The starting point is to have a learning model that predicts stock  movements well.

However, this is only a small part of the whole problem. Many other issues have to be taken into account when building a successful system. We will also review some of these issues. In particular, we will consider the following issues:

1) Types of trading systems: trend-following/contrarian/value; categories of systems: arbitrage, sector switching, intraday, etc.

2) Inputs/indicators: technical: moving averages, patterns; fundamental: financial statement data, earnings surprises; techno-fundamental: insider sales, short ratio; market wide indicators: A/D ratio; economic indicators.

3) Learning models: neural networks, SVM, kernel regression, k-nearest neighbor, mixtures of experts, etc.

4) Design considerations: overfitting, data snooping, test of the null hypothesis of whether being profitable, survival bias, forward looking, market regime switch.

5) Other considerations: transactions costs, exit stops and profit targets, order types, etc.

6) Performance and risk measures: Sharpe ratio, maximum drawdown, Sterling ratio, etc.

7) Brief review of risk management and portfolio optimization.

Presenter biography: Amir Atiya has obtained his Ph.D. from Caltech, and has been active in research in the areas of neural networks, machine learning, computational methods, and computational finance. He has 13 years experience in trading system design. Currently he is an Associate Professor at Cairo University. He held a Visiting Associate position at Caltech from 1997-2001, and had research positions in several financial firms, such as Qantxx, Tradelink, Simplex Technology, Countrywide, and Dunn Capital Management. He has been active in the academic community as well, as a member of the organization committee for the yearly Computational Finance Conference, Neural Networks in the Capital Markets Conference, and as program cochair for IEEE Conference on Computational Intelligence for Financial Engineering (CIFER’2003). He has been a Guest Editor of the Special Issue of IEEE Transactions Neural Networks on “Neural Networks in Financial Engineering”, that appeared in July 2001. He got several awards, including the INNS Young Investigator Award in 1996 and the Kuwait Prize in 2005. Currently, he is an associate editor for IEEE Transactions Neural Networks. He has published close to 100 papers on the topics of neural networks, statistical learning theory and applications of these. 

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