SearcharxivSearch

arXiv subjects

Andrew Y. Chen

Publications and source records attributed to Andrew Y. Chen.

11 recordsLinked to original sources

Hedging the Singularity

AI stocks trade at extraordinary valuations. We develop an asset pricing model in which investors use AI stocks to hedge against an AI singularity that displaces their consumption. Because markets are incomplete -- investors cannot trade private AI capital -- AI stocks command a premium. Market incompleteness distorts both valuations and the efficient development of AI, creating a rationale for government transfers that becomes compelling when singularity-driven growth overwhelms deadweight costs. This paper was generated by AI, using https://github.com/chenandrewy/ralph-wiggum-asset-pricing/.

q-fin.GN

What Useful Alphas?

This paper examines about 200 published long-short anomaly equity portfolios (Chen and Zimmermann, 2022). Over the period through 2005 (December 2005 and earlier) and across all stocks, their median zero-investment return was an impressive 48 bp per month. Using only post-2005 years (January 2006 onward) reduces this to 19 bp. Using only "non-micro" top-3,000 stocks in the top 90% of market capitalization reduces this to 26 bp. Using only post-2005 and non-micro stocks reduces this to 7 bp. Even modest allowances for luck or transaction costs would have eliminated even these 7 bp. The evidence strongly suggests that published academic anomalies have been useless to non-micro-cap portfolio managers in the 21st century. Public stock markets were very efficient.

q-fin.GN

Magnetically Responsive Microprintable Soft Nanocomposites with Tunable Nanoparticle Loading

Magnetic remote actuation of soft materials is attractive for applications such as transforming materials and medical robots. However, due to manufacturing limitations, microscale magnetoactive devices are scarce -- light-based additive manufacturing methods, despite achieving microscale resolution, struggle with particle-induced light scattering. Moreover, large hard-magnetic microparticles restrict ultimate feature sizes, and deformation of soft-magnetic nanoparticle composites requires impractically high loading and field gradients. Among successfully fabricated microscale soft-magnetic composites, limited control over particle loading, distribution, and matrix-phase stiffness has hindered their functionality. Here, we combine two-photon polymerization with iron oxide nanoparticle coprecipitation to fabricate 3D-printed microscale nanocomposites with spatially tunable nanoparticle distribution. We control nanoparticle content by locally modulating the two-photon dose, imbuing parts with varied magnetic functionality and achieving millimeter-scale elastic deformations, demonstrated by a soft robotic gripper and a bistable bit register and sensor. Our approach enables precise control of mechanical and magnetic properties towards microscale metamaterial and robotics applications.

cond-mat.soft

Does Peer-Reviewed Research Help Predict Stock Returns?

Mining 29,000 accounting ratios for t-statistics $> 2.0$ leads to cross-sectional return predictability similar to the peer review process. For both, $\approx50\%$ of predictability remains after the original sample periods. This finding holds for many categories of research, including research with risk or equilibrium foundations. Only research agnostic about the theoretical explanation for predictability shows signs of outperformance. Our results imply that inferences about post-sample performance depend little on whether the predictor is peer-reviewed or data mined. They also have implications for the importance of empirical vs theoretical evidence, investors' learning from academic research, and the effectiveness of data mining.

q-fin.GN

Most claimed statistical findings in cross-sectional return predictability are likely true

The false discovery rate (FDR) measures the share of false positives in a set of statistical tests. I develop simple and intuitive bounds on the FDR in cross-sectional predictability publications. The simplest bound requires just a few lines of math and finds $\text{FDR} \le 25\%$ based on summary statistics in eight out of nine previous studies. A more refined bound finds $\text{FDR} \le 9\%$. The FDR is small because randomly selecting accounting ratios produces statistically significant predictability far more often than would occur if there were no predictability. The bounds also reconcile the disparate FDR estimates in the literature.

q-fin.GN

Optimal Post-Hoc Theorizing

For many economic questions, the empirical results are not interesting unless they are strong. For these questions, theorizing before the results are known is not always optimal. Instead, the optimal sequencing of theory and empirics trades off a ``Darwinian Learning'' effect from theorizing first with a ``Statistical Learning'' effect from examining the data first. This short paper formalizes the tradeoff in a Bayesian model. In the modern era of mature economic theory and enormous datasets, I argue that post hoc theorizing is typically optimal.

econ.EM

High-Throughput Asset Pricing

We apply empirical Bayes (EB) to mine data on 136,000 long-short strategies constructed from accounting ratios, past returns, and ticker symbols. This ``high-throughput asset pricing'' matches the out-of-sample performance of top journals while eliminating look-ahead bias. Naively mining for the largest Sharpe ratios leads to similar performance, consistent with our theoretical results, though EB uniquely provides unbiased predictions with transparent intuition. Predictability is concentrated in accounting strategies, small stocks, and pre-2004 periods, consistent with limited attention theories. Multiple testing methods popular in finance fail to identify most out-of-sample performers. High-throughput methods provide a rigorous, unbiased framework for understanding asset prices.

q-fin.GN

Mechanics of three-dimensional micro-architected interpenetrating phase composites

Composite materials are used across engineering applications for their superior mechanical performance, a result of efficient load transfer between the structure and matrix phases. However, the inherently two-dimensional structure of laminated composites reduces their robustness to shear and out-of-plane loads, while unpredictable interlaminar failure and fiber pull-out can cause a catastrophic loss of load capacity. Meanwhile, advances toward uncovering structure-property relations in architected materials have led to highly tunable mechanical properties, deformation, and even failure. Some of these architected materials have reached near-theoretical limits; however, the majority of current work focuses on describing the response of a single-material network in air, and the effect of adding a load-bearing second phase to a three-dimensional architecture is not well understood. Here, we develop facile fabrication methods for realizing centimeter-scale polymer- and carbon-based architected interpenetrating phase composite (IPC) materials, i.e., two-phase materials consisting of a continuous 3D architecture surrounded by a load-bearing matrix across length scales, and determine the effect of geometry and constituent material properties on the mechanics of these architected IPCs. Using these experiments together with computational models, we show that the matrix phase distributes stress effectively, resulting in a high-strength, stable response. Notably, failure delocalization enhances energy dissipation of the composite, achieving specific energy absorption (SEA) values comparable to those of wound fiber tubes. Finally, we demonstrate that the stress state in an IPC can be tuned using geometric design and introduce an example in an architected composite. Altogether, this work bridges the gap between mechanically efficient composites and tunable architected materials.

cond-mat.soft

Do t-Statistic Hurdles Need to be Raised?

Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the FDR) can be strongly identified, as data on published findings is plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature.

q-fin.GN

Missing Values Handling for Machine Learning Portfolios

We characterize the structure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with cross-sectional means performs well compared to rigorous expectation-maximization methods. This stems from three facts about predictor data: (1) missingness occurs in large blocks organized by time, (2) cross-sectional correlations are small, and (3) missingness tends to occur in blocks organized by the underlying data source. As a result, observed data provide little information about missing data. Sophisticated imputations introduce estimation noise that can lead to underperformance if machine learning is not carefully applied.

stat.ME

Publication Bias in Asset Pricing Research

Researchers are more likely to share notable findings. As a result, published findings tend to overstate the magnitude of real-world phenomena. This bias is a natural concern for asset pricing research, which has found hundreds of return predictors and little consensus on their origins. Empirical evidence on publication bias comes from large scale meta-studies. Meta-studies of cross-sectional return predictability have settled on four stylized facts that demonstrate publication bias is not a dominant factor: (1) almost all findings can be replicated, (2) predictability persists out-of-sample, (3) empirical $t$-statistics are much larger than 2.0, and (4) predictors are weakly correlated. Each of these facts has been demonstrated in at least three meta-studies. Empirical Bayes statistics turn these facts into publication bias corrections. Estimates from three meta-studies find that the average correction (shrinkage) accounts for only 10 to 15 percent of in-sample mean returns and that the risk of inference going in the wrong direction (the false discovery rate) is less than 10%. Meta-studies also find that $t$-statistic hurdles exceed 3.0 in multiple testing algorithms and that returns are 30 to 50 percent weaker in alternative portfolio tests. These facts are easily misinterpreted as evidence of publication bias effects. We clarify these misinterpretations and others, including the conflating of ``mostly false findings'' with ``many insignificant findings,'' ``data snooping'' with ``liquidity effects,'' and ``failed replications'' with ``insignificant ad-hoc trading strategies.'' Meta-studies outside of the cross-sectional literature are rare. The four facts from cross-sectional meta-studies provide a framework for future research. We illustrate with a preliminary re-examination of equity premium predictability.

q-fin.GN