Portfolio Performance Evaluation Research Articles

Understanding Investment Decision-making: A Qualitative Inquiry into High-Frequency Trading, Investment Strategies, and Portfolio Performance in the Financial Market

This qualitative study explores investment decision-making in the context of high-frequency trading (HFT), investment strategies, and portfolio performance within the financial market. The research aims to provide insights into the complex dynamics influencing investment decisions and their implications for market participants. Adopting a qualitative research design, the study conducts a comprehensive review and analysis of existing literature from academic journals, books, and conference proceedings. The data collection process involves synthesizing insights from diverse sources to uncover underlying themes, patterns, and contradictions surrounding investment decision-making. Through thematic analysis and constant comparison, the study identifies key findings related to the role of HFT, diversity of investment strategies, and evaluation of portfolio performance. The results highlight the transformative impact of HFT on market liquidity, efficiency, and stability, while also raising concerns about market fragmentation and systemic risks. Additionally, the study explores the evolution of investment strategies, ranging from traditional approaches like fundamental analysis to emerging techniques such as algorithmic trading and quantitative strategies. It underscores the importance of tailoring investment strategies to individual preferences and market conditions for optimizing portfolio performance. Furthermore, the study evaluates alternative frameworks such as post-modern portfolio theory (PMPT) and factor investing, offering promising avenues for enhancing portfolio resilience and risk-adjusted returns. Overall, the research contributes to a deeper understanding of investment decision-making processes and informs stakeholders in the financial industry about effective strategies for navigating the dynamic landscape of the financial market.

Constructing Portfolio Optimization: Analysis in Indonesia Non-Cyclical Industry (Markowitz Approach and Skewness and Kurtosis)

Purpose: The purpose of this research is to examine the application of portfolio optimization in the context of real-world financial issues, particularly in light of the challenges posed by the COVID-19 pandemic. Traditional portfolio optimization strategies, such as those proposed by Markowitz, often rely on the assumption of normally distributed returns, which may not accurately capture the risks associated with extreme events like the COVID-19 crisis. This study aims to shed new light on portfolio optimization methods by exploring various approaches and considering the implications of non-normally distributed returns on portfolio construction. Methods: This research employs a quantitative approach to analyze portfolio optimization techniques in the face of non-normally distributed returns. Using data from financial markets impacted by the COVID-19 pandemic, the study investigates different portfolio construction methods, including risk-free rate for equally weighted portfolios, optimal risk portfolios, minimum variance weights, and maximum expected returns. Various risk metrics such as variance, Sharpe ratio, and standard deviation are considered to evaluate portfolio performance under different constraints. Results and discussion: The empirical findings highlight the limitations of traditional portfolio optimization techniques, particularly in accurately assessing and managing risk in the presence of non-normally distributed returns. Assets exhibit heavily tailed returns, leading to an underestimation of risk when using standard approaches. The study identifies that certain assets offer high returns but also entail significant risks, necessitating a nuanced approach to portfolio construction. By considering stable distribution models and optimizing for both maximum expected return and minimum variance weight, investors can build more profitable and diversified portfolios while managing risk effectively. Implications of the research: The research findings have important implications for investors and financial practitioners, particularly in navigating uncertain market conditions such as those brought about by the COVID-19 pandemic. By recognizing the limitations of traditional portfolio optimization methods and embracing more sophisticated approaches that account for non-normally distributed returns, investors can make more informed decisions and better manage portfolio risk. These insights can inform the development of robust investment strategies tailored to mitigate the impact of extreme events on portfolio performance. Originality/value: This research contributes to the literature by offering a fresh perspective on portfolio optimization under the backdrop of the COVID-19 pandemic. By systematically evaluating different portfolio construction methods and considering the implications of non-normally distributed returns, the study advances understanding in the field of financial risk management. The identification of stable distribution models and the emphasis on balancing maximum expected return with minimum variance weight provide practical guidance for investors seeking to build resilient and profitable portfolios in turbulent market environments. Overall, this research underscores the importance of adapting portfolio optimization strategies to address the realities of contemporary financial markets.

A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.

Analysis and Prediction of the Trend of Chinese and American Stock Market Indexes Based on ARIMA Model

The comparison and analysis of Chinese and American stock market indexes are important in the context of global financial markets. As the global economy becomes more interconnected, the trend of stock market indexes in China and the US has important implications for global investors and policymakers. Through in-depth study of the trend, influencing factors and forecasts of the Chinese and American stock market indexes, we can better provide decision support for international investment and risk management. This study adopts ARIMA model as the main analysis tool to fit and forecast the historical data of important indexes of Chinese and American stock markets. Additionally, this study discusses the factors affecting the Chinese and American stock market indexes, so as to better explain the volatility and periodicity of the stock market. The results of the study show that the predictions of the ARIMA model are close to the actual data, which has important implications for investors, financial analysts and policymakers. This study is significant for understanding the trend of Chinese and American stock market indexes and their influencing factors, and provides valuable information for investors, analysts and policymakers to help them formulate effective investment strategies and evaluate portfolio performance.

Performance evaluation of possibilistic fuzzy portfolios with different investor risk attitudes based on DEA approach

By considering the stock market’s fuzzy uncertainty and investors’ psychological factors, this paper studies the portfolio performance evaluation problems with different risk attitudes (optimistic, pessimistic, and neutral) by the Data Envelopment Analysis (DEA) approach. In this work, the return rates of assets are characterized as trapezoidal fuzzy numbers, whose membership functions with risk attitude parameters are described by exponential expression. Firstly, these characteristics with risk attitude are strictly derived including the possibilistic mean, variance, semi-variance, and semi-absolute deviation based on possibility theory. Secondly, three portfolio models (mean-variance, mean-semi-variance, and mean-semi-absolute-deviation) with different risk attitudes are proposed. Thirdly, we prove the real frontiers determined by our models are concave functions through mathematical theoretical derivation. In addition, two novel indicators are defined by difference and ratio formulas to characterize the correlation between DEA efficiency and portfolio efficiency. Finally, numerical examples are given to verify the feasibility and effectiveness of our model. No matter what risk attitude an investor holds, the DEA can generate approximate real frontiers. Correlation analysis indicates that our proposed approach outperforms in evaluating portfolios with risk attitudes. At the same time, our model is an improvement of Tsaur’s work (2013) which did not study the different risk measures, and an extension of Chen et al.’s work (2018) which only considered risk-neutral attitude.

Analysis Of Idx 30 Stock Portfolio Performance During Covid 19 Pandemic

Investors need to do proper analysis before making a decision in investing. If the investor does not do the right analysis, the investor will experience significant losses. One of the analyzes used is CAPM, and for the next step the researcher evaluates the performance of the stock portfolio. This study aims to analyze the performance evaluation of stock portfolios during the covid 19 pandemic. The research objects were companies registered in IDX 30 during the period January 2020 to July 2022. The result of this study is the Treynor index is the most indicative of conformity without differences between the three measures, as Treynor. Its value is similar to Sharpe and the Jensen index has the smallest difference.

Analisis perbandingan evaluasi knerja portofolio saham sebelum dan di masa pandemi COVID-19 di IDX 30

Economic conditions during the Covid-19 pandemic caused portfolio changes when the stock market experienced volatility in March 2020. This impact had an impact on the weak performance of the IDX30 so that many investors experienced panic selling at that time. This is due to the lack of investor knowledge about the formation of stock portfolios, one of which is by using CAPM. This study aims to analyze the formation of a stock portfolio measured using CAPM and analyze the presence / absence of differences in the evaluation of stock portfolio performance as measured using the Sharpe index, Treynor index and Jensen index. These results show the H0 hypothesis is accepted which means that there is no significant difference between testing with the Sharpe, Treynor, and Jensen methods both before and during the Covid 19 pandemic. The test results between treatments both before the Covid-19 pandemic showed that the third difference in the average ranking of the Treynor index, while during the covid-19 pandemic it was the Jensen Index which showed the greatest consistency with the uncertainty between the three measurements.

PEMBENTUKAN PORTOFOLIO SAHAM OPTIMAL DENGAN MEAN ABSOLUTE DEVIATION PADA DATA SAHAM JAKARTA ISLAMIC INDEX

In 2017 to 2020 the Jakarta Islamic Index (JII) showed a positive trend and was quite stable compared to the LQ45 index. The selection of the JII stock index in this study is intended to obtain maximum profits. Investors are expected to create a series of portfolios to get maximum profit. One of the ways to identify stocks for portfolio formation is to use factor analysis. Factor analysis is used to summarize a large number of variables into new, smaller factors. This new factor is called the portfolio. The Mean Absolute Deviation (MAD) method is used for the formation of an optimal portfolio as well as an improvement on the Markowitz method in terms of non-linear (quadratic) mathematical models. The MAD method is the mean of the absolute value of the deviation between the realized return and the expected return. The optimization technique used in the MAD portfolio is the simplex method. Optimizing the objective function by constraining the set of constraints on the simplex method is done by forming a simplex table. Based on the processing using the simplex method, the investment weight for each of the stocks that make up the first portfolio is 30% CPIN shares; 29.23% of JPFA's shares; 10.77% shares of SMGR; and 30% shares in UNVR. Meanwhile, the investment weight of the constituent stocks for the second portfolio is 30% ACES shares; 10% of ERAA's shares; 30% of INCO's shares; 30% of PGAS shares; and 0% WIKA shares. The results of portfolio performance evaluation show that portfolio 2 is better than portfolio 1, by looking at the Sharpe Index for portfolio 2 of 0.0135629 and portfolio 1 of -0.0281177.

Forecasting the Chinese Stock Market Volatility with G7 Stock Market Volatilities: A Scaled PCA Approach

ABSTRACT In this study, we forecast the realized volatility (RV) of the Chinese stock market using the heterogeneous autoregressive (HAR) model and various extended models. To extract the volatility information from the G7 stock markets, we employ a newly proposed approach, the scaled principal component analysis (SPCA), to produce a diffusion index and extend the HAR benchmark (HAR-SPCA). To validate the effectiveness of the SPCA approach, we employ three other dimension reduction approaches, the kitchen sink model, and five popular forecast combinations to deal with multivariate information and make competing forecasts. The results suggest that the combined volatility information from the G7 stock markets significantly predicts Chinese stock market volatility. More importantly, the forecasts from the HAR-SPCA model are steadily more accurate than the benchmark and other competing models under various evaluation criteria. Finally, our results are persistent to various robustness checks and the evaluation of portfolio performance.

Analisis Kinerja Portofolio Saham Perusahaan Pada Sektor Jasa Penerbangan Di Beberapa Negara Asean

The Covid-19 pandemic that occurred in early 2020 in the world resulted in many businesses and companies being affected, handling companies in the aviation sector in ASEAN countries. As an investor who is going to invest, of course, you have to assess how the performance of the stock is, whether it will provide good and profitable returns, because the purpose of investors in investing in the capital market is of course to get a good increase (return). Portfolio performance evaluation is the final stage of the investment management process which aims to assess whether the portfolio that has been formed has a good performance and is in accordance with investment objectives. Among them are using the Sharpe, Treynor, and Jensen measurement models. The results showed that the Sharpe Method of Measurement found that the Garuda Indonesia company (-0.9144) had a greater risk and its performance was arguably the lowest compared to the other 3 companies because the Sharpe index value of Garuda Indonesia was higher risk than AirAsia Berhad-Malaysia (-0, 4823), Bangkok Airline-Thailand (-0.4703), and Nok Airlines Public Company-Thailand (-0.1133). None of the portfolios of these 4 companies produced better performance, in fact showed less performance than the benchmark. Meanwhile, the Treynor and Jensen methods also show that the performance of these companies underperforms against the benchmark.

Evaluation of Optimal Stock Portfolio Performance by Grouping Issuers Based on Stock Price Movements

Stock investment has a high risk, one of which is caused by changes in stock prices that occur in trading on the stock exchange every day, thus affecting the level of stock returns which also changes. Investors will face uncertainty in making choices and evaluating stock performance in the future because the returns obtained are uncertain and depend on the risks that affect it. Risk in investing can be reduced by investing in various types of stocks by forming an optimal stock portfolio. These problems are solved in this study by taking a calculation approach in selecting stocks and determining the optimal portfolio with the advantage of a single index model that can explain the relationship between the returns of each stock and the market index returns to calculate the variance of a stock. portfolio. There are 4 stocks with the largest excess return to beta, namely MEGA, JECC, UNIC, and KICI stocks. Performance measurement in this study was carried out using the Treynor Index, because this index used systematic risk as measured by Beta. Keywords: Single Index Model, Treynor, Dynamic Time Wraping.

An examination of the level of engagement of modern portfolio techniques for portfolio management by real estate firms in Lagos State, Nigeria

Portfolio management in real estate practices was examined in this study with a view to ascertaining the techniques engaged in carrying out the risk and return analysis as well as the evaluating portfolio performance in the firm. Questionnaires were administered to respondents from 208 estate surveying and valuation firms in Ikeja, Lagos Island, Ikoyi and Victoria Island, Lagos State.Response rate achieved was 86.5%. Basic descriptive tools of percentage, weighted mean, relative importance index were used to analyse the data.The study found that the number of firms that offer portfolio management services is rather low compared to other areas of services.Results further showed that the firms ranked discounted cashflow and contemporary growth models higher than the modern portfolio techniques for asset return assessment whereas simulation and modern portfolio tools ranked higher among the techniques for risk assessment. Finally, the results showed that most firms make use of contemporary growth models, benchmark, style or market comparison to evaluate the performance of the portfolio. The study therefore suggests that estate firms embark on staff capacity development for effective application of modern portfolio theory techniques and making the service widespread among practicing firms.

Modeling asset allocations and a new portfolio performance score.

We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper, we focus on two specific questions. The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy. Certain features in stock markets lead to this type of anomaly detection: Given the assets’ returns, we describe the relationship between portfolios’ return and volatility by means of a copula, without making any assumption on investors’ strategies. We examine a recent method relying on copulae to construct an appropriate indicator that allows us to automate crisis detection. On real data the indicator detects all past crashes in the cryptocurrency market and from the DJ600-Europe index, from 1990 to 2008, the indicator identifies correctly 4 crises and issues one false positive for which we offer an explanation. Our second contribution is to introduce an original computational framework to model asset allocation strategies, which is of independent interest for digital finance and its applications. Our approach addresses the crucial question of evaluating portfolio management, and is relevant the individual managers as well as financial institutions. To evaluate portfolio performance, we provide a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on statistical properties of portfolios, and we show how they can be computed efficiently.

Leveraged ETF Pairs: An Empirical Evaluation of Portfolio Performance

We examine the performance of investment strategies involving leveraged and inverse leveraged ETF pairs. As in Jiang and Peterburgsky’s (2017) simulation analysis, the empirical analysis in this article indicates that simple portfolios of bull/bear short positions constructed to approximately mimic an underlying index (such as the S&amp;P 500) outperform the index on a risk-adjusted basis. The outperformance asymptotically diminishes as the investor’s rebalance bounds tighten. Additionally, results indicate that the volatility effect dominates the trend effect for bull leveraged ETFs. This finding is relevant for the debate on the regulation of leveraged ETF markets. <b>Key Findings</b> ▪ Simple portfolios of bull/bear short positions in leveraged and inverse leveraged ETFs constructed to approximately mimic an underlying index (such as the S&amp;P 500) outperform the index on a risk-adjusted basis. ▪ The outperformance asymptotically diminishes as the investor’s rebalance bounds tighten. ▪ The volatility effect dominates the trend effect for bull leveraged ETFs.

The impact of COVID-19 on formation and evaluation of portfolio performance: A case of Indonesia

This paper examines how to build a portfolio and assess the impact of the COVID-19 on portfolio performance using the Sharpe single index model. The research sample consists of ten high market capitalization stocks representing five price fractions of the population listed stocks on the Indonesia Stock Exchange during the COVID-19 outbreak from March 1 to May 31, 2020. The results show that there are four stocks that are included in the portfolio formation, namely CASA with a proportion of 50%, BNLI with a proportion of 26 %, UNVR with a proportion of 15%, and HMSP with a proportion of 9%. Based on portfolio performance testing using the Sharpe single index model, it is known that the portfolio during the COVID-19 has a negative Sharpe ratio, meaning that portfolio performance is underperforming. The findings provide evidence that COVID-19 has had a negative impact on the stock market so that many investors have suffered losses on their portfolios. The implications of findings are that investors must evaluate portfolio performance and restructure the formation of new portfolios by considering the COVID-19 pandemic outbreak as a systematic risk factor that can determine the expected returns.

The Effect of Macroeconomic Variables on Stock Portfolio Performance Based on Traditional and Modern Network

Evaluation of stock portfolio performance is considered one of the important issues in the capital market and investment management in stocks. Proper evaluation of portfolio performance requires recognizing the factors affecting it. The macroeconomic variables are important and effective factors due to affecting the systematic risk of companies. In this research, ordinary least squares method (OLS) was used to evaluate the effect of macroeconomic variables including inflation, interest rate, liquidity growth rate, oil price and currency rate (Rial versus Dollar) on the stock portfolio performance based on traditional and modern network theory. Performance of portfolios including growth portfolio, growth-value portfolio, and value portfolio, and offensive portfolio, indifferent and defensive portfolio was measured based on seasonal data from 2006 to 2016 using the Teriner Index. The research results show that at the error level of 5%, macroeconomic variables have an impact on the performance of both traditional and modern networks. However, the Akaike information criterion for the modern network model is equal to 5.822, which is less than the traditional network value with the value of 6.724. This suggests that the interpretation of macroeconomic variables in a modern network portfolio is better than that of traditional one. In addition, the effect of macro variables on the performance of the six portfolios will be different

Estimation of portfolio efficiency considering social responsibility: evidence from the multi-horizon diversification DEA

With the introduction of the concept of social responsibility investment/green investment, more and more investors have realized the importance of such investment, which has prompted portfolio managers to more comprehensively consider both financial and non-financial performance of portfolios in different time horizons. DEA (Data Envelopment Analysis), as a data-driven evaluation approach, has been widely used in performance evaluation of portfolios. However, the existing studies are mostly limited to single-horizon problems, and the evaluation indicators are mostly financial indicators, while ignoring the impact of non-financial indicators (e.g., social responsibility indicators). More importantly, the input-output process of portfolios in the multi-horizon framework also needs to be clarified. In this paper, we first define the input-output process of portfolios from the multi-horizon perspective, and then propose the corresponding stochastic output possibility sets based on portfolio returns and social responsibility indicators. We use the expectation and variance measures to derive the deterministic estimation of the above stochastic sets, where the expectations and variances of portfolio returns and social responsibility indicators are all regarded as outputs. We construct the multi-horizon diversification DEA models both with and without social responsibility constraints. Finally, we select the 20 component stocks of China ESG100 index to illustrate the difference between the multi-horizon models and the single-horizon models, and further discuss the impact of social responsibility on the portfolio efficiency and its ranking. The empirical results show that compared with the single-horizon models, the proposed models can provide portfolio managers with an improvement strategy to balance the performance of portfolio returns and social responsibility indicators in different time horizons. Further, we also find that the social responsibility has a greater impact on the portfolio efficiency and its ranking, especially when the portfolio managers pay more attention to the social responsibility performance.

The Evaluation of Portfolio Performance with Jensen Index Method toward Samuel Securities in January-December 2020

The Evaluation of Portfolio Performance with Jensen Index Method toward Samuel Securities in January-December 2020

Portfolio Performance Evaluation based on Post Modern Portfolio Theory

Portfolio Performance Evaluation based on Post Modern Portfolio Theory

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

Portfolio performance evaluation is a major data envelopment analysis (DEA) application in the finance field. Most proposed DEA approaches focus on single-period portfolio performance assessment based on aggregated historical data. However, such an evaluation setting may result in the loss of valuable information in past individual time periods, and violate real-world portfolio managers’ and investors’ decision making, which generally involves multiple time periods. Furthermore, to our knowledge, all proposed DEA approaches treat the financial assets comprising a portfolio as a “black box”: thus there is no information about their individual performance. Moreover, ideal portfolio evaluation models should enable the target portfolio to compare with all possible portfolios, i.e., enabling full diversification of portfolios across all financial assets. Hence, this research aims at developing nested dynamic network DEA models, an additive model being nested within a slacks-based measure (SBM) DEA model, that explicitly utilizes the information in each individual time period to fully and simultaneously measure the multi-period efficiency of a portfolio and its comprised financial assets. The proposed nested dynamic network DEA models, referred to as NDN DEA models, are linear programs with conditional value-at-risk (CVaR) constraints, and infinitely many decision making units (DMUs). In conducting the empirical study, this research applies the NDN DEA models to a real-world case study, in which Markov chain Monte Carlo Bayesian algorithms are used to obtain future performance forecasts in today's highly volatile investment environments.