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# Financial Time Series: Concept and Forecast (dsth Meetup#2)

Financial Time Series: Concept and Forecast
Chainarong Kesamoon, PhD
Data Science Thailand Team
chainaron.kes@gmail.com
datascienceth.com

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### Financial Time Series: Concept and Forecast (dsth Meetup#2)

1. 1. Financial Time Series: Concept and Forecast Chainarong Kesamoon, PhD Data Science Thailand Team  chainaron.kes@gmail.com Data Science Thailand Meetup #2 6 Nov 2015
2. 2. Outline: What is time series? How to model ﬁnancial time series? Forecasting
3. 3. What is time series? Numeric data 1, 1, 2, 3, 5, 8,… 2.8, 1.9, -10, 25, -6.7,… Time series measured by time Time matters!!!
4. 4. Examples
5. 5. Financial Time Series Stock prices Market index Money Exchange rates gold, oil
6. 6. Typical data set source: Google Finance
7. 7. We would like to know: Tomorrow prices!!! Chance of proﬁt or loss Future value of our money That’s all about “Return & Risk”
8. 8. Return Today’s return=today’s price - yesterday’s price Percentage return = Today’s return/yesterday’s price Log return = log(today’s price) - log(yesterday’s price) know return then we also know price
9. 9. Better analyze returns than prices
10. 10. Let’s analyze!
11. 11. Correlation Positive correlation : More experience, more salary Where there’s a will, there’s a way. Negative correlation: The higher the Doy, the lower the temperature. The more one works, the less free time one has. No correlation: The color of your shirt, the color of my shoes.
12. 12. Coefﬁcient of Correlation mpg Miles/(US) gallon cyl Number of cylinders disp Displacement (cu.in.) hp Gross horsepower drat Rear axle ratio wt Weight (lb/1000) qsec 1/4 mile time vs V/S am Transmission (0 = automatic, 1 = manual) gear Number of forward gears carb Number of carburetors
13. 13. Correlation between returns on different days
14. 14. Can we forecast the return? Imagine you have tossed a normal coin ten times, last ﬁve outcomes were all head. Do you expect that the next outcome would be head? If there is no correlation, the next outcome would be like tossing a coin. Seem hopeless T__T H T T H T H H H H H 1 2 3 4 5 6 7 8 9 10 11 ?
15. 15. Efﬁcient Market Hypothesis A ﬁnancial economist and passionate defender of the efﬁcient markets hypothesis (EMH) was walking down the street with a friend. The friend stops and says, "Look, there's a \$20 bill on the ground." The economist turns and says, "Boy, this must be our lucky day! Better pick that up quick because the market is so efﬁcient it won't be there for very long. Finding a \$20 bill lying around happens so infrequently that it would be foolish to spend our time searching for more of them. Certainly, after assigning a value to the time spent in the effort, an 'investment' in trying to ﬁnd money lying on the street just waiting to be picked up would be a poor one. I am also certainly not aware of lots of people, if any, getting rich mining beaches with metal detectors." When he had ﬁnished they both look down and the \$20 bill was gone! source: http://www.etf.com/sections/features/123.html
16. 16. Let’s try another way
17. 17. Why squared return? The variance of return is calculated from squared returns. Why variance? What is it? Variance is the degree of variation High variance => high volatility=> high risk Volatility is forecastable High risk, high return (but return can be either + or - )
18. 18. Major Stylized Facts for Return I. The distribution of returns is not normal, it has a high peak and fat tails. II. There is almost no correlation between returns for different days. III. There is positive correlation between squared returns on nearby days, likewise for absolute returns.
19. 19. Time series models General time series models: MA : moving average AR : autoregressive ARMA : autoregressive moving average ARIMA, ARFIMA,… Financial time series models: EWMA : exponentially weighted moving average (G)ARCH : (generalized) autoregressive conditional heteroskedastic SV : stochastic volatility Asset pricing model Black-Scholes model
20. 20. Robert F. Engle Tim Bollerslev Nobel Prize in Economic Sciences 2003 GARCH model Generalized AutoRegressive Conditional Heteroskedasticity Model rt+1 = µ + t+1✏t+1 2 t+1 = ! + ↵(rt µ)2 + 2 t where ✏t+1 ⇠ N(0, 1)
21. 21. Examples GARCH volatility forecasting Using data up to 5 Nov 2015 Date Return SD 6 Nov 0 0.384 7 Nov 0 0.390 8 Nov 0 0.396 9 Nov 0 0.401 10 Nov 0 0.407 11 Nov 0 0.412 12 Nov 0 0.416 13 Nov 0 0.421
22. 22. "When Professors Scholes and Merton and I invested in warrants, Professor Merton lost the most money. And I lost the least” – Fischer Black – Nobel Prize in Economic Sciences1997: Fischer Black, Myron Scholes, and Robert Merton
23. 23. Challenging, isn’t it? Financial time series is challenging as it is quite difﬁcult to forecast. Multivariate time series is also of interest, but it is even more difﬁcult to model multiple time series together. Most ﬁnancial models were created some years ago, at the time that less data were available. Nowadays, we can access more and more data, that would be a good opportunity to explore and create better models for ﬁnancial market.
24. 24. –Chainarong Kesamoon “Moltes Gracies”