Skip to main content

Linear and logistic regression analysis to find arbitrage opportunities in art markets

In this project, I worked with a group of three other students with the goal to find arbitrage opportunities between the New York, London, and Paris art markets. We developed two models; a multivariate regression model to predict the hammer price and a logistic regression model to predict the likelihood of a sale. 



This project demonstrates the following technical skills:

- Collecting and cleaning data

- Advanced statistical modeling

- Testing statistical models

- Machine Learning

- R

- STATA

- Excel


please click here or the link below for the full PDF report

Labels:

Comments

  1. Kale Co JakimAugust 11, 2026 at 12:42 AM

    The project explores the possibility of identifying arbitrage opportunities between the New York, London, and Paris art markets. Working as part of a four-person team, the author developed two complementary models: a multivariate regression model to predict hammer prices and a logistic regression model to estimate the likelihood of a sale. This provides an interesting example of applying statistical modeling and machine learning techniques to financial and art market data.

    ReplyDelete
  2. Kale Co JakimAugust 11, 2026 at 12:42 AM

    The project also demonstrates the importance of data preparation before applying statistical models. Collecting and cleaning relevant information helps establish a reliable dataset for subsequent analysis, while testing statistical models provides an opportunity to evaluate whether the selected approaches are appropriate. Developing these analytical foundations can be supported through Data Analysis Course.

    ReplyDelete
  3. Kale Co JakimAugust 11, 2026 at 12:42 AM

    Using both multivariate and logistic regression allows the project to examine different aspects of the art market, including expected prices and the probability of a successful sale. Combining statistical analysis with machine learning also demonstrates how multiple analytical approaches can be used to investigate complex market behavior. Visual exploration of such datasets can further support interpretation through Data Visualization Course.

    ReplyDelete
  4. Kale Co JakimAugust 11, 2026 at 12:43 AM

    The project is a good example of how regression techniques can be applied to real-world datasets to investigate relationships and make predictions. Working with tools such as R, STATA, and Excel also demonstrates the value of using different analytical environments when developing and testing models. Similar concepts can be explored through practical Regression Projects that apply predictive modeling to different types of datasets.

    ReplyDelete

Post a Comment

Popular posts from this blog

RDD analysis on time-series financial data

For this project, I set out to understand how changes in Facebook's advertisement algorithm impacts facebook's stock prices. In addition to Facebook, four other stock ticker and the S&P index were used to tease out any larger market and industry price changes. Using a RDD model, I was able to find two significant algorithmic/acquisitional updates that impacted facebooks stock prices in a statistically significant manner.  This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - MATLAB - Excel - Working with financial time-series data please click here or the below link for the full Google Drive Slides https://docs.google.com/presentation/d/1YYNv3wCx7a-qx0ruth1E_LsoAwWioZqY/edit?usp=sharing&ouid=107570216653474841263&rtpof=true&sd=true
Post a Comment
Read more

Classifying profitable soccer players

Overview In this project I set out to create a classification model in order to predict key characteristics where soccer players are expected to show significant market value growth in the short to mid-term future. I relied on data available on Transfermarkt.com, a European soccer player market valuation database/website used by soccer clubs around the world.  Data Collection and Cleaning In order to collect the relevant data, I developed a scrapping program using Beautiful Soup on Python. After scraping all of the necessary data, I used python to remove any null values and standardized all data types. After collecting and cleaning the data through Python. I was finally ready to perform preliminary data exploration to find any key trends of themes in the dataset.  Exploratory Data Analysis Size = Change in market value, X = age, Y = Market Value From an initial review of the dataset, I found that there was most likely a strong relationship between the age of the player and exp...
5 comments
Read more

The Collateralization of Art: A Primer and Practical Assessment of the Art-Secured Lending Industry

For this project, I worked with a graduate student at the Duke Law School to perform exploratory analysis on loan backed fine art. As a topic that has minimal prior research, this project was particularly interesting as we had to create and collect out own datasets and provided original insight in the topic.  Through our analysis, we were able to find that collateralized works tend to sell for a higher hammer price and that the volume of art backed loans negatively correlates to the housing market. This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - STATA - Excel - Working with financial time-series data
Post a Comment
Read more