Financial Applications of Neural Networks

1. Financial Applications of Neural Networks Lecture 1

2. Introduction • Purpose of the Course • General • to gain an understanding of the stock market • to learn about trading methods, indicators and strategies • to practice trading using computer technology • Introduction to Indicators • Technical • Fundamental

3. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

4. Trading – the last fontier • The life of a successful trader • You can be free • You can live anywhere in the world • You can be independent of routine and not answer to anyone • Many aspire but few succeed. Why? • The game is hard • Ignorance • Lack of discipline • Amateurs • An amateur looks at the quote screen and sees millions of dollars in front of his face. He reaches for the money and loses, reaches again and loses again.

5. Strategies and Psychology • Learn about the market • Learn to separate good an bad information • Use hunches about economic or political trends • Use inside information

6. Strategies and Psychology • Control feelings • Anxious to jump in • Afraid of losing • Procrastinate • Elated or humiliated after trade • Cutting losses • The feelings of thousands of traders merge into huge psychological tides that move the markets.

7. Strategies and Psychology • Management of Emotions • You can succeed in trading and/or investing only if you handle it as a serious intellectual pursuit. Emotional trading is lethal. • To insure success it is necessary to practice defensive money management • A good trader/investor watches his capital as carefully s a scuba diver watches his air supply • Stick to a game plan

8. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

9. Terminology • Randomness • Data can be random, and yet exhibit trends that can be measured and predicted on the bases of past history. • Stationarity • This refers to the notion that the past statistical properties of the data may no longer be applicable to the present. An event such as an earnings surprise (good or bad) influences future trading decisions. • Decision-making • The problem is to use readily available data in a meaningful way to extract information for decision-making.

10. Terminology • Data • Fundamental – earnings, sales, profits, P/E • News – general data on the economy, company announcements, progress of industries and groups, takeover rumors … • Technical – based on proce and volume history using a variety of measures • Information • The first two (above) report on what has already happened and are strongly biased by the provider of the information. Tha last is a true measure of what investors have actuall done, or are doing at the moment

11. Example • Apple Computer • In this example we observe the behavior of two technical indicators, Bollinger Bands and OBV, in addition to price. The divergence in price and OBV, in combination with a narrowing of Bollinger Bands signals a price increase • This pattern does not occur too often, but when it does it is a good bet.

12. Example

13. Example This is an excellent example of the power of technical analysis. The reason for the divergence of price and OBV is that knowledgeable investors are accumulating the stock but are managing their trades so as not draw attention to their actions. They may have inside information about a coming announcement, e.g. Once the announcement is made, the price rises due to “normal” market forces and the knowledgeable investors have made a nice profit.

14. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

15. Models • Game • The market is a game that pits the smart traders/investors against the not-so-smart • Auction • For every buyer there is a seller in the setting of an auction • Cycles • The market and individual stocks move in cycles that can be predicted

16. Models • Dow • The stock market reflects the combined knowledge, hopes and fears of the entire financial community. It discounts everything that is known or surmised • There are three distince trends • Primary – several months to years • Secondary – several weeks to months • Day-to-day – ignored by the Dow

17. Models • Short-term model • Based on the interplay between the bid-ask price and whether a buyer or seller has initiated the transaction • Random Model • Based on statistical averages and the assumption of stationarity; I.e., that the statistics governing the data are constant in time • Intermediate- Long-term • Based on the use of fundamental data in the valuation of the investment potential of individual stocks, and in the behavior of the market in general

18. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

19. Classes of Indicators • High/Low indicator • A count of the number of stocks making new 52 week highs and the number making new 52 week lows. Highs > 300 is a market top. Lows> 500 is a bottom. If the market is rising, but the new highs are not expanding, exercise caution.

20. Classes of Indicators • Advance/Decline Line • A measure derived from the number of stocks advancing divided by the number declining. If there is a divergence between price movement and AID, bet on the latter. • Sentiment indicators • Put/Call ratio and Short Interest

21. Classes of Indicators • Technical Indicators • Moving average - simple or exponential • MACD - difference of two moving averages • Stochastics - measure of whether the closing price in nearer a daily high or low. • OBV - directional volume indicator • Bollinger Bands - measure of price variation over a recent period

22. Classes of Indicators • Fundamental Indicators • Price • Earnings Growth • Profitability • P/E Ratio

23. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

24. Price • What is it? • Perceived value • That which a person is willing to pay • That which the last person paid • That which the next person is willing to pay

25. Price • What it is based on • Book value • Asset value • Dividend yield • Potential sales due to new inventions

26. Price • Some terminology • Bid is what a buyer offers • Ask is what the seller asks • The “market maker” keeps an orderly market • A trade occurs when there is a meeting of the minds • Each tick on the quote screen represents a deal between a buyer and a seller

27. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

28. The Art of Trading • Time scale • Short-term - intraday or a few days • Intermediate-term - weeks to several months • Long-term - months to years • Timing • Cycles can make long term investing problematical • Market timing helps to solve this problem • Information is the key to successful market timing

29. The Art of Trading • Accuracy • Accuracy of predictions is only part of the goal. The other is a trading strategy which employs some common sense guards against losses and against purchases outside the bid ask range. • Discipline • It is pretty easy to devise or select a winning strategy. However, its maddeningly difficult to adhere to that strategy. • Be alert to a change of state due to world events, special reports and, yes, deliberate misinformation

30. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

31. Challenges • Blending analysis techniques • Technical • Fundamental • Blending several markets • Stocks, Bonds, Commodities • Using advanced computer analysis methods • Neural networks • Fuzzy sets. • Genetic algorithms

32. Outline • General Comments • Some Terminology • Models • Classes of Indicators • Price • The Art of Trading • Challenges • Conclusions

33. Conclusions • In this course we want to learn enough about the market to be able to use our engineering skills to good advantage. • We want to learn enough about advanced algorithms to apply our business background to best advantage.

34. Conclusions • We want to become aware of the vast amount of information on the Internet and learn how to use it. • We will select a topic for in depth study and write a report describing the purpose, goals, tasks results and conclusions. • Hopefully we will become successful investors and/or traders