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Student Projects Sponsored by Corporate Sponsors and Lehigh Alumni
Below is a compilation of projects on which Lehigh's MS in Financial Engineering students are working that are sponsored by corporate sponsors and Lehigh alumni. Learn about the hard work these students have put in outside of the classroom and the impact these projects are making.
Lehigh Business MFE students created wiki that helps users discover effective and useful tools to enhance a user's experience with ChatGPT.
ChatGPT, developed by OpenAI, is an advanced language model designed to generate human-like text based on the input provided. Trained on a vast dataset as of September 2021, it spans a wide knowledge base. The premium version of ChatGPT introduces 'Plug-ins,'extending its capabilities with features like language translation, document summarization, and synthetic market research surveys. However, with millions of plug-ins available, identifying the most useful ones can be daunting.
The wiki addresses this by providing comprehensive reviews of tested plug-ins, guiding users to discover effective tools for diverse tasks. The student project team welcomes comments and feedback to continually enhance this resource.
The project sponsor is Allan Frank '76, '78 MBA, '79G.
The PA-100 Index It provides a condensed view of the PA economy and how it has developed over time.
It is a MS in Financial Engineering capstone project which is part of GBUS 484/485/487. It has been continuously student-led from 2021. In summer 2022, it was featured as part of Data for Impact as part of the Mountaintop Summer Experience.
The project involves constructing and maintaining an equity index of the top 100 companies of Pennsylvania. The construction methodology is a free-float market capitalization methodology (same as the S&P 500), and follows standard industry practices.
It provides a condensed view of the PA economy and how it has developed over time. It can be used as a gauge for economic outlook/performance of PA, similar to how broad market indices may be used as a gauge for the overall economy.
Pennsylvania is home to America’s oldest stock market exchange, the Philadelphia Stock Exchange (now the Nasdaq PHLX), which first opened in 1790. In that day, the state was known primarily for its industrial production, including steel, but now Pennsylvania is home to the headquarters of a range of businesses across all sectors.
Nandu Nayar, chair of the Perella department of finance at Lehigh’s College of Business, ideated a project for its Master's of Financial Engineering (MFE) program that might cater to innovation within the state, while giving students hands-on learning opportunities. “I thought, ‘Is there a way to quantify what the state has done in terms of actual commercial activity?’” says Nayar.
The project sponsor is Alex Matturri '80.
Two students in the Masters of Financial Engineering program are creating a method for using alternative data signals to make trade decisions.
Meteorologists use weather models to make their forecasts. Quants (quantitative analysts) use the weather to forecast the ups and downs of the commodities market. Perusing weather patterns instead of financial statements to predict future financial performance is considered.
The project sponsor is Gurraj Sangha.
Middle market private company CEOs/CFOs and PE investors were invited to participate in a 15 minute data collection survey in order to receive research survey results and analyses in January 2023.
MFE capstone project students collaborated with our private equity advisory firm sponsor Coopertown Ventures to research and analyze the impact of 2022 rapidly rising interest rates on private middle market company valuations, investment and operating actions.
The project sponsor is Craig Johson.
Deep Q-Network Interpertability: Applications to ETF Trading
Written by: Bryan Yekelchik MFE ‘22
Abstract: We present an interpertability infrastructure for reinforcement learning (RL) based trading strategies. For all audiences to be able to answer the the question of 'how does the algorithm work?', we provide a visual and user-friendly approach, in contrast to a more quantitative approach. This allows not only a technical audience to consume insights derived from an RL-based trading approach. In this application, we introduce a three module approach in understanding value-based RL, specifically Deep Q-Learning. We demonstrate this infrastructure and possible derived outcomes of using this infrastructrure when applied to trading a market ETF in a given time interval.
The project sponsor is Hariom Tastat.