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on Forecasting |
| By: | Knüppel, Malte; Krüger, Fabian; Pohle, Marc-Oliver |
| Abstract: | Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate extensions of PIT-based calibration tests face various challenges. We therefore introduce a general framework for calibration testing in the multivariate case and propose two new tests that arise from it. Both approaches use proper scoring rules and are simple to implement even in large dimensions. The first employs the PIT of the score. The second is based on comparing the expected performance of the forecast distribution (i.e., the expected score) to its actual performance based on realized observations (i.e., the realized score). The tests have good size and power properties in simulations and solve various problems of existing tests. We apply the new tests to forecast distributions for macroeconomic and financial time series data. |
| Keywords: | Forecast Evaluation, Density Forecasts, Ensemble Forecasts |
| JEL: | C12 C52 C53 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:502022&r=for |
| By: | Fateme Shahabi Nejad; Mohammad Mehdi Ebadzadeh |
| Abstract: | Applying machine learning methods to forecast stock prices has been one of the research topics of interest in recent years. Almost few studies have been reported based on generative adversarial networks (GANs) in this area, but their results are promising. GANs are powerful generative models successfully applied in different areas but suffer from inherent challenges such as training instability and mode collapse. Also, a primary concern is capturing correlations in stock prices. Therefore, our challenges fall into two main categories: capturing correlations and inherent problems of GANs. In this paper, we have introduced a novel framework based on DRAGAN and feature matching for stock price forecasting, which improves training stability and alleviates mode collapse. We have employed windowing to acquire temporal correlations by the generator. Also, we have exploited conditioning on discriminator inputs to capture temporal correlations and correlations between prices and features. Experimental results on data from several stocks indicate that our proposed method outperformed long short-term memory (LSTM) as a baseline method, also basic GANs and WGAN-GP as two different variants of GANs. |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.05693&r=for |
| By: | Tanja Aue; Adam Jatowt; Michael F\"arber |
| Abstract: | Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years. With the requirements of compulsory reporting being implemented and investors incorporating sustainability in their investment decisions, the demand for transparent and reliable ESG ratings is increasing. However, automatic approaches for forecasting ESG ratings have been quite scarce despite the increasing importance of the topic. In this paper, we build a model to predict ESG ratings from news articles using the combination of multivariate timeseries construction and deep learning techniques. A news dataset for about 3, 000 US companies together with their ratings is also created and released for training. Through the experimental evaluation we find out that our approach provides accurate results outperforming the state-of-the-art, and can be used in practice to support a manual determination or analysis of ESG ratings. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.11765&r=for |