000			a
999			_c33006 _d33006
008			240318b xxu||||| |||| 00| 0 eng d
020			_a9783030472504
082			_a006.31 _bMOR
100			_aMoraes Sarmento, Simao
245			_aA machine learning based pairs trading investment strategy
260			_bSpringer, _c2021 _aCham :
300			_aix, 104 p. ; _bill., (some col.), _c24 cm
365			_b59.99 _c€ _d93.50
490			_aSpringerBriefs in Computational Intelligence, _v2625-3704
504			_aIncludes bibliographical references.
520			_aThis book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.
650			_aMachine learning
650			_aEconomics
650			_aTrading
650			_aInvestment
700			_aHorta, Nuno
942			_2ddc _cBK