Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: +'kW`pP4OXY}R3yK[c]S^$Dh9EM]#[~r^CJ_4!-I?pFNq6AlSKgf!^F26

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 5

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

Autorzy: 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(14/2022)390. Investor Sentiment in Asset Pricing Models: A Review

Autorzy: Lis Szymon
Despite the number of works on investor sentiment in asset pricing models the results did not allow to obtain a coherent knowledge about this sentiment. Most of the researchers used different measures and various models to study the impact of sentime…

WP390. Investor Sentiment in Asset Pricing Models: A Review

Despite the number of works on investor sentiment in asset pricing models the results did not allow to obtain a coherent knowledge about this sentiment. Most of the researchers used different measures and various models to study the impact of sentiment on stocks returns. However, the empirical relationship between investor sentiment and stock market behavior remains unclear. This study focuses on reviewing the methodologies and empirical findings of 71 papers published between 2000 and 2021 that apply different investor sentiment measures for modeling returns. The research confirmed two out of the three research hypotheses that the investor sentiment proxies and higher complexity of the model with the investor sentiment indicator improve the coefficient of determination. The second one was rejected, however, this may be due to too small a sample. For the hypothesis that models with more complex sentiment have better predictive power than those with simpler proxies, the number of studies was insufficient to refer strongly to the hypothesis.


Lis Szymon
Investor sentiment Asset pricing Multifactor models Behavioral finance Risk factors stock market behavior

WP(10/2016)201. Applying Exogenous Variables and Regime Switching to Multifactor Models on Equity Indices

Autorzy: Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
This article aims to extend the evaluation of classic multifactor models of Carhart(1997) for the case of global equity indices and to expand analysis performed in Sakowski, Slepaczuk, and Wywial (2015). Our intention is to test several modifications…

WP201. Applying Exogenous Variables and Regime Switching to Multifactor Models on Equity Indices

This article aims to extend the evaluation of classic multifactor models of Carhart(1997) for the case of global equity indices and to expand analysis performed in Sakowski, Slepaczuk, and Wywial (2015). Our intention is to test several modifications of these models to take into account different dynamics of equity excess returns between emerging and developed equity indices. Proposed extensions include volatility regime switching mechanism (using dummy variables and the Markov approach) and three new risk factors based on realized volatility of index returns, percentage deviation from nominal GDP trend and capitalisation relative to GDP factor. Additional modifications include introduction of common and country specific variables in order to control for global risk where fluctuations of volatility of various assets, prices of commodities, currencies and rates is really important. Moreover, instead of using data for individual stocks (which is a common approach in the literature), we evaluate the performance of these models for weekly data of 81 world investable equity indices in the period of 2000-2015. Such approach is proposed to estimate equity risk premium for a single country. Empirical evidence from the first part reveals important differences between results for classical models estimated on single stocks (either in international or US-only frame work) and models evaluated for equity indices. Additionally, we observe substantial discrepancies between results for developed countries and emerging markets. The last part of this research helps us to understand results revealed in the first part. Thanks to introduction of new risk factors, and additional common and country specific variables we were able to increase explanatory power of our factor models especially in case of emerging market indices. Finally, using weekly data for the last 15 years we illustrate importance of model risk and data overfitting effects when drawing conclusions upon results of multifactor models.


Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
multi-factor models asset pricing models equity risk premia equity indices new risk factors sensitivity analysis book to market momentum market price of risk emerging and developed equity indices

WP(9/2016)200. Can We Invest Based on Equity Risk Premia and Risk Factors from Multi-Factor Models?

Autorzy: Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
We find that detailed analysis of multi-factor models makes it possible to propose investment strategies based on equity risk premium disequlibrium. We examine two investment algorithms built on weekly data of world equity indices for emerging and de…

WP200. Can We Invest Based on Equity Risk Premia and Risk Factors from Multi-Factor Models?

We find that detailed analysis of multi-factor models makes it possible to propose investment strategies based on equity risk premium disequlibrium. We examine two investment algorithms built on weekly data of world equity indices for emerging and developed countries in the period of 2000-2015. We create seven risk factors using additional data about market capitalisation, book value, country GDP and betas of equity indices. The first strategy utilises theoretical value of equity risk premium from seven-factor Markov-switching model with variables common for all countries and variables specific to developed/emerging countries. We compare theoretical with realised equity risk premium for a given index to undertake the buy/sell decisions. The second algorithm works only on eight risk factors and applies them as input variables to Markowitz models with alternative optimisation criteria (target risk, target return, maximum Sharpe ratio, minimum variance and equally weighted assets). Finally, we notice that the impact of risk factors on final results of investment strategy is much more important than the selection of a particular econometric model in order to correctly evaluate equity risk premium.


Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
investment algorithms multi-factor models Markov switching model asset pricing models equity risk premia risk factors Markowitz model

WP(8/2016)199. Do Multi-Factor Models Produce Robust Results? Econometric and Diagnostic Issues in Equity Risk Premia Study

Autorzy: Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
In recent decades numerous studies verified empirical validity of the CAPM model. Many of them showed that CAPM alone is not able to explain cross-sectional variation of stock returns. Researchers revealed various risk factors which explained outperf…

WP199. Do Multi-Factor Models Produce Robust Results? Econometric and Diagnostic Issues in Equity Risk Premia Study

In recent decades numerous studies verified empirical validity of the CAPM model. Many of them showed that CAPM alone is not able to explain cross-sectional variation of stock returns. Researchers revealed various risk factors which explained outperformance of given groups of stocks or proposed modifcations to existing multi-factor models. 
Surprisingly, we hardly find any discussion in financial literature about potential drawbacks of applying standard OLS method to estimate parameters of such models. Yet, the question of robustness of OLS results to invalid assumptions shouldn't be ignored. This article aims to address diagnostic and econometric issues which can influence results of a time-series multifactor model. 
Based on the preliminary results of a five-factor model for 81 emerging and developed equity indices (Sakowski, Slepaczuk and Wywial, 2016a) obtained with OLS we check the robustness of these results to popular violations of OLS assumptions. We find autocorrelation of error term, heteroscedasticity and ARCH effects for most of 81 regressions and apply an AR-GARCH model using MLE to remove them. We also identify outliers and diagnose collinearity problems. Additionally, we apply GMM to avoid strong assumption of IID error term. 
Finally, we present comparison of parameters estimates and Rsquared values obtained by three different methods of estimation: OLS, MLE and GMM. We find that results do not differ substantially between these three methods and allow to draw the same conclusions from the investigated five-factor model.


Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
multi-factor models asset prising models equity risk premia OLS MLE GMM autocorrelation heteroscedasticity outliers collinearity normality econometric diagnostics
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