Software Development For The Financial Industry
We can help managers, traders, advisors and financial analysts working in financial institutions or in financial consultancy services with several mathematical modelling tasks in the following areas:
- Analysis and evaluation of trading strategies.
- Analysis and evaluation of financial and investment products and concepts.
- Asset Liability Management modelling.
- Risk modelling, management and reporting.
- Debt management modelling.
- Scenario analysis (What if analysis as well as scenario optimization).
- Pricing of financial products.
We apply analytical methods from mathematics and engineering, to help you in your decision process. For example if you are considering launching a new product or a trading strategy, we can help you with an analysis of pros and cons of different specifications you have been considering. We improve your team’s efficiency when your project needs analytical input. By presenting second opinions and alternative solution methods, we can provide you with that last bit of information you need to make the best decisions. We can help you within a large range of projects from long term strategic planning to short term tactical decisions.
Why Use Us
We have analysts in different areas of specialization. That could be methodological such as statistics, time series analysis, stochastic processes, operations research and information technology, or business oriented such as asset management, fixed income analysis, FX trading, debt management, risk management etc. Decision problems in these areas are in general very complex and often require expertise from many disciplines, so it often requires that you can combine teams of experts in several areas. With our wide access to specialists in different fields we can solve problems that normally require that you have your own research and development group within decision making.
How It Works
Our analyst teams start projects by listening to managers describe problems. We ask questions and search for data that may help to formally define a problem. For example, in an analysis for deciding profitability and risks associated with a new financial product we may be asked to determine the best transaction cost structure and the best hedging strategy for a few different risk levels.
Techniques used may include Monte Carlo simulations, scenario generation and analysis, linear and nonlinear programming, stochastic programming, econometric methods, data denvelopment analysis, neural networks and expert systems. Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system.
Using these models, the team can explicitly describe the different components and clarify the relationships among them. The model’s inputs can then be altered to examine what might happen to the system under different circumstances. In most cases, a computer program is used to numerically evaluate the model. The team will often run the model with a variety of different inputs to determine the results of each change.
Based on the results of the analysis, we present recommendations to managers. Managers may ask our analysts to modify and rerun the model with different inputs or change some aspect of the model before making their decisions. Once a manager reaches a final decision, the team usually works with others in the organization to ensure the plan’s successful implementation.