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The securities market is extremely volatile and difficult to prognosticate. Stock prices are depending upon numerous factors. To reduce the risk of volatility, it is very important to apply an accurate mechanism to forecast stock prices. The importance of share price prediction forecasting in finance and economics has sparked researchers’ interest in creating more reliable forecasting models over time. In this research paper, the researchers try to explore two different applications based on linear and nonlinear (RNN) functions. The criteria for the stock price predictions are evolved using Auto Regressive Integrated Moving Average (ARIMA) which takes the linearity function from the past share prices. The ARIMA model assumes the future prices usually be similar to past. Sudden changes may not reflect in this model. The nonlinearity Recurrent Neural Network (RNN) is going to be applied for share price prediction so that it can be taken into account the quick changes that are occurring in the market environment. To test the RNN, the study used the Long Short Term Memory (LSTM) model which takes the support of Artificial Intelligence. Taking the sample of share prices of banks listed in the NIFTY index, the ARIMA and LSTM have been performed and analyzed. Stock price predictions for banks listed in the NIFTY bank index are found better with the ARIMA model than with the LSTM model.

Real estate investment has recently been advancing rapidly in both volume and complexity. A sound understanding of behavioral issues in this sector benefits all stakeholders, such as investors, regulators and local residents. We focus on one of the most robust behavioral anomalies in business and finance research: overconfidence. Overconfidence significantly influences financial decision and investment performance. However, theoretical and empirical studies are lacking in real estate sector. We conduct a critical review of the overconfidence literature to bridge this gap, identify future research directions for the study of overconfidence in real estate markets, and suggest strategies to handle technical issues, such as the robustness of overconfidence measurement and data availability. Findings provide useful guidelines for researchers and practitioners to design and implement overconfidence studies in real estate research.

With the rapid development of tourism economics, personalized and diversified demand of tourists and the advancement of modern information technologies, the investment of scenic spots faces multitudinous uncertainties and emergencies and can be considered as a nonlinear dynamic system. Given this situation, the existing investment decision-making model (e.g. net present value, NPV) is difficult to estimate the realistic value of scenic spots, and new theory and decision-making model are needed in the area. Based on real option theory and option function, the paper establishes a nonlinear dynamic investment decision model of scenic spots with uncertainties and emergencies, and then, the model is verified with an example. The results show that with the impacts of emergencies, when the investment value is considered, the option value of the investment project in the scenic spot can be maximized, thus maximizing the total value of the investment project. The proposed nonlinear dynamic investment decision model proves that the favourable impact of emergencies on the investment of scenic spots increases the investment opportunity value of scenic projects, thus increasing the investment value of scenic projects, emphasizes more on the dynamic nature of investment and the favourable conditions in the face of crisis, and helps enterprises investing in scenic spots make full use of its uncertain environment and the favourable impact of emergencies to choose the optimal investment opportunity. Therefore, this study has both theoretical and practical significance.

In this paper we focus on the incentive to invest or disinvest in equity shares to benefit from discrepancies between their real value and their market prices, based on privileged information. Such a situation arises in particular when a manager trades his company's own stock. An existing simple model for the impact of transactions on prices is extended to the case of discrete transactions. This model is used to represent the impact of the informed agent's transactions. A probabilistic approach is proposed to determine the optimal control applied to the market price by the informed agent. Analytical solutions are derived to calculate the value of "realigning the price" for an informed market participant, and the properties of the controlled market price are discussed.

Since the energy markets liberalization at the beginning of the 1990s in Europe, electricity monopolies have gone through a profound evolution process. From an industrial organization point of view, they lost their monopoly on their historical business, but gained the capacity to develop in any sector. Companies went public and had to upgrade their financial risk management process to international standards and implement modern risk management concepts and reporting processes (VaR, EaR…). Even though important evolutions have been accomplished, we argue here that the long-term risk management process of utility companies has not yet reached its full maturity and is still facing two main challenges. The first one concerns the time consistency of long-term and mid-term risk management processes. We show that consistencies issues are coming from the different classical financial parameters carrying information on firms' risk aversion (cost of capital and short-term risk limits) and the concepts inherited from the monopoly period, like the loss of load value, that are still involved in the utility company decision-making process. The second challenge concerns the need for quantitative models to assess their business model. With the deregulation, utilities have to address the question of their boundaries. Although intuition can provide insights on the benefits of some firm structures like vertical integration, only sound and tractable quantitative models can bring answers to the optimality of different possible firm structures.

The prosperity of emerging technology may be triggered by the occurrence of rare events, and once emerging technology booms, there may be an enormous market demand for it. This paper explores the firm's commercial and R&D investment decisions on emerging technology in real options framework. We think that, for the fear that a rare event which occurs prior to R&D success would trigger the emerging technology boom, the firm should carry out prospective R&D in the emerging technology innovation. So the impacts of the R&D hazard rate and the intensity of rare event arrival on the prospective R&D investment strategy are mainly addressed. There are two results. First, the firm is most likely to take the strategy at low, rather than high, probability of technological boom in market-driven innovation. Second, in technology-driven innovation, the huge potential profit of emerging technology in booming period can arouse more attention toward the firm. When the growth rate of profit flow in booming period rises to some degree, the firm may take this strategy unconditionally.

Online social networks (OSNs) are a terrifically emerging platform for information dissemination around the world. Like other settings, acceptance and adoption of OSNs among the individual capital market investors are extensive. The study developed a conceptual model for behavioural finance integrating a technology acceptance model (TAM) and valence framework from the information systems and marketing disciplines, respectively. The integrated model added some persuasive constructs from social capital and diffusion innovation theory with a view to explore the key factors swaying investors’ intention to adopt and use the OSN’s services. By using an online and offline structured questionnaire, 510 data were collected from individual capital market investors in Bangladesh. Structural Equation Modelling (SEM) was used for data analysis. The study determined that the proposed integrated model with additional constructs outperformed other models. Perceived usefulness (PU), perceived enjoyment (PE), trust and personal innovativeness in IT (PIIT) had a substantial sway on the investor’s intention to use OSNs. Hedonic value is more robust predictor of intention to use OSNs than utilitarian value. Intention to use properly mediated the relationships and had strong significant impact on investor’s investment decision. But perceived ease of use (PEOU) and perceived risk had no direct significant effect on intention to use. PEOU had significant impact on intention to use through PU and PE. Gender moderated the relationships of different constructs with the intention to use OSNs for investment decisions in the capital market. It contributes knowledge by including the integration of different models in stock market perspectives and the inclusion of technological aspect in the behavioural finance literature. The findings of the study will also succor different firms and regulatory authorities to adopt OSNs as an information dissemination platform.

This chapter analyzes the errors-in-variables problems in a simultaneous equation estimation in dividend and investment decisions. We first investigate the effects of measurement errors in exogenous variables on the estimation of a just-identified or an over-identified simultaneous equations system. The impacts of measurement errors on the estimation of structural parameters are discussed. Moreover, we use a simultaneous system in terms of dividend and investment policies to illustrate how theoretically the unknown variance of measurement errors can be identified by the over-identified information. Finally, we summarize the findings.

This paper considers strategic entry decisions in a duopoly market when the underlying state variable follows a diffusion with volatility that depends on the current state variable. The extension to this case is more than marginal, since empirical studies have suggested that the volatility is indeed non-constant in real options practices. It is shown that, even in the extended model, three types of equilibria exist in the case of strategic substitution, as for the geometric Brownian case, when the revenue functions are linear. Also, the presence of strategic interactions may push a firm with cost advantage to invest earlier, and the firm value as well as the optimal threshold for the investment decision increases as the market uncertainty increases.