Markus Pelger

Finance · 2018–2020

A researcher in the Science corpus: 8 papers; trusts 114 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Finance — 175 citations into it; #5,472 of 10,518 by trust here, #5,728 by citations
• Economics and Econometrics — 165 citations into it; #11,829 of 23,481 by trust here, #14,051 by citations
• Statistics and Probability — 73 citations into it; #6,407 of 12,932 by trust here, #11,487 by citations
• Management Science and Operations Research — 39 citations into it; #5,145 of 11,946 by trust here, #8,469 by citations
• Accounting — 25 citations into it; #4,040 of 8,970 by trust here, #7,462 by citations
• Strategy and Management — 12 citations into it
• Statistical Learning Theory — 11 citations into it
• Artificial Intelligence — 9 citations into it

Whom Markus Pelger trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. A. Onatski
2. Jushan Bai
3. Ole E. Barndorff-Nielsen
4. Serena Ng
5. Torben G. Andersen
6. James H. Stock
7. Martin Lettau
8. Tim Bollerslev
9. Gary Chamberlain
10. Yacine Aït-Sahalia

Who trusts Markus Pelger overall

1. Ruoxuan Xiong
2. Luyang Chen
3. Jason Zhu
4. Martin Lettau
5. Yacine Aït-Sahalia
6. D. Xiu
7. Svetlana Bryzgalova
8. Chaoxing Dai

Papers from Markus Pelger's perspective

Ordered by how strongly the researchers Markus Pelger trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Regression Shrinkage and Selection via the Lasso
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Large-dimensional factor modeling based on high-frequency observations (2019) — 104 citations
• Factors That Fit the Time Series and Cross-Section of Stock Returns (2018) — 212 citations
• Understanding Systematic Risk: A High‐Frequency Approach (2020) — 50 citations
• Forest Through the Trees: Building Cross-Sections of Stock Returns (2020) — 84 citations
• Deep Learning in Asset Pricing (2019) — 358 citations
• Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019) — 53 citations
• Estimating latent asset-pricing factors (2018) — 141 citations
• State-Varying Factor Models of Large Dimensions (2018) — 51 citations