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The purpose of this study is to investigate if a firm's exchange complexity profile (that is, the linkages between the firm and its environment) influences investor behavior at the negotiation table where a firm valuation is derived.

The authors utilize Qualitative Comparative Analysis (QCA). Specifically, the authors utilize fuzzy-set Qualitative Comparative Analysis (fsQCA), a QCA variant that allows the researcher to assign graduated membership in sets.

When the authors dichotomize their positions as either higher stakes that favor the seller (high capital, low equity, high valuation) or lower stakes that favor the buyer (low capital, high equity, low valuation), and when the authors focus primarily on the equity outcome, the authors find that investors adopt a reductionist stance that adheres to a transaction cost economics logic under conditions of lower stakes and higher complexity as well as higher stakes and lower complexity conditions. The authors interpret this to mean that equity serves as a counter-balancing lever for a firm's exchange complexity configuration.

On a theoretical level, the authors showcase the exchange complexity framework and differentiate its position within the extant frameworks that address a firm's competitive advantage. More generally, the authors note that this framework brings the discipline of micro-economics and the field of strategic management closer together, providing scholars with a new tool enabling research across industries for the portfolio level of analysis.

Extensive macro- and micro-economics research has been conducted on China's tax reform, which replaced business tax with value-added tax (VAT). However, existing studies have not clarified the reform's impact on firm-level investment decisions. Hence, this study explored the effect of replacing business tax with VAT on firms' investment efficiency.

The study used 2010–2018 data from China's A-share listed companies and a difference-in-differences (DID) model to explore the effect of the reform on firm-level investment decisions.

The authors found that China's tax reform has improved investment efficiency in underinvested firms, increased liquidity and decreased the level of reliance on external financing. The tax reform had a greater effect on investment efficiency in firms with lower liquidity and higher external financing reliance. Its effect was also more significant among non-state-owned and small companies.

This study fills the aforementioned research gap by exploring the effects of China's tax reform, thus providing a theoretical reference and a basis for policymaking.

The purpose of this study is to extend the extant literature on the relationship between financial risk tolerance (FRT), awareness of Islamic financial principles (AWIF) and positive financial behaviour (FB) on financial satisfaction (FS) of micro, small and medium enterprise (MSME) owners by principally investigating the mediating effect of access to Islamic financing (AIF) on these relationships.

A quantitative survey method of data collection through a self-administered questionnaire. The sample of 384 MSME owners was selected in which 208 questionnaires were retrieved and analysed using the partial least square structural equation modelling (SEM).

The result shows that the relationships between FRT and AIF as well as FB and AIF are not significant. However, the AWIF–AIF relationship was found to be positively significant. Moreover, only the mediating effect of AIF on the AWIF–FS relationship was established.

The result implies that AIF could strongly influence the FS of MSME owners, and the AWIF–FS relationship is better explained with sufficient AIF. However, AIF could not mediate the relationships between FRT–FS and FB–FS. Therefore, policymakers and MSME owners should emphasize on AWIF and AIF to enhance FS.

This study pioneers the examination of the mediating influence of AIF on FRT, AWIF, FB and FS of MSME owners in a single framework. Despite the importance of MSME owners on economic sustainability, literature on MSME owners' FS is lacking expressly among developing countries, particularly in Nigeria. This study also revealed new theoretical and practical knowledge by illuminating the mediating effect of AIF on AWIF–FS relationship.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.