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.

Please send your paper by e-mail: rL3Kp~Ax6]-DC%bvVG!n85ZhJTt]#[j8$3Zm!irNm33BZe8PufEu?^SEa 

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 3

WP(27/2023)434. Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models

Authors: Teymurzade Sahil, Ślepaczuk Robert
This paper implements automated trading strategies with buy/sell signals based on Autoregressive Integrated Moving Average (ARIMA) and Vector autoregression (VAR) models. ARIMA and VAR models are compared based on several forecast error measures and …

WP434. Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models

This paper implements automated trading strategies with buy/sell signals based on Autoregressive Integrated Moving Average (ARIMA) and Vector autoregression (VAR) models. ARIMA and VAR models are compared based on several forecast error measures and investment performance statistics. The data used in this thesis are daily closing prices of Dow Jones Industrial Average, NASDAQ Composite and NYSE Composite indices. The trading period covers 20 years of data from 2000-11-30 to 2020-11-30. The sensitivity analysis is made by changing the initial parameters to test how robust the methods are to these changes. Results show that although ARIMA model performed remarkably well during the volatile periods, VAR based strategy had better investment performance and was less robust to the changes compared to the ARIMA based strategy. Additionally, we have found that error metrics might be insufficient to evaluate performance of forecasting models, as VAR with higher forecast errors outperformed ARIMA model in algorithmic trading strategies.


Teymurzade Sahil, Ślepaczuk Robert
ARIMA model VAR model time series analysis algorithmic trading strategies investment systems statistical models forecasting stock prices

WP(27/2021)375. Robust optimisation in algorithmic investment strategies

Authors: Gómez Castellano Sergio, Ślepaczuk Robert
This research develops a portfolio of four algorithmic strategies that produce Long/Short signals based on t+1 close price predictions of the underlying instrument. The main instrument used is S&P 500 index, and the data covers the period from 19…

WP375. Robust optimisation in algorithmic investment strategies

This research develops a portfolio of four algorithmic strategies that produce Long/Short signals based on t+1 close price predictions of the underlying instrument. The main instrument used is S&P 500 index, and the data covers the period from 1990-01-01 to 2021-04-23. Each strategy is based on a different theory and aims to perform well in different market regimes. The objective is to have a set of uncorrelated investment strategies based on different logics such as trend-following, contrarian approach, statistical methods, and macro-economic news. Each strategy was individually generated following a personalized Walk-Forward optimisation, in which the model seeks to choose the most robust combination of parameters rather than the best one, in terms of risk-adjusted returns. The robustness of all strategies was tested by changing all parameters selected at the beginning of the optimisation. Additionally, the robustness of the portfolio of strategies is tested by applying it to another American index, Nasdaq Composite. Finally, the ensemble model was created based on the combination of the signals from all investment strategies for our two basis instruments. Results show that the portfolio obtains returns four (seven) times larger than the Buy & Hold strategy on S&P 500 (Nasdaq Composite) with a similar level of risk in the last 31 years.


Gómez Castellano Sergio, Ślepaczuk Robert
algorithmic trading strategies robust optimisation criteria overoptimisation walk-forward optimisation ensemble investment model

WP(39/2020)345. Applying Hurst Exponent in Pair Trading Strategies

Authors: Bui Quynh, Ślepaczuk Robert
This research aims to seek an alternative approach to pair selection for the purpose of pair trading strategy. We try to build an effective pair trading strategy based on 103 stocks listed in NASDAQ-100 index. The dataset has daily frequency and cove…

WP345. Applying Hurst Exponent in Pair Trading Strategies

This research aims to seek an alternative approach to pair selection for the purpose of pair trading strategy. We try to build an effective pair trading strategy based on 103 stocks listed in NASDAQ-100 index. The dataset has daily frequency and covers the period from 01/01/2000 to 31/12/2018. In this study, Generalized Hurst Exponent, Correlation and Cointegration are employed to detect the mean-reverting pattern in the time series of linear combination of each pair of stock. The result show that Hurst method cannot outperform the benchmark, which implies that market is efficient. This result is quite sensitive to varying number of pairs traded and rebalancing period and less sensitive to financial leverage degree. Moreover, Hurst method is better than cointegration method but is not superior as compared to correlation method.


Bui Quynh, Ślepaczuk Robert
generalized Hurst & Hurst exponent algorithmic trading strategies portfolio choice mean-reversion strategy pair trading correlation & cointegration trading efficient market hypothesis
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