Systematic Strategy
The major focus of Investment Analytics’s consulting work is in the research and development of systematic trading strategy that are custom-designed to meet our client’s investment objectives for risk and return. Our consulting process begins with a detailed review of the client’s requirements and an identification of the terms of reference of the research to be carried out, including the asset class(es) and investment universe to be considered, return objectives, risk controls and strategy constraints, which typically may require market or sector neutrality, liquidity and event risk limits, and minimum and maximum turnover or holding periods.
Our research may cover a broad menu of investment concepts, both fundamental and technical, including: Factor models Mean reversion and momentum models Pairs trading models Basket trading models Dispersion trading systems Correlation trading Relative value models Basis arbitraje Cointegration analysis Risk models Volatility arbitraje Pattern recognition techniques Behavioral finance models Nonlinear models
A typical research project will also entail carrying out extensive back-testing of the proposed strategy to ensure its robustness under varying market conditions.Investment Analytics is often also engaged in the implementation stage of the process, designing and developing the computer algorithms, and interfaces to trading platforms, ECNs and Reporting systems.
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Algorithmic Trading
Often an essential component of systematic trading, particularly in high frequency strategies, algo trading is widely used by hedge funds, pension funds, mutual funds, and other institutional traders to divide up a large trade into several smaller trades in order to manage market impact, opportunity cost, and risk. It is also increasingly used to make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information, with the computer algorithm deciding on certain aspects of the order such as the timing, price, or even the final quantity of the order. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically.
Investment Analytics has experience of researching and developing custom algo trading strategies with a wide variety of trading applications, such as:
Transaction cost reduction Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging". The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period.
Arbitrage A classical arbitrage strategy might involve three or four securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are sufficiently different from those implied in the model to cover transactions cost then four transactions can be made to guarantee a risk-free profit. Algorithmic trading allows similar arbitrages using models of greater complexity involving many more than 4 securities
Market Making Algorithmic trading systems have been successfully deployed as engines for market making strategies in equities, F/X and fixed income markets.
Complex Strategies A "benchmarking" algorithm can be deployed to mimic an index's return. Algo strategies are often used to access dark pools - alternative electronic stock exchanges where trading takes place anonymously, with most orders hidden or "iceberged”. Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order. They then front run the order.
High Frequency Finance
High-frequency finance applications present numerous complexities as well as opportunities:
The number of observations can overwhelm conventional systems. The daily average of quotes of currency spot market can easily reach tens of thousands while an active stock in a major exchange can produce orders of magnitude more ticks. Streams of data must be segregated by currency pairs or range of stock symbols to allow computation. Data is recorded with errors. Data is published with errors and must be cleaned before analysis. Some of the frequent errors are random peaks, data gaps and sequences out of order. Data is irregularly spaced in time. Time series are irregularly spaced in time with random daily number of observations. Taken together, these characteristics present very difficult challenges for conventional discrete or continuous time models and for computer modeling systems designed to handle data volumes orders of magnitude smaller.
Modern Solutions to The Challenge of High Frequency Finance Investment Analytics has developed modeling frameworks designed to meet these challenges and provide a superior description of the characteristics of markets operating in real time, representing behaviors that are often highly non-linear. We use Complex Event Programming and parallel/distributed processing techniques to develop systems capable of handling massive data volumes on the fly, minimizing latency and response times to new information. Combine dwith algorithmic trading techniques, these technologies provide a formidable advantage to clients engaged in systematic strategies.
Mathematical Modeling and Financial Engineering
Model Development and Validation We specialize in the design, development and validation of mathematical models for pricing, trading and risk management of cash and derivatives securities in the equity, fixed income, foreign exchange and credit markets. In addition to developing customized models. We can also assist with the validation of clients' existing models and the evaluation of third-party systems.
OTC Derivative Valuation We provide independent pricing and month-end valuation of over-the-counter products using proprietary yield curve, credit and volatility surface models and ensure that senior management has a sound grasp of key valuation and risk parameters.
Financial Engineering We apply mathematical, statistical and econometric models to assist our clients in the design and engineering of complex derivative products and in the structuring of special purpose vehicles used to provide investment, credit or risk management solutions.
Risk Management Investment Analytics has consulted on risk management with some of the leading Wall Street institutions and developed systems for managing market risk exposures in credit, f/x, equity, fixed income and derivatives markets, as well counterparty credit risk.Our risk control ssytems have ben vetted and approved by leading ratings agencies, while our proprietary risk management methodology provides protection against extreme adverse market movements and monitors the risk to clients’ fixed income or equity portfolios on an ongoing basis.
Contact us:
Suite 1212, 48 Par-La-Ville Road, Hamilton HM11, Bermuda USA Telephone: +1 212 786 1781 Email: enquiries@investment-analytics.com