Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures.

Overview

NLP_0-project

Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures1. We are a "democratic" and collaborative group of five, and I mentioned our names based on our initial work division below 😄 .

Here is the outline of our project:

Data collection.

@LeiyuanHuo, jyang130, FanFanShark, xdc1999, gaojiamin1116

  • Based on file data-WRDS-list.csv, write a web-scraping algorithm to download all 10-Ks (html format) these companies filed to the SEC within 2010 to 2022 at Historical EDGAR documents, and rename them data-10K-COMPNAME-Year.html.
  • Parse html files to extract Business and MD&A sections.

Text Processing: feature extraction2

  • Part of Speech Tagging (POS) (mainly this method) to get product name, descriptions. Store these for each company.
  • Named Entity Recognition (NER) (also mainly this method) to get mentioned competitor names. Store these for each company.
  • Product texts: BoW and tf-idf for each company's product(s), and hopefully we have a term-product matrix then.
  • Competitor texts: definitely BoW, as we care about the frequency of being mentioned.
  • ‼️ We also need to combine sector and firm size/market power into competitor texts and re-count.

Text Processing: feature transformation and representation2

  • Term-product matrix: calculate cosine similarity scores for products pairwise; use score threshold to cluster products into similar groups.
  • Term-product matrix: directly apply clustering method (e.g., KMeans clustering) to product vectors, and cluster them.

Econometric Analysis and Hypothesis Testing2

  • Multivariate regression: DV is profitability (e.g., sales, revenue, Tobin's q), IV is competition measures (one from similar product count, one from mentions as competitors), also include relevant control variables.
  • Cross-section portfolios: our competition measures are cross-sectional (one for each year), so we can create long-short portfolios for both measures, and examine stock return effects.

Footnotes

  1. Two papers inspired this project. Citations: Eisdorfer, A., Froot, K., Ozik, G., & Sadka, R. (2021). Competition Links and Stock Returns. The Review of Financial Studies, The Review of financial studies, 2021-12-20. && Hoberg, G., & Phillips, G. (2016). Text-Based Network Industries and Endogenous Product Differentiation. The Journal of Political Economy, 124(5), 1423-1465.

  2. Text processing processes are based on MFIN7036 Lecture_Notes and a review paper. Citation: Marty, T., Vanstone, B., & Hahn, T. (2020). News media analytics in finance: A survey. Accounting and Finance (Parkville), 60(2), 1385-1434. 2 3

Deep High-Resolution Representation Learning for Human Pose Estimation

Deep High-Resolution Representation Learning for Human Pose Estimation (accepted to CVPR2019) News If you are interested in internship or research pos

HRNet 167 Dec 27, 2022
Python package to generate image embeddings with CLIP without PyTorch/TensorFlow

imgbeddings A Python package to generate embedding vectors from images, using OpenAI's robust CLIP model via Hugging Face transformers. These image em

Max Woolf 81 Jan 04, 2023
Speech-Emotion-Analyzer - The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)

Speech Emotion Analyzer The idea behind creating this project was to build a machine learning model that could detect emotions from the speech we have

Mitesh Puthran 965 Dec 24, 2022
Multi Task Vision and Language

12-in-1: Multi-Task Vision and Language Representation Learning Please cite the following if you use this code. Code and pre-trained models for 12-in-

Facebook Research 712 Dec 19, 2022
Pytorch implementation of FlowNet by Dosovitskiy et al.

FlowNetPytorch Pytorch implementation of FlowNet by Dosovitskiy et al. This repository is a torch implementation of FlowNet, by Alexey Dosovitskiy et

Clément Pinard 762 Jan 02, 2023
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

DV Lab 137 Dec 14, 2022
Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition"

Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition" Pre-trained Deep Convo

Ankush Malaker 5 Nov 11, 2022
AI Based Smart Exam Proctoring Package

AI Based Smart Exam Proctoring Package It takes image (base64) as input: Provide Output as: Detection of Mobile phone. Detection of More than 1 person

NARENDER KESWANI 3 Sep 09, 2022
This script runs neural style transfer against the provided content image.

Neural Style Transfer Content Style Output Description: This script runs neural style transfer against the provided content image. The content image m

Martynas Subonis 0 Nov 25, 2021
The Python ensemble sampling toolkit for affine-invariant MCMC

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Dan Foreman-Mackey 1.3k Dec 31, 2022
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

GPT2-Pytorch with Text-Generator Better Language Models and Their Implications Our model, called GPT-2 (a successor to GPT), was trained simply to pre

Tae-Hwan Jung 775 Jan 08, 2023
Pytorch implementation of NeurIPS 2021 paper: Geometry Processing with Neural Fields.

Geometry Processing with Neural Fields Pytorch implementation for the NeurIPS 2021 paper: Geometry Processing with Neural Fields Guandao Yang, Serge B

Guandao Yang 162 Dec 16, 2022
PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

Improving Visual-Semantic Embeddings with Hard Negatives Code for the image-caption retrieval methods from VSE++: Improving Visual-Semantic Embeddings

Fartash Faghri 441 Dec 05, 2022
A rule-based log analyzer & filter

Flog 一个根据规则集来处理文本日志的工具。 前言 在日常开发过程中,由于缺乏必要的日志规范,导致很多人乱打一通,一个日志文件夹解压缩后往往有几十万行。 日志泛滥会导致信息密度骤减,给排查问题带来了不小的麻烦。 以前都是用grep之类的工具先挑选出有用的,再逐条进行排查,费时费力。在忍无可忍之后决

上山打老虎 9 Jun 23, 2022
A keras-based real-time model for medical image segmentation (CFPNet-M)

CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation This repository contains the implementat

268 Nov 27, 2022
use machine learning to recognize gesture on raspberrypi

Raspberrypi_Gesture-Recognition use machine learning to recognize gesture on raspberrypi 說明 利用 tensorflow lite 訓練手部辨識模型 分辨 "剪刀"、"石頭"、"布" 之手勢 再將訓練模型匯入

1 Dec 10, 2021
2021 credit card consuming recommendation

2021 credit card consuming recommendation

Wang, Chung-Che 7 Mar 08, 2022
QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing Environment Tested on Ubuntu 14.04 64bit and 16.04 64bit Installation # disabl

gts3.org (<a href=[email protected])"> 581 Dec 30, 2022
[Machine Learning Engineer Basic Guide] 부스트캠프 AI Tech - Product Serving 자료

Boostcamp-AI-Tech-Product-Serving 부스트캠프 AI Tech - Product Serving 자료 Repository 구조 part1(MLOps 개론, Model Serving, 머신러닝 프로젝트 라이프 사이클은 별도의 코드가 없으며, part

Sung Yun Byeon 269 Dec 21, 2022
Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

AmbientGAN: Generative models from lossy measurements This repository provides code to reproduce results from the paper AmbientGAN: Generative models

Ashish Bora 87 Oct 19, 2022