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: SA3D|4C]fYk8=@gilN+WF'Uz?s6]#[K-$+f}%NHJQ#.W_XRW|OOh6pHd} 

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 4

WP(13/2024)449. The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models

Authors: Natalia Roszyk, Ślepaczuk Robert
Predicting the S&P 500 index's volatility is crucial for investors and financial analysts as it helps in assessing market risk and making informed investment decisions. Volatility represents the level of uncertainty or risk related to the siz…

WP449. The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models

Predicting the S&P 500 index's volatility is crucial for investors and financial analysts as it helps in assessing market risk and making informed investment decisions. Volatility represents the level of uncertainty or risk related to the size of changes in a security's value, making it an essential indicator for financial planning. This study explores four methods to improve the accuracy of volatility forecasts for the S&P 500: the established GARCH model, known for capturing historical volatility patterns; an LSTM network that utilizes past volatility and log returns; a hybrid LSTM-GARCH model that combines the strengths of both approaches; and an advanced version of the hybrid model that also factors in the VIX index to gauge market sentiment. This analysis is based on a daily dataset that includes data for S&P 500 and VIX index, covering the period from January 3, 2000, to December 21, 2023. Through rigorous testing and comparison, we found that machine learning approaches, particularly the hybrid LSTM models, significantly outperform the traditional GARCH model. The inclusion of the VIX index in the hybrid model further enhances its forecasting ability by incorporating real-time market sentiment. The results of this study offer valuable insights for achieving more accurate volatility predictions, enabling better risk management and strategic investment decisions in the volatile environment of the S&P 500.


DOI: https://doi.org/10.33138/2957-0506.2024.13.449
Natalia Roszyk, Ślepaczuk Robert
volatility forecasting LSTM-GARCH S&P 500 index hybrid forecasting models VIX index machine learning financial time series analysis walk-forward process hyperparameters tuning deep learning recurrent neural networks

WP(7/2024)443. LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies

Authors: Kashif Kamil, Ślepaczuk Robert
This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boost results of this RNN by…

WP443. LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies

This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boost results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across three equity indices (S&P 500, FTSE 100, and CAC 40) using daily frequency data spanning from January, 2000 to August, 2023. The architecture of testing is based on the walk-forward procedure which is applied for hyperparameter tunning phase that uses using Random Search and backtesting the algorithms. The selection of the optimal model is determined based on adequately selected performance metrics combining focused on risk-adjusted return measures. We considered two strategies for each algorithm: Long-Only and Long-Short in order to present situation of two various groups of investors with different investment policy restrictions. For each strategy and equity index, we compute the performance metrics and visualize the equity curve to identify the best strategy with the highest modified information ratio. The findings conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms across all the equity indices what confirms strong potential behind hybrid ML-TS (machine learning - time series) models in searching for the optimal algorithmic investment strategies.


DOI: https://doi.org/10.33138/2957-0506.2024.7.443
Kashif Kamil, Ślepaczuk Robert
Deep Learning Recurrent Neural Networks Algorithmic Investment Strategy LSTM ARIMA Hybrid/Ensemble Models Walk-Forward Process

WP(5/2023)412. The performance of time series forecasting based on classical and machine learning methods for S&P 500 index

Authors: Uzzal Maudud Hassan, Ślepaczuk Robert
Based on one step ahead forecasts, this study compares the forecasting abilities of the traditional technique (ARIMA) with recurrent neural network (LSTM). In order to check the possible use of these forecasts in different asset management methods, t…

WP412. The performance of time series forecasting based on classical and machine learning methods for S&P 500 index

Based on one step ahead forecasts, this study compares the forecasting abilities of the traditional technique (ARIMA) with recurrent neural network (LSTM). In order to check the possible use of these forecasts in different asset management methods, these forecasts are afterwards included into trading signals of investment strategies. As a benchmark method, the Random Walk model producing naive forecasts has been utilized. This research examines daily data from the S&P 500 index for 20 years, from 2000 to 2020, and it includes information on some significant market turbulence. The methods were tested in terms of robustness to changes in parameters and hyperparameters and evaluated based on various error metrics (MAE, MAPE, RMSE MSE). The results show that ARIMA outperforms LSTM in terms of one step ahead forecasts. Finally, LSTM model with a variety of hyperparameters - including a number of epochs, a loss function, an optimizer, activation functions, a number of units, a batch size, and a learning rate - was tested in order to check its robustness.


Uzzal Maudud Hassan, Ślepaczuk Robert
deep learning recurrent neural networks ARIMA algorithmic investment strategies trading systems LSTM walk-forward process optimization

WP(21/2022)397. A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy

Authors: Baranochnikov Illia, Ślepaczuk Robert
The aim of this work is to build a profitable algorithmic investment strategy on various types of assets. The algorithm is built using recurrent neural networks (LSTM and GRU) as the primary source of  signals to buy/sell financial instruments. …

WP397. A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy

The aim of this work is to build a profitable algorithmic investment strategy on various types of assets. The algorithm is built using recurrent neural networks (LSTM and GRU) as the primary source of  signals to buy/sell financial instruments. LSTM and GRU architectures are compared in terms of obtaining the best results and beating the market. The algorithm is tested for four financial instruments (Bitcoin, Tesla, Brent Oil and Gold) on daily and hourly data frequencies. The out-of-sample period is from 1 January 2021 to 1 April 2022. A walk-forward process is responsible for training models and selecting the best model to forecast asset prices in the future. Ten model architectures with various hyperparameters are trained during each step of the walk-forward process. The model architecture with the highest Information Ratio (IR*) in the validation period is used for forecasting in the out-of-sample period. For each strategy, the performance metrics are calculated based on which the profitability of the algorithm is evaluated. At the end, a detailed sensitivity analysis with regards to the main hyperparameters is conducted. The results reveal that LSTM outperforms GRU in most of the cases and that investment strategy built based on LSTM/GRU architecture is able to beat the market only on 50% of tested cases. 


Baranochnikov Illia, Ślepaczuk Robert
deep learning recurrent neural networks algorithm trading strategy LSTM GRU walk-forward process
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