yahoo Finance and Investing.com. Furthermore, the Unit Root Test conducted found that the variables are stationary after 1st differencing. Moreover, since there was no Cointegration discovered between the variables, VAR was performed to determine the long-run causality effect, while the Wald Test was conducted to examine the short-run causality effect. The findings revealed that none of the variables contained a long-run relationship, whereas Bitcoin (BTC), Crude Palm Oil (CPO), and Gold (XAU) contained a short-run relationship with the independent variable (KLSE).

Macroeconomic Environment and Stock Price Movement in Nigeria: An Evaluation of Fama’s Model of Efficient Market

The study examined the link between macroeconomic environmental dynamics and stock price movements in Nigeria using Fama’s model of efficient market approach. The macroeconomic climate posed issues for Nigerian stock markets. The buying power of local currency occasionally impacted unfavourable changes in the majority of macroeconomic variables. In this study, an ex post facto research design was used. The study looked at certain macroeconomic variables and the evolution of Nigerian stock prices between 1985 and 2021. To test the hypothesis, quantile regression estimates of the Fama-French three-factor coefficient were derived. The findings showed that the interest rate and stock price movement have a negative and negligible association. Additionally, a strong and positive correlation was observed between the exchange rate and changes in stock prices. It was recommended that in order to promote stock market activity and, by extension, enhance stock prices in Nigeria, a suitable monetary policy and a sufficient amount of money supply must be implemented. Also, well-diversified portfolios that mirror the entire market portfolio should be recommended to investors to protect themselves against stock price volatility. This would serve as a guide to protect them against shocks resulting from the macroeconomic environment.

Impact of sentimental factors on stock portfolio returns –an empirical analysis

This study aims to introduce an integrated model for understanding the influence of various sentimental factors in conjunction with macroeconomic factors on portfolio returns across ten industry sectors within the US market. These sentimental factors are categorized into market-wide, consumer, and individual stock market factors to assess their impact on industry portfolio returns. Employing the Autoregressive Distributed Lag (ARDL) model, the study evaluates the effects of macroeconomic and sentimental factors on stock market portfolio returns. The findings reveal a negative relationship between short-term interest rates and portfolio returns in specific industry sectors like manufacturing, telecom, and wholesale/retail. The study finds a positive relationship between the Hi-tech sector's risk spread and portfolio returns. Market sentimental factors positively influence portfolio returns of durable, non-durable, utility, and other sectors. Individual sentimental factors negatively impact portfolio returns in hi-tech, utility, durable, energy, and other sectors. The stock market-related individual, sentimental factor of the number of IPOs has a positive impact on portfolio returns in the energy sector and a negative impact on portfolio returns in other sectors. Consumer sentimental factors are significant positive determinants for portfolio returns in durable, energy, telecom, health, and other sectors. Discounts on closed-end funds may provide vital fundamental information regarding lower future earnings for stocks in the durable and energy sectors. The study provides valuable insights for investors to optimize their portfolio strategies in response to macroeconomic and sentimental factors within specific industry sectors.

Risk Modeling, Return Forecasting, and Optimal Portfolio Selection

The main objective of this research is to form an optimal investment portfolio consisting of a number of stocks selected according to specific criteria in Amman Stock Exchange and to test the ability of a various economic models to predict the performance of this portfolio in the foreseeable future. A time series for the return of the selected portfolio and for the return of a market index are formed. A set of tests were conducted to reach a stationary time series return and, then, to follow the Box-Jenkins methodology in order to build predictive models (ARMA) and to examine the residuals of models and to model them using ARCH and GARCH models to reach the best prediction of the performance of the portfolio and the market index in the forecasted periods. The data were tracked on a daily basis for the study sample and the market index simultaneously for a period of three years. Twenty-one companies were selected in the investment portfolio distributed among several sectors. The study concluded that the formed portfolio achieved a good diversification and gave a high return in relation to the lowest possible risk according to the Sharpe scale. Also, it is concluded that the model ARMA (1,1) is the most suitable for estimating market portfolio returns and forecasting risks for the market index return, and ARMA (2,1) and the model ARMA - GARCH are the most capable one of achieving good results that can be relied upon in tracking the performance of the studied investment portfolio.

Testing the Weak Efficient Market Hypothesis of Chinese Stock Market--Based on the Market Performance of Representative Stock Investment Portfolios in the HS300 Index

Chinas domestic stock market has developed very quickly overall in the more than 30 years since it was established, but information effectiveness in the Chinese stock market still faces multiple problems, so testing the weak efficient market hypothesis of the Chinese stock market is a good way to test its information quality and give investors relevant advice. This article selected representative stocks from the HS300 index, gathered the stocks historical data, used the index model to construct an optimal portfolio, and used another part of historical data to test if the portfolio has continuous excess return compared with the market portfolio and risk-free asset. The article finds excess return for the optimal portfolio during a long period and cannot prove that the Chinese stock market is weak and efficient. This result will provide a certain theoretical basis for investors who use technical analysis for stock investment and may also indicate that many issues still need to be addressed in the Chinese stock market, such as information asymmetry, high transaction costs, and insufficient regulation.

Performance of negatively screened sustainable investments during crisis

We investigate the market performance of negatively screened environment social and governance (ESG) portfolio or sustainable investments prior to and during crisis. A general and simple method is developed under the ESG Capital Asset Pricing Model (CAPM) framework for the assessment. The novelty is that this method can be employed when the parent portfolio is not a market portfolio. In this situation, both coefficients, alpha and beta, in the reduced form of regression have special interpretations and are informative. This paper examines 24 negatively screened ESG indices from the S&P, DJSI and MSCI data across various regions, firm sizes, and criteria of screening, for 2017 to 2021. Markov Switching Autoregressive (MSAR) model is adopted to identify the crisis regime. Our results show that the negatively screened ESG indices provide positive investors’ surpluses for ESG-motivated investors during the crisis, when the corresponding parent indices are the market portfolios. For ESG investments where market portfolios are not their parent indices, half of ESG indices under consideration still provide positive surplus with similar systematic risks as their parent indices during the crisis. The remaining ESG indices under-performs but has relatively lower systematic risks, implying resilience as compared to the corresponding parent indices during the crisis. Furthermore, we demonstrate the sensitivity analysis of treating a parent index as a market portfolio.

Sentiment trading with large language models

We analyse the performance of the large language models (LLMs) OPT, BERT, and FinBERT, alongside the traditional Loughran-McDonald dictionary, in the sentiment analysis of 965,375 U.S. financial news articles from 2010 to 2023. Our findings reveal that the GPT-3-based OPT model significantly outperforms the others, predicting stock market returns with an accuracy of 74.4%. A long-short strategy based on OPT, accounting for 10 basis points (bps) in transaction costs, yields an exceptional Sharpe ratio of 3.05. From August 2021 to July 2023, this strategy produces an impressive 355% gain, outperforming other strategies and traditional market portfolios. This underscores the transformative potential of LLMs in financial market prediction and portfolio management and the necessity of employing sophisticated language models to develop effective investment strategies based on news sentiment.

Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture

SummaryThis paper replicates the results of the stochastic volatility–Dirichlet process mixture (SV‐DPM) models proposed in Jensen and Maheu (2010) in both a narrow and a wide sense. By using a normal‐Wishart prior and the collapsed Gibbs sampling method, our algorithm can be applied for more general settings, and it is more efficient for sampling the Dirichlet process mixture. For the stochastic volatility component, we adopt the method in Chan (2017) to further increase the overall efficiency of our algorithm. Using the same dataset, we obtain mixed results. Some of the results have significant differences. If we use recent time period dataset, which includes the COVID‐19 pandemic period, the log market portfolio volatility seems to increase in terms of the number of clusters and size of magnitude.

A nationwide multi-location multi-resource stochastic programming based energy planning framework

With the escalating global energy demands, nations face greater urgency in diversifying their electricity market portfolio with various energy sources. The efficient utilization of these fuel resources necessitates the integrated optimization of the entire energy portfolio. However, the inherent uncertainty of variable renewable energy generators renders traditional deterministic planning models ineffective. Conversely, expansive stochastic models that capture all the complexities of the power generation problem are often computationally intractable. This paper introduces a scalable energy planning framework tailored for policymakers, which is adaptable to diverse geopolitical contexts, including individual countries, regions, or coalitions formed through energy trade agreements. A two-stage stochastic programming approach, including a scenario-based Benders decomposition method, is employed to achieve computational efficiency. Scenarios for representing parameter uncertainty are developed using a k-means clustering algorithm. An illustrative case study is presented to show the practical application of the proposed framework and its ability to inform policymaking by identifying actionable cost-reduction strategies. The findings underscore the importance of promoting resource coordination and demonstrate the impact of increasing interchange and renewable energy capacity on overall gains. Notably, the study shows the superiority of stochastic optimization over deterministic models in energy planning.