Applying Artificial Intelligence to Financial Markets

By Ondrej Martinsky

This book deals with the issue of problematic market price prediction in the context of crowd behaviour affected by the psychology of the masses. It highlights the contrast between a phenomenon of mass psychology and the efficient market hypothesis, which is essentially based on a common economic theory. The basic assumption is that if there is a model of interaction between masses and agents participating in markets, then there also exist means for prediction of the whole market’s behaviour, though nevertheless the behaviour of every single agent is not predictable.

From a practical point of view, this book describes technical analysis methods used to predict price movements, and discusses a soft computing approach used in a composition of automated trading systems. This book brings alternative, soft computing computational models to trading strategies and innovatively combines two different areas of science – artificial intelligence and technical analysis. One of the main benefits of this book is a demonstration that the soft computing approach in a combination with the “soft” social sciences accounts more reliable results than the conventional mathematical models.

This book is for anyone interested in trading, financial markets and security exchanges, as well as for those who have theoretical or practical knowledge from the fields of artificial intelligence and soft computing, and want to know how these topics can be applied in financial markets.

Ondrej Martinsky has 15 years of experience in quantitative finance, specializing in building Rates/FX pricing and risk management systems. He is currently based in Hong Kong and holds a position at HSBC Bank as a quantitative strategist for their market-making business. Previously, he held various similar positions in investment banks in London.

Ondrej is also the author of several scientific papers presented at international conferences and publishes on his blog, www.quantandfinancial.com.

In his book "Intelligent Trading Systems", he describes the application of advanced machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms in building automated trading strategies. The result of his research is a unique interconnection of knowledge from the fields of computer science and financial markets.