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The chapter provides a theory of war and conflict issues, and applies the theory to the arms race and the possibility of war in the South Asian subcontinent. We try to give a new perspective on an old question: wars are not rational since they destroy the contestable resource over which disputes arise; yet, states that are rational frequently undertake them rather than going for the less costly option of settlement. In the chapter, a war game is played in which two states first build armaments and then, if they cannot achieve a settlement, fight a war, the outcome of which depends on strength of armaments, where at stake is a contestable resource. The anticipated outcome determines the bargaining threat point. “Technology” is a factor in any war, and so too is the cost of building armaments. States typically differ in technology and may also miscalculate their own relative technical position and war-fighting capability. Alternative models of settlement and war are presented in which states either believe the opposing state has the same perception of technical advantage, or else know the opposing state’s differing perception. Dynamic models, which include the effects of decay in information over time and strategic concerns, are examined. Finally, the results of the models are applied to the stylized facts of India-Pakistan rivalry and conflict, paying particular attention to institutional issues. It is demonstrated that the stylized facts of the Indo-Pakistani conflict and wars fit well with the theoretical conjectures of the analytical models. External conflicts and wars in South Asia are often related to internal causes, which allow the possibility of incomplete information; the two contending states miscalculate their own power in terms of war-fighting capability, so that war occurs.

Income distribution policies have been one of the most important issues that policy makers in less developed countries (LDCs) have had to face over the last decade. There has been a great revival of interest about the different aspects of distributing the national income, even irrespective of its size. Growth per se is still considered to be important, but without appropriate links with suitable distributive measures, loses much of its charismatic appeal. Further, the interconnections between growth and distribution have been stressed in recent policy literature, particularly the beneficial effects that a more egalitarian income stream may have in raising the national product.

Military expenditure in developing countries is large and rising; its economic effects are a matter of vital concern. This article analyses the impact of military spending on the mobilisation of physical resources in less developed economies; resource creation is proxied by the saving‐income ratio. Both theoretical and empirical arguments are provided to show that, though there are some beneficial spin‐offs from defence, the overall impact is to lower the rate of resource creation significantly. This may have sizable negative effect on growth and development.

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

Co-marketing strategies play an important role in enabling firms to improve their competitive position. However, despite its increasing implementation, it remains a topic that is largely not researched. The purpose of this paper is to analyze existing contributions to the field of co-marketing research and the different perspectives scholars have adopted in analyzing the topic.

A literature review has been developed, as its lack seems to be a major hindrance to the development of related studies. A specific focus has been made on the adopted approaches. Five approaches have been identified, and multidimensional scaling (MDS) has been used to analyze the differences among them.

First, the analysis of the typologies of studies on co-marketing alliances is made. Also, the identified approaches are strategic-based, consumer-based, relational-based, specificity-based and evaluation-based. What emerges from the MDS is that there are two perspectives of analysis of the alliance that characterize them: the inside–outside and the wide–narrow points of view.

Limitations are mostly referred to the methodologies and the level of subjectivity they imply. For example, they are not only the choices made concerning keywords to be used and, consequently, the articles included in the analysis, but also the MDS that offers broad autonomy to the researchers in interpreting the data.

The originality of this research is that it fills an emerged gap concerning a literature review on co-marketing alliances, supporting future research in this field of study. The identification of the approaches underlines what may be lacking, providing interesting insights on possible avenues for future research.

Considers two forms of strategic alliances, technological and marketing, and examines how these alliances foster formation and maintenance of intellectual capital. Empirical evidence suggests that on average, strategic alliances do create value for shareholders that is consistent with the creation of intellectual capital. Between the two, technological alliances are potentially more beneficial than marketing alliances, and more likely to create intellectual capital. Empirical evidence is consistent with the notion that the gains from alliances are not shared equally by all the partners. When intellectual capital is created by the smaller or financially weaker partner, the return may be appropriately captured by the owner of such capital through strategic alliances. However, if the intellectual capital is created by the larger or financially stronger firm which moves first in an alliance relationship, the return on this intellectual capital may be subject to opportunistic exploitation by the late moving partner.

Refers to previous research on the accuracy of financial analysts’ earnings forecasts and explores the differences in accuracy for loss making and non‐loss making firms using 1985‐1993 US data. Finds an optimistic bias for both types of firms (smaller for more recent forecasts); which is greater for loss making firms even after controlling for forecast horizon, year of forecast and industry. Considers possible explanations for this finding and consistency with other research.