Integrity. Innovation. Impact.

Michael Rechenthin

Michael Rechenthin

Ph.D. student in management sciences

What is the most important factor that made you choose to study here over other institutions?

My advisor, Dr. Nick Street, was the main reason I chose to study at the University of Iowa. I had a very clear idea about what I wanted to study before I came here and after researching professionals in the field, I found his work to be right in line with my interests. After one conversation with him via phone, I knew this was the right place for me. Our interests have stayed well aligned throughout my time here.

Is studying for a Ph.D. as intense as most people think?

During the course of my program here, I've learned that the Ph.D. isn't so much about becoming an expert in my particular area of interest as much as it is about becoming an expert in learning HOW to do research. Of course, mastering my topic is important, but really learning how to access all of my available resources and taking full advantage of the amazing professionals I have right at my fingertips is integral to my success. I've had the opportunity to discuss my research with experts in so many different disciplines and it has given me a very well-rounded view of my topic. The Ph.D. is a ton of work, yes, but if you stay focused and use your resources wisely, you'll do great. Everything you need to succeed is here.

What do you do to relieve the stress of grad school?

Fortunately, I really enjoy my research topic, so although there is stress in my work, it's exciting and I enjoy it. Writing papers, studying for exams, running experiments, and presenting results to my advisor involve some level of anxiety—but I get through it! When I'm done with a particularly hectic week, I go out to dinner with friends or head back to Chicago for the weekend with my wife. The drive is just over three hours and we have good friends there.

What do you enjoy most about the Iowa City community?

Iowa City is a fun place to be. There's always something interesting to do around town, including live music, street festivals, farmers markets, cultural performances, and access to parks and outdoor activities. It's a convenient place to live since the university is in the heart of downtown. You can walk everywhere, feel safe, and establish a sense of community easily. Some of my favorite hangouts are the Java House coffee shop, the steps of the Old Capitol building, and the Ped Mall.

Because most Ph.D. programs at Tippie are small, is this of benefit to you?

The small size of my department provides a high level of accessibility to the faculty and interaction with the other Ph.D. students. I have weekly meetings with my advisor and I have colleagues right in my office with whom I can brainstorm as I work through my ideas. I know the relationships I have developed here will remain throughout my career.

What kind of support is available to Ph.D. students?

Support is plentiful. I have weekly meetings with my advisor where we discuss our research and brainstorm future direction. I also participate in two dfferent groups with fellow Ph.D. students and faculty. One is the Data Mining Group ( We meet every Friday afternoon to read and present papers on our research topics. The other is Prof. Padmini Srinivasan's Text Retrieval and Text Mining Journal Club ( Her group meets on Mondays and includes students from my department, computer science, and even political science. Participating in these sessions gives me an opportunity to collaborate with students from other disciplines, practice my presentation skills, and expose myself to other research in my field.

What is your research focus?

My research implements computer-aided techniques to predict short-term stock price direction. I've been interested in stock and financial markets since I was in high school, and before I went to graduate school, I worked as a trader at the Chicago Stock Exchange for seven years. As a trader, we often held positions for under 15 minutes in a very volatile market. My work explores how to predict stock price direction in the short term—sometimes less than a minute. It's an incredibly complex problem when the market is constantly moving and algorithms that we use quickly become obsolete. Determining future market direction in practice requires special consideration since streaming stock data may arrive faster than an algorithm may produce results. For example, a model that takes 30 minutes to arrive at a prediction is of little value if the objective is to predict one minute in the future. We experiment with machine learning techniques to address these challenges.

What are you most looking forward to after graduation?

This is my fifth year in the Ph.D. program, so graduation is right around the corner. I'm looking forward to finding a good position and continuing my research.