Predicting financial time series with incomplete information due to late publications of financial reports

Predicting financial time series with incomplete information due to late publications of financial reports

Esseveld, J.R. (TU Delft Electrical Engineering, Mathematics and Computer Science)

Loog, M. (mentor)
Rambier, E. (mentor)
van Gemert, J.C. (graduation committee)
Lofi, C. (graduation committee)

Delft University of Technology

2020-07-10

Records from ledgers of Dutch companies all across the Netherlands are used in this study. Records can be submitted in the ledgers with various lags, because the data of many different bookkeepers is involved with different workflows. Bookkeepers can be punctual or late, therefore records can be submitted with various lags in the ledgers. This causes missing data, which results in a deformation of a time series that is constructed from these records. Using a technique called now­casting, a prediction can be made of how these series with no missing data would have looked like.
This study sheds light on how information of an incomplete time series from ledgers of Dutch companies can be used to now­cast on that series, without the use of external indicators. To better utilize the information available from the series, an addition to the Seasonal Auto Regressive Integrated Moving Average with eXogenous regressors (SARIMAX) model is proposed. The addition to the SARIMAX model is presented in two forms: the additive­ and multiplicative relation between indicator­ and target series. These are modeled with the goal to improve the information utilization and therefore improve the now­casting accuracy. Experiments have shown that this addition to the model does not give a direct improvement in accuracy compared to an ordinary SARIMAX model. Thereafter an iterative now­cast procedure is proposed to utilize information from highly lagged records. It has been shown that this gives a slight increase in accuracy for the overall now­cast.

Nowcasting
Financial Time Series
SARIMAX
Forecasting
Incomplete Time Series

http://resolver.tudelft.nl/uuid:0a196013-6999-4e81-b955-efbf64a9dc1c

2021-01-01

Student theses

master thesis

© 2020 J.R. Esseveld