Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="7"><hr/></td></tr><tr class="thead"><td class="align_left"> </td><td class="align_center">Net return</td><td class="align_center"><span style="width: 27.6269ptpx;"><svg height="11.8161pt" id="M190" style="vertical-align:-3.291121pt" version="1.1" viewbox="-0.0498162 -8.52498 27.6269 11.8161" width="27.6269pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M687 650H462V622C543 612 549 605 530 547C498 447 422 252 372 126H370C302 298 229 492 204 563C188 607 191 615 262 622V650H17V622C77 616 93 608 122 534C180 389 262 172 329 -11H360C436 196 541 450 568 516C606 605 619 614 687 622V650Z"></path></g><g transform="matrix(.013,0,0,-0.013,7.847,0)"><path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 307 422C285 440 255 449 225 449C168 437 102 399 75 379C56 365 44 353 44 339C44 315 69 296 87 296C101 296 111 303 116 319C124 349 133 371 145 385C156 397 171 404 190 404C241 404 275 364 275 291V274C253 256 180 229 120 209C65 190 39 159 39 110C39 47 88 -12 159 -12C189 -12 237 25 277 52C282 35 288 21 301 8C312 -3 333 -12 348 -12L433 39ZM275 84C256 65 221 48 195 48C164 48 124 73 124 124C124 161 146 180 185 198C206 208 254 229 275 240V84Z"></path></g><g transform="matrix(.013,0,0,-0.013,13.554,0)"><path d="M631 18C609 24 585 35 559 65C534 91 514 117 478 169C448 214 406 281 389 313C462 346 516 399 516 485C516 545 490 590 449 616C412 641 363 650 290 650H42V622C120 615 128 612 128 527V125C128 40 120 34 38 28V0H300V28C221 34 212 40 212 125V284H244C295 284 312 272 329 244C359 195 395 133 430 84C475 19 516 -3 592 -7C603 -8 615 -8 627 -8L631 18ZM212 316V563C212 591 215 602 223 607C231 613 248 617 277 617C352 617 423 577 423 469C423 415 407 375 368 345C343 324 310 316 260 316H212Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,21.713,3.132)"><path d="M558 106L533 129C501 84 476 67 464 67C446 67 435 116 414 239C457 297 508 367 550 440L539 451L484 438C458 390 429 338 404 300H402C375 407 351 451 285 451C168 451 24 312 24 153C24 56 66 -12 132 -12C206 -12 284 64 345 156H347C367 24 386 -12 422 -12C457 -12 503 7 558 106ZM338 206C272 99 215 56 173 56C140 56 116 96 116 170C116 303 194 405 255 405C304 405 324 299 338 206Z"></path></g></svg></span></td><td class="align_center"><span style="width: 36.3043ptpx;"><svg height="12.0444pt" id="M191" style="vertical-align:-3.29106pt" version="1.1" viewbox="-0.0498162 -8.75334 36.3043 12.0444" width="36.3043pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"></path></g><g transform="matrix(.013,0,0,-0.013,8.645,0)"><path d="M687 650H462V622C543 612 549 605 530 547C498 447 422 252 372 126H370C302 298 229 492 204 563C188 607 191 615 262 622V650H17V622C77 616 93 608 122 534C180 389 262 172 329 -11H360C436 196 541 450 568 516C606 605 619 614 687 622V650Z"></path></g><g transform="matrix(.013,0,0,-0.013,16.492,0)"><path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 307 422C285 440 255 449 225 449C168 437 102 399 75 379C56 365 44 353 44 339C44 315 69 296 87 296C101 296 111 303 116 319C124 349 133 371 145 385C156 397 171 404 190 404C241 404 275 364 275 291V274C253 256 180 229 120 209C65 190 39 159 39 110C39 47 88 -12 159 -12C189 -12 237 25 277 52C282 35 288 21 301 8C312 -3 333 -12 348 -12L433 39ZM275 84C256 65 221 48 195 48C164 48 124 73 124 124C124 161 146 180 185 198C206 208 254 229 275 240V84Z"></path></g><g transform="matrix(.013,0,0,-0.013,22.199,0)"><path d="M631 18C609 24 585 35 559 65C534 91 514 117 478 169C448 214 406 281 389 313C462 346 516 399 516 485C516 545 490 590 449 616C412 641 363 650 290 650H42V622C120 615 128 612 128 527V125C128 40 120 34 38 28V0H300V28C221 34 212 40 212 125V284H244C295 284 312 272 329 244C359 195 395 133 430 84C475 19 516 -3 592 -7C603 -8 615 -8 627 -8L631 18ZM212 316V563C212 591 215 602 223 607C231 613 248 617 277 617C352 617 423 577 423 469C423 415 407 375 368 345C343 324 310 316 260 316H212Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,30.39,3.132)"><path d="M558 106L533 129C501 84 476 67 464 67C446 67 435 116 414 239C457 297 508 367 550 440L539 451L484 438C458 390 429 338 404 300H402C375 407 351 451 285 451C168 451 24 312 24 153C24 56 66 -12 132 -12C206 -12 284 64 345 156H347C367 24 386 -12 422 -12C457 -12 503 7 558 106ZM338 206C272 99 215 56 173 56C140 56 116 96 116 170C116 303 194 405 255 405C304 405 324 299 338 206Z"></path></g></svg></span></td><td class="align_center">Skewness</td><td class="align_center">Kurtosis</td><td class="align_center">SE</td></tr><tr><td class="thead-hr" colspan="7"><hr/></td></tr><tr><td class="align_left">FTSE100</td><td class="align_center">−0.1038</td><td class="align_center">−0.0134</td><td class="align_center">−0.0179</td><td class="align_center">−0.2501</td><td class="align_center">3.9934</td><td class="align_center">3.9905</td></tr><tr><td class="align_left">Equal weight</td><td class="align_center">−0.1057</td><td class="align_center">−0.0142</td><td class="align_center">−0.0178</td><td class="align_center">−0.3211</td><td class="align_center">3.5008</td><td class="align_center">4.6150</td></tr><tr class="table-tr"><td colspan="7"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The indicators of FTSE100 and equal weight.</div>

Computational Intelligence and Neuroscience

tab4

Table 4

Table 4: Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization 