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Artigos de revistas sobre o tema "Portfolio rebalancing"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 25 de julho de 2025

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1

Lim, Qing Yang Eddy, Qi Cao, and Chai Quek. "Dynamic portfolio rebalancing through reinforcement learning." Neural Computing and Applications 34, no. 9 (2021): 7125–39. http://dx.doi.org/10.1007/s00521-021-06853-3.

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AbstractPortfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebala

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Bányai, Attila, Tibor Tatay, Gergő Thalmeiner, and László Pataki. "The Impact of Rebalancing Strategies on ETF Portfolio Performance." Journal of Risk and Financial Management 17, no. 12 (2024): 533. http://dx.doi.org/10.3390/jrfm17120533.

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This research explores the efficacy of rebalancing strategies in a diversified portfolio constructed exclusively with exchange-traded funds (ETFs). We selected five ETF types: short-term U.S. Treasury bonds, U.S. equities, global commodities, U.S. real estate investment trusts (REITs), and a multi-strategy hedge fund. Using a 10-year historical period, we applied a unique simulation model to generate random portfolios with varying asset weights and rebalancing tolerance bands, assessing the impact of rebalancing premiums on portfolio performance. Our study reveals a significant positive correl

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Almeida, Joana, and Raquel M. Gaspar. "Portfolio Performance of European Target Prices." Journal of Risk and Financial Management 16, no. 8 (2023): 347. http://dx.doi.org/10.3390/jrfm16080347.

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This paper examines the performance of actively managed portfolios constructed using target price recommendations provided by analysts. We propose two methods for constructing portfolios based on Bloomberg’s 12-month target price consensus, which serves as a signal to buy or sell assets. Using a sample of 50 European stocks over a 19-year period (from 1 April 2004 to 31 March 2023), we compare the performance of target-price-based portfolios to traditional alternatives, such as a naïve homogeneous portfolio and the Eurostoxx 50 index, as well as to passive portfolios based on average recommend

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4

Horn, Matthias, and Andreas Oehler. "Automated portfolio rebalancing: Automatic erosion of investment performance?" Journal of Asset Management 21, no. 6 (2020): 489–505. http://dx.doi.org/10.1057/s41260-020-00183-0.

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Abstract Robo-advisers enable investors to establish an automated rebalancing strategy for a portfolio usually consisting of stocks and bonds. Since households’ portfolios additionally include further frequently tradable assets like real estate funds, articles of great value and cash(-equivalents), we analyze whether households would benefit from a service that automatically rebalances a portfolio which additionally includes the latter assets. In contrast to previous studies, this paper relies on real-world household portfolios, which are derived from the German central bank’s (Deutsche Bundes

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Sornmayura, Sutta, Nichanan Sakolvieng, and Kaimook Numgaroonaroonroj. "Optimizing Cryptocurrency Portfolios: A Comparative Study of Rebalancing Strategies." GATR Journal of Finance and Banking Review Vol. 8 (4) January - March 2024 8, no. 4 (2024): 01–16. http://dx.doi.org/10.35609/jfbr.2024.8.4(1).

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Objective – This study aims to contribute to the field of cryptocurrency portfolio management and rebalancing strategies by empirically investigating the impact of different allocation frequencies and threshold percentages on the risk-adjusted returns of cryptocurrency portfolios. Methodology/Technique – Utilizing a simulation of 10,000 cryptocurrency portfolios comprising seven assets, including Ethereum (ETH), Bitcoin (BTC), Tether (USDT), Litecoin (LTC), Solana (SOL), Dogecoin (DOGE), and Polygon (MATIC), this study examines and compares the effects of different allocation frequencies (dail

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Demos, Guilherme, Thomas Pires, and Guilherme Valle Moura. "Rebalanceamento Endógeno para Portfólios de Variância Mínima." Brazilian Review of Finance 13, no. 4 (2015): 544. http://dx.doi.org/10.12660/rbfin.v13n4.2015.49112.

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Investment managers often rebalance portfolios using it ad-hoc criteria due the trade-off between gains from updating optimal weight and costs incurred from changing the portfolio composition. A common solution for this stalemate is rebalancing the portfolio based on some exogenous criteria. By monitoring the optimal weights of the portfolio through control charts, the authors propose a portfolio rebalance strategy based solely on endogenous information. The optimal portfolio weights are thus monitored daily and if statistical significant changes are detected we either rebalance or not the por

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Ielasi, Federica, Paolo Ceccherini, and Pietro Zito. "Integrating ESG Analysis into Smart Beta Strategies." Sustainability 12, no. 22 (2020): 9351. http://dx.doi.org/10.3390/su12229351.

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Smart beta strategy is an increasingly frequent approach to investment analysis for portfolio selection and optimization and it can be combined with environmental, social, and governance (ESG) considerations. In order to verify the impact of the integration between ESG and smart beta analysis, first we apply a portfolio rebalancing based on ESG scores on securities selected according to different smart beta strategies (ex-post ESG rebalancing approach). Secondly, we apply different smart beta approaches to sustainable portfolios, screened according to the issuers’ ESG scores (ex-ante ESG scree

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Lowe, Stephen. "Rebalancing the Portfolio." AIMR Conference Proceedings 1998, no. 6 (1998): 117–25. http://dx.doi.org/10.2469/cp.v1998.n6.12.

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Sahu, Sonal, José Hugo Ochoa Vázquez, Alejandro Fonseca Ramírez, and Jong-Min Kim. "Analyzing Portfolio Optimization in Cryptocurrency Markets: A Comparative Study of Short-Term Investment Strategies Using Hourly Data Approach." Journal of Risk and Financial Management 17, no. 3 (2024): 125. http://dx.doi.org/10.3390/jrfm17030125.

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This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to construct and assess portfolios across various rebalancing frequencies. Our empirical analysis reveals significant volatility, skewness, and kurtosis in cryptocurrencies, highlighting the need for sophisticated portfolio management techniques. We discover that the Kurtosis Mi

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Wang, Meihua, Cheng Li, Honggang Xue, and Fengmin Xu. "A New Portfolio Rebalancing Model with Transaction Costs." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/942374.

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A portfolio rebalancing model with self-finance strategy and consideration of V-shaped transaction cost is presented in this paper. Our main contribution is that a new constraint is introduced to confirm that the rebalance necessity of the existing portfolio needs to be adjusted. The constraint is constructed by considering both the transaction amount and transaction cost without any additional supply to the investment amount. The V-shaped transaction cost function is used to calculate the transaction cost of the portfolio, and conditional value at risk (CVaR) is used to measure the risk of th

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Jha, Manoj, and Namita Srivastava. "Portfolio Rebalancing Model Using Fuzzy Optimization." International Journal of Scientific Engineering and Research 1, no. 4 (2013): 59–70. https://doi.org/10.70729/j201378.

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Haneefa, Muhasin. "The Act of Rebalancing the Portfolio." Paripex - Indian Journal Of Research 3, no. 6 (2012): 105–6. http://dx.doi.org/10.15373/22501991/june2014/33.

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Meirelles, Sofia Kusiak, and Marcelo Fernandes. "Estratégias de Imunização de Carteiras de Renda Fixa no Brasil." Brazilian Review of Finance 16, no. 2 (2018): 179. http://dx.doi.org/10.12660/rbfin.v16n2.2018.69279.

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This paper aims to statistically compare the performance of two hedging strategies for Brazilian fixed income portfolios, with discrete rebalancing. The first hedging strategy matches duration, and hence it considers only small parallel changes in the yield curve. The alternative methodology ponders level, curvature and convexity shifts through a factor model. We first estimate the yield curve using the polynomial model of Nelson & Siegel (1987) and Diebold & Li (2006) and then immunize the fixed income portfolio using Litterman & Scheinkman’s (1991) hedging procedure. The alternat

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Boyante, Roba, Willy Muturi, and Mouni Gekara. "Moderating Influence of Portfolio Rebalancing on the Relationship between Asset Allocation and Financial Performance of Pension Funds in Kenya." International Journal of Finance and Accounting 7, no. 3 (2022): 56–67. http://dx.doi.org/10.47604/ijfa.1644.

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Purpose: This paper examined the moderating influence of portfolio rebalancing on the relationship between asset allocation and financial performance of pension funds in Kenya. Methodology: The study used a descriptive research design with data collection form used to gather secondary data. The target population for this study was 1,258 registered schemes as per RBA as of 31 December 2021. The sample consisted of 294 registered schemes. Secondary data was obtained from the Retirement Benefits Authority (RBA) for the study variables for the six-year period between 2016- 2021. The data was

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Purata-Aldaz, José, Juan Frausto-Solís, Guadalupe Castilla-Valdez, Javier González-Barbosa, and Juan Paulo Sánchez Hernández. "MASIP: A Methodology for Assets Selection in Investment Portfolios." Mathematical and Computational Applications 30, no. 2 (2025): 34. https://doi.org/10.3390/mca30020034.

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This paper proposes a Methodology for Assets Selection in Investment Portfolios (MASIP) focused on creating investment portfolios using heuristic algorithms based on the Markowitz and Sharpe models. MASIP selects and allocates financial assets by applying heuristic methods to accomplish three assignments: (a) Select the stock candidates in an initial portfolio; (b) Forecast the asset values for the short and medium term; and (c) Optimize the investment portfolio by using the Sharpe metric. Once MASIP creates the initial portfolio and forecasts its assets, an optimization process is started in

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Gadde, Nishant. "Machine Learning for Real-Time Portfolio Rebalancing: A Novel Approach to Financial Optimization." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 19–23. http://dx.doi.org/10.22214/ijraset.2024.64375.

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Portfolio rebalancing is an important practice in finance, which keeps the asset allocations of an investor's portfolio in conformance with his or her risk tolerance and financial goals. Traditional rebalancing strategies are largely of a static nature, with rebalancing being periodically performed or when the portfolio valuations exceed fixed thresholds. Obviously, these methods do not take into consideration the dynamic and rapidly changing nature of financial markets, wherein the prices of various assets can change drastically due to macroeconomic events, market sentiments, and other factor

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Chaiyarit, Yotaek, and Pongsutti Phuensane. "Optimizing Portfolio Efficiency in the Digital Era: A Data Envelopment Analysis of Range-Rebalanced Asset Investments." International Journal of Analysis and Applications 22 (July 29, 2024): 122. http://dx.doi.org/10.28924/2291-8639-22-2024-122.

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In the digital era, the advent of new asset classes like cryptocurrencies and the application of advanced analytical tools have significantly reshaped portfolio management. This study employs Data Envelopment Analysis (DEA) to assess the efficiency of range-rebalanced investment portfolios incorporating diverse assets such as cryptocurrencies, major currencies, technology securities, and commodities. The analysis spans from October 1, 2016, to June 30, 2022, evaluating various rebalancing strategies including Allowed Range, Threshold, Drifting Mix, and Tactical approaches during different mark

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Boďa, Martin, and Mária Kanderová. "What is the True Effect of Rebalancing – A Higher Return or a Lower Risk?" Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 6 (2018): 1417–30. http://dx.doi.org/10.11118/actaun201866061417.

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The paper is motivated by the fact that rebalancing in portfolio management has an effect recognisable with both return and risk, although its purported ambition is to control (or decrease) portfolio risk. Focusing upon rebalancing strategies in quadratic tracking, the paper investigates whether rebalancing contributes to higher returns or lower risks. The investigation is conducted as a case study of tracking the S&P 500 Index by means of its constituents in four different time periods spanning from 2011 to 2017. Different approaches to stock pre‑selection (according to investment styles

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Sornmayura, Sutta, Nichanan Sakolvieng, and Kaimook Numgaroonaroonroj. "Cryptocurrency Portfolio Rebalancing: A Comparative Analysis of Time-Based and Threshold-Based Rebalancing Strategies." 15TH GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES ON 14 - 15 SEPTEMBER 2023, NOVOTEL BANGKOK PLATINUM PRATUNAM, THAILAND 15, no. 1 (2023): 175. http://dx.doi.org/10.35609/gcbssproceeding.2023.1(175).

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The rise of cryptocurrencies as an asset class has attracted the attention of investors and portfolio managers alike (Baur & Hendershott, 2018; Chuen, Guo, & Wang, 2017). One of the key challenges in managing a cryptocurrency portfolio is the high volatility and uncertainty of the market. To address these challenges, rebalancing, which involves adjusting the weights of assets in a portfolio to align with a predetermined asset allocation, is applied to maintain a diversified portfolio and manage the risks of digital assets (Bakry, Rashid, Al-Mohamad, & El-Kanj, 2021). For any tradit

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20

Hilliard, Jimmy E., and Jitka Hilliard. "A Comparison of Rebalanced and Buy and Hold Portfolios: Does Monetary Policy Matter?" Review of Pacific Basin Financial Markets and Policies 18, no. 01 (2015): 1550006. http://dx.doi.org/10.1142/s021909151550006x.

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We examine buy and hold and a number of rebalancing strategies on a portfolio of indices that are tracked by ETFs. The indices include Barkley's treasuries and MSCI indices on emerging markets, Pacific and European markets, value funds, and growth funds. Portfolios are rebalanced using threshold schemes and compared with portfolios rebalanced using weights from different points on the Markowitz efficient frontier. We also examine portfolios that are rebalanced across monetary policy regimes. We find that portfolios rebalanced using Markowitz weights that allow for shifts in monetary policy reg

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21

D. Mattei, Michael, and Daniel Bauer. "Rebalance Your Investment Portfolio Periodically: Mantra or Myth?" International Journal of Business & Management Studies 05, no. 03 (2024): 14–17. http://dx.doi.org/10.56734/ijbms.v5n3a3.

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The investment mantra goes something like this: “have a well-diversified mix of stocks and bonds and rebalance it to maintain your strategic asset allocation.” Recent publications in the financial press have begun to question the value of periodic portfolio rebalancing. This research examines the equity portion of a well-diversified investment portfolio and shows that the “numbers” just don’t support the mantra. In fact, this research indicates that traditional rebalancing is the worst strategy, on a risk adjusted basis, for long-term portfolio growth when compared to three other strategies. W

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22

Morhachov, I. V. "FEATURES OF DIVERSIFICATION AND REBALANCING OF THE SECURITIES PORTFOLIO: ASPECTS OF ORGANIZATION OF INVESTMENT FUNDS." Economics and Law, no. 1 (May 10, 2022): 98–108. http://dx.doi.org/10.15407/econlaw.2022.01.098.

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Features of diversification and formation of individual parts of the securities portfolio, as well as the parameters of bringing it to the planned parameters have a significant impact on investment efficiency and risk. The urgency of rebalancing the securities portfolio in the activities of investment funds has been clarified, as such entities often have a certain policy on the structure of assets and liabilities. The aim of the work is to clarify the features of rebalancing the securities portfolio, which ensure the optimal parameters for the level of return on investment and risk. The need t

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Škrinjarić, Tihana, and Boško Šego. "Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach." Business Systems Research Journal 7, no. 2 (2016): 78–90. http://dx.doi.org/10.1515/bsrj-2016-0014.

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Abstract Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation and restructuring over time. Objectives: This study utilizes MGARCH methodology on Croatian fina

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Meher, Premananda, and Rohita Kumar Mishra. "Risk-Adjusted Portfolio Optimization: Monte Carlo Simulation and Rebalancing." Australasian Business, Accounting and Finance Journal 18, no. 3 (2024): 85–101. http://dx.doi.org/10.14453/aabfj.v18i3.06.

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This study evaluates the risk-adjusted performance of a diversified portfolio in the Indian financial market from 2011 to 2021, incorporating Nifty 50 stocks and new-age assets. Leveraging Monte Carlo simulations and mathematical optimization, the research identifies an optimal portfolio on the efficient frontier. Integration of the Black-Litterman model provides a comparative analysis, emphasizing the impact of investor views. Despite transaction costs, optimized portfolios outperform the Nifty 50 index, with the rebalanced portfolio demonstrating higher cumulative returns. Key findings inclu

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Luo, Ronghua, Yi Liu, and Wei Lan. "A penalized expected risk criterion for portfolio selection." China Finance Review International 9, no. 3 (2019): 386–400. http://dx.doi.org/10.1108/cfri-12-2017-0226.

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Purpose Under the classical mean-variance framework, the purpose of this paper is to investigate the properties of the instability of minimal variance portfolio and then propose a novel penalized expected risk criterion (PERC) for optimal portfolio selection. Design/methodology/approach The proposed method considers not only a portfolio’s expected risk, but also its instability that is quantified by the variance of the estimated portfolio weights. This study tests the out-of-sample performance of various portfolio selection methods on both China and US stock markets. Findings It is very useful

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Sher, G., and G. D. I. Barr. "Portfolio rebalancing in South Africa." South African Journal of Accounting Research 25, no. 1 (2011): 59–80. http://dx.doi.org/10.1080/10291954.2011.11435153.

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Kimball, Miles S., Matthew D. Shapiro, Tyler Shumway, and Jing Zhang. "Portfolio rebalancing in general equilibrium." Journal of Financial Economics 135, no. 3 (2020): 816–34. http://dx.doi.org/10.1016/j.jfineco.2019.08.007.

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Irvan, Liunardi Senjaya, Erman Sumirat S.E M. Buss CSA CRP CIB AK. Dr., and Dr. Ir. Sudarso Kaderi Wiryono DEA. Prof. "Portfolio Rebalancing with GARCH Model at Jarvis Balanced Fund." International Journal of Current Science Research and Review 06, no. 02 (2023): 1362–73. https://doi.org/10.5281/zenodo.7645909.

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Abstract : With high inflation economy, investors must find another way to minimize their risk, but also maximize their return of the portfolio. Various instruments used for finding the most suitable amount of portfolio allocation. Single instruments, such as stocks, bonds, and time deposits is chosen by investors to secure their assets from inflation. The other investors chose mutual funds to grow their investments. One of the solutions to find the portfolio allocation is to rebalance the portfolio. In other perspectives, time-series model will help investors to predict the volatility that wi

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Curcuru, Stephanie E., Charles P. Thomas, Francis E. Warnock, and Jon Wongswan. "US International Equity Investment and Past and Prospective Returns." American Economic Review 101, no. 7 (2011): 3440–55. http://dx.doi.org/10.1257/aer.101.7.3440.

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Counter to extant stylized facts, using newly available data on country allocations in US investors' foreign equity portfolios we find that (i) US investors do not exhibit returns-chasing behavior, but, consistent with partial portfolio rebalancing, tend to sell past winners; and (ii) US investors increase portfolio weights on a country's equity market just prior to its strong performance, behavior inconsistent with an informational disadvantage. Over the past two decades, US investors' foreign equity portfolios outperformed a value-weighted foreign benchmark by 160 basis points per year. JEL:

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Sant'Anna, Leonardo Riegel, Tiago Pascoal Filomena, and Denis Borenstein. "Index Tracking com Controle do Número de Ativos." Brazilian Review of Finance 12, no. 1 (2014): 89. http://dx.doi.org/10.12660/rbfin.v12n1.2014.10622.

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Index tracking is a passive investment strategy, which aims at generating portfolios to reproduce a specific market index’s performance. This article proposes a model for a index tracking problem with control on the number of assets in the portfolio, which corresponds to a restriction in transaction costs. The model is applied to Ibovespa (sample: 67 stocks) from January/2009 to July/2012. Portfolios were formed without limiting the amount of stocks and limiting this amount to 40, 30 and 20 stocks, with rebalancing periods of 20, 40 and 60 trading days. The results were satisfactory especially

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Clarissa, Adeline, and Deddy Priatmodjo Koesrindartoto. "Strategic portfolio rebalancing: Integrating predictive models and adaptive optimization objectives in a dynamic market." Investment Management and Financial Innovations 21, no. 3 (2024): 304–16. http://dx.doi.org/10.21511/imfi.21(3).2024.25.

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Adjusting investment strategy is one of the ways to handle dynamic market conditions. This study proposes a novel portfolio management strategy using appropriate optimization objectives for different stock market trends while also incorporating market trends and stock return predictions The optimization objectives that will be evaluated for different market trends are maximizing the Sharpe ratio, minimizing risk, and minimizing expected shortfall. This study utilizes simulation modelling with various predictive models on building the portfolios. The results show that, in an upward market trend

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Fischer, Andreas M., Rafael P. Greminger, Christian Grisse, and Sylvia Kaufmann. "Portfolio rebalancing in times of stress." Journal of International Money and Finance 113 (May 2021): 102360. http://dx.doi.org/10.1016/j.jimonfin.2021.102360.

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Israelov, Roni, and Harsha Tummala. "An Alternative Option to Portfolio Rebalancing." Journal of Derivatives 25, no. 3 (2018): 7–32. http://dx.doi.org/10.3905/jod.2018.25.3.007.

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Tokat, Yesim, and Nelson W. Wicas. "Portfolio Rebalancing in Theory and Practice." Journal of Investing 16, no. 2 (2007): 52–59. http://dx.doi.org/10.3905/joi.2007.686411.

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Donohue, Christopher, and Kenneth Yip. "Optimal Portfolio Rebalancing with Transaction Costs." Journal of Portfolio Management 29, no. 4 (2003): 49–63. http://dx.doi.org/10.3905/jpm.2003.319894.

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Liu, Ding. "Analytical solutions of optimal portfolio rebalancing." Quantitative Finance 19, no. 4 (2018): 683–97. http://dx.doi.org/10.1080/14697688.2018.1520394.

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de Villiers, Johann U. "Portfolio Rebalancing in Theory and Practice." CFA Digest 37, no. 4 (2007): 85–86. http://dx.doi.org/10.2469/dig.v37.n4.4889.

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Guastaroba, Gianfranco, Renata Mansini, and M. Grazia Speranza. "Models and Simulations for Portfolio Rebalancing." Computational Economics 33, no. 3 (2008): 237–62. http://dx.doi.org/10.1007/s10614-008-9158-y.

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Yu, Jing-Rung, and Wen-Yi Lee. "Portfolio rebalancing model using multiple criteria." European Journal of Operational Research 209, no. 2 (2011): 166–75. http://dx.doi.org/10.1016/j.ejor.2010.09.018.

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Perdue, Grady, and Joseph McCormack. "TIME INTERVALS FOR REBALANCING A PORTFOLIO." Journal of International Finance Studies 14, no. 3 (2014): 53–58. http://dx.doi.org/10.18374/jifs-14-3.4.

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Kim, Kyungkeun, and Dongwon Lee. "Equity market integration and portfolio rebalancing." Journal of Banking & Finance 113 (April 2020): 105775. http://dx.doi.org/10.1016/j.jbankfin.2020.105775.

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Mattei, Michael D., and Nicholas Mattei. "Analysis of fixed and biased asset allocation rebalancing strategies." Managerial Finance 42, no. 1 (2015): 42–50. http://dx.doi.org/10.1108/mf-10-2015-0264.

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Purpose – Over the years a number of tactical, dynamic and strategic approaches for asset allocation have been developed to improve the objectivity of portfolio management. One of the most popular approaches is to annually rebalance a portfolio of six to ten assets classes back to an equal or fixed percentage. Most researchers agree that this is essentially a contrarian strategy. The purpose of this paper is to develop and evaluate an asset allocation methodology using a biasing factor that can implement a momentum strategy for investors who might prefer momentum investing. Design/methodology/

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Bányai, Attila, Tibor Tatay, Gergő Thalmeiner, and László Pataki. "Consumer Expenditure-Based Portfolio Optimization." International Journal of Financial Studies 13, no. 2 (2025): 99. https://doi.org/10.3390/ijfs13020099.

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This study examines whether portfolio optimization can be effectively based on annual changes in the harmonized index of consumer prices (HICP) data. Specifically, we assess whether asset allocation based on consumer expenditure can generate superior returns compared to static or equal-weighted asset allocation. To explore this, we use consumer expenditure data from HICP statistics categorized by COICOP. Our findings indicate that this strategy outperforms a buy-and-hold benchmark by 13.32% in terms of the Sharpe Ratio and exceeds an annual equal-weighted rebalancing strategy by 3.11%. Additio

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Blouin, Jennifer L., Brian J. Bushee, and Stephanie A. Sikes. "Measuring Tax-Sensitive Institutional Investor Ownership." Accounting Review 92, no. 6 (2017): 49–76. http://dx.doi.org/10.2308/accr-51719.

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ABSTRACT We classify all institutional investors that file Form 13-F over the period 1995–2013 as either “tax-sensitive” or “tax-insensitive” based on their trading behavior and portfolio characteristics. We examine tests of the effects of investor tax-sensitivity on portfolio rebalancing, price pressure, and fund performance, and compare our measure of tax-sensitive institutional investor ownership to three measures used in prior studies. We show that our measure of tax-sensitive investors dominates other measures in the portfolio rebalancing and price pressure tests. In the fund performance

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Zhou, Xintong. "From Theory to Practice: Applying the Markowitz Model in Stock Portfolio Management under ESG." International Journal of Global Economics and Management 2, no. 3 (2024): 369–85. http://dx.doi.org/10.62051/ijgem.v2n3.44.

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The study consisted of a month-long simulated stock market operation focusing on the comprehensive analysis of equity portfolio creation and management. Against the backdrop of growing concern for environmental protection, the S&P 500 Net Zero 2050 Climate Transition ESG Index was deemed the appropriate benchmark for the portfolios due to the average level of risk tolerance of customers. The investigation began with an in-depth assessment of macroeconomic and sector conditions, followed by careful selection of securities using both fundamental and technical analysis techniques. The portfol

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West, Tracey, and Andrew C. Worthington. "Life Events and Portfolio Rebalancing of the Family Home." Journal of Financial Counseling and Planning 29, no. 1 (2018): 103–13. http://dx.doi.org/10.1891/1052-3073.29.1.103.

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This article investigates the impacts of financial shocks on the role of the family home in asset portfolios of Australian households using longitudinal data from the Household, Income, and Labour Dynamics in Australia (HILDA) survey. The life events considered are serious illness or injury, death of a spouse, fired or made redundant, and separation from a spouse. We use a static and dynamic Tobit models to assess the impact and duration of the life events on the portfolio share of the family home. The insights gained from this study may be important for financial planners, as adverse wealth o

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Jain, Prayut, and Shashi Jain. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification." Risks 7, no. 3 (2019): 74. http://dx.doi.org/10.3390/risks7030074.

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The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk-based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it does not require its invertibility. In this paper, we first study the impact of covariance misspecification on the performance of the different allocation methods. Next, we study under an appropriate covariance forecast model whether the machine learning based HRP outperforms the traditio

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DePrince, Albert, and Pamela Morris. "Assessing Alternative Equal-Weight Asset Re-Balancing Rules." Journal of Finance Issues 8, no. 1 (2010): 86–96. http://dx.doi.org/10.58886/jfi.v8i1.2357.

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This paper evaluates a hypothetical portfolio based on an equal-weight investment rule involving four asset classes: domestics, industrialized countries excluding the U.S., emerging market countries, and global bonds. Portfolio results using three alternative re-balancing rules are compared with a composite benchmark index. The simulation period runs from July 1996 through January 2010, which is dictated by the availability of data on the underlying asset classes. The equal-weight investment strategy is superior to investing the composite benchmark index in all three rules examined. The first

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González-Bueno, Jairo, Rima Tamošiūnienė, Camilo Gómez Morales, and Gladys Rueda-Barrios. "Applying the mean-variance framework: portfolio optimization and comparative performance analysis in the emerging Colombian capital market." Business, Management and Economics Engineering 23, no. 01 (2025): 164–88. https://doi.org/10.3846/bmee.2025.22695.

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Purpose – this paper adopts the mean-variance approach in optimizing portfolios within the Colombian capital market, a setting full of complications such as lack of liquidity and market concentration. It delivers actionable messages for emerging market stakeholders and formulates guidance aimed at enhancing risk-adjusted returns and informing portfolio management in markets with similar structural and economic conditions. Research methodology – a bi-objective mean-variance model has been used for analyzing the stock prices of 17 stocks on a weekly basis from 2009–2024. Annual rebalancing has m

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Kulikov, Alexander, Dmitriy Polozov, and Nikita Volkov. "Long-term investment optimization based on Markowitz diversification." Business Informatics 18, no. 3 (2024): 56–69. http://dx.doi.org/10.17323/2587-814x.2024.3.56.69.

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The article introduces a long-term investment algorithm that identifies optimal solutions in lower dimensional spaces constructed through principal component analysis or kernel principal component analysis. Portfolio weights optimization is carried out using the Markowitz method. Hyperparameters of the model include window size, smoothing parameter, rebalancing period and the fraction of explained variance in dimensionality reduction methods. The algorithm presented incorporates weights regularization taking into account portfolio rebalancing transaction costs. Hyperparameters’ selection is ba

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