116 research outputs found
The Time-Varying Risk Return Tradeoff in the Long-Run
Lundblad(2007,JFE) shows that the risk-return tradeoff is unequivocally positive with a two-century history of equity market data. A further examination of the relation with the UK monthly stock returns from 1836 to 2010 produces rather weak risk-return relation. I show that the risk-return relation is mostly positive but varies considerably over time based on a new nonlinear ICAPM with multivariate GARCH-M terms with the time-varying risk-return tradeoffs and hedging coefficients. The often observed negative risk-return relation is also statistically insignificant with the 95 % confidence bounds. The hedging coefficients also vary significantly across time. This complex nonlinearity seems to be the main culprit of the weak risk-return relation
New return anomalies and new-Keynesian ICAPM
AbstractI propose a new multi-factor asset pricing model with new-Keynesian factors to explain stock return anomalies from 1972Q1 to 2009Q2. This new model explains the average returns across testing portfolios formed on financial distress, momentum, and standardized unexpected earnings with misspecification-robust statistics. Test portfolios formed on net stock issues and total accruals are also partly explained by new-Keynesian factors. Two monetary policy factors play an important role in explaining these new anomalies. The credit aspect of these new anomalies suggests an economic rationale for the model through capital market imperfections and the credit channel of monetary policy mechanism
Slow Recovery of Output after the 2007−09 Financial Crisis: U.S. Shortfall Spillovers and the U.K. Productivity Puzzle
Asymmetric Co-fluctuations of U.S. Output and Unemployment: Friedman’s Plucking Model and Okun’s Law
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Real-world graphs naturally exhibit hierarchical or cyclical structures that
are unfit for the typical Euclidean space. While there exist graph neural
networks that leverage hyperbolic or spherical spaces to learn representations
that embed such structures more accurately, these methods are confined under
the message-passing paradigm, making the models vulnerable against side-effects
such as oversmoothing and oversquashing. More recent work have proposed global
attention-based graph Transformers that can easily model long-range
interactions, but their extensions towards non-Euclidean geometry are yet
unexplored. To bridge this gap, we propose Fully Product-Stereographic
Transformer, a generalization of Transformers towards operating entirely on the
product of constant curvature spaces. When combined with tokenized graph
Transformers, our model can learn the curvature appropriate for the input graph
in an end-to-end fashion, without the need of additional tuning on different
curvature initializations. We also provide a kernelized approach to
non-Euclidean attention, which enables our model to run in time and memory cost
linear to the number of nodes and edges while respecting the underlying
geometry. Experiments on graph reconstruction and node classification
demonstrate the benefits of generalizing Transformers to the non-Euclidean
domain.Comment: 19 pages, 7 figure
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
To overcome the quadratic cost of self-attention, recent works have proposed
various sparse attention modules, most of which fall under one of two groups:
1) sparse attention under a hand-crafted patterns and 2) full attention
followed by a sparse variant of softmax such as -entmax. Unfortunately,
the first group lacks adaptability to data while the second still requires
quadratic cost in training. In this work, we propose SBM-Transformer, a model
that resolves both problems by endowing each attention head with a
mixed-membership Stochastic Block Model (SBM). Then, each attention head
data-adaptively samples a bipartite graph, the adjacency of which is used as an
attention mask for each input. During backpropagation, a straight-through
estimator is used to flow gradients beyond the discrete sampling step and
adjust the probabilities of sampled edges based on the predictive loss. The
forward and backward cost are thus linear to the number of edges, which each
attention head can also choose flexibly based on the input. By assessing the
distribution of graphs, we theoretically show that SBM-Transformer is a
universal approximator for arbitrary sequence-to-sequence functions in
expectation. Empirical evaluations under the LRA and GLUE benchmarks
demonstrate that our model outperforms previous efficient variants as well as
the original Transformer with full attention. Our implementation can be found
in https://github.com/sc782/SBM-Transformer .Comment: 19 pages, 8 figure
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Graph pooling is a crucial operation for encoding hierarchical structures
within graphs. Most existing graph pooling approaches formulate the problem as
a node clustering task which effectively captures the graph topology.
Conventional methods ask users to specify an appropriate number of clusters as
a hyperparameter, then assume that all input graphs share the same number of
clusters. In inductive settings where the number of clusters can vary, however,
the model should be able to represent this variation in its pooling layers in
order to learn suitable clusters. Thus we propose GMPool, a novel
differentiable graph pooling architecture that automatically determines the
appropriate number of clusters based on the input data. The main intuition
involves a grouping matrix defined as a quadratic form of the pooling operator,
which induces use of binary classification probabilities of pairwise
combinations of nodes. GMPool obtains the pooling operator by first computing
the grouping matrix, then decomposing it. Extensive evaluations on molecular
property prediction tasks demonstrate that our method outperforms conventional
methods.Comment: 10 pages, 3 figure
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Pretraining molecular representations from large unlabeled data is essential
for molecular property prediction due to the high cost of obtaining
ground-truth labels. While there exist various 2D graph-based molecular
pretraining approaches, these methods struggle to show statistically
significant gains in predictive performance. Recent work have thus instead
proposed 3D conformer-based pretraining under the task of denoising, which led
to promising results. During downstream finetuning, however, models trained
with 3D conformers require accurate atom-coordinates of previously unseen
molecules, which are computationally expensive to acquire at scale. In light of
this limitation, we propose D&D, a self-supervised molecular representation
learning framework that pretrains a 2D graph encoder by distilling
representations from a 3D denoiser. With denoising followed by cross-modal
knowledge distillation, our approach enjoys use of knowledge obtained from
denoising as well as painless application to downstream tasks with no access to
accurate conformers. Experiments on real-world molecular property prediction
datasets show that the graph encoder trained via D&D can infer 3D information
based on the 2D graph and shows superior performance and label-efficiency
against other baselines.Comment: 16 pages, 5 figure
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