Leverage data sources such as Twitter, Facebook, YouTube, and LinkedIn, shorish research can provide social network analysis from a variety of keywords, queries or other terminology.
shorish research can tailor a custom solution to the needs of the financial institution or planner, taking into consideration (for example) heavy-tailed returns distributions, non-normal distributions, and other real-world financial complications.
Solutions are meant to be implemented. With expert knowledge for back-end and front-end programming and Cloud infrastructure utilization, shorish research moves from concept to concrete with tailor-made precision.
With over 20 years of experience in modeling and empirical analysis, shorish research leverages modern technology to tackle problems in financial asset pricing and forecasting, market sentiment assessment and ‘big data’ applications.
Social Network Analysis
Harnessing the Power of Social Media
The advent of social networking has created previously impossible opportunities for the collection and analysis of enormous data sets. These data sets, when properly treated, have applications to network formation and analysis, group dynamics, and meme diffusion (e.g. the diffusion of product information and/or advertising, market news events, regional and global events, etc.).
Using data sources such as Twitter, Tumblr, Facebook, YouTube, LinkedIn–to name some of the most popular social networking platforms–shorish research can field products designed to extrapolate a multi-layer network according to a preselected (or endogenously generated) set of keywords, queries or other terminology.
Shorish Research conducts frontier research into the solution of social networking “big data” challenges, such as the combination of social networking information with hyperlink web information. Through its partnership with Uberlink Corporation, a first-class analysis of web networks can be provided using currently available Internet data.
Networks are complicated. But shorish research uses the latest methods and algorithms to extract meaning from complexity. Common factors, both quantitative and qualitative, can be filtered using machine learning (e.g. classifier systems, natural language processing, robust optimization, etc.) and used to assess the topology of the resulting network.
From this, we can trace the impact of a particular meme (such as a news item) through the network, indicating how individual and group sentiment can change over time. Understanding this impact can form a crucial input into the decision-making procedures for e.g. advertising and marketing.
In complex modeling environments, shorish reseach can create insights for use in financial asset pricing models, such as statistically robust proxies for the prediction of turning points, or for the refinement of suspected or explicitly recognized trends in quantitative financial data.
Creating a web experience can be challenging. Shorish Research uses some of the most popular Content Management System (CMS) and Web Frameworks to create both front- and back-end solutions that are HTML5-compliant. Drupal, WordPress and Joomla are available for website creation and administration. And Ruby On Rails and Django can turn your web interface into a web application.
Real World Finance
The Great Recession (2007-2009) showed how crucial modeling for the real world has become, to avoid yesterday’s mistakes and prepare for tomorrow’s opportunities. Although off-the-shelf financial pricing and forecasting solutions exist, they often rely upon assumptions which are oversimplified for tractability. In the Great Recession this led to mispricing of complex derivatives and aggregated securities (such as Collateralized Debt Obligations, or CDOs), and an underestimation of the downside risk of extreme events.
Shorish Research can tailor a custom solution to the needs of the financial institution or planner, taking into consideration (for example) heavy-tailed returns distributions, non-normal distributions, and other real-world financial complications.
Before the explosion of crowdsourcing, Web 2.0 and social media, assessing the sentiment of consumers and investors relied upon surveys or polling data, which can be expensive.
By accessing the wealth of information available from social media, Shorish Research provides tools to properly filter and assess sentiment, trends and outlooks. The cost of generating sentiment estimates has never been lower.
Research and Teaching
- Intermediate Microeconomics (University of Aarhus; University of Illinois at Urbana-Champaign)
- Introductory Economics (Carnegie Mellon University)
- Information Economics and Contract Theory (Institute for Advanced Studies)
- Applied Game Theory (Institute for Advanced Studies)
- Introduction to Game Theory (Institute for Advanced Studies)
- Mathematics for Economists (Institute for Advanced Studies)
- Numerical Methods for Hard Optimization (Institute for Advanced Studies)
- Introduction to Computer Programming (Institute for Advanced Studies)
- Microeconomics (Institute for Advanced Studies)
- General Equilibrium Theory (Institute for Advanced Studies)
- Mathematical Macroeconomics (University of Aarhus)
- Dynamic Macroeconomics (University of Aarhus; Institute for Advanced Studies)
Dr Shorish’s work has appeared in such peer-reviewed journals as The Journal of Economic Theory, Economic Theory, Annals of Finance, Computational Economics, The Berkeley Electronic Journal of Theoretical Economics, and others.
With academic interests ranging over mathematical economics and finance, computer science, software programming and engineering, shorish research devotes much of its resources toward frontier academic research. Education and outreach continues to be and will remain a high priority.
Contact Us For Further Information
3078 Meerbeek, Belgium