Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Please send 2 files:

(1) the main text without the title of the article and the authors (DOC/DOCX file) and

(2) the title page including: the title of the paper, the authors and their affiliation (DOC/DOCX file).

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Number of Results 2

WP(17/2023)424. Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models

Authors: Ślusarczyk Damian, Ślepaczuk Robert
We aim to answer the question of whether using forecasted stock returns based on machine learning and time series models in a mean-variance portfolio framework yields better results than relying on historical returns. Nevertheless, the problem of the…

WP424. Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models

We aim to answer the question of whether using forecasted stock returns based on machine learning and time series models in a mean-variance portfolio framework yields better results than relying on historical returns. Nevertheless, the problem of the efficient stock selection has been tested for more than 50 years, the issue of adequate construction of mean-variance portfolio framework and incorporating forecasts of returns in it has not been solved yet. Stock returns portfolios were created using ’raw’ historical returns and forecasted return based on ARIMA-GARCH and the XGBoost models. Two optimization problems were concerned: global maximum information ratio and global mini-mum variance. Then strategies were compared with two benchmarks – an equally weighted portfolio and buy and hold on the DJIA index. Strategies were tested on Dow Jones Industrial Average stocks in the period from 2007-01-01 to 2022-12-31 and daily data was used. The main portfolio performance metrics were information ratio* and information ratio**. The results showed that using forecasted returns we can enhance our portfolio selection based on Markowitz framework, but it is not a universal solution, and we have to control all the parameters and hyperparameters of selected models.


Ślusarczyk Damian, Ślepaczuk Robert
Algorithmic Investment Strategies Markowitz framework portfolio optimization forecasting ARIMA GARCH XGBoost minimum variance

WP(41/2020)347. Verification of Investment Opportunities on the Cryptocurrency Market within the Markowitz Framework

Authors: Sakowski Paweł, Turovtseva Anna
The aim of the paper is to reveal if the classical approach for asset allocation can be reflected on an innovative market of cryptocurrencies. Markowitz rebalanced portfolio technique is employed for this purpose. The filtering of coins for optimizat…

WP347. Verification of Investment Opportunities on the Cryptocurrency Market within the Markowitz Framework

The aim of the paper is to reveal if the classical approach for asset allocation can be reflected on an innovative market of cryptocurrencies. Markowitz rebalanced portfolio technique is employed for this purpose. The filtering of coins for optimization is done on the whole scope of cryptocurrencies available for the time horizon of the study and only 52 coins get into the portfolio at least once. There are four primary strategies produced within a set of assumed optimization parameters together with four benchmarks for each. The benchmarks are Bitcoin buy-and-hold, S&P500 buy-and-hold, equally weighted portfolio and portfolio weighted by market capitalization. While looking at the performance measures, it is concluded that Markowitz strategies outperform their benchmarks for every set of parameters. The results of the sensitivity analysis suggest that there is a big potential in finding profitable strategy of investment on cryptocurrencies. The change of parameters: look-back period, rebalancing window, transaction cost as well as optimization objective impact the strategies performance significantly. Eventually, there appear a strategy in the sensitivity analysis, which performs better than the primary ones due to the prolonged parameters of look-back period and rebalancing window.


Sakowski Paweł, Turovtseva Anna
Portfolio analysis Markowitz framework cryptocurrencies investment strategies asset allocation
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