Hybrid quantum investment optimization with minimal holding period

Abstract

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a 1 year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.

Implementation of Quantum Annealing: A Systematic Review

“…The findings are presented in the following Table IX. Portfolio 3 [166,194,219] Big data 3 [43,103,105] Hydrology 2 [39,237] Database 2 [55,198] Sensor 2 [178,212] The table shows that eight research including [37], [93], [179], [208], [210], [222], and [228] are categorized under two problem domains. Meanwhile, machine learning was found to be the most investigated topic using the QA approach as indicated by 26.40% and this was followed by graphics with 24.72%, mathematics with 17.98%, routing with 6.74%, scheduling 6.74%, chemistry computation 5.06%, biology computation 3.37%, security 2.25%, portfolio 1.69%, big data 1.69%, hydrology 1.12%, database 1.12%, and sensors with 1.12%.…”

Wasserstein Solution Quality and the Quantum Approximate Optimization Algorithm: A Portfolio Optimization Case Study

“…have not yet reached a comparable scale. As seeded by this scale discrepancy, QA-based MVPO has been the topic of several studies [23][24][25][26][27][28][29][30][31] while QAOA-based MVPO [32][33][34][35][36] has received much less attention. So much so, that until the present work, systematic benchmarking of the QAOA-based approach has not been performed.…”

Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets

“…They strongly believe that in studying some of the financial and social systems, violating the laws of classical probability, a deeper uncertainty principle, relative to the uncertainty represented by classical probability theory, exists 1,2 . The number of applications from applying quantum theory to social and financial problems, ranges from cognitive science and psychology, to economy, and quantum computing for finance [3][4][5][6][7][8] . In finance, more specifically, quantum modeling of risks and decision-making to the analysis of the financial market [9][10][11][12] , has concerned interest rates and option pricing [13][14][15] .…”