1. Economic Crisis: Technology is the answer Edward Tsang Centre For Computational Finance and Economics University of Essex IEEE Technical Committee on Finance and Economics

2. Motivation Technology has changed every aspect of our lives Why not economics!? Do we understand the economy? Very little! Hence the trouble. Unique opportunities for computing

3. Market as hard science: Observe micro-behaviour Discover regularities Agent-based studies: Simulate markets Look for stylised facts Artificial Market EDDIE Intelligence EDDIE Fundamental EDDIE Noise Technical Analysis: Discover regularities by analysing past series Studying Financial Markets Classical economics: Mathematical analysis Cal. fundamental values E(R i ) = R f + β im (E(R m ) - R f ) β im = Cov(R i, R m ) / Var(R m ) A Wiki approach? Assume rationality Automated trading is future

4. Classical Economics To model economy and prices mathematically Classic: Capital Asset Pricing Model (CAPM) E(R i ) = R f + β im (E(R m ) - R f ) where β im = Cov(R i, R m ) / Var(R m ) –E(R i ) is the expected return on the capital asset –R f is the risk-free rate of interest –β im is the sensitivity of the asset to market returns –E(R m ) is the expected return of the market Built on important assumptions –e.g. perfect rationality, market efficiency, homogeneity

5. Rationality Rationality is the assumption behind many economic theories What does being rational mean? Are we rational?

6. 10 September 2015All Rights Reserved, Edward Tsang CIDER: Computational Intelligence Determines Effective Rationality (1) You have a product to sell. One customer offers £10 Another offers £20 Who should you sell to? Obvious choice for a rational seller

7. 10 September 2015All Rights Reserved, Edward Tsang CIDER: Computational Intelligence Determines Effective Rationality (2) You are offered two choices: –to pay £100 now, or –to pay £10 per month for 12 months Given cost of capital, and basic mathematical training Not a difficult choice …

8. 10 September 2015All Rights Reserved, Edward Tsang CIDER: Computational Intelligence Determines Effective Rationality (3) Task: –You need to visit 50 customers. –You want to minimize travelling cost. –Customers have different time availability. In what order should you visit them?  This is a very hard problem  Some could make wiser decisions than others

9. 10 September 2015All Rights Reserved, Edward Tsang What is Rationality? Are we all logical? What if Computation is involved? If we know P is true and P  Q, then we know Q is true We know all the rules in Chess, but not the optimal moves “Rationality” depends on computation power! –Think faster  “more rational”

10. Technical Analysis (Chartists) Attempt to find patterns in the chart in order to predict future movements Refer to EDDIE for forecastingEDDIE for forecasting EDDIE uses Genetic Programming, a branch of computational evolution

11. 10 September 2015All Rights Reserved, Edward Tsang Computer vs Human Traders Programs work day and night, humans can’t Programs react in miliseconds, humans can’t Programs can be fully audited, humans can’t When programs make mistakes, one can learn and change the culprit codes –Failed human traders simply change jobs Expertise in computer programs accumulates –Human traders leave with his/her experience Not to mention costs, emotion, hidden agenda, etc.

12. Automated Trading is Future Traders have to program Or work with programmers Traders provide strategies Programmers produce programs Programs trade –Markets are 24 hours –Already true for foreign exchange –No reason for markets to pause in weekends

13. 10 September 2015All Rights Reserved, Edward Tsang FAQ in Automated Trading Is the market predictable? –It doesn’t have to be: just code your own expertise –Market is not efficient anyway, herding has patterns How can you predict exceptional events? –No, we can’t –Neither can human traders How can you be sure that your program works? –No, we can’t –Neither were we sure about Nick Leeson at Barrings –Codes are more auditable than humans –If you can improve your odds from 50-50 to 60-40 in your favour, you should be happy

14. 10 September 2015All Rights Reserved, Edward Tsang Agent-based Market Studies 5 Modify agent models according to discrepancies Artificial Market Agent 1 Agent 2 Agent n 2. Simulate their interaction 3. Observe their results 1. Try to model the agents exogenous endogenous 4. Compare results with real markets “All models are wrong, some models are useful” “More calculation is better than less, Some calculation is better than none”

15. The Hard Science of Markets (Richard Olsen) How is biology studied? –E.g. one observes the growth of plants –Measure certain chemical contents –Write down regularities –Generalize regularities if possible Markets are results of micro-behaviour –Technical analysis only studies the results (prices) –Much deeper knowledge can be observed from studying micro-behaviour … Richard Olsen Forex OANDA

16. Agent-based Market Studies If I can model every investor, I can predict the market But I can’t accurately model investors However, I know exactly what orders were placed I know what happened after each order was placed –Whether it was transacted –Its immediate impact to prices –Price movements afterwards Can’t we learn anything from these? –By recording details and looking for regularities –As we do in biology

17. High Frequency Data: Example of an Order Book PriceVolumeOrders Seller 43.862,0001 Seller 33.8510,0005 Seller 23.845,0001 Seller 13.831,0001 Buyer 13.826,0003 Buyer 23.818,0003 Buyer 33.805,0001 Buyer 43.7917,0003

18. Theory of fractals Financial markets are fractal: statistical properties are self similar.

19. Different investors react differently to the same piece of information The length of the coast line (profit opportunities) depends on how you measure it A trader that reacts monthly (red line) has higher potential for profit than one who reacts quarterly (blue line) Even with perfect foresight, one may be buying when the other is selling (April) JanAprOctJul Jan Time Price

20. Definitions of directional changes

21. Directional Changes (DC) A Directional Change Event can be a –Downturn Event or an –Upturn Event. A Downward Run is a period between a Downturn Event and the next Upturn Event. An Upward Run is a period between an Upturn Event and the next Downturn Event.

22. DC Definition (2) In a Downward Run, a Last Low is constantly updated to the minimum of –(a) the current price and –(b) the Last Low. In an Upward Run, a Last High is constantly updated to the maximum of –(a) the current price and –(b) the Last High.

23. DC Definition (3) In a Downward Run, given a Threshold (%), an Upturn Event is an event when the price is higher than the Last Low by the Threshold. An Upturn Event terminates a Downward Run, and starts an Upward Run. In an Upward Run, given a Threshold, a Downturn Event is an event when the price is lower than the Last High by the Threshold. A Downturn Event terminates an Upward Run, and starts an Downward Run.

24. DC Definition (4) A Directional Changes Sequence (DC Sequence) is a sequence: (Start_date, Start_price, Return, Period, Return, Period,...) The above definitions are mutual recursive. Operationally, we set the Last High and the Last Low to the Start_price at the beginning of the sequence.

25. Length of coastline Maximum profit opportunity after transaction costs with no leverage and perfect foresight Long coast line (>2,000%) means huge opportunities to be exploited! opportunities

26. Striking observation 17 scaling laws discovered so far, e.g. –When a directional change of r% occurs, it is followed by an overshoot of r% –The time for the overshoot to happen is also highly correlated to the time taken for the change of direction to happen! Further observation and analysis needed Machine learning needed for function fitting

27. High frequency data You only see what you look at 25,000 data points per day Which is roughly 100 years daily data

28. Significance of HFF  Average daily turnover : about US$3.98 trillion [1]  2008 World GDP [2] : WorldUS$60.69 trillion 1.USUS$14.26 trillion 2.JapanUS$ 4.92 trillion 3.ChinaUS$ 4.40 trillion 4.GermanyUS$ 3.67 trillion 5.FranceUS$ 2.87 trillion [1] 2009 estimation based on Bank for International Settlements, 2007 [2] International Monetary Fund, 2008

29. Plotting Long/Short against Win/Lost positions USD/JPY on 4 th July 2009 –There are more losers than winners –There are more short than long positions Rich information to be analysed (PhD projects) Observation from OANDA Those who bought USD with JPY at prices higher than current price (i.e. in potential loss) Green: in profit Blue: in loss Those who short USD for JPY at prices higher than current price (i.e. with unrealized profit) Long Short Current price

30. Market Analysis You can play Forex game at OANDA

31. 17 new scaling laws Example: trend scaling law trend of 1% will on average continue for another 1%, trend of 2% for another 2%. Scaling laws establish definite frame of reference for financial modelling. Tick scaling law Scaling law indentifies fixed relationship between averages of two variables.

32. Scaling law of directional changes

33. Price Distribution

34. Micro behaviour analysis Approach: –Modelling trading agents –Consequences analysis on big offers/bids –Finding patterns, such as scaling laws Hope to explain market behaviour that conventional economics failed to explain –No perfect rationality –No homogeneous behaviour by traders An exciting way forward

35. Proposal Indonesian tsunami 2004 led to construction of early warning systems Economic turmoil demands the same! Banking sector lost in the crisis over US$1,000bn (779bn Euro, £702bn, 6,529bn KON) Investing 2%, or US$2bn is not too much –Richard Olsen, OANDA and CCFEA –Clive Cookson, Financial Times

36. Comptuer Science: new challenges Routes: a programming environment for real time applications

37. High-frequency Finance Research Platform EconomistVisualization Expert Computational Intelligence Expert High-frequency data (including foreign exchange rates, stock and option prices, interest rates, etc) Inter- connected modules implementing models Modules interact with each other or users Users...... Users upload or retrieve modules Possibly through automated interaction Web-based Open-source

38. Concluding Summary Classical economics build castles on sand Technical analysis only scratches the surface Agent-based help understand markets –Repeatable, enabling scientific studies “Market science” looks into micro-behaviour –Chartists look at end results, why not look at causes!? –If chartists can make money, so can market scientists! –Exciting, uncharted area [demanding expertise!] Technology will play a big part in economics!

39. Reference Richard Olsen & Clive Cookson, How science can prevent the next bubble, FT.com, 12 February 2009

40. References Martinez-Jaramillo, S. & Tsang, E.P.K., An heterogeneous, endogenous and co-evolutionary GP- based financial market, IEEE Transactions on Evolutionary Computation, Vol.13, No.1, 2009, 33-55 Tsang, E.P.K., Forecasting – where computational intelligence meets the stock market, Frontiers of Computer Science in China, Springer, Vol.3, No.1, March 2009, 53-63 Tsang, E.P.K., Computational intelligence determines effective rationality, International Journal on Automation and Control, Vol.5, No.1, January 2008, 63-66 http://www.bracil.net/finance/papers.html