Showing 1 - 3 of 3 Items

A Machine Learning Approach to Sector Based Market Efficiency

Date: 2023-01-01

Creator: Angus Zuklie

Access: Open access

In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency. By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the basic comparisons of the weak form efficiency of four sectors over the same time period: utilities, healthcare, technology and energy. Finally, Within these sectors, I was able to detect inefficiencies in the stock market up to four years closer to modern day than previous studies.

Statistically Principled Deep Learning for SAR Image Segmentation

Date: 2024-01-01

Creator: Cassandra Goldberg

Access: Open access

This project explores novel approaches for Synthetic Aperture Radar (SAR) image segmentation that integrate established statistical properties of SAR into deep learning models. First, Perlin Noise and Generalized Gamma distribution sampling methods were utilized to generate a synthetic dataset that effectively captures the statistical attributes of SAR data. Subsequently, deep learning segmentation architectures were developed that utilize average pooling and 1x1 convolutions to perform statistical moment computations. Finally, supervised and unsupervised disparity-based losses were incorporated into model training. The experimental outcomes yielded promising results: the synthetic dataset effectively trained deep learning models for real SAR data segmentation, the statistically-informed architectures demonstrated comparable or superior performance to benchmark models, and the unsupervised disparity-based loss facilitated the delineation of regions within the SAR data. These findings indicate that employing statistically-informed deep learning methodologies could enhance SAR image analysis, with broader implications for various remote sensing applications and the general field of computer vision. The code developed for this project can be found here: https://github.com/cgoldber/Statistically-Principled-SAR-Segmentation.git.

Ideal Point Models with Social Interactions Applied to Spheres of Legislation

Access to this record is restricted to members of the Bowdoin community. Log in here to view.

Date: 2020-01-01

Creator: Luca Ostertag-Hill

Access: Access restricted to the Bowdoin Community