Momentum

The purpose of this study is to compare the impacts of momentum on stock returns of companies listed in Tehran Stock Exchange in different market conditions. For this purpose, the sample size is 120 months from 2008 to 2017. The research hypotheses are estimated using multivariate linear regression using time series method. Based on the results of the hypotheses test, the momentum in each of the market conditions, including normal, ascending and descending conditions, has a positive and direct effect on the stock returns of the companies listed in Tehran Stock Exchange, which indicates the principle of investors' insatiability in the stock exchange Tehran seeking to maximize its return on investment with a certain risk that in a downside mode of market, their insatiability exits less and faster than their momentum conditions, which is a reason for investors' loss evasion in this situation.

Momentum strategy, despite its outstanding performance, offers different results at different time intervals. In this study, we aimed to provide an opti-mal model for stock selection based on momentum, reverse and hybrid trad-ing strategies using the data panel model. The present research method was applied on the information of 180 companies in the period 2011 to 2021 was used to estimate the model. (Eviews12) software has been used to estimate the models. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48 and 60 months based on different momentum and inverse strategies and a combination of loser, winner and loser-winner, winner-loser were analyzed. According to the data panel method, the studied strategies in small companies give more additional returns to investors than large companies. Also, based on the results of hybrid strategies, investors will receive more additional returns in the long run than simple momentum strategies.

This article introduces an integrated approach for addressing uncertainty and improving human-oriented risk control by combining differential equation modeling with neural and fuzzy logic enhancements. Differential equation modeling provides a structured mathematical foundation for capturing price dynamics over time, while neural and fuzzy logic components adaptively adjust the model to account for nonlinear behaviors and uncertain market signals. The proposed framework is applied within volatility-aware trading strategies, comparing fixed-exposure and downside-scaled momentum approaches. Using daily data from five major digital currencies spanning 2016 to 2024, the model demonstrates improved prediction accuracy and controlled exposure under volatile conditions. While the adaptive strategy offers reduced drawdowns and more stable weight distributions, it does not universally outperform in return-to-risk metrics. However, the integrated system consistently shows better alignment with market risk regimes, particularly in directional accuracy, confidence calibration, and drawdown control enhancing its practical viability for real-world deployment.