arXiv subjects

Shinya Tanaka

Publications and source records attributed to Shinya Tanaka.

4 recordsLinked to original sources

arXiv2026

Post-Screening Portfolio Selection

We propose post-screening portfolio selection (PS$^2$), a two-step framework for high-dimensional mean--variance investing. First, assets are screened by Lasso-type regression of a constant on excess returns without an intercept. Second, portfolio weights are estimated on the selected set using standard low-dimensional methods. Because strong factors can destroy sparsity in real data, we further introduce PS$^2$ with factors (FPS$^2$), which defactors returns before screening and allows factor investing in the final step. We establish theoretical guarantees, and simulations and an empirical application show competitive performance, especially when sparse screening is appropriate or strong factors are explicitly accommodated.

q-fin.PM↗

arXiv2025

Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification

Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 254 cases comprising five major tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central nervous system lymphoma, and metastatic tumors. Comparing state-of-the-art foundation models with conventional approaches, we found that foundation models demonstrated robust classification performance with as few as 10 patches per case, despite the traditional assumption that extensive per-case image sampling is necessary. Furthermore, our evaluation revealed that simple transfer learning strategies like linear probing were sufficient, while fine-tuning often degraded model performance. These findings suggest a paradigm shift from "training encoders on extensive pathological data" to "querying pre-trained encoders with labeled datasets", providing practical implications for implementing AI-assisted diagnosis in clinical pathology.

eess.IV↗

arXiv2023

Locations of logistics facilities for e-commerce: a case of the Tokyo Metropolitan Area

The rapid growth of the e-commerce market creates new dynamics in the logistics landscape, which has been evolving for decades in cities around the world. It is a challenge for businesses and planners to meet the high demand for logistics facilities for e-commerce order fulfillment and goods handling. In the Tokyo Metropolitan Area, mega-scale multi-tenant logistics facilities have been developed in both the port area near the urban center and the periphery of the city, while delivery service providers locate many last-mile delivery stations, varying in number depending on the urban density. We analyze the spatial distribution and location factors of both mega-scale multi-tenant facilities and last-mile delivery facilities. We found that, due to the scarcity of land, newly developed multi-tenant facilities are more likely to be in less accessible places that have high-level development restrictions. The result also indicates the heterogeneity of the distribution of delivery service providers' facilities, reflecting the heterogeneity in business strategies.

physics.soc-ph↗

arXiv2017

Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach

This paper studies macroeconomic forecasting and variable selection using a folded-concave penalized regression with a very large number of predictors. The penalized regression approach leads to sparse estimates of the regression coefficients, and is applicable even if the dimensionality of the model is much larger than the sample size. The first half of the paper discusses the theoretical aspects of a folded-concave penalized regression when the model exhibits time series dependence. Specifically, we show the oracle inequality and the oracle property for ultrahigh-dimensional time-dependent regressors. The latter half of the paper shows the validity of the penalized regression using two motivating empirical applications. The first forecasts U.S. GDP with the FRED-MD data using the MIDAS regression framework, where there are more than 1000 covariates, while the sample size is at most 200. The second examines how well the penalized regression screens the hidden portfolio with around 40 stocks from more than 1800 potential stocks using NYSE stock price data. Both applications reveal that the penalized regression provides remarkable results in terms of forecasting performance and variable selection.

stat.AP↗