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To enhance the effectiveness of the conventional mean-variance portfolio model, this study introduces a parametric portfolio policy that incorporates a momentum-based sentiment characteristic vector. This vector enables the identification of outperforming assets by capturing both historical returns and market sentiment. Drawing on a decade of rebalancing data from the S&P 500 and Dow Jones 30 constituent stocks, the proposed model optimizes the interrelationships among portfolio holdings, a benchmark portfolio, and the constructed characteristic vectors. In contrast to conventional static back testing approaches, the proposed model accounts for transaction costs and is evaluated over a 15-year investment horizon. Empirical results demonstrate that the proposed model significantly outperforms the benchmark, particularly the minimum-variance model that does not incorporate sentiment-driven parametric adjustments. During periods of financial crisis, the model selects sentiment-based momentum more frequently, leading to differing asset allocations and potentially higher utility for investors. The sentiment-augmented momentum strategy exhibits superior performance compared to the conventional mean-variance approach. The findings underscore the importance of integrating market sentiment into characteristic vector construction, affirming the value of parametric portfolio policies in improving asset allocation and risk-adjusted returns.

This study develops the Omega model integrated with momentum and reversal strategies using high-frequency data on the component stocks of the S&P 500 Index and the NASDAQ 100. The Omega model based on the momentum strategy (M_Omega), the reversal strategy (R_Omega), and both strategies (M_R_Omega) are designed to simulate trading over three periods. The portfolio is rebalanced every transaction day to optimize asset allocation by incorporating intraday winners or losers’ information and trading cost. The study finds that the proposed models generate positive returns (net of trading costs), in spite of fact that intraday trading frequently erodes profits. The M_Omega and R_Omega models produce a higher return than that of the S&P 500 index or NASDAQ 100 index, considering the intraday trading cost. The performance of the Omega model integrated with the momentum or reversal strategy is more profitable in a volatile market or period. The M_Omega and R_Omega reach the highest final market value from 2020 to 2021, when COVID 19 pandemic emerged. The rebalancing of the momentum or reversal strategy is suitable for the short term but not recommended in the long term for intraday trading as the trading costs become increasingly significant over time.

The logistic model is adopted in order to fit growth trends of innovative products for a single growth process. In the current competitive environment, we are incapable of predicting a product’s life cycle such that it can be described as a smooth S curve. Given this, we propose the use of a fuzzy piecewise regression model as a revision of the traditional logistic model. While no proper probability distribution for market share data currently exists, the proposed method is not only able to detect change-points, but can also identify predicted intervals when the growth trend of an analyzed generation is affected by other product generations. The market shares of four television technologies are used in order to demonstrate the performance of the proposed model. The results show that the proposed model outperforms the logistic model, providing both the best and worst possible market shares for the corresponding generation, and highlighting the time of impact of external influences by identifying change-points.

How to construct effective investment strategies is a core issue for modern finance. In this paper, we investigate the benefits of various models by rebalancing portfolios using the daily stock return data in Taiwan. We further consider investment constraints in portfolios to ensure the feasibility of their applications. Using five performance criteria, we find the risk models, particularly the CVaR, yield higher ex ante and ex post performance than a naïve buy-and-hold portfolio. The two-stage regressions show that high return benefits are associated with a bear market while high reduction in risk is positively related to high volatility. Though VaR is regarded as a standard model applied in the real world, our findings suggest that CVaR can serve as a good alternative.

Blueweb is a self-organizing Bluetooth-based multihop network equipped with a scatternet formation algorithm and a hybrid routing protocol. The routing protocol combines the reactive method globally and the proactive method locally to discover the optimal path for packet transmission. In Blueweb, the route master maintains the global topology information and each master maintains its own N-tier routing information. In this paper, a tier number decision algorithm is used in Blueweb to determine the optimal number of tiers for all the other masters. Our computer simulation results show that this algorithm can efficiently improve the routing performance and reduce the routing maintenance cost for Blueweb routing protocol.

To enhance the effectiveness of the conventional mean-variance portfolio model, this study introduces a parametric portfolio policy that incorporates a momentum-based sentiment characteristic vector. This vector enables the identification of outperforming assets by capturing both historical returns and market sentiment. Drawing on a decade of rebalancing data from the S&P 500 and Dow Jones 30 constituent stocks, the proposed model optimizes the interrelationships among portfolio holdings, a benchmark portfolio, and the constructed characteristic vectors. In contrast to conventional static back testing approaches, the proposed model accounts for transaction costs and is evaluated over a 15-year investment horizon. Empirical results demonstrate that the proposed model significantly outperforms the benchmark, particularly the minimum-variance model that does not incorporate sentiment-driven parametric adjustments. During periods of financial crisis, the model selects sentiment-based momentum more frequently, leading to differing asset allocations and potentially higher utility for investors. The sentiment-augmented momentum strategy exhibits superior performance compared to the conventional mean-variance approach. The findings underscore the importance of integrating market sentiment into characteristic vector construction, affirming the value of parametric portfolio policies in improving asset allocation and risk-adjusted returns.