A Fuzzy Index Tracking Portfolio Selection Model

Enhanced index tracking (EIT) is an active research area in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. To solve the EIT problem, many approaches have been proposed. However, it still remains a critical challenge to efficiently generate a portfolio with good quality. In this study, we propose a learning-based approach named IntelliPortfolio for the EIT problem. IntelliPortfolio uses PCA and clustering to select stock and estimates the investment weight for each constituent stock using a long short-term memory (LSTM) network. Two advantages of the proposed algorithm are as follows. (1) It considers both the fundamentals and the price information for stocks and can balance the trade-off between the performance and the diversity of the selected stocks. (2) It uses a LSTM model to estimate investment weights, which is more capable to handle long sequences of input and is more robust to predict the future trend of stock market. Experimental results on the five real-world datasets of the international stock market illustrate the significant performance superiority of the proposed approach in comparison with five state-of-the-art algorithms.

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“…Fang and Wang [7] proposed a bi-objective programming model for the index tracking portfolio selection problem.…”

Section: Statistic-based Approachmentioning

confidence: 99%

IntelliPortfolio: Intelligent Portfolio for Enhanced Index Tracking Using Clustering and LSTM

Jin

Wang

et al. 2022

Mathematical Problems in Engineering

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“…Conversely, to prove that constraints ( 9)-( 13) imply (2f), we consider the following cases. i) If w i ≥ Lb + (so that λ(w i ) = 1), then (12) implies v i = 1, so that (11)…”

Section: Proofmentioning

confidence: 99%

Exact and Heuristic Approaches for the Index Tracking Problem with UCITS Constraints

et al. 2012

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“…In [28] a constraint aggregation method for the mathematical formulation introduced in [2,4] is presented, thus obtaining a mixed integer non linear programming problem that is solved for the Hang Seng 31 stock market. Fang and Wang [11] formulate the Index Tracking problem as a bi-objective programming problem with the aim of optimizing the excess return and the absolute downside deviation from the index return. A fuzzy approach leading to the solution of a linear programming problem is presented and applied to the Shanghai 180 index with 30 stocks.…”

Section: Introductionmentioning

confidence: 99%

Exact and heuristic approaches for the index tracking problem with UCITS constraints

et al. 2012

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Index tracking aims at determining an optimal portfolio that replicates the performance of an index or benchmark by investing in a smaller number of constituents or assets. The tracking portfolio should be cheap to maintain and update, i.e., invest in a smaller number of constituents than the index, have low turnover and low transaction costs, and should avoid large positions in few assets, as required by the European Union Directive UCITS (Undertaking for Collective Investments in Transferable Securities) rules. The UCITS rules make the problem hard to be satisfactorily modeled and solved to optimality: no exact methods but only heuristics have been proposed so far. The aim of this paper is twofold. First, we present the first Mixed Integer Quadratic Programming (MIQP) formulation for the constrained index tracking problem with the UCITS rules compliance. This allows us to obtain exact solutions for small-and medium-size problems based on real-world datasets. Second, we compare these solutions with the ones provided by the state-of-art heuristic Differential Evolution and Combinatorial Search for Index Tracking (DECS-IT), obtaining information about the heuristic performance and its reliability for the solution of large-size problems that cannot be solved with the exact approach. Empirical results show that DECS-IT is indeed appropriate to tackle the index tracking problem in such cases. Furthermore, we propose a method that combines the good characteristics of the exact and of the heuristic approaches.

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A Fuzzy Index Tracking Portfolio Selection Model

Cited by 9 publications

References 10 publications

IntelliPortfolio: Intelligent Portfolio for Enhanced Index Tracking Using Clustering and LSTM

IntelliPortfolio: Intelligent Portfolio for Enhanced Index Tracking Using Clustering and LSTM

Exact and Heuristic Approaches for the Index Tracking Problem with UCITS Constraints

Exact and heuristic approaches for the index tracking problem with UCITS constraints

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