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: *n'b1]@^V`.Ky[jWOkL_3=hfwCi]#[|^uNuL~O4Qn=mrbF1tBW@zM\$0V 

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).

Please read the detailed editing requirements before submitting your text. 

Number of Results 2

WP(18/2024)454. Explaining and forecasting abnormal returns and volume by investor sentiment indicators

Authors: Lis Szymon, Ślepaczuk Robert, Sakowski Paweł
This study investigates the impact of investor sentiment on stock returns and trading volume, challenging the efficient market hypothesis. Using CRSP data from May 1998 to March 2022, methods like Fama-MacBeth and quantile regression were applied to …

WP454. Explaining and forecasting abnormal returns and volume by investor sentiment indicators

This study investigates the impact of investor sentiment on stock returns and trading volume, challenging the efficient market hypothesis. Using CRSP data from May 1998 to March 2022, methods like Fama-MacBeth and quantile regression were applied to analyze sentiment indicators such as the VIX, AAII Investor Sentiment Survey, Consumer Confidence, and Baker-Wurgler Index. The findings reveal that investor sentiment significantly influences stock returns and trading volume, especially during uncertain times. Sentiment also affects financial metrics like SMB, HML, RMW, and CMA uniquely. This research provides new insights and practical implications for investors and analysts, emphasizing the importance of considering sentiment in investment strategies to better anticipate market movements and manage risks.


Lis Szymon, Ślepaczuk Robert, Sakowski Paweł
market sentiment factor models stock returns EMH Fama-MacBeth model quantile regression Baker-Wurgler index VIX index

WP(4/2012)70. Investment strategies beating the market. What can we squeeze from the market?

Authors: Ślepaczuk Robert, Zakrzewski Grzegorz, Sakowski Paweł
The paper presents the new approach to optimizing automatic transactional systems. We propose the multi-stage technique which enables us to find investment strategies beating the market. Additionally, new measures of combined risk and returns are app…

WP70. Investment strategies beating the market. What can we squeeze from the market?

The paper presents the new approach to optimizing automatic transactional systems. We propose the multi-stage technique which enables us to find investment strategies beating the market. Additionally, new measures of combined risk and returns are applied in the process of optimization. Moreover, we define new elements of risk control system based on volatility measures and consecutive signals confirmation. As the result, we formulate three complex investment systems, which maximize returns and simultaneously minimize risk in comparison to all other alternative investments (IR=2, Maximum Drawdown <21%, Maximum Loss Duration=0.75 year). Our analysis is based on historical daily data (1998-2010, in- and out-of-sample period) for index and commodity futures. Afterwards, systems are reoptimized and reallocated each half a year in order to include the most recent financial data. Finally, we show the results for joint model consisting of our three systems.


Ślepaczuk Robert, Zakrzewski Grzegorz, Sakowski Paweł
investment strategies automatic trading systems optimization technical and fundamental analysis market volatility efficient risk and return measures EMH mutual and hedge funds
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