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: {PTJD-hW/c\p$?&*'8w|}5nSZt3]#[s@G2*vPHkTBctV{shEmt+uSIcez

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 7

WP(10/2024)446. Predictive modeling of foreign exchange trading signals using machine learning techniques

Autorzy: Sugarbayar Enkhbayar, Ślepaczuk Robert
This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based strategies and traditional trend-following strategies with benchmark strategies. It differs from other s…

WP446. Predictive modeling of foreign exchange trading signals using machine learning techniques

This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based strategies and traditional trend-following strategies with benchmark strategies. It differs from other studies in that it considered a wide variety of cases including different foreign exchange pairs, return methods, data frequency, and individual and integrated trading strategies. Ridge regression, KNN, RF, XGBoost, GBDT, ANN, LSTM, and GRU models were used for the machine learning-based strategy, while the MA cross strategy was employed for the trend-following strategy. Backtests were performed on 6 major pairs in the period from January 1, 2000, to June 30, 2023, and daily, and intraday data were used. The Sharpe ratio was considered as a metric used to refer to economic significance, and the independent t-test was used to determine statistical significance. The general findings of the study suggested that the currency market has become more efficient. The rise in efficiency is probably caused by the fact that more algorithms are being used in this market, and information spreads much faster. Instead of finding one trading strategy that works well on all major foreign exchange pairs, our study showed it’s possible to find an effective algorithmic trading strategy that generates a more effective trading signal in each specific case.


DOI: https://doi.org/10.33138/2957-0506.2024.10.446
Sugarbayar Enkhbayar, Ślepaczuk Robert
machine learning algorithmic trading foreign exchange market rolling walk-forward optimization technical indicators

WP(15/2023)422. Ensembled LSTM with Walk Forward Optimization in Algorithmic Trading

Autorzy: Chojnacki Karol, Ślepaczuk Robert
This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were …

WP422. Ensembled LSTM with Walk Forward Optimization in Algorithmic Trading

This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compared to Buy and Hold benchmark and evaluated using Performance Metrics, that is Annualized Return Compounded, Maximum Drawdown, Maximum Loss Duration, and three types of Information Ratio. This research uses daily S&P 500 index data ranging from 2000 to 2023. Every strategy was optimized with novel walk forward approach consisting of numerous in sample and out of sample periods. MAC and best performing ML methods were subjected to sensitivity analysis. The results show that LSTM ensembled with XGBoost and MAC yields the most promising results in terms of risk-adjusted returns which suggest further research focused on ensembling of individual ML strategies. Finally, we show that classical methods of technical analysis (that is, MAC) are much less robust and indifferent to change in hyperparameters than machine learning based algorithms, especially LSTM.


Chojnacki Karol, Ślepaczuk Robert
Algorithmic Investment Strategies Machine Learning Recurrent Neural Networks Long Short-Term Memory XGBoost Walk Forward Optimization Trading algorithms Technical Analysis Indicators

WP(29/2022)405. The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index

Autorzy: Nguyen Thi Thu Giang, Ślepaczuk Robert
The study compares the use of various Long Short-Term Memory (LSTM) variants to conventional technical indicators for trading the S&P 500 index between 2011 and 2022. Two methods were used to test each strategy: a fixed training data set from 200…

WP405. The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index

The study compares the use of various Long Short-Term Memory (LSTM) variants to conventional technical indicators for trading the S&P 500 index between 2011 and 2022. Two methods were used to test each strategy: a fixed training data set from 2001–2010 and a rolling train–test window. Due to the input sensitivity of LSTM models, we concentrated on data processing and hyperparameter tuning to find the best model. Instead of using the traditional MSE function, we used the Mean Absolute Directional Loss (MADL) function based on recent research to enhance model performance. The models were assessed using the Information Ratio and the Modified Information Ratio, which considers the maximum drawdown and the sign of the annualized return compounded (ARC). LSTM models' performance was compared to benchmark strategies using the SMA, MACD, RSI, and Buy&Hold strategies. We rejected the hypothesis that algorithmic investment strategy using signals from LSTM model consisting only from daily returns in its input layer is more efficient. However, we could not reject the hypothesis that signals generated by LSTM model combining daily returns and technical indicators in its input layer are more efficient. The LSTM Extended model that combined daily returns with MACD and RSI in the input layer generated a better result than Buy&Hold and other strategies using a single technical indicator. The results of the sensitivity analysis show how sensitive this model is to inputs like sequence length, batch size, technical indicators, and the length of the rolling train - test window.


Nguyen Thi Thu Giang, Ślepaczuk Robert
algorithmic investment strategies machine learning testing architecture deep learning recurrent neural networks LSTM technical indicators forecasting financial-time series technical indicators hyperparameter tuning S&P 500 Index

WP(37/2020)343. Predicting well-being based on features visible from space – the case of Warsaw

Autorzy: Andruszek Krystian, Wójcik Piotr
In recent years, availability of satellite imagery has grown rapidly. In addition, deep neural networks gained popularity and become widely used in various applications. This article focuses on using innovative deep learning and machine learning meth…

WP343. Predicting well-being based on features visible from space – the case of Warsaw

In recent years, availability of satellite imagery has grown rapidly. In addition, deep neural networks gained popularity and become widely used in various applications. This article focuses on using innovative deep learning and machine learning methods with combination of data that is describing objects visible from space. High resolution daytime satellite images are used to extract features for particular areas with the use of transfer learning and convolutional neural networks. Then extracted features are used in machine learning models (LASSO and random forest) as predictors of various socio-economic indicators. The analysis is performed on a local level of Warsaw districts. The findings from such approach can be a great help to get almost continuous measurement of the economic well-being, independently of statistical offices.


Andruszek Krystian, Wójcik Piotr
well-being economic indicators Open Street Map satellite images Warsaw

WP(17/2019)302. Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach

Autorzy: Korzeń Kamil, Ślepaczuk Robert
The purpose of this research is to test the potential returns and robustness of an automated investment strategy. The strategy is based on momentum and macroeconomic approach, that consists of the technical core – momentum, and the additional m…

WP302. Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach

The purpose of this research is to test the potential returns and robustness of an automated investment strategy. The strategy is based on momentum and macroeconomic approach, that consists of the technical core – momentum, and the additional macro screening, which is used to determine whether investment signals generate relevant investment opportunities or just technical noise. In order to check whether the macroeconomic factor is the value added to the momentum strategy, the hybrid approach is tested and compared with the simple momentum and the macroeconomic strategy alone and then assessed on a risk-adjusted return basis. The main aim of this paper is to answer the question, whether an investor can gain surplus risk-adjusted returns from merging short-term momentum strategy with the long-term macroeconomic approach. Strategies are based on the data for the selected companies from the S&P500 index in the period ranging from 02/01/1990 to 31/12/2018.


Korzeń Kamil, Ślepaczuk Robert
investment strategy momentum macroeconomic indicators algorithmic trading risk adjusted returns

WP(21/2018)280. Hybrid choice models vs. endogeneity of indicator variables: a Monte Carlo investigation

Autorzy: Budziński Wiktor, Czajkowski Mikołaj
We investigate the problem of endogeneity in the context of hybrid choice (integrated choice and latent variable) models. We first provide a thorough analysis of potential causes of endogeneity and propose a working taxonomy. We demonstrate that alth…

WP280. Hybrid choice models vs. endogeneity of indicator variables: a Monte Carlo investigation

We investigate the problem of endogeneity in the context of hybrid choice (integrated choice and latent variable) models. We first provide a thorough analysis of potential causes of endogeneity and propose a working taxonomy. We demonstrate that although it is widely believed that the hybrid choice framework is devoid of the endogeneity problem, there is no theoretical reason to expect that this is the case. We then demonstrate empirically that the problem exists in the hybrid choice framework too. By conducting a Monte Carlo experiment, we display the extent of the bias resulting from measurement and endogeneity biases. Finally, we propose two novel solutions to address the problem: by explicitly accounting for correlation between structural and discrete choice component error terms (or with random parameters in a utility function), or by introducing additional latent variables. Using simulated data, we demonstrate that these approaches work as expected, that is, they result in unbiased estimates of all model parameters.


DOI: http://doi.org.10.26405/WP/WNE/2018/280/021
Budziński Wiktor, Czajkowski Mikołaj
hybrid choice models endogeneity measurement bias attitudinal variables indicators

WP(1/2011)41. The coincident and the leading business cycle indicators for Poland

Autorzy: Woźniak Rafał
In the paper, two indicators, the coincident and the leading indicator, are proposed to represent the aggregated economic activity in Poland. The indicators are constructed with stochastic cycle and trend model. Not only does the presented approach s…

WP41. The coincident and the leading business cycle indicators for Poland

In the paper, two indicators, the coincident and the leading indicator, are proposed to represent the aggregated economic activity in Poland. The indicators are constructed with stochastic cycle and trend model. Not only does the presented approach solve the problem of existence of stochastic trends in the observed series, but it also allows to account for a different data span of each series in dataset.


Woźniak Rafał
business cycles leading indicators coincident indicators dynamic factor model stochastic cycle stochastic trend
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