Academic Article Competition
Our Academic Articles are aimed at academics at Undergraduate level or higher. As a result, all Mathematical and Theoretical concepts are explained - step by step - as they would be in professors’ lecture notes. We do not shy away from exemplifying many working-out steps, explaining used nomenclature and providing further material and references.
Academic Studies, Term Papers, Dissertations, Theses, and more can all be coded using code exemplified in our articles and our powerful set of APIs and Feeds. Our web APIs provide a broad range of language support so you have unlimited integration options. These include Python so you can access all wide set of open source libraries that are available.
The range of possible studies with such data is limitless. You may find – in this landing page – examples constructing Mathematical Models such as Econometric Models (Linear/Non-Linear Mean/Variance Models using Ordinary Least Squares or Maximum Likelihood Estimation), Forecasting and Interpolation frameworks (e.g.: using Holt-Winters Models).
This competition aims at helping students - from undergraduate to postdoctorate level - to publish their work on internationally recognized platforms and to aid them in (i) academic style writing, (ii) implementing code in their study, (iii) coding, (iv) financial knowledge, (v) developing a contacts network in finance, (vi) generating ideas for economics/finance papers.
This competition runs every quarter. Any work submitted today will be eligible for the current round/quarter. You may submit your work here.
It will have to be an economic or financial study, and in a style akin to these articles:
- Estimating Monthly GDP Figures Via an Income Approach and the Holt-Winters Model
- Investigating the effect of Company Announcements on their Share Price following COVID-19
- Information Demand and Stock Return Predictability (Coded in R)
- Computing Risk Free Rates and Excess Returns from Zero-Coupon-Bonds
- Previous competition winner: Beneish's M-Score and Altman's Z-Score for Analyzing Stock Returns
- Previous competition winner: A Probabilistic Relative Valuation for the Financial Sector Using Deep Learning
- Previous competition winner: Monte Carlo Dropout for Predicting Prices with Deep Learning and Tensorflow
- Previous competition winner: Create an ESG Pitchbook with React and Next.js using the Refinitiv Data Library for TypeScript
As per these examples, the length of the study in question is not a limit; there is no min. or max. length your article has to be to qualify. However, it needs to show and explain the code used in its study. It can be in any (reasonable) format (such as LaTeX, PDF, Word, Markdown, …) but it would preferably be a Jupyter Notebook (you may choose to use CodeBook to write it). You will also need to use Refintiv APIs/data-sets before being published on our Developer Portal – were it to win the competition.
There is no min. or max. number of winners per quarter; any work exceeding our standards will be selected for publication. Thus, do not hesitate to share this with your colleagues, informing them of this opportunity, they are not competitors, but collaborators; you may even choose to work together on this project.
We are ready and happy to provide any help in writing such articles. You can simply email Haykaz Aramyan - the Competition Leader - at email@example.com
Computing Risk Free Rates and Excess Returns Part 2: From Sovereign Coupon-Paying-Bonds (and Bootstrapping / Spot Rates / Discount-Factors)
Beneish's M-Score and Altman's Z-Score for analyzing stock returns of the companies listed in the S&P500