Orpheus is MIT ranked fintech and was shortlisted by AIconics Awards as one of the world’s best AI Innovators. The company specializes in Quantitative and Artificial Intelligence solutions for the financial services and other sectors. RMI® models have consistently outperformed respective benchmarks at similar risk by a range of 3 to 5% annually. The ‘Data Universality’ framework works across asset classes (equity, fixed income, commodities, currencies, and alternatives), Regions (America, Europe, Asia) and styles (active, passive, long-short, tactical etc.).
RMI® Methodology is based on a universal framework which considers markets as natural dynamic systems that are driven by the statistical forces of reversion and diversion. These forces create variability in asset performance and market functioning. This is why conventional factors like value, momentum, size, quality, volatility etc. are inconsistent in their behavior and hence fail to perform. RMI® Methodology is consistent and delivers risk-weighted excess returns because it combines the two diverse statistical forces into a unique factor which delivers consistently across assets, regions, styles and strategies.
Mukul is from the MIT FinTech Class (’16). He has an MSc. Econometrics and Applied Statistics from UBB Romania and MBA Finance from the Institute of Chartered Financial Analysts of India (ICFAI) Business School. He is a member of the American Statistical Association and a Chartered Market Technician (CMT) from the Market Technicians Association, New York. Mukul started his career as a lecturer of Finance and Economics at Institute for Management Development, India (‘99). He continued to work in the derivatives domain working for top Institutions like Bombay Stock Exchange, HDFC, and Edelweiss before starting Orpheus in 2005. Mukul is a ranked author on the Social Science Research Network and has several filed patents on data innovation. His work on Mean Reversion Framework, Statistical Finance, and Architecture of Complexity has received global attention on the network. He has been invited to speak at Bombay Stock Exchange, Market Technicians Association, Thomson Reuters Conferences, TED, Princeton University, University of Chicago and other.
Dan is the technology brain behind Orpheus. Dan has more than a decade of experience in managing technology teams for various global financial service companies. He oversees all technological aspects and provides solutions architecture for Orpheus, which delivers data solutions, analytics and RMI strategies to global institutional clients. He leads a team of developers and system administrators who apply and scale new technologies for Orpheus’ second phase cross-domain expansion. His project management experience along with his functional background equips him to lead Orpheus to the global fin-tech stage. Currently, Dan is overseeing the integration of a machine-learning platform into the Orpheus infrastructure.
Cristina is an MSc. from UBB, Romania. Her area of expertise includes Finance, Econometrics, and Statistics. Cristina as head of business perfects the key role of bridging research and technology systems. Her background in R Programming and other programming environments, top research capability, and leadership skills assists her to deliver many successful projects. She led the RMI listing on NASDAQ which delivered more than 38% above the Nasdaq Composite for a 24 month period since January 2015 making it the top performing equity model in the world. Cristina is currently integrating our AI research process into a scalable technology framework that can drive our next generation of global financial and non-financial solutions.
Elena’s team at Heraldist & Wondermarks powers the digital marketing strategy for Orpheus. Before joining Heraldist & Wondermarks, Elena led Coca-Cola Company’s advertising across Eastern European markets on behalf of McCann, as well as Coke’s sponsorship of the Sochi Olympics. Previously, Elena headed marketing communication activities for Pepsico and was awarded Grand Effie, world’s most important recognition for marketing excellence. She has 12 years of experience in advertising and held senior positions with major global agencies such as BBDO, McCann, Ogilvy or Publicis.
Ionut has a decade-long experience as a professor of Corporate Finance, Public Finance and Behavioral Finance. He graduated the Faculty of Economic Sciences and Business Administration from UBB, Romania, completed his Ph.D. in Finance in 2006 followed by Postdoctoral studies from Kobe University, Japan in 2010. Ionut has authored many papers and a book on Behavioral Finance. He is fluent in Japanese, Romanian and English. He is associated with Orpheus since 2007 and continues to spearhead the company’s academic initiative.
Dacian is a Physicist by training. He completed his Bachelor’s in Electrical Engineering at the Technical University from Cluj-Napoca, Romania. His interest in phase change systems and complex networks got him interested in working with Orpheus. His bachelor thesis, “The Study of Embedded Permanent Magnet Synchronous Motors for Automotive Electrical Propulsion” was judged as one of the best papers from Scientific Students Symposion-2015. His research interests in complex networks and dynamics systems come from his love for mathematics, physics and from his need to understand universal systems and the intelligence that drives them. Currently, he is working on the idea of preferential attachment and detachment in complex networks and how they can be applied in stock market systems.
Octavian is currently pursuing his bachelors in Economics at UBB Romania and handles multiple roles at Orpheus starting from fixing data feeds, preparing statistical R plots, to monitoring corporate actions across global equities , maintaining and building ETF fixed income models and preparing the Orpheus team for global fintech and AI competitions. His research focus is on applying reinforcement learning to portable alpha. Tavi, as we call him, is a firm believer that ultra-smart AI bot managers are around the corner and will soon take over the discretionary alpha process.
Ionut has worked as a data scientist in both corporate and academic environments. His experience at Hewlett Packard was oriented towards mastering tools such as SAS and R in quick succession. However, during his stint as a graduate student at CEU’s graduate school of political science he focused on inferential solutions to complex puzzles, succinct visualizations and open source data collection. His master thesis applied a non-parametric model based on multidimensional scaling to primary data aimed at predicting voting preferences and turnout in various European elections. At Orpheus he is working on machine learning problem sets to enhance our asset management models. His instrument of choice is R but he is not a stranger to Python. He is always on the lookout for the next top non-parametric model.