From 27ff5b0676777d301a2810b964ca7455bb04cbb2 Mon Sep 17 00:00:00 2001 From: Markus Bilz Date: Tue, 27 Feb 2024 16:36:53 +0100 Subject: [PATCH] feat: add background, motivation, and contributions --- reports/Content/main-summary.tex | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/reports/Content/main-summary.tex b/reports/Content/main-summary.tex index d5e6f259..424d0d70 100644 --- a/reports/Content/main-summary.tex +++ b/reports/Content/main-summary.tex @@ -14,7 +14,7 @@ % other tasks by applying it successfully to English constituency parsing both with % large and limited training data -\section{Motivation} +\section{Background and Motivation} Every option trade has a buyer and seller side. For a plethora of problems in option research, it’s also crucial to determine the party that initiated the transaction. Applications include the study of option demand \autocite[][]{garleanuDemandBasedOptionPricing2009}, of informational content in option trading \autocites[][]{huDoesOptionTrading2014}[][]{panInformationOptionVolume2006}[][]{caoInformationalContentOption2005}, of order flow \autocite[][]{muravyevOrderFlowExpected2016}, or of trading costs \autocite[][]{muravyevOptionsTradingCosts2020}. @@ -26,7 +26,10 @@ \section{Motivation} The goal of our empirical study is to investigate if machine learning-based classifier improve upon the accuracy of state-of-the-art approaches for option trade classification? +\section{Contributions} + % Thereby, our work addresses several addressed shortcomings. +% TODO: by how much? Our contributions are as follows: (I) By employing gradient-boosted trees and transformers we are able to establish a new state-of-the-art in terms of classification accuracy. (II) Our work is the first to consider both the supervised and the semi-supervised setting, where trades are partially-labelled. (III) Through a feature importance analysis based on Shapley values, we consistently attribute performance gains of rule-based and machine learning-based classifiers to feature groups. We discover that both paradigms share common features, but machine learning-based more effectively exploits the data. % Additional insights are gained from probing the Transformers' attention heads. % consistently attribute probing attention heads