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Stock portfolio problems are one of the most relevant real-world problems. In this study, we discuss the portfolio's risk amount, rate of risk-return, and expected return rate under a Fermatean fuzzy environment. A linear programming problem is used to formulate a Fermatean fuzzy portfolio. The Fermatean fuzzy portfolio is converted to a deterministic form using the score function. Lingo software is used to solve these deterministic portfolio problems. The main feature of this model is that investors can select a risk coefficient to enhance predicted returns and customize their strategies according to their circumstances. An example is offered that illustrates the effectiveness and dependability of the proposed approach. [ABSTRACT FROM AUTHOR]

Momentum and reversals are two phenomena to explain the past return trend. Originally introduced by Jegadeesh and Titman in 1993, momentum is now a common investment strategy when investors are trading securities. It points out the stock price may have a relationship with their past performance. A large number of researchers have been trying to find out the momentum investment effect based on empirical evidence in different markets in different investment periods, which include short term, medium term and long term. Moreover, a series of research concludes that the momentum investment strategy tends to help investors to get a higher return. In recent years, a large number of researchers have focused more on analysing financial markets in China, and they have paid more attention to improving the traditional Jegadeesh and Titman models. In addition, an increasing number of researchers also point out that noise trading is quite important in the security investment strategy, and the investors who are using a momentum investment strategy to trade their portfolios are supposed to take the noise trading strategy into consideration. Then a series of papers have been tried to explain the sources of the momentum effect, either riskbased or behavioural-based. When it assumes that the market is efficient, the past market prices could be reflected, and it is hard to get excess returns by observing the stock's past prices performance. Nevertheless, the momentum effect is likely to examine that the market is inefficient. When it tries to explain the momentum effect in risk-based sources, the abnormal price return may be derived from a risk that is undiversifiable. As for the behaviour-based explanation, several behavioural biases applied by different researchers could be used to study the momentum effect, such as cognitive errors, including the conservatism bias, the representative bias, and the emotional bias, including the loss aversion bias, the overconfidence bias and the self-attribution bias. Another possible explanation within this behavioural source is that overreaction to the news leads to the existence of price momentum. [ABSTRACT FROM AUTHOR]

Smart beta products using common factors like value, low volatility, quality, and small cap experienced an underwhelming performance from 2005–2022. On average, long-only factor portfolios built from a wider set of global factors identified in the finance literature generated significantly positive excess returns across countries, suggesting diversifying across many factors is more prudent than selecting a handful that have performed the best. Moreover, long-only portfolios built from expected returns fit to these 87 factors using linear ridge and nonlinear machine learning models like gradient boosting generated larger and more statistically significant excess returns in nearly all countries. A long-only portfolio optimized to maximize return given an aversion to tracking error delivered yet higher excess returns and information ratios across countries. Taken together, these results provide strong evidence against the claim that most of the documented factors are datamined and without investment merit. [ABSTRACT FROM AUTHOR]

The design and operation of transportation systems, as with any large complex technical system, are marked by indetermination—risks and uncertainties (scientific/methodologic and/or socio-economic). This paper analyzes the occurrence and consequences of uncertainties, defined as completely unknown random events ("unknown unknowns"), on transportation system performances. Interest in the topic is justified by the considerable value and long life of transportation system components. In order to reduce the effects of uncertainties, a holistic approach to all technical infrastructures in society, regardless of the flow category (material, energy, information), is necessary. Technological progress and changes in territorial activity systems historically confirm the dynamism of the competition and complementarity relations between civil and industrial infrastructures and transport infrastructures, as well as among different modal transport/traffic infrastructures. Declining discount rates are applied to compensate for the effects of uncertainties on investment project opportunities on long time horizons. There is no unanimous agreement on the discount rate values. Unforeseen exogenous events are considered differentiated/non-systemic or undifferentiated/systemic uncertainties. They can have significant consequences on the performance of a transport system, including a change in the transport market share. Therefore, an adaptive policy is required to reduce the methodological/scientific and socio-economic uncertainties that affect the design and operation of any transportation system. [ABSTRACT FROM AUTHOR]