Seminarium online QFRG i DSLab “LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index" [21.03.2022]

Serdecznie zapraszamy do wzięcia udziału w kolejnym seminarium z cyklu spotkań organizowanych przez QFRG (Quantitative Finance Research Group) i DSLab (Data Science Lab).

Podczas spotkania zespół badaczy: Jakub Michańków oraz dr Paweł Sakowski i dr hab. Robert Ślepaczuk z Katedry Finansów Ilościowych Wydziału Nauk Ekonomicznych przedstawią wyniki badania “LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index".

Spotkanie odbędzie się 21 marca 2022 r. od godz. 17:00 za pośrednictwem platformy Google Meet i będzie prowadzone w języku angielskim.

Link do spotkania: https://meet.google.com/gnr-gfbh-fxc

Szczegółowe informacje są dostępne w zaproszeniu.

Abstrakt:

We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of the forecasting ability of the LSTM model in algorithmic investment strategies. Based on the forecasts from the LSTM model we generate buy and sell investment signals, employ them in algorithmic investment strategies and create equity lines for our investment. For this purpose we use various combinations of LSTM models, optimized on in-sample period and tested on out-of-sample period, using a rolling window approach. We pay special attention to data preprocessing in the input layer, to avoid overfitting in the estimation and optimization process, and assure correct selection of hyperparameters at the beginning of our tests. The next stage is devoted to the conjunction of signals from various frequencies into one ensemble model, and the selection of best combinations for the out-of-sample period, through optimization of the given criterion in a similar way as in the portfolio analysis. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.