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While machine learning's role in financial trading has advanced considerably, algorithmic transparency and explainability challenges still exist. This research enriches prior studies focused on high‐frequency financial data prediction by introducing an explainable reinforcement learning model for portfolio management. This model transcends basic asset prediction, formulating concrete, actionable trading strategies. The methodology is applied in a custom trading environment mimicking the CAC‐40 index's financial conditions, allowing the model to adapt dynamically to market changes based on iterative learning from historical data. Empirical findings reveal that the model outperforms an equally weighted portfolio in out‐of‐sample tests. The study offers a dual contribution: it elevates algorithmic planning while significantly boosting transparency and interpretability in financial machine learning. This approach tackles the enduring 'black‐box' issue and provides a holistic, transparent framework for managing investment portfolios. [ABSTRACT FROM AUTHOR]

Existing studies on commodity market risk spillovers recognize the pivotal role of geopolitical risk (GPR), but scarcely address how it drives tail risk spillover networks. This study adopts the Tail‐Event driven NETwork methodology to explore high‐dimensional Conditional Value at Risk (CoVaR) spillovers within energy and other strategic commodity markets. Our findings indicate that (1) In both lower and upper tail networks, metal and food commodities primarily act as net risk transmitters, whereas energy commodities are mainly net risk receivers. Additionally, these roles undergo short‐term reversals during periods of heightened market uncertainty. (2) There exists an asymmetrical pattern of CoVaR co‐movements in these commodity markets. The total connectedness (TC) in both the upper and lower tails demonstrates distinct responses to various extreme events. GPR tends to weaken the lower tail TC and strengthen the upper tail. (3) Incorporating GPR substantially improves the effectiveness of Minimum Connectedness Portfolio (MCoP) for these strategic commodities. [ABSTRACT FROM AUTHOR]

Nowadays a lot of organizations and project-oriented governmental and private companies have been leading their approach from project-oriented management to project-oriented basket management to answer to pointed problems, the necessity of having meaningful relation of approaches, themes, and projects and aim of them to more efficiency to themes and their projects. Project basket management is a new approach is grown from project management knowledge and the meaning was focused on the management of combining targeted projects and special situations it deemed to be a high level of project management and themes of them in organizations and offering the model of effective criterion models and optimal projects in selecting portfolios of constructions projects were studied in this survey. The results showed that five effective and basic indexes for optimal selecting of construction projects are: budget indexes and supply of resources, scheduling of management indexes, management indexes and risk reduction, detailed documental indexes, and parallel operation of indexes. The results showed that there is a meaningful and positive effect in surveys of hypothesis 1, budget indexes, and supplying resources in optimal management of construction project portfolios. The meaningful levels of hypothesis equals 0 and lower than 0.05 according to results and then the project hypothesis is approved by this meaning that budget and supplying resources have a positive and meaningful effect on optimal management of construction project portfolios. The amount of this effect is 0.513 By noticing this coefficient is positive and the effect of that is direct. In other words, changing a unit (increasing) budget index and supplying resources by the amount of 0.513% leads to increasing optimal management on construction project portfolios. The meaningful level of the hypothesis was equal to 0 and lower than 0.05 according to the results. The meaningful hypothesis of this hypothesis is approved by the meaning that parallel operation of them on construction projects of portfolio and optimal management have a meaningful and positive effect. The amount of this effect is 0.615 and the effect of that is direct by noticing that this coefficient is positive. In other words, changing a unit of parallel operation by the amount of 0.165 percent is increased in the optimal management of construction project portfolios. [ABSTRACT FROM AUTHOR]

This study refines the methodology for solving stochastic optimal control problems with quality criteria that include the sum of the quality functional of the classical formulation and an extremal measure. A two-level optimization solution of these kinds of problems is presented already for the case where the quality functional consists only of the extremal measure. Our study shows the possibility of solving the original time inconsistency problem through solving a two-level optimization problem, where the outer problem is solved by gradient methods since the value function is convex and the inner problem is solved by classical methods. Some experiments were carried out and confirmed the validity of the theory. The results of the study can be applied to the case of portfolio management with quality criteria containing the Conditional Value-at-Risk (CVaR) metric. [ABSTRACT FROM AUTHOR]

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance. [ABSTRACT FROM AUTHOR]

This survey aims to provide insightful and objective perspectives on the research history of quantitative portfolio management strategies with suggestions for the future of research. The relevant literature can be clustered into four broad themes: portfolio optimization, risk-parity, style integration, and machine learning. Portfolio optimization attempts to find the optimal trade-off of future returns per unit of risk. Risk-parity attempts to match the exposure of various asset classes such that no single asset class dominates portfolio risk. Style integration combines risk factors on a security level such that rebalancing differences cancel out. Finally, machine learning utilizes large arrays of tunable parameters to predict future asset behavior and solve non-convex optimization problems. We conclude that machine learning will likely be the focus of future research. [ABSTRACT FROM AUTHOR]

Purpose: Organizations have always faced the possibility of disruptions. Traditional approaches, such as shifting risks through insurance or improving organizational resiliency, view disruptions as threats. This study aims to propose a new perspective where disruptions can also be opportunities. By adopting project portfolio management (PPM), organizations can develop proactive capabilities to manage uncertainty and prepare to exploit future disruptions. Design/methodology/approach: Drawing on publicly available research reports, company reports, professional standards and press reports, this study describes key features of PPM and provides detailed practical guidance on how to apply PPM in daily operations, especially in preparation for the next disruption. Findings: The key steps in applying PPM in daily operations are: align portfolios and projects with strategic goals and objectives; establish a robust governance framework; optimize resource capability and capacity; build and implement appropriate implementation methodologies; continuously monitor, review and optimize the project portfolio; and develop a culture that embraces risks, innovation and adaptability. Research limitations/implications: This research has several limitations and implications. On limitations, the study was constrained by publicly available data, an in-depth interview with a consulting firm and a survey based on convenient sampling. These limitations will impact the generalizability of the findings. On implications, this paper shows how organizations can prepare for future disruptions by applying PPM. There are other ways to prepare for the unpredictable future, and further research is needed to explore other methods. Practical implications: The results of this study have important practical implications for all organizations and in all sectors. Major disruptions are matters of "when," not "how," and responsible organizations need to pay attention. Based on the PPM discipline, this research provides an approach for business executives and project management practitioners to tackle this challenge. Furthermore, portfolio managers should use this information to promote and advocate for more disciplined planning to confront the uncertain future. Social implications: The findings of this paper carry important social implications. As the recent events showed the vastness of disruptions, from extreme heat to fires in Maui, sitting idly and waiting passively for an unpredictable future is not an option. This paper advocates the need for more awareness and preparation for future disruption by applying PPM. Furthermore, this research provides concrete guidelines for organizations and practitioners to consider as they confront the unknown. Additional research should investigate other effective strategies to meet the challenges of an uncertain and volatile future. Originality/value: This study offers practical steps on how organizations may manage not only to survive but also to thrive in an uncertain and volatile world. [ABSTRACT FROM AUTHOR]

Purpose: New product introductions, particularly line extensions (LEs), are common in consumer goods categories. Despite their commonality, the success of LEs are not guaranteed. The purpose of this study is to provide brands that introduce LEs a benchmark about what success to expect. Design/methodology/approach: This study investigates the success of 36,994 LEs in each quarter for the first three years after introduction. Four indicators are calculated using consumer panel data to benchmark how long LEs survive (failure rate), how competitive they are in the category (market share) and how they are adopted by category buyers (penetration and repeat buyer rate). Findings: Most LEs survive after the first year, but many cease to exist or perform well in the long term. Around 50% of LEs fail a year after launch, but this failure rate halves once seasonal LEs are removed. Failure rates start to approach 80% after three years. Most LEs do not perform better than existing products. Around three in four LEs have a market share or penetration near or below the category norm. Although this percentage decreases the longer after launch, most LEs are still below the category norm. Practical implications: These new product success benchmarks provide guidelines to practitioners about what success the "typical" LE will achieve. This research can help guide new product investment decisions because it provides context on what is feasible to achieve. Originality/value: Four market success measures are used, a departure from past benchmarking research which uses practitioner evaluation on metrics seldom used in practice. The authors provide guidelines about when and how to measure LE and new product success more broadly. [ABSTRACT FROM AUTHOR]

Adjusting investment strategy is one of the ways to handle dynamic market conditions. This study proposes a novel portfolio management strategy using appropriate optimization objectives for different stock market trends while also incorporating market trends and stock return predictions The optimization objectives that will be evaluated for different market trends are maximizing the Sharpe ratio, minimizing risk, and minimizing expected shortfall. This study utilizes simulation modelling with various predictive models on building the portfolios. The results show that, in an upward market trend, the strategy is to choose stocks with positive returns, and the objective is to maximize the Sharpe ratio. The portfolio that follows this strategy during upward market trends has greater returns than both the Indonesian Composite Index and LQ45, which serve as stock market benchmarks, with 90% certainty. Meanwhile, during the downward market trend, the strategy is to choose stocks with a negative correlation with the Indonesian Composite Index, and the proper optimization objective is to minimize risk. A portfolio that follows this strategy during downward market trends has greater returns than stock market benchmarks with 95% certainty. Across the evaluation period from 2018 to 2023, the portfolio using the proposed strategy outperforms both stock market benchmarks, with a higher quarterly Sharpe ratio of 0.3047 and cumulative return of 107.90%. The proposed portfolio has a higher quarterly return than the stock market benchmark with 99% certainty. Therefore, the proposed strategy shows a promising result in a dynamic market.

Our study develops a unique Security Risk Assessment based Smart Beta (SB) portfolio construction model for Robo Advising investors belonging to different risk categories. This model will cater to the Gen Z tech-savvy retail investors who have become more active and are interested in online investment platforms like Robo Advising. Our study differs from prior studies as it proposes a portfolio construction model for equity investors belonging to different risk categories while traditional approaches map debt portfolios to low risk investors and equity portfolios to high risk investors. Investors are generally risk-averse but prior studies have developed SB portfolios without considering their risk appetite. In this study, we assess the riskiness of stocks and then categorise them into different risk categories by mapping SB factors such as quality, value, alpha, momentum, etc. We further construct SB portfolios that minimise risk for each category of stocks to cater to investors belonging to low, moderate, high, as well as very high risk categories, using Machine Learning (ML) algorithms. Through a wide range of risk and return performance indicators, we provide evidence that our model offers higher returns at lower risk than human-managed portfolios. Our model proves to be more reliable and encourages Robo Advisors to offer SB portfolios catering to retail investors’ needs and their risk appetite. The study contributes to the evolving literature on Robo Advising, SB investing and to the debate on whether algorithms can replace human portfolio managers.

The present study examines the Indian small-cap cycle between April 2011 and March 2022. The ordinary least squares (OLS) estimate shows that investors can benefit from investing in the Nifty Small-Cap 100 index by following a proper exponential moving average strategy. The study findings also highlighted that among macroeconomic factors, ‘term spread’ might influence the Nifty Small-Cap 100 index returns (NIFSC100R). The daily returns of the Russell 2000 index, the relative change in international crude oil prices (RCO), and the relative change in the exchange rate between USD and INR (RUSDINR) show no statistically significant impact on NIFSC100R.

Using the constituent stocks of the iShares MSCI US ESG Select Index ETF, a matrix of technical indicators, returns, and covariance is incorporated to represent the inherent information characteristics of the stock market. In this study, based on the proposed Deep Reinforcement Learning for Portfolio Management on Environmental, Social, and Governance (DRLPMESG) architecture model, investors who use active portfolio management reap the greatest rewards, as the portfolio with 5 stocks performing the best, with an annualized return of 46.58%, a Sharpe ratio of 1.37, and a cumulative return of 115.18%, indicating that the results have the potential to win the market and generate excess profits. In contrast to the efficient market hypothesis, this new understanding of proven effectiveness in obtaining satisfactory rewards would help improve investment strategies for portfolio management. Furthermore, this study proposed that holding 5 stocks in a portfolio can lead to higher returns, laying the foundation for future research on the number of holdings. Moreover, when compared to previous static strategies, this model offering a dynamic strategy may generate a more stable return in the face of market fluctuations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) models can help investors find portfolios in which the focus is to optimize the risk-return relationship. There are several algorithms and techniques in the literature that allow the application of tests to a set of historical data for the selection and validation of investment portfolios. Based on this, this research intends to examine the contribution of the main machine learning techniques used in portfolio management through a systematic literature review. By using the Methodi Ordinatio for selection and ranking of articles, we classified papers considering object of study, type of AI used, period of analysis, data frequency, balance and cardinality. In addition, we detail the main contributions and trends conceived until the year 2020. Therefore, our findings reveal gaps and suggest future works on the topic. [ABSTRACT FROM AUTHOR]

This paper generalizes the concept of capacity from the portfolio level to the investment process for systematic equity strategies. Capacity is often understood as the maximum asset under management, above which additional inflows would have too great a negative impact on performance. The concept of capacity is often limited to the study of a given portfolio. However, setting up a capacity management framework must consider what the portfolio might look like in the future. This is obviously complicated for discretionary portfolios but theoretically conceivable for portfolios implementing systematic strategies, if we can simulate all possible scenarios. In our framework, we extend the traditional definition of capacity from a number to a random variable, allowing portfolio managers to integrate it into their risk considerations. We provide examples of how portfolio managers can approach this problem, with full-search or modelling methods. Our framework includes several capacity metrics that can be used jointly or selected to align better with the features of each strategy. [ABSTRACT FROM AUTHOR]

Integrating environmental, social, and governance (ESG) considerations into investment decisions has become increasingly popular. In 2020, global assets under management incorporating ESG factors reached $35 trillion, a 55% increase from 2016. Given its growing relevance within the financial and academic community, this paper analyzes the historical evolution of ESG Investing and its most recent developments. It describes the extent to which ESG considerations affect financial performance and outlines the main strategies used by investors when incorporating ESG factors into their financial decisions. This study also introduces Islamic finance, and sheds a light on the main differences, but also similarities, with ESG Investing through a novel comparative approach. Finally, we offer a summary of the main research findings on the performance of socially aware mutual funds through a comprehensive literature review of more than 40 papers (1993–2022), which we hope it will be of practical assistance to scholars and industry professionals looking to develop or refine their investment strategies. [ABSTRACT FROM AUTHOR]

International portfolio management is influenced by the existence of "frictions", factors or events that interfere with trade, which are linked in financial literature to market-specific factors, such as available information, restrictions, investor protection, or market liquidity. Given the wide variety of factors that can be included in these categories, scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact. We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values. We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction. By employing grey clustering analysis, we cluster these capital markets into three categories—strongly recommended, recommended with some reserve, and not recommended—based on the importance given by the decision maker to these factors. The results show that some of the studied markets are in the same cluster, irrespective of the chosen scenario. The only market always included in the "strongly recommended" category is Hungary, indicating that it is a good investment option for international participants. Bulgaria and Slovakia are always regarded as "recommended with reserve" markets, whereas the Republic of Moldova is part of the "not recommended" category. The other markets show a degree of variability that can be explained by different investor perspectives. This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis. Investors can use this approach during the decision-making process related to their investments. [ABSTRACT FROM AUTHOR]

Existing portfolio management models based on deep reinforcement learning adjust funds dynamically by constructing an action space with specific trading behaviors. However, this method cannot effectively adapt to complex investment environments. This paper proposes an LSTM-ER-DCPPO portfolio management model based on the improved PPO algorithm. The model provides stable policy learning, enhanced strategy exploration, and trend identification, enabling more precise capital allocation through continuous asset weight output. To evaluate the model, a simulated market trading environment based on real stock data was created. The experimental results show that the LSTM-ER-DCPPO model achieved an annualized return of 58.62%, a maximum drawdown of 8.53%, a Calmar ratio of 6.873, and a Sharpe ratio of 2.434, outperforming other benchmark models. [ABSTRACT FROM AUTHOR]

Various factor models extended by Jensen's (J Financ 23:389–416, 1968) alpha have been used to measure the retail investors' portfolio (under-) performance compared to the market portfolio. The previous studies tried to explain this anomaly in behavioral finance by examining retail investors' cognitive biases that induce irrational trading behavior. While operationalizing these cognitive biases in trading is not trivial, researchers still have found measures to proxy for biases and prove their statistical and economic significance. However, these studies only focused on linking one or a subset of behavioral biases and their effect on portfolio performance. In addition, different measures of biases across studies complicate the comparability of results. Therefore, this paper provides a structured overview of the current state of the literature regarding behavioral biases and their measurements to design a behavioral factor model that should help to explain the performance alpha from a behavioral finance perspective. The paper presents an overview of 11 behavioral bias factors and 29 corresponding measurements to consider inputting in such a model. With an application-oriented focus, it is recommended to include the most researched bias factors in a factor model, which are also the most detrimental to portfolio performance, as well as to include the most frequently used and least complex measures, which results in the primary inclusion of the following eight behavioral bias factors: disposition effect, under-diversification, home bias, local bias, lottery stock preference, trend chasing, overtrading, and trade clustering. [ABSTRACT FROM AUTHOR]

Background: Many retirees in South Africa face the challenge of either outliving their retirement savings or living below their means. Studies suggest a ‘safe’ withdrawal rate of between 4% and 5%, which is below the average fund size-weighted drawdown rate of approximately 6.66%. Aim: To provide a scientific basis for the success rate of a ‘hybrid’ retirement strategy, whereby a retiree invests a proportion of their savings in a life annuity and the remaining proportion in a living annuity, to increase the success rate for South African retirees. Setting: Historical asset class returns (equities, bonds and inflation) for South Africa were sourced for the period 1900–2020. Method: Bootstrap sampling of historical asset returns was employed to simulate 10 000 random scenarios to investigate the success rate of various compositions of the ‘hybrid’ retirement strategy. Results: The success rate of all ‘hybrid’ portfolio compositions is significantly greater than the success rate of a pure living annuity when the withdrawal rate is less than 8%. Conclusion: In a South African context, a ‘hybrid’ retirement portfolio increases the probability of success for retirees withdrawing less than 8% from their portfolio – which constitutes approximately 50% of the current annuatised population – and may increase the inheritance of a retiree’s heir. Contribution: Where other studies have focussed solely on the success rate of a living annuity, we have shown that a ‘hybrid’ retirement strategy increases a South African retiree’s likelihood of retiring successfully when the withdrawal rate is less than 8%, which is approximately 50% of the annuatised population.

Earned Value Management (EVM) has become a de facto standard for project planning after decades of applications worldwide, and most industry contracts that require some of the reporting features of EVM. However, EVM has shortcomings such as too early release of work and no process perspective to facilitate improvements. The Last Planner System (LPS) was developed to overcome the shortcomings of EVM utilizing insights from lean manufacturing. Unfortunately, LPS has not reached the same level of acceptance as EVM. A new approach, Lean Project Planning (LPP), is developed to take advantage of the strengths of both approaches. In this paper, LPS and EVM are compared to each other, and the resulting LPP is presented. The paper discusses a LPP implementation across the project portfolio at a Norwegian pressure vessel fabrication company. The results show that LPP provided accurate Estimate At Completion (EAC) for both costs and delivery times despite taking on many major Variation Orders. The approach is arguably a step forward for turning plans into planning in project-based industries such as shipbuilding and fabrication of pressure vessels. The complexity of pressure vessel fabrication stress-tested the LPP, and some improvements were identified for future work.

One of the primary concerns of the Iranian National Pension Fund is managing its investment portfolio. In this respect, the present study aimed to examine the long-term investment portfolio, the largest subset of which is V-sandoq. The analysis used the R2 connectedness approach proposed by Naeem et al. (2023) over the period from September 17, 2013, to September 22, 2023. The study focused on the immediate influence and susceptibility to influence of the stocks within the National Pension Fund. The results showed that, in terms of net influence and susceptibility, the stocks of Group 1 (i.e., Kechad, Foulad, Kegol, and Sheranol) were the most influential, transferring risk to the network. Conversely, the stocks of Group 2 (i.e., Shepas, Pasa, Shekabir, and Vebshahr) were the most influenced by the network. Therefore, risk is transferred from Group 1 stocks to the network, impacting Group 2 stocks the most. In network analysis, during a bear market with a threshold of -4%, there is a high degree of connectivity among the stocks in the portfolio. This suggests that portfolio adjustments are necessary under bear market conditions. Conversely, in a bull market with a threshold of +4%, there is no connectivity between the stocks, indicating that no portfolio adjustments are needed under such conditions.1.IntroductionIn recent years, Iran has consistently faced challenges with pension funds and the inability to generate adequate income to pay retirement salaries. With the number of retirees expected to increase in the coming years (particularly from the 1980s generation), effective management of the investment portfolio of the National Pension Fund’s subsidiaries has become increasingly critical. Many state-owned companies were transferred to the National Pension Fund to finance retired pay from their profitability. However, budget evidence indicates that over 80% of retirement salaries are still financed through the government budget. This underscores the importance and necessity of revising the investment portfolio of the National Pension Fund’s investment holdings. In this respect, the present study aimed to examine the portfolio management of one of the largest subsidiaries of the National Pension Fund, namely the Investment Company of the National Pension Fund or V-sandoq, over the period from September 17, 2013, to September 22, 2023. The study used the vector autoregression (VAR) model with time-varying parameters and R2 connectedness, as an immediate response, proposed by Naeem et al. (2023). The immediate impact analysis of variables on/from each other was chosen because any national, regional, or global event has immediate effects, and providing an appropriate response in portfolio management is of great importance.2.Materials and MethodsThe study employed the TVP-VAR algorithm and the Kalman filter introduced by Antonakakis et al. (2020), in conjunction with the approach proposed by Naeem et al. (2023). The key econometric structure of the TVP-VAR model is outlined below. For the sake of simplicity, it is presented in the form of a first-order VAR. Thus, the TVP-VAR model is as follows:(1) (2) Time-varying parameters and time-varying error variances are essential components for the generalized impulse response functions (GIRF) and generalized forecast error variance decomposition (GFEVD) developed by Koop et al. (1996) and Pesaran and Shin (1998). These components underpin the connectivity approach of Diebold and Yılmaz (2012, 2014). To obtain GIRF and GFEVD, the TVP-VAR needs to be converted to TVP-VMA by applying the Wold representation theorem. According to this theorem, GIRFs i j,t (K) at a forecast horizon K do not assume or depend on the ordering of shocks, providing a more robust interpretation of VAR models compared to standard IRFs, which are sensitive to the order of variables in the econometric system. The GIRF approach reflects the dynamic differences between all variables jjj. Mathematically, it can be expressed as Equation (3):(3) (4) Subsequently, GFEVD ψij,t(K)\psi_{ij,t} (K)ψij,t​(K) represents the unique contribution of each variable to the forecast error variance of variable iii, interpreted as the percentage impact of one variable on the forecast error variance of another variable. This can be expressed as Equation (5):(5) The criteria for GIRF and GFEVD can help determine how much variable iii is influenced by others and how much it influences others. Three metrics are used for this purpose.First, we must determine how much other variables in the system influence variable iii. This is obtained by summing the error variance shares for variable iii relative to variable jjj. The influence from others is then calculated using Equation (6):(6) Second, the impact of variable iii on others in the system is calculated through the measurement known as influence on others. This measurement is derived by summing the effects (error variance) that variable iii imposes on the forecast error variance of other variables:(7) The total connectivity index (TCI) is calculated based on the Monte Carlo simulations presented by Chatzanzinou et al. (2021). It demonstrates that the self-variance share consistently exceeds or equals all cross-variance shares. Since the average co-movement of the network is expressed as a percentage, which should be between [0,1], TCI needs to be slightly adjusted:(8) Finally, the TCI definition is modified to obtain pairwise partial connectivity index (PCI) scores between variables iii and jjj as follows:(9) 3.Results and DiscussionFigure 2 illustrates the temporal dynamics of stock influences received from other stocks. It shows the extent to which each stock has transferred or received risk from others. The stocks above the zero line indicate a net influence on the network, while those below indicate a net reception from the network during the examined period. Notably, Kechad, Foulad, Kegol, and Sheranol (Group 1) predominantly acted as influencers, transferring risk to the network. In contrast, Shepas, Pasa, Shekabir, and Vabshahr (Group 2) exhibited the highest reception from the network. Therefore, it can be inferred that external shocks transfer risk from Group 1 to the network, notably impacting the stocks in Group 2.It is crucial to recognize that this influence/reception patterns vary over time and exhibit significant fluctuations. Specifically, the chart shows that the influence/reception of stocks on/from the network decreased with the outbreak of the COVID–19 pandemic from January 19, 2021. Conversely, the disclosure of the letter regarding the increase in petrochemical feed rates on May 7, 2023 heightened the risk transfer from petrochemical stocks to the studied network. This underscores that external shocks do not uniformly affect the portfolio under review, necessitating separate examination of each. Figure1: Net Influence/Reception of Stocks on/from Each Other Source: Research findings4.ConclusionThe results of the long-term portfolio analysis indicated varying levels of interconnectedness influenced by economic, political, military, and health conditions—with the connectivity averaging around 45%. This reflects a high risk for the long-term portfolio. In terms of net influence and reception, Kechad, Foulad, Kegol, and Sheranol (Group 1) generally exerted influence by transferring risk to the network. In contrast, Shepas, Pasa, Shekabir, and Vabshahr (Group 2) predominantly received risk from the network. Thus, during external shocks, risk tends to shift away from Group 1 stocks, thus impacting Group 2 significantly. The outbreak of the COVID–19 pandemic on January 19, 2021 led to a decrease in the influence/reception of stocks on or from the network. Conversely, the disclosure of an increase in petrochemical feed rates on May 7, 2023 heightened risk transfer from petrochemical stocks to the studied network. Concerning the network analysis, there is a high degree of connectivity among the stocks in the portfolio during a bear market with a threshold of -4%. This suggests that portfolio adjustments are necessary under bear market conditions. In bearish markets, it thus becomes imperative to select stocks that have less connectivity. On the contrary, in a bull market with a threshold of +4%, there is no connectivity between the stocks, indicating that no portfolio adjustments are needed under such conditions. Hence, while the examined portfolio is optimal during bull markets, adjustments are essential during bear markets to mitigate risks associated with high connectivity.