How do financial models' predictive power change over time?
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The predictive power of financial models evolves over time due to changing dynamics in the financial sector. Studies show that relationships and predictability of financial variables have shifted significantly in the past decade, with some variables maintaining or even improving prediction quality . Additionally, the incorporation of latent graph representations through Graph Representation Learning has proven valuable in capturing similarity-based patterns in financial transactions, offering insights into the system's dynamics, especially during events like the Covid-19 pandemic . Furthermore, the use of sentiment analysis from news articles and user browsing activity has shown to enhance forecasting power by identifying and weighting relevant news, indicating a "wisdom of the crowd" effect in improving predictive capabilities .
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| Papers (5) | Insight |
|---|---|
Open access 01 Jan 2010 3 Citations | Financial models' predictive power can improve over time by using learning neural networks to dynamically adapt stochastic parameters, enhancing forecasting accuracy for asset processes. |
| Financial models' predictive power can improve by combining sentiment from news and user browsing data, enhancing forecasting accuracy for over 50% of investigated companies in the short term. | |
| The predictive power of monetary models may vary during financial crises compared to non-crisis periods, with mixed results observed in predicting the Swedish krona's value. | |
08 Feb 2015 1 Citations | Financial models' predictive power fluctuates over time, with some variables showing improved predictability while others exhibit changes in relationships and predictability, reflecting evolving dynamics in the financial sector. |
15 Oct 2020 | Financial models' predictive power evolves over time due to shifts in latent trajectories captured by Graph Representation Learning, reflecting real-world events like the Covid-19 pandemic's impact on consumer patterns. |
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