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Entropy Rate and Profitability      of Technical Analysis:       Experiments on the       NYSE US 100 StocksNicolas NAVET ...
Estimating entropyActive field of research in neuroscienceMaximum-likehood (“Plug-in”):     construct an n-th order Markov...
Performance of the estimatorExperiments :  Uniformly distributed r.v. in {1,2,..,8} –                        r.v.         ...
Preprocessing the data (2/2)  Discretization is tricky – 2 problems:      How many bins? (size of the alphabet)      How m...
Entropy is high but price time                         series are not random!          50                                 ...
Autocorrelation analysis         Typical high                         Typical low    complexity stock (OXY)               ...
GP : the big picture         Training              Validation          Out-of-sample         interval               interv...
Experimental setupData preprocessed with 100-days MATrading systems:  Entry (long): GP induced rule with a classical  set ...
Results: low entropy stocks          GP net profits     LT net profits   GP>LT?    LT>GP?  TWX        −9K$             −1.5K...
Explanations (2/2)The 2 cases where GP outperforms LT : trainingquite similar to out-of-sample                 out- of-   ...
PerspectivesHigher predictability level can be observedat intraday timeframe (what about highertimeframes?)Experiments nee...
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Cief2007 nn shc_slides

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  1. 1. Entropy Rate and Profitability of Technical Analysis: Experiments on the NYSE US 100 StocksNicolas NAVET – INRIAFrancennavet@loria.frShu-Heng CHEN – AIECON/NCCUShu-Taiwanchchen@nccu.edu.twCIEF2007 - 07/22/2007 1 OutlineEntropy Rate : uncertainty remaining in thenext information produced given knowledge ofthe past measure of predictabilityQuestions : Do stocks exhibit differing entropy rates? Does low entropy imply profitability of TA?Methodology : NYSE US 100 Stocks – daily data – 2000-2006 2000- TA rules induced using Genetic Programming 2 1
  2. 2. Estimating entropyActive field of research in neuroscienceMaximum-likehood (“Plug-in”): construct an n-th order Markov chain not suited to capture long/medium term dependenciesCompression-based techniques : Lempel-Ziv algorithm, Context-Tree Weighting Lempel- Context- Fast convergence rate – suited to long/medium term dependencies 3 Selected estimatorKontoyannis et al 1998 Ã n !−1 ˆ 1X hSM = Λi log2 n n i=1Λi : length of the shortest string that does not appear in the i previous symbolsExample: 0 1 1 0 0 1 0 1 1 0 0 Λ6 = 3 4 2
  3. 3. Performance of the estimatorExperiments : Uniformly distributed r.v. in {1,2,..,8} – r.v. P theoretical entropy = − 8 1/8 log2 p = 3 b.p.c. i=1 Boost C++ random generator Sample of size 10000 ˆ hSM = 2.96 ˆ Note 1 : with sample of size 100000, hSM ≥ 2.99 Note 2 : with standard C rand() function and ˆsample size = 10000, hSM = 2.77 5Preprocessing the data (1/2) ptLog ratio between closing prices: rt = ln( pt−1 )Discretization : {rt } ∈ R → {At } ∈ N 3,4,1,0,2,6,2,… 6 3
  4. 4. Preprocessing the data (2/2) Discretization is tricky – 2 problems: How many bins? (size of the alphabet) How many values in each bin? Guidelines : maximize entropy with a number ofbins in link with the sample size Here : alphabet of size 8 same number of values in each bin (“homogenous partitioning”) 7 Entropy of NYSE US 100 stocks – period 2000-2006 25 20 Mean = Median = 2.75 Max = 2.79 15 Min = 2.68 Density 10 Rand() C lib = 2.77 ! 5 0 2.66 2.68 2.70 2.72 2.74 2.76 2.78 2.80 entropyNote : a normal distribution of same mean and standard deviation isplotted for comparison. 8 4
  5. 5. Entropy is high but price time series are not random! 50 50 40 40 30 30Density Density 20 20 10 10 0 0 2.65 2.70 2.75 2.80 2.85 2.65 2.70 2.75 2.80 2.85 Entropy (original data) Entropy (shuffled data) Original time series Randomly shuffled time series 9 Stocks under study Symbol Entropy OXY 2.789 Highest entropy VLO 2.787 MRO 2.785 time series BAX 2.78 WAG 2.776 Symbol Entropy TWX 2.677 Lowest entropy EMC 2.694 C 2.712 time series JPM 2.716 GE 2.723 10 5
  6. 6. Autocorrelation analysis Typical high Typical low complexity stock (OXY) complexity stock (EMC) Up to a lag 100, there are on average 6 autocorrelations outsidethe 99% confidence bands for the lowest entropy stocks versus 2for the highest entropy stocks 11 Part 2 : does low entropy imply better profitability of TA? Addressed here: are GP-induced GP- rules more efficient on low-entropy low- stocks ? 12 6
  7. 7. GP : the big picture Training Validation Out-of-sample interval interval interval 1 ) Creation of Further Performance the trading rules selection on evaluation using GP unseen data 2) Selection of - the best resulting One strategy is strategies chosen for out-of-sample2000 2002 2004 2006 13GP performance assessmentBuy and Hold is not a good benchmarkGP is compared with lottery trading (LT) of same frequency : avg nb of transactions same intensity : time during which a position is heldImplementation of LT: random sequenceswith the right characteristics, e.g: e.g:0,0,1,1,1,0,0,0,0,0,1,1,1,1,1,1,0,0,1,1,0,1,0,0,0,0,0,0,1,1,1,1,1,1,…0,0,1,1,1,0,0,0,0,0,1,1,1,1,1,1,0,0,1,1,0,1,0,0,0,0,0,0,1,1,1,1,1,1,…GP>LT ? LT>GP ? Student’s t-test at 95% t-confidence level – 20 GP runs / 1000 LT runs 14 7
  8. 8. Experimental setupData preprocessed with 100-days MATrading systems: Entry (long): GP induced rule with a classical set of functions / terminals Exit: Stop loss : 5% Profit target : 10% 90-days stop 90-Fitness: net return - Initial equity: 100K$- position sizing : 100% 15Results: high entropy stocks GP net profits LT net profits GP>LT? LT>GP?OXY 15.5K$ 14K$ No NoV LO 7K$ 11.5K$ No NoM RO 15K$ 18.5K$ No NoBAX 24K$ 13K$ Yes NoW AG 6K$ −0.5K$ Yes NoGP is always profitableLT is never better than GP (at a 95% confidencelevel)GP outperforms LT 2 times out of 5 (at a 95%confidence level) 16 8
  9. 9. Results: low entropy stocks GP net profits LT net profits GP>LT? LT>GP? TWX −9K$ −1.5K$ No Yes EM C −16.5K$ −11K$ No Yes C 15K$ 18.5K$ No No JP M 6K$ 10K$ No No GE −0.5K$ 0.5K$ No No GP is never better than LT (at a 95% confidence level) LT outperforms GP 2 times out of 5 (at a 95% confidence level) 17 Explanations (1/2) GP is not good when training period is very different from out-of-sample e.g. out- of- Typical low Typical high complexity stock (EMC) complexity stock (MRO)2000 2006 2000 2006 18 9
  10. 10. Explanations (2/2)The 2 cases where GP outperforms LT : trainingquite similar to out-of-sample out- of- BAX WAG 19 ConclusionsEOD NYSE time series have high butdiffering entropiesThere are (weak) temporal dependenciesHere, more predictable ≠ less risksGP works well if training is similar to out-of-sample 20 10
  11. 11. PerspectivesHigher predictability level can be observedat intraday timeframe (what about highertimeframes?)Experiments needed with stocks lesssimilar than the ones from NYSE US 100Predictability tells us about the existenceof temporal patterns – but how easy /difficult to discover them ?? 21 ? 22 11

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