1. NEURAL TRADING .KEYS TO PROFIT Term Paper Vinod Gupta School of ManagementIIT Kharagpur Submitted in partial fulfilment of the Management Information Systems (BM61014) Course at the Vinod Gupta School of Management, IIT Kharagpur.Submitted to: Submitted by: Prof. Prithwis Mukerjee Pallav Maheshwari 10BM60057 1 Neural Trading-Keys to Profit

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Table of Contents Abstract ................................................................................................................................................... 3 Introduction to the idea and importance ............................................................................................... 3Why ANN? ........................................................................................................................................... 3So, What is required: .......................................................................................................................... 3 Fundamental Analysis ............................................................................................................................. 4 Technical Analysis ................................................................................................................................... 5 Things to take care while training Neuronets ......................................................................................... 7 Some Myths about Neural Trading ....................................................................................................... 11 Future as seen by Experts ..................................................................................................................... 11 Current Players in the Market............................................................................................................... 12 Road Blocks for Neural Trading............................................................................................................. 12 Real World Constraints ......................................................................................................................... 13 Conclusion and the way forward .......................................................................................................... 13 References ............................................................................................................................................ 14 2 Neural Trading-Keys to Profit 3. AbstractThe Human nervous system is one of the most complex systems in the universe. Apart from its superior processing speed and memory, it has the ability to learn and adapt This makes the brain different from computer. Computer scientists now are writing software which tries to mimic this cognitive learning of adapting. This adapting is seen as learning from past experiences, which a normal computer today is unable to do. This term paper focuses in applying this learning in trading. We focus on a number of trading strategies and see how neural trading can reap rich benefits and do away with the need to understand patterns which have been previously observed. The same technology can also be used in forecasting and helping in deciding on future strategy in derivatives and forex markets. This will help in generating more than normal profits. This paper aims to discuss a neural trading strategy, give insight into its development and eventually operate it given the real world constraints. Introduction to the idea and importance Why ANN? Artificial Neural Networks (ANNs) are the best known non linear approximators. They show great tolerance to imprecision and perform even in noisy data environments. Financial Markets are very noisy in terms of volatility, unpredictability and risk. These very features make financial trading both challenging and rewarding. ANNs therefore are best suited for mimicking and understanding the movements of financial markets. Researchers have been trying to apply ANN to develop mechanical trading systems. In this context a mechanical trading system operates as per a set of rules and has no discretionally components. Developing economically viable trading system is a tough task, but the recent developments in the technology are very promising. ANNs have the methods of back-propagation with feedback to reduce the training error. One of the objectives which the mechanical trading system can put to use is to maximize profit subject to a given level of risk, an opportunity which is not available with ANNs. The mechanical trading system try to mimic behaviour expectations of the traders and at the same time maintain a balance with the traditional ANN training methods. This happens because of proper choice of inputs and outputs. All this methodology if properly implemented with a robust mechanical system would work wonders in years to come. So, What is required:3Neural Trading-Keys to Profit 4. From a trading system point of view, we will focus on real world applications of the technique along with a technique of benchmarking. First of all, we need to select the variables which are likely to influence our desired outcome. These methods for the starting point for creation of ANN and are likely to heavily influence the outcome. Various number of methods exist to find out the variables which are likely to influence our outcome heavily. There are categories under which theyll fall, the fundamental analysis or the technical analysis. We need to first understand such these two types of analysis. Since both of them are complementary to each other an intelligent approach in selection of inputs should be used. These inputs should be used to match them to expected outputs in terms of their effect and duration so that an intelligent choice of inputs can be made, and these can be matched to likely outputs in terms of their possible effect and duration. Fundamental AnalysisWe all know that financial ratios are used worldwide for the fundamental analysis of companies future performance. They give an indication of future earnings and a potential future price direction. Various books have been selling this investment wisdom and contain details on building portfolios and selecting top picks for the near future. One of the wisest investor approaches was developed by Graham. He urged investors to pay attention to three fundamental variables The size of the firm The capitalization and The P/E ration Oppenheimer and Schlarbaum stated that . . .it is reasonable to conclude that our evidence contradicts the semi-strong form of the efficient market hypothesis. Graham had also published a list of ten attributes of an undervalued stock, which could be used by investors to get more than normal returns. These 10 attributes were: E/P yield >= twice the AAA bond yield, P/E = 2/3 of the AAA bond yield, Price