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The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis.

The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with the k-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting.

Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence.

The paper provides important implications for policymakers, real estate professionals and researchers. For example, policymakers will be able to design policies that target the most influential factors of housing affordability in their region.

This study introduces a new urban spatial house price-to-income ratio, and it examines how macroeconomic indicators, government regulation, real estate market supply and urban infrastructure level have a significant impact on housing affordability. The problem of having too many variables in the decision-making process is minimized through the post-LASSO methodology, which varies the parameters of the model to allow for the ranking of the importance of the variables. As a result, this approach allows policymakers and stakeholders in the real estate market more flexibility in determining policy interventions. In addition, through the k-fold cross-validation methodology, the study ensures a high degree of accuracy and credibility when using drivers to predict housing affordability.

This study seeks to explore the strategies purchasing and supply managers can employ to navigate the challenges presented by low-probability-high-impact (LPHI) disruptions. The core aim is to create a process framework that provides a systematic, step-by-step method to help purchasing and supply managers effectively deal with the chaos triggered by LPHI events.

The study draws on qualitative data collected from eight firms operating within different industries (healthcare, fishing, food retail and manufacturing), where two firms represented each industry. The data underwent a thorough analytical process involving open coding, axial coding and aggregation of categories, resulting in the identification and formulation of overarching themes.

The analysis unveiled five primary challenges purchasing and supply management (PSM) encountered during the COVID-19 pandemic. These include supply shortages, supplier opportunism, the imperative to build a new supply base, price volatility and the need to make critical decisions based on limited information. It also identified contingent factors that influenced the magnitude of these challenges and approaches applied to address them. Additionally, it identified five responses to the challenges and two contingent factors that affected the responses.

This study extends the existing body of knowledge in purchasing and supply management by developing a process framework tailored to assist purchasing and supply managers in effectively addressing LPHI disruptions. To the best of our knowledge, this is one of the first studies to offer a structured, step-by-step approach that guides PSM professionals in navigating the chaos likely to be caused by such events.

India’s historical fiscal performance has featured elevated deficit levels. Driven by the imperative need for fiscal stimulus measures in response to the crisis, efforts toward fiscal consolidation from 2003 to 2008 were reversed in 2008–2009 due to the financial crisis. These stimulus actions are believed to have wielded a notable influence on inflation dynamics. Presumably, a high inflation rate hinders growth and inflicts severe welfare costs. Accordingly, the principal objective of this paper is to scrutinise the threshold effects of fiscal deficit on inflation within the context of the Indian economy.

We employed the Smooth Transition Autoregressive (STAR) Model, a robust tool for capturing non-linear relationships, to discern the specific threshold level of fiscal deficit. Our analysis encompasses annual data spanning from 1971 to 2020. Additionally, we have leveraged the Toda-Yamamoto causality test to establish the existence and direction of a causal connection between fiscal deficit and inflation in the Indian economy.

Our analysis pinpointed a critical threshold level of 3.40% for fiscal deficit, a value beyond which inflation dynamics in India undergo a marked transition, signifying the presence of significant non-linear effects. Moreover, the results derived from the Toda-Yamamoto causality test offer substantiating evidence of a causal relationship originating from the fiscal deficit and leading to inflation within the Indian economic framework.

The findings of our study carry significant implications, particularly for the formulation and execution of both fiscal and monetary policies. Understanding the threshold effects of fiscal deficit on inflation in India provides policymakers with valuable insights into achieving a harmonious balance between these two critical economic variables.

To the best of our knowledge, this study is the first of its kind to empirically investigate threshold effects of fiscal deficit on inflation in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model.

This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions.

This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis.

In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles.

First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions.

This study aims to examine the relationship between climate change disclosure performance (CCDP) and audit pricing. The moderating effect of corporate governance characteristics on that relationship is also investigated.

Using a sample of top 300 Australian Securities Exchange listed non-financial firms over the period 2008–2019, this study investigates the association between CCDP and audit fees. The findings are robust to a difference-in-difference test thereby alleviating potential endogeneity concerns.

CCDP is found to be significantly positively related to external auditor fees.

The findings show some important implications for firm management, regulators, investors and auditors. This study presents empirical evidence that climate change, as a factor of external risk, influences audit fees.

Firms with governance structures characterized by larger more independent boards, larger audit committees and audit committees with a higher level of independence significantly moderate the relationship between CCDP and audit fees.

Investors’ demand for firm transparency and disclosure of information regarding the risks of climate change, effects and opportunities has increased significantly over the past decade, as these factors could have a significant effect on valuation and investment decisions.

Importantly, stakeholders need to be aware of the costs of climate change, the quantification of climate change impacts and how firms address climate change in their business risk management processes. This study quantifies the impact of CCDP on auditor risk assessments via audit fees.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Climate change has led to a rise in the frequency, intensity and scope of droughts, posing significant implications for businesses. This study examines the impact of local community drought levels on audit pricing. Additionally, it explores the moderating effects of high-tech industries, auditor busyness and the level of local community concern regarding the drought crisis.

This study employs a mixed-methods approach to rigorously test the research hypotheses. The quantitative phase of the study utilizes a sample of 1,278 firm-year observations from Iran’s capital market. For the analysis of the quantitative data, ordinary least squares regression with clustered robust standard errors is used. Additionally, this research supplements its quantitative findings with qualitative evidence obtained through semi-structured interviews with 19 Iranian audit partners.

The results suggest that firms operating in provinces facing severe droughts experience notably higher audit fees. Furthermore, the positive relationship between drought and audit fees is weakened when auditors are busy, local community concern regarding the drought crisis is high or the firm operates within high-tech industries. These findings are supported by a range of robustness checks and qualitative evidence gathered from the field.

This research contributes to the growing literature on climate change by examining the influence of local community drought levels on audit pricing within an Iranian context. Additionally, our study sheds light on how high-tech industries, auditor workload and the level of local community concern regarding the drought crisis moderate the relationship between drought and audit fees. Importantly, our study pioneers in providing mixed-methods evidence of the association between drought severity and audit fees.

The purpose of this study was to determine the determinants of financial statement fraud: the perspective of pentagon fraud theory.

This study used quantitative methods with an explanatory research design by applying secondary data on Islamic banking companies listed on the Indonesia Stock Exchange (IDX).

External pressure affects financial statement fraud, ineffective monitoring affects financial statement fraud, external auditor quality affects financial statement fraud, change in auditor affects financial statement fraud, frequent number of CEO’s picture affects financial statement fraud, external pressure affects firm size, ineffective monitoring affects firm size, external auditor quality affects firm size, change in auditor affects firm size, frequent number of CEO’s picture affects firm size, firm size affects financial statement fraud, firm size mediates the relationship between external pressure on financial statement fraud, firm size mediates the relationship between ineffective monitoring on financial statement fraud, firm size mediates the relationship between external auditor quality and financial statement fraud, firm size mediates the relationship between change in auditor and financial statement fraud, firm size mediates the relationship between frequent number of CEO’s picture and financial statement fraud.

The limitations of this research were found during the research process and can be used as input for further research and related parties in conducting the research to obtain better research results. The limitations of this study are as follows: this study only focused on Islamic banking, so it cannot be generalized to other sectors. Besides, this study only tested five independent variables, one dependent variable and one mediating variable.

For external auditors, financial statement fraud by management might be caused by many factors and is a social as well as an economic problem that must be addressed immediately. Therefore, in carrying out the duties and roles as an external auditor, they must have an attitude of independence (not taking sides) in the mental attitude that must be maintained by the auditor related to the assignment. Auditors must have sufficient technical expertise and training as auditors. In carrying out the audit, the auditor should use their professional skills in responding carefully and thoroughly. Moreover, in carrying out audit work, the auditor must have a plan, must know adequate internal control and obtain sufficiently competent audit evidence.

To the best of the authors’ knowledge, very few studies in Indonesia have applied the Beneish model. There is only one study that implemented the Beneish model, and the study examined only a few companies listed on the IDX. The findings of the present study have important implications not only for banks but also for users of financial statement accounts in Indonesia, especially for investors, auditors, regulators, taxation and other state authorities.

Since 1966, the Severn crossing has been connecting England and Wales. In January 2018, its ownership returned to the UK Government, and this marked the start of a toll-free journey across the two countries and made commuting between the regions more affordable. In this paper, we examine the impact of the toll removal on the property market.

We employ property-level data from the Land Registry and a difference-in-differences (DiD) empirical model for the periods 2016–2018 and 2019–2021 to capture the pre- and post-toll removal dynamics. The DiD estimation allows us to examine the causal relationship between policy changes and property prices.

Our findings suggest that property prices in Newport and Monmouthshire (South East Wales) are positively affected by the policy, which results in a statistically significant increase of 5.8% more than those located in the South West England (Bristol and South Gloucestershire) region in the period 2019–2021. The impact can reach up to 13.1% for properties located in a 10 km radius of the bridge. The results indicate that the toll removal enables the ripple effect across the two markets by reducing commuting costs.

This is the first paper that examines the Severn Crossing case study. Its contribution is significant since we provide empirical evidence on how reduced transportation costs increase property prices in the lowest income region and have the opposite effect on the area with higher incomes and economic activity levels.