Search results

The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results.

This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data.

MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models.

Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market.

This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.

The purpose of this paper is to analyze the intensity of transmission of shocks from USA to BRICS countries in the long-run and short-run deviations and swiftness of recovery during US subprime mortgage crisis. This analysis enables the authors to explore the evolving patterns of relationships between these markets and examine whether their co-movements altered either in response to international shocks that originated in advanced markets like USA or due to their domestic fluctuations.

Employing data of daily stock market indices (open and close) of BRICS countries for the period January 2, 2001 to May 31, 2012, this paper examines the interactions and characteristics of price movements of BRICS with US market by applying co-integration tests, vector error correction model and Granger causality relationship. The daily stock market indices data are derived from respective stock exchange web sites.

The results exhibit that both long-run co-integration relationships and short-run Granger causality relationships exist between the stock markets of US-BRICS. Furthermore, this nexus is amplified in the short-run during 2007-2009, when the subprime mortgage financial crisis in the USA cropped up. This finding lends support to the prominence of developed (US) market links in the proliferation of persistent co-movements of BRICS stock markets.

The findings imply an increasing degree of global market integration due to quick dissemination of global shocks originating from developed market like USA, and swift recovery which can be attributed to the increased resilience, consistent with the moderated level of domestically driven risk in the BRICS markets. In spite of their similarities, long-run and short-run interdependences with the US stock market exhibit differences among the BRICS. This can be attributed to the regional heterogeneity in long-run risk and return co-movements with the USA.

Changes from the US index easily affect these stock markets in the short-run, which implies that the US index may act as a leading indicator for investing funds in BRICS markets.

This study would enable the authors to understand whether BRICS economies actually remain resilient to adverse developments in USA and could serve as alternative investment destinations for global portfolio diversification.

Ports handle a significant portion of international cargo, so the performance of ports plays a major role in the economic development of a country. This paper aims to study how port performance depends on various interdependent factors and how it requires a holistic approach, which accounts for all the necessary criteria that contribute to the overall efficiency and performance.

An integrated fuzzy DEMATEL-TOPSIS approach with an objective to evaluate the operational capability and financial performance of the ports is proposed. A case study is presented with an analysis of the major South Indian ports to assess port operational performance and evaluate various financial ratios to assess financial performance.

Through a review of the literature and based on the inputs from experts, six criteria affecting the operational performance and six financial criteria were identified. The debt coverage ratios turned out to be the most important, while the liquidity ratios were the least important. The six operational criteria have almost similar importance. The final results indicate a consistent overall performance by the Ennore Port, except during one financial year.

The proposed solution approach helps to identify and concentrate on the criteria that affect port performance. It will also help to evaluate and understand the dynamics involved in the performance of ports.

This work highlights the key measurable operational and financial criteria that affect the efficiency of ports. The integrated fuzzy DEMATEL-TOPSIS approach provides a better way to evaluate and benchmark port performance.

The purpose of this paper is to examine the impact of financial inclusion on poverty alleviation through cooperative banks.

In order to fulfil the objectives of the study, primary data were collected from 540 beneficiaries of cooperative banks operating in three northern states of India, i.e., J&K, Himachal Pradesh (HP) and Punjab using purposive sampling during July-December 2015. The technique of factor analysis had been used for summarisation of the total data into minimum factors. For checking the validity and reliability of the data, the second-order CFA was performed. Statistical techniques like one-way ANOVA, t-test and SEM were used for data analysis.

The study results reveal that financial inclusion through cooperative banks has a direct and significant impact on poverty alleviation. The study highlights that access to basic financial services such as savings, loans, insurance, credit, etc., through financial inclusion has generated a positive impact on the lives of the poor and help them to come out of the clutches of poverty.

The study was conducted amidst few limitations. First, the in-depth analysis of the study is restricted to three northern states only because of limited resources and time availability. Second, the study is limited to the perception of financial inclusion beneficiaries only, which, in future, could be carried further on the perception of other stakeholders such as bank officials, business correspondents, village panchayats, etc.

The study makes contribution towards the financial inclusion literature relating to poverty alleviation and fulfils the research gap to some extent by assessing the impact of financial inclusion on poverty alleviation through cooperative banks. This paper can help the policymakers and other stakeholders of cooperative banks in promoting banking habits among poor rural households both at the national and international level.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

The validity and legitimacy of auditing profession depends on trust as one of the most important assets of auditing profession that trust itself is directly derived from capacity of this profession in responding to responsibilities. The present research has tried to identify 15 ethical values in auditing and analyzing causal relationships of ethical values in auditing from Islam’s perspective using fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. The purpose of this paper is analyzing causal relationships of ethical values in auditing from Islam’s perspective because ethical values improve auditing and keep the auditor’s independence.

Reviewing the literature and conducting a survey among 14 certified auditing experts, 15 ethical values in auditing were identified according to Islamic law using fuzzy DEMATEL techniques. Adopting this approach, the study was then to determine causal relationships and to identify influential values.

Based on the findings, the metrics “independence” with (D−R) = +1.045 was identified to be the cornerstone of the auditing profession. Independence is an essential component for users’ trust in the financial statements. If it is ignored, many other values will further make no sense. Truth-seeking as a value with (D + R) = +3.289 has the greatest interaction with other values, and this reveals that truth-seeking is one of the most significant auditing missions. Additionally, the value “trust” with (D−R) = −1.605 is mostly affected by other variables, suggesting that other values strongly influence this value, and it will be distorted if other values are ignored.

Auditing ethics is one of the most important and striking factors that maintains the credibility and dignity of the auditing profession. Today, the society’s expectations from auditing and accounting professions have increased. Increasing these expectations requires official accountants to provide their professional services in the form of ethical auditing. As this model has been designed using the ideas of the Muslim professional experts, the results may be different from those of non-Muslim professional experts.

Considering that identified values are of great importance in Islam religion, it seems that using Islamic culture and pragmatic tone by professionals, scholars and legislative bodies is necessary to create changes required in the current business culture and as the result to create fundamental changes in professional behaviors of auditing area. This study is the first to provide evidence on the improvement of auditing based on a review of auditing studies in Islam.

The purpose of this paper is to explore key factors or criteria of sustainable supply chain management (SSCM) influencing Indonesian coal companies, using “adoption, implementation, and performance” (A-I-P) of SSCM, thru three means: first, to investigate which criteria have higher weight to achieve SSCM in Indonesia; second, to see whether there are any differences between the Indonesia case and existing literature of SSCM; third, to highlight any causal relationships between the Indonesia case and the SSCM theory.

DEMATEL-based analytic network process (DANP) questionnaire survey with a theoretical SSCM model is applied to conduct an empirical test for the coal production and supply chain companies in Indonesia.

The “performance” dimension out of the A-I-P of the SSCM is the most important one, while the “adoption” dimension is the least. Out of the 12 criteria under the three dimensions, the “operational, economic, environmental, and social” factors under the category of the performance dimension and the “ISO 14001 certification” criteria belonging to the dimension of “implementation” are the top five key factors in the SSCM.

There are some limitations in this study. First and foremost is the relatively small sample size with a limited geographic area, although they are unavoidable owing to one country case study.

The test results are helpful to draw guidance for sustainable supply chain managers in implementing efficient SSCM in the wave of tough competition and changing marketplace.

This study contributes first to developing a theoretical framework for SSCM under the A-I-P model and second, to applying DANP to an empirical case of SSCM of the coal industry in Indonesia. As a result, the authors draw helpful guidelines and policy implications for SSCM of the coal industry, referring to the A-I-P dimension as drivers and enablers for the SSCM performances of the industry.

Borsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.

This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.

The most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.

Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.

This study aims to investigate the long-run relationship between financial inclusion and agricultural growth in Pakistan for the period of 1960–2018.

The autoregressive distributed lag (ARDL) approach, the Johansen co-integration test and the dynamic ordinary least squared (DOLS) method are used for the evaluation.

The results show that in both short- and long run, domestic credit has a significantly negative impact on the agricultural growth, while broad money and cropped area positively affected the agricultural growth in Pakistan in both cases.

The government and policymakers need to develop strategies that bring together agriculturalists on a single platform so that the government can clearly distinguish the interests of these farmers and can obtain precise information for allocating agricultural expenditure and easing access to credit for small-scale agriculturalists.

This is the first study to evaluate the impact of financial inclusion on the agricultural growth in Pakistan by using different econometric techniques, including the ARDL-bound approach, Johansen co-integration test and DOLS method.

The purpose of this paper is to investigate the impact of financial inclusion on the enhancement of paddy farmers’ technical efficiency (TE). The impact was evaluated rigorously from different dimensions which could be useful in the policy discussion for enhancing efficiency in utilizing productive resources.

A cross-sectional data of randomly selected 120 paddy farmers from Khulna district in the Southwest region of Bangladesh were collected for this study. Initially, a stochastic production frontier approach was used for estimating farmers’ TE. Thereafter, ordinary least squares and quantile regression models were applied for unveiling the existing relationship between TE and various dimensions of financial inclusion after controlling all other socio-economic characteristics.

The study findings revealed that farmers were around 86 percent technically efficient and amongst them, credit takers were more efficient than non-credit takers. A non-monotonic relationship between TE and amount of credit was observed where TE was maximized at amount around 20,000 Bangladeshi Taka (USD255), a medium credit in terms of its amount. In addition, credit literacy was identified as a significant factor for improving TE. Though difference in the choice of sources for accessing credit had little impact on mean TE, its effect was found significantly higher for low scored technically efficient farmers compared to high scored farmers.

The policy toward widening the coverage of financial inclusion would be more effective than providing larger amount of credit to a limited number of farmers for improving their TE.

Such an in-depth assessment of the impact of financial inclusion on TE is probably the first effort in the Khulna district of Bangladesh.