When investing in the stock market, the first problem and one of paramount importance which investors have to face is making the proper stock selection. Selecting the stocks that simultaneously offer high return and low risk is a difficult problem that is worth investigating. However, the traditional risk calculation based on the modern portfolio theory (MPT) of portfolios has some defects. The MPT method requires the calculations of every relationship between each pair of stocks in the portfolio, entailing high computation complexity, which grows exponentially with the increased number of stocks. Besides, the traditional calculation is unable to calculate the coefficient of variation, and merely considers the relationship between each pair of stocks, so it cannot accurately assess portfolio risk. Therefore, this paper proposes a novel method, funds standardization, and utilizes it to represent the portfolio return and calculate portfolio risk. The fluctuation of portfolio funds standardization shows not only the relationships between each pair of stocks, but also the interactions among all stocks. Hence, utilizing funds standardization can accurately assess portfolio risk and completely represent the mood swings of investors. Compared with the traditional method, the proposed method significantly reduces the computation complexity because the complexity does not increase when the portfolios stock number increases. We combine the genetic algorithm, Sharpe ratio and funds standardization to find the optimal portfolio. In addition, we utilize the sliding window to avoid the over-fitting problem, which is common in this field, and test the effect of all kinds of training and testing periods. The experimental results show that the portfolio can spread the risk effectively, and that the portfolio risk can be assessed accurately by utilizing the funds standardization. Comparing with the traditional method, our method can identify the optimal portfolio efficiently and establish a portfolio that has lower risk and stable return.

Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to high-dependence problems, they experience difficulty in finding the global optimum. To address this problem, this paper proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its $Q$ -bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependence problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum not gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependence problems and accelerate the optimization process by using entangled states. This paper uses nine benchmark functions to test the search ability of the entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency.

Stock selection is a paramount importance in stock investment. Selecting stocks generally requires considering return and risk simultaneously. The common assessment strategy is to use the Sharpe ratio. Sharpe ratio uses the standard deviation to determine portfolio risk but a portfolio with an uptrend has high risk, defying the logic of most investors. Therefore, this paper proposes a novel assessment strategy, trend ratio. The trend ratio uses linear regression, which is improved with the initial funds when seeking to find the trend of a portfolio. The slope of the trend line is daily expected return, and the difference between the trend line and the funds standardization of a portfolio is daily risk. The trend ratio not only can find the portfolio that has a stable uptrend, but also solves the problem of the Sharpe ratio when a portfolio with an uptrend has high risk. This paper does not limit the number of stocks in a portfolio; it uses the quantum-inspired tabu search algorithm, which is improved by an adaptive strategy, the current best-known solution, and the quantum not gate (ANQTS) to find the best portfolio in a large solution space. This paper employs the sliding window to avoid the over-fitting problem. We use the traditional sliding window, which uses the previous period as training data, as well as the year-on-year sliding window, which uses the same period in the last year as the training data. In summary, this paper combines the trend ratio, ANQTS, and the sliding window to solve the problem of stock selection. The experimental results show that our method finds better portfolios than the Sharpe ratio. Moreover, the year-on-year sliding window, as proposed herein, has promising performance in investment and finds out some suitable economic cycle lengths for stock investment.

Quantum secret sharing (QSS) is a significant branch of quantum cryptography and can be widely used in various applications. Quantum secret sharing schemes can be developed by utilizing different features of quantum mechanics, and quantum secure direct communication (QSDC) is an effective way to achieve secret sharing using single qubits. The utilization of QSDC offers certain benefits, such as low cost, high security, and great potential for implementation with current technologies. However, the purpose of QSDC is different from that of QSS, which causes some vulnerabilities, such as dishonest participant attacks. We discover two critical factors that affect the security of traditional protocols. Firstly, they skip a few steps from the QSDC protocol to the QSS protocol. Secondly, the participants have different privileges. This can lead to participants with more privileges engaging in potential attack behavior. In light of these issues, this study proposes a new multiparty QSS scheme to address these vulnerabilities. The proposed protocol ensures the independence of each participant and grants them equal privileges. Analysis results demonstrate that it can defend against malicious attackers, retain the advantages of the QSDC protocol, and further reduce transmission costs. It achieves an excellent balance between security and performance.