Portfolio strategy search is an essential topic in the financial and economic networks. Various risk preferences of investors should be considered to accommodate their investment choices, thereby introducing multi-objective portfolio optimization. This study utilizes an innovative and accurate evaluation model, the trend ratio (TR), to assess return and risk and construct a multi-objective optimization (MOO) model. Within a huge search space, computational intelligence techniques achieve high-performance computing through effective nature-inspired computing. The proposed TR-based MOO quantum-inspired model with an entanglement-enhanced local search (EL) mech-anism can search for Pareto optimal solutions (POSs) in global investment markets. The experimental results demonstrate that the TR-based EL mechanism effectively achieves a larger number of POSs and improves efficient frontiers to meet the different risk profiles of investors across global markets. A visualized Pareto front (PF) of the TR model helps a user identify a more straightforward return and risk balance evaluation, helping investors make informed decisions. The entanglement-simulated local search mechanism has been proven to improve the search for a better PF significantly. The TR-based, quantum-inspired MOO portfolio strategy search has paved the way for flexible solutions in global financial networks.

Quantum-inspired optimization is an emerging computational intelligence technique that achieves quantum advan-tages through inspiration from the mechanisms of quantum properties. Parameter control is vital for optimization methods to address complex problems with extensive solution spaces. However, static parameters lack adaptability, leading to issues like premature convergence, becoming trapped in local optima, and failing to uncover the global optimum. Therefore, we develop a novel adaptive mechanism to dynamically adjust parameters during algorithm execution to strike a remarkable balance between exploration and exploitation. This study first proposes the knowledge-navigated quantum-inspired tabu search (KNQTS) algorithm, which acquires insights from the ongoing process to adjust the environment-sensitive parameters adaptively and non-linearly. Differences between inter-iteration solutions can signal the extent of convergence and guide bidirectional adjustments of a parameter, facilitating the handling of com-plex optimization problems. KNQTS is applied to tackle real-world application portfolio construction problems in the financial technology field. Comprehensive experiments are conducted to demonstrate the substantial potential and robustness of KNQTS in rapidly identifying better solutions with higher probabilities in shorter durations compared to the other state-of-the-art methods. Statistical tests substantiate the significant superiority of KNQTS over these comparative algorithms.

Quantum-inspired optimization (QIO) has garnered attention for attempting to retain quantum benefits on classical computers, thereby improving search efficiency in solving complex optimization problems. Portfolio optimization is one of the complicated real-world applications that concerns conflicting objectives of profit and risk simultaneously, making it a biobjective problem. This study exploits the advantage of the QIO to propose a multi-objective quantum-inspired tabu search algorithm (MoQTS) for constructing the Pareto front (PF) for portfolio optimization based on the innovative bi-objective trend ratio model. MoQTS initially employs the superposition encoding mechanism and Q-gate to search for potential areas quickly while maintaining the memory of PF information. Then, the entanglement move expands the search direction along with the current PF with more diversity. This study provides exhaustive search results to examine the completeness of optimal solutions in PF. The experimental results demonstrate that MoQTS exhibits competitive performance compared to classical methods across various metrics, including inverted generational distance (IGD), hypervolume (HV), and others. MoQTS shows significant potential in generating the PF using fewer computational resources.

Financial management is a critical and complicated issue. People tend to invest in the stock market and expect a stable uptrend portfolio to spread the investment risk effectively. As global economies affect each other, investing in global markets is the best way to address systematic risk for effective financial management. Selecting an appropriate portfolio across global markets involves a substantial solution space; thus, an efficient and effective evolutionary computation is proposed. The novel entanglement-enhanced quantum-inspired optimization technique is proposed as an efficient mechanism to search for the relationship between stocks with high-dependency solutions. The novel indicator trend ratio aims to evaluate the investor-desired portfolio with a stable uptrend under a perfect balance between return and risk. Therefore, this study is the first attempt to evaluate the major global markets in the Group of Seven (G7) countries to expand the generality of trend ratio usage. The experiments demonstrate that the proposed novel approach has better and more precise searchability than other state-of-the-art optimization. The proposed intelligent model outperforms the Sharpe ratio, benchmark strategy, and index performance regarding the trend ratio, risk, maximum drawdown, and profit factor. The results reveal that risk dispersion substantially improves investment performance. Under the evaluation of the trend ratio, the proposed system integrates G7 markets as an expansion of a prospective avenue for global asset management,

Computational intelligence (CI) has been extensively used in financial technology areas to help make smart decisions within enormous solution spaces. This study proposes a new portfolio optimization model using hybrid strategies to concurrently consider long and short investments and compose effective portfolios assessed by an emerging indicator to reach an excellent balance between return and risk. It exploits the trend ratio investment strategy to a detailed extent to evaluate the steady uptrend and downtrend portfolios and provides an in-depth discussion of the U.S. stock market. The proposed method combines long and short selling through a single fund to enhance investment efficiency. Quantum-inspired CI is adopted to construct a near-optimal portfolio efficiently and effectively, and sliding windows are applied to rebalance the portfolio and dynamically discover appropriate periods. The experiment conducts comprehensive statistical tests to show that hybrid strategies significantly improve traditional approaches. The comparison with other traditional methods shows outstanding performances in MDD, PF, RoMaD, return, and trend ratio. The proposed hybrid portfolio optimization application can be complementary, offset the volatility and increase the return, and show great potential in the financial industry.

With the growing popularity of online trading platforms, investors can consider investing in the global market without regard to their geographical location. This study analyzes the investment performance of Group of Seven (G7) members, who greatly influence the global economy and stock market. Regardless of the market in which they invest, investors seek long-term and stable gains in the stock market; therefore, investing in a risk diversification option is paramount. By constructing a portfolio, investment risk can be decreased. The relationship between return and risk is crucial. In this study, the proposed intelligent investing system with a novel trend ratio indicator aids in constructing an efficient portfolio. In a volatile stock market, the daily ups and downs are all profit opportunities; therefore, the proposed intelligent system constructs long-selling and short-selling portfolios. This study expands the investment discussion market to the G7 market and combines trading strategies into an innovative hybrid trading strategy to evaluate the G7 market's overall performance. As the trading strategies and markets under consideration are more inclusive, the proposed quantum-inspired algorithm with the entanglement technique can address the vast portfolio construction solution space. This study generalizes trend ratios in long-selling and short-selling portfolios and G7 markets. The experiment's result reveals that the hybrid strategy can increase portfolio profitability and efficiency.

When investors invest in the stock market, the first decision is invested in which stock market and how to decide on the invested stock. The high return stock is accompanied by high risk. Constructing a portfolio can help to diversify the risk in the investment. This paper proposes a decision support system that can help select stocks and construct a portfolio with high return and low risk precisely in the Singapore stock market, which is one of the top performances in Asia. The proposed system uses the novel assessment indicator trend ratio to simultaneously consider the portfolio return and risk and then uses the global-best guided quantum-inspired tabu search algorithm with quantum-NOT gate (GNQTS) to search for the near-optimal solution. The decision-making system also includes 13 sliding windows to retain the freshest data and identify the suitable investment period. According to the experimental results, the system consisting of evolutionary computation can perform robust and rational decisions to efficiently construct a stable uptrend portfolio with the highest trend ratio in the Singapore stock market.

Selecting the best combination of stocks with low risk and high return simultaneously is a significant challenge for investors. The Sharpe ratio is a common way to evaluate the performance of portfolios. However, the Sharp ratio has some defects such as that portfolios in uptrend will be calculated as high risk. Therefore, trend ratio on linear regression is proposed to find the portfolio that has a stable uptrend. Since the linear regression just roughly represents the portfolio trend and cannot be approached precisely, this paper proposes the quadratic regression trend to define the portfolio trend more accurately. In addition, the trend ratio on quadratic regression can signify the change of magnitude slowing down or speeding up in an uptrend or downtrend direction to help this model predict the future trend more precisely. This paper uses the adaptive quantum-inspired tabu search with not gate (ANQTS) to search the best portfolio and employs the 2-phase sliding window to consider more investment situations. The experiment results show that the proposed portfolio optimization model has better performance than the Sharp ratio and trend ratio on linear regression.