arXiv 8 Sep 2024 · Finance — Trading
arXiv:2409.05192 · PDF · DOI · OpenAlex · Extracted main text
In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predicting future market movements. Then, we utilize the information from this successful neural network predictor to pinpoint the individual trades within each data point (trading window) that had the most impact on the optimized neural network's prediction of future price movements. This approach helps us uncover important insights about the heterogeneity in information content provided by trades of different sizes, venues, trading contexts, and over time.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016) Deep Learning | 0.874 | 5 | 2 | 100% |
| 2 | Oliver Hansch and Hyuk Choe (2005) Which trades move stock prices in the internet age? | 0.644 | 2 | 2 | 100% |
| 3 | David Easley, Nicholas M. Kiefer, and Maureen O'Hara (1997) One day in the life of a very common stock | 0.511 | 2 | 1 | 100% |
| 4 | James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl (2011) Algorithms for hyper-parameter optimization | 0.511 | 2 | 1 | 100% |
| 5 | Joel Hasbrouck Measuring the information content of stock trades | 0.511 | 2 | 1 | 100% |
| 6 | Matthew D Zeiler and Rob Fergus (2014) Deep inside convolutional networks: Visualising image classification models and saliency maps | 0.511 | 2 | 1 | 100% |
| 7 | Mukund Sundararajan, Ankur Taly, and Qiqi Yan (2017) Axiomatic attribution for deep networks | 0.511 | 2 | 1 | 100% |
| 8 | Shuhei Watanabe (2023) Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance | 0.511 | 2 | 1 | 100% |
| 9 | Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hub… (1989) Backpropagation applied to handwritten zip code recognition | 0.405 | 1 | 1 | 100% |
| 10 | Christopher M. Bishop (2006) Pattern Recognition and Machine Learning | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 52 scored citations.