Econophysics and Data Driven Modelling of Market Dynamics - download pdf or read online

By Frédéric Abergel, Hideaki Aoyama, Bikas K. Chakrabarti, Anirban Chakraborti, Asim Ghosh

ISBN-10: 3319084720

ISBN-13: 9783319084725

This e-book offers the works and examine findings of physicists, economists, mathematicians, statisticians, and monetary engineers who've undertaken data-driven modelling of industry dynamics and different empirical reports within the box of Econophysics. in the course of fresh many years, the monetary industry panorama has replaced dramatically with the deregulation of markets and the transforming into complexity of goods. The ever-increasing velocity and reducing bills of computational strength and networks have resulted in the emergence of massive databases. the supply of those facts may still let the improvement of versions which are larger based empirically, and econophysicists have hence been advocating that one should still count totally on the empirical observations as a way to build versions and validate them. the hot turmoil in monetary markets and the 2008 crash seem to provide a robust purpose for brand new versions and techniques. The Econophysics neighborhood therefore has an immense destiny function to play in marketplace modelling. The Econophys-Kolkata VIII convention lawsuits are dedicated to the presentation of many such modelling efforts and handle contemporary advancements. a couple of top researchers from around the globe file on their contemporary paintings, touch upon the newest matters, and overview the modern literature.

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Get Econophysics and Data Driven Modelling of Market Dynamics PDF

This publication provides the works and learn findings of physicists, economists, mathematicians, statisticians, and fiscal engineers who've undertaken data-driven modelling of industry dynamics and different empirical stories within the box of Econophysics. in the course of fresh many years, the monetary marketplace panorama has replaced dramatically with the deregulation of markets and the turning out to be complexity of goods.

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5 bp trading costs) Order book imbalance Flow quantity Past return Stock Gain σ (Gain) Gain σ (Gain) Gain σ (Gain) INTERBREW AIR LIQUIDE ALLIANZ ASML Holding NV BASF AG BAYER AG BBVARGENTARIA BAY MOT WERKE DANONE −56 −30 67 −107 −117 −29 −104 −115 −117 952 679 866 1,008 857 991 1,273 942 811 −33 −46 −22 −9 28 −95 −8 22 −82 835 686 910 934 903 919 1,186 918 806 −88 −99 −38 −63 −51 −95 53 −78 −50 937 735 858 1,045 862 886 1,212 868 810 (continued) 36 M. Anane and F. ON AG TOTAL GENERALI ASSIC SOCIETE GENERALE GDF SUEZ IBERDROLA I ING INTESABCI INDITEX LVMH MUNICH RE LOREAL PHILIPS ELECTR REPSOL RWE ST BANCO SAN CENTRAL HISPANO SANOFI SAP AG SAINT GOBAIN SIEMENS AG SCHNEIDER ELECTRIC SA TELEFONICA UNICREDIT SPA UNILEVER CERT VIVENDI UNIVERSAL VOLKSWAGEN Order book imbalance Gain σ (Gain) Flow quantity Gain σ (Gain) Past return Gain σ (Gain) −113 20 48 −159 74 129 95 −18 −51 −11 5 −36 41 −139 −28 25 47 −82 −103 −20 −38 −49 −14 −97 −97 34 −53 1,274 1,153 1,121 1,067 906 1,091 839 856 931 1,044 745 968 756 1066 1,556 909 1,112 1,348 1,459 976 856 787 842 847 1,012 1,243 1,228 −96 −79 48 −120 −79 −171 8 16 −1 −16 −88 −57 78 −39 4 −34 −60 −86 −48 −88 −55 −50 43 −57 −65 81 −137 1,254 1,209 1,290 1,093 966 1,120 827 866 922 1,041 767 983 706 1,124 1,453 888 1,035 1,257 1,429 986 871 758 805 844 1,023 1,263 1,208 15 −122 −58 −84 −47 17 86 −15 −26 46 −27 23 13 −92 −2 17 75 −69 −15 24 −112 −61 17 −20 8 33 −23 1,253 1,065 1,263 1,185 950 1,120 802 794 973 1,015 774 967 744 1,208 1,464 880 1,073 1,325 1,438 899 806 746 879 898 998 1,227 1,177 −93 15 −14 101 −15 107 154 −8 −53 75 928 776 1,020 912 984 926 1,708 606 947 1,111 1 −54 57 −68 −155 8 −33 −89 3 −32 953 864 1,075 911 917 910 1,593 639 935 1,116 −63 −20 −73 −71 37 24 10 −51 −66 10 890 716 1,074 893 938 881 1,628 659 932 1,138 Empirical Evidence of Market Inefficiency: Predicting Single-Stock Returns 37 Appendix 2: Four-Class Classification See Tables 12, 13, 14, 15, 16, 17, 18 and 19.

Abergel Fig. 15 The quality of the LASSO prediction: similar as the Ridge regression, the LASSO regression gives a better profitability than the OLS one. Notice that for the 1-min case, the LASSO method improves the performances by 165 % compared to the OLS. Eventhough the LASSO metho is using less regressors than the OLS method, (and thus less signal), the out of sample results are significantly better in the LASSO case. This result confirms the importance of the signal by noise ratio and highlights the importance of the regularization when addressing an ill-conditioned problem are given in Tables 30, 31 and 32 of Appendix 5.

ON AG TOTAL GENERALI ASSIC SOCIETE GENERALE 1,388 1,603 2,775 1,969 1,156 1,269 1,954 1,330 1,591 1,120 1,878 4,144 2,003 1,380 1,251 1,410 1,586 1,762 3,723 2,996 2,245 1,256 3,977 1,195 1,201 1,112 1,219 1,278 1,102 1,055 1,537 1,219 993 1,608 1,572 1,881 1,373 1,275 1,372 1,113 1,416 1,315 1,655 1,185 1,193 956 1,764 1,763 1,107 996 221 1,244 921 1,142 1,866 1,240 958 831 1,461 2,853 674 1,130 905 1,252 848 1,523 295 321 481 831 177 853 1,308 1,005 1,107 1,316 1,311 1,251 1,700 1,325 1,143 1,620 1,601 1,691 1,428 1,228 1,405 1,211 1,196 1,295 1,384 1,161 1,722 977 1,324 1,896 174 169 638 190 2 289 595 347 231 526 600 1,496 582 208 310 376 308 12 1,219 1,109 323 326 1,210 643 1,264 936 1,175 1,419 1,185 1,296 1,934 1,394 1,196 1,911 1,665 1,542 1,603 1,390 1,672 1,113 1,298 1,281 1,307 1,201 1,445 950 1,577 2,060 (continued) Empirical Evidence of Market Inefficiency: Predicting Single-Stock Returns 27 Table 4 (continued) Stock GDF SUEZ IBERDROLA I ING INTESABCI INDITEX LVMH MUNICH RE LOREAL PHILIPS ELECTR REPSOL RWE ST BANCO SAN CENTRAL HISPANO SANOFI SAP AG SAINT GOBAIN SIEMENS AG SCHNEIDER ELECTRIC SA TELEFONICA UNICREDIT SPA UNILEVER CERT VIVENDI UNIVERSAL VOLKSWAGEN Order book imbalance Gain σ (Gain) Flow quantity Gain σ (Gain) Past return Gain σ (Gain) 2,031 2,220 1,511 4,019 2,481 2,445 1,895 2,367 1,978 2,694 1,323 1,717 1,227 1,433 1,564 1,911 1,452 1,220 1,107 1,109 1,173 1,451 1,348 1,535 934 1,626 1,493 153 1,742 533 791 894 1,670 1,700 1,475 1,393 1,389 1,514 1,491 1,787 1,525 1,148 1,485 1,242 1,565 1,607 1,880 1,577 156 566 217 1,048 145 613 194 438 182 292 307 383 1,355 1,403 1,720 1,954 1,344 1,267 1,006 1,220 1,251 1,558 1,747 1,684 1,368 1,225 1,612 1,108 1,419 2,694 3,039 1,402 2,142 2,044 1,040 1,022 1,359 983 1,294 1,267 2,025 766 1,223 1,440 1,118 939 1,209 967 1,014 1,156 382 551 1,114 1,165 1,123 1,071 1,449 1,196 1,275 1,341 1,850 860 1,391 1,397 107 117 455 164 379 290 683 222 244 225 1,190 1,084 1,607 1,124 1,436 1,194 2,002 949 1,326 1,359 Table 5 The quality of the binary prediction: the daily gain average and standard deviation for the 1-min prediction (with trading costs) Order book imbalance Flow quantity Past return Stock Gain σ (Gain) Gain σ (Gain) Gain σ (Gain) INTERBREW AIR LIQUIDE ALLIANZ ASML Holding NV BASF AG BAYER AG BBVARGENTARIA BAY MOT WERKE DANONE −191 81 1,141 370 −422 −363 303 −260 −40 1,189 1,112 1,063 1,179 1,064 1,002 1,477 1,176 963 −788 −980 −1,199 −697 −955 −734 −58 −530 −906 1,325 1,057 1,309 1,335 1,338 1,249 1,681 1,263 1,164 −1,222 −1,211 −952 −1,301 −1,298 −1,122 −910 −1,256 −1,246 1,531 1,164 1,162 1,574 1,558 1,503 2,027 1,510 1,369 (continued) 28 M.

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Econophysics and Data Driven Modelling of Market Dynamics by Frédéric Abergel, Hideaki Aoyama, Bikas K. Chakrabarti, Anirban Chakraborti, Asim Ghosh


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