Journal articles: 'Series " Finance'

1

Shin, Minchul, and Molin Zhong. "Finance and Economics Discussion Series." Finance and Economics Discussion Series 2016, no. 40 (2016): 1–55. http://dx.doi.org/10.17016/feds.2016.040.

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Ritchey, Philip C., and Vernon J. Rego. "Synthetic Steganographic Series and Finance." Journal of Information Security and Cryptography (Enigma) 1, no. 1 (2015): 24. http://dx.doi.org/10.17648/enig.v1i1.15.

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Chimka, Justin R. "Time Series: Applications to Finance." IIE Transactions 35, no. 12 (2003): 1143–44. http://dx.doi.org/10.1080/714044436.

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Fang, Zheng, Jianying Xie, Ruiming Peng, and Sheng Wang. "Climate Finance: Mapping Air Pollution and Finance Market in Time Series." Econometrics 9, no. 4 (2021): 43. http://dx.doi.org/10.3390/econometrics9040043.

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Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, CO, and O3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process.

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Hansen, Lars Peter. "Time-Series Econometrics in Macroeconomics and Finance." Journal of Political Economy 125, no. 6 (2017): 1774–82. http://dx.doi.org/10.1086/694625.

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Habibi, Reza. "Bayesian Online Change Point Detection in Finance." Financial Internet Quarterly 17, no. 4 (2021): 27–33. http://dx.doi.org/10.2478/fiqf-2021-0025.

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Abstract It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is an important task in financial time series analysis. Change point detection is the identification of abrupt changes in the generative parameters of sequential data. In application areas such as finance, online rather than offline detection of change points in time series is mostly required, due to their use in predictive tasks, possibly embedded in automatic trading systems. However, the complex structure of the data generation processes makes this a challenging endeavor. This paper is concerned with online change point detection in financial time series using the Bayesian setting. To this end, the Bayesian posterior probability of change at a specific time is proposed and some procedures are presented for selecting the priors and estimation of parameters. Applications in simulated financial time series are given. Finally, conclusions are proposed.

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Mahmoud, Ola. "Discrete Time Series, Processes, and Applications in Finance." Quantitative Finance 14, no. 12 (2014): 2073–74. http://dx.doi.org/10.1080/14697688.2014.911414.

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LeBaron, Blake. "Non-Linear Time Series Models in Empirical Finance,." International Journal of Forecasting 19, no. 4 (2003): 751–52. http://dx.doi.org/10.1016/s0169-2070(03)00054-2.

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Janacek, G. "Non-linear Time Series Models in Empirical Finance." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 4 (2003): 696. http://dx.doi.org/10.1046/j.1467-9884.2003.t01-9-00383_11.x.

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Peia, Oana, and Kasper Roszbach. "Finance and growth: Time series evidence on causality." Journal of Financial Stability 19 (August 2015): 105–18. http://dx.doi.org/10.1016/j.jfs.2014.11.005.

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Jiang, Xiang Rong, and Ying Ying Cui. "Time Series Model Based Earning Forecasting." Advanced Materials Research 791-793 (September 2013): 2147–50. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2147.

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We propose a procedure to forecast earning of listed companies. It is a modification of method developed for forecasting series with stable seasonal patterns. The new method is motivated by the observations that seasonal patterns, which may be evolving over time and remain relative stability, arise in finance market. The method can be applied to forecast individual observations as well as the end-of-season totals. Empirical study will be conducted with data from finance market to evaluate the performance of the proposed method. The new method is proved more effective than traditional time series models.

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Wang, Zilin. "Science and Technology Finance Policy and Corporate Investment Efficiency -- A Quasi-Natural Experiment Based on Pilot Policies for Integrating Technology with Financer." Highlights in Business, Economics and Management 36 (July 17, 2024): 580–97. http://dx.doi.org/10.54097/yse68r33.

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In the context of China's economic transformation and rapid technological advancement, based on the implementation of the "Pilot Policy for Promoting the Integration of Technology and Finance", this paper constructs a panel data of prefecture-level cities in China from 2005 to 2017, and examines the impact and mechanism of science and technology financel policy on the corporate Investment efficiency by using the DID method. The DID method is used to investigate the impact and mechanism of technology financel policy on the corporate Investment efficiency. It is found that the science and technology financel policy significantly improves the corporate Investment efficiency, and this conclusion remains valid after a series of robustness tests such as the parallel trend test, the placebo test, and the Propensity Score Matching-Difference in Differences (PSM-DID) analyses. Further mechanism analysis in this paper reveals that the science and technology finance policy mainly enhances the corporate Investment efficiency by alleviating the corporate financing constraints, promoting the digital transformation of enterprises, and increasing the capitalized R&D investment of enterprises. In addition, the effect of the implementation of science and technology finance policy has a certain degree of heterogeneity, and this policy has a more significant impact on the improvement of investment efficiency of non-state-owned enterprises and large enterprises. This paper delves into the impact of technology finance on the investment behavior of micro-enterprises, providing theoretical support and empirical guidance for the advancement of science and technology finance practices and high-quality economic development.

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Chen, Huangen, Lu Yu, Yilin Wang, and Miaomei Guo. "Synchronization of a Hyperchaotic Finance System." Complexity 2021 (March 30, 2021): 1–7. http://dx.doi.org/10.1155/2021/6618435.

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In this article, we propose a series of control strategies to synchronize two chaotic financial systems. Due to the characteristics of chaotic systems, the system is very sensitive to its initial values. Thus, the behaviour of two systems with different initial values will be completely different. In order to realize the synchronization of two financial chaotic systems, we designed a series of controls including controllers to realize global asymptotic synchronization and controllers to realize global exponential synchronization to make the two systems fully synchronized. We provide mathematical proofs to show that the designed controls are effective. Numerical methods are used to verify the effectiveness of the controls.

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Karachun, Irina, Lyubov Vinnichek, and Andrey Tuskov. "Machine learning methods in finance." SHS Web of Conferences 110 (2021): 05012. http://dx.doi.org/10.1051/shsconf/202111005012.

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This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.

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KK, D. R. Cox, D. V. Hinkley, and O. E. Barndorff-Nielsen. "Time Series Models in Econometrics, Finance and Other Fields." Journal of the American Statistical Association 92, no. 438 (1997): 799. http://dx.doi.org/10.2307/2965747.

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Chen, Cathy W. S., Feng-Chi Liu, and Mike K. P. So. "A review of threshold time series models in finance." Statistics and Its Interface 4, no. 2 (2011): 167–81. http://dx.doi.org/10.4310/sii.2011.v4.n2.a12.

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Saidane, Mohamed, and Christian Lavergne. "Conditionally heteroscedastic factorial HMMs for time series in finance." Applied Stochastic Models in Business and Industry 23, no. 6 (2007): 503–29. http://dx.doi.org/10.1002/asmb.687.

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Yoon, Young-In, Jun-Seong Yang, and Hye-Young Jeong. "Uncertainty Quantification from time series image encoding in finance." Journal of Korean Institute of Intelligent Systems 34, no. 1 (2024): 8–14. http://dx.doi.org/10.5391/jkiis.2024.34.1.8.

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Hakim, Luqman, and Renny. "Analysis Of Financial Ratio on The Financial Performance of Banking Companies Before and During the Covid-19 Pandemic Recorded on The Indonesian Stock Exchange in the LQ-45 Index of The Banking Sub-Sector in The Period 2016–2022." International Journal of Scientific and Management Research 07, no. 02 (2024): 11–25. http://dx.doi.org/10.37502/ijsmr.2024.7202.

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Study this for analyze performance finance company banking before and during exists Covid19 pandemic which is listed on the Indonesian Stock Exchange in the LQ-45 sub-sector index banking in the period 2016–2022 with use analysis ratio liquidity, solvency, profitability, and activity. As for objects study in discussion This is performance finance company banking in the sub-sector LQ-45 index banks listed on the Indonesian Stock Exchange, use analysis ratio as base evaluation performance finance with use report finance in form balance sheets and reports make a loss profit period seven-year final namely 2016-2022. As a result, from 5 companies per bank before and during exists Covid-19 pandemic which is listed on the Indonesia Stock Exchange (BEI) in the sub-sector LQ-45 index banking in the period 2016– 2022, companies that own performance best finances seen from analysis ratio finance, crosssection analysis technique, and time series analysis technique are PT Bank Central Asia Tbk, then followed by PT Bank Rakyat Indonesia Tbk, then PT Bank Mandiri Tbk, then PT Bank Negara Indonesia Tbk, and finally PT Bank Tabungan Negara Tbk

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Laux, Judy. "Topics In Finance Part VIIDividend Policy." American Journal of Business Education (AJBE) 4, no. 11 (2011): 25–32. http://dx.doi.org/10.19030/ajbe.v4i11.6490.

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This series inspects the major topics in finance, reviewing the roles of stockholder wealth maximization, the risk-return tradeoff, and agency conflicts.The current article, devoted to dividend policy, also reviews the topic as presented in textbooks and the literature.

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Landy Godbold, A. "Geometry, Iteration, and Finance." Mathematics Teacher 89, no. 8 (1996): 646–51. http://dx.doi.org/10.5951/mt.89.8.0646.

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The mathematics offinance has long been a staple of the algebra-precalculus-calculus curriculum continuum. Whether in the study of geometnc sequences and series, logarithms and exponential functions, or limits and derivatives, topics from finance have presented interesting settings in which to apply the mathematics being studied. However, in all but the most advanced classes, such applications usually begin with the familiar assumption associated with exponential behavior—no deposits or withdrawals are permitted to or from any account. This article develops a view of linear functions that permits insight into the processes behind realistic financial applications.

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Wu, Chunchi, Chihwa Kao, and Cheng F. Lee. "Time-Series Properties of Financial Series and Implications for Modeling." Journal of Accounting, Auditing & Finance 11, no. 2 (1996): 277–303. http://dx.doi.org/10.1177/0148558x9601100207.

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This paper investigates the time-series properties of a wide range of corporate financial and accounting series. Unit root tests developed by Dickey and Fuller (1979) are applied to these series. The results support the hypothesis that most of these series contain both permanent (random walk) and transitory components. The results show that most financial series are dominated by a random walk component. However, for some series, such as net sales, net income, earnings per share, and returns on investments, there is a relatively significant stationary component, which suggests the presence of successful smoothing for these series. We show that smoothing may reduce volatility of financial series but it cannot produce a deterministic growth trend. Implications of nonstationarity for financial modeling are explored.

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Laux, Judy. "Topics In Finance Part II - Financial Analysis." American Journal of Business Education (AJBE) 3, no. 3 (2010): 81–88. http://dx.doi.org/10.19030/ajbe.v3i3.402.

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The second article in a series designed to supplement the introductory financial management course, this essay addresses financial statement analysis, including its impact on stock valuation, disclosure, and managerial behavior.

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Hernwall, Patrik, and Inga-Lill Söderberg. "Elevers förståelse av grundläggande privatekonomiska principer – implikationer för undervisning i HKK." Forskning om undervisning och lärande 7, no. 2 (2019): 111–36. http://dx.doi.org/10.61998/forskul.v7i2.27280.

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Ämnet hem- och konsumentkunskap (HKK) bär ansvaret för undervisning i privatekonomi. Detta till trots saknas kunskap om hur elever förstår grundläggande privatekonomiska principer. Med utgångspunkt i en serie om tre workshoppar på tre skolor med totalt 191 elever har tematisk analys använts för att besvara frågeställningen ”Hur förstår elever, i årskurserna 4 till 6, privatekonomi?”. Artikeln visar att elevers förståelse av ekonomi generellt och villkoren för undervisning om ekonomi specifikt är sparsamt beforskat. Genom att relatera den empiriska studien till HKK-ämnet konstateras att det finns goda förutsättningar att utveckla och förstärka såväl undervisningen i som elevers förståelse av ekonomi. Analysen visar att resurs, värde och tid är de centrala principer eleverna använder för att förstå privatekonomi. Artikeln avslutas med en dikussion om hur elevers förståelse av ekonomi kan bidra till en utveckling av undervisning i privatekonomi inom ramen för ämnet hem- och konsumentkunskap. Children’s understanding of basic principles of personal financial - implications for teaching in HCS The subject of home and consumer studies (hem- och konsumentkunskap: HKK) holds the responsibility to teach personal finance. Even so, there is a lack knowledge on how children understand basic principles of personal finance. Based on a series of three workshops in three schools with a total of 191 students, thematic analysis has been used to answer the question “How do children, in grades 4 to 6, understand personal finance?” The article shows that research on children’s understanding of personal finances in general and the conditions for education on personal finances are sparsely studied. By relating the empirical study to the HKK subject, it is noted that there are good conditions for developing and strengthening both the education as well as children’s understanding of personal finances. The analysis shows that resource, value and time are the key principles that the children use to understand personal finance. The article concludes with a discussion on how children’s understanding of personal finances can contribute to the development of personal finance education in the field of home and consumer studies.

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Ismanto, Hadi, Tantri Pratiwi, Nurin Zulianti, and Akhmad Abdul Qofur. "THE ROLE OF COMPETENCIES, FINANCIAL PLANNING, AND ACCESS TO FINANCE IN EXPLAINING SMES FINANCIAL INNOVATION." Jurnal Manajemen dan Kewirausahaan 24, no. 1 (2022): 44–51. http://dx.doi.org/10.9744/jmk.24.1.44-51.

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Finance was a crucial aspect for the survival of SMEs. This study explored the factors that can influence financial innovation. Data was collected from the results of distributing questionnaires to 285 SMEs using a door-to-door approach and filling assistance. Sampling was based on the proportion of small and medium-sized industries. The data were tested with a series of statistical tests to obtain a suitable regression result. These results indicated that financial innovation was influenced by the owner or manager's entrepreneurial competence. Meanwhile, access to finance and financial planning did not affect financial innovation. Additional funds from outside the company were deemed necessary when the owner or manager of SMEs applies their maximum competence for business growth and performance. Therefore, it forced SMEs to be smart in exploring financial sources. SMEs tent to use their finance access to finance their business operations rather than making financial innovations. SMEs did not think about exploring their finances anymore because they feelt they had enough loans obtained from one institution. Even though the knowledge of SME owner or manager financial planning was quite good; they prefered not to make financial planning and let the cash flow normally.

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Srivastava, Praveen Ranjan, Zuopeng (Justin) Zhang, and Prajwal Eachempati. "Deep Neural Network and Time Series Approach for Finance Systems." Journal of Organizational and End User Computing 33, no. 5 (2021): 204–26. http://dx.doi.org/10.4018/joeuc.20210901.oa10.

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The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

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Cruz, P., and H. Cruz. "Piecewise Linear Representation of Finance Time Series: Quantum Mechanical Tool." Acta Physica Polonica A 138, no. 1 (2020): 21–24. http://dx.doi.org/10.12693/aphyspola.138.21.

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Parnes, Dror. "Time series patterns in credit ratings." Finance Research Letters 4, no. 4 (2007): 217–26. http://dx.doi.org/10.1016/j.frl.2007.09.001.

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Sadaf, Mustafa. "Impact of Terrorism on FDI Inflow in Pakistan- A Time Series Analysis." American Based Research Journal 8, no. 3 (2019): 33–39. https://doi.org/10.5281/zenodo.3456760.

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<em>Finance is needed to grow every sector of life. The internal finance can be raised through public saving, direct and indirect taxes, etc. while the external sources of finance include foreign direct investment and loans from the foreign and local lending institutions. Foreign direct investment is considered very important as an external source of finance as it has long term effects in both quantitative and qualitative terms and doesn’t need to be paid off back with heavy interest charges like in the case of loans. FDI plays a key role in bringing capital, advanced technology, skills transfer and increase standard of living in developing countries. Pakistan as a developing country needs a huge amount of finance for development and non-development expenditures. With its heavy budgetary deficits, the country took and is still taking loans from different lending institutions. The country didn’t focus on raising FDI in the starting decades, but then it realized the importance. Over the last two decades, Pakistan has also made significant efforts in increasing the flow of foreign investment. However, the country has suffered terrible terrorism after the 9/11 attacks which have also impacted the flow of investment in the country. Terrorism is the actions of the people who are working against the law and the government. The flow of investment a decade before and after the 9/11 terror attacks has been analyzed that had hit the country with massive terrorism disrupting the economic scenarioa</em>

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Gumanti, Dessyta, Salman M. Noer, Reni Respita, et al. "Meningkatkan Personal Finance Management Siswa SMA PGRI 2 Padang." Journal of Community Service and Society Empowerment 3, no. 02 (2025): 146–55. https://doi.org/10.59653/jcsse.v3i02.1735.

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Personal finance management is very important, especially for the younger generation such as students, for their future. However, in reality, there are still many students who do not understand how to manage finances effectively. Community service activities are carried out for students of SMA PGRI 2 Padang with the hope that students will be able to manage their personal finances from an early age. This activity is carried out through a series of workshops and interactive discussions that involve students in activities based on good financial planning and management. Through a simple and practical approach, students are invited to learn the basic concepts of money management, create a personal budget, and understand the importance of saving and investing. The results of this service show an increase in students' understanding of the importance of personal financial management and their ability to plan a more structured budget.

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Laux, Judy. "Topics In Finance Part I-Introduction And Stockholder Wealth Maximization." American Journal of Business Education (AJBE) 3, no. 2 (2010): 15–22. http://dx.doi.org/10.19030/ajbe.v3i2.381.

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The following article represents the first in a series dedicated to presenting students the opportunity to better understand the key theoretical constructs in the introductory financial management course. The current essay offers an introduction to the series and covers the topics of stockholder wealth maximization and its close cousin, agency theory.

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Wang, Hongyi. "The Macroeconomic Impact of Internet Finance." Proceedings of Business and Economic Studies 7, no. 3 (2024): 166–72. http://dx.doi.org/10.26689/pbes.v7i3.7512.

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The arrival of the era of Internet finance has brought significant opportunities for the development of the global macroeconomy. Under the influence of the Internet finance model, the world’s macroeconomy has experienced rapid growth in recent years. However, while promoting macroeconomic development, Internet finance also introduces a series of negative impacts. This paper analyzes the impact of Internet finance on the macroeconomy from both positive and negative perspectives and proposes effective coping strategies.

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Pan, Luyu. "Integrated development of inclusive finance and green finance promotes rural revitalization." Advances in Economics and Management Research 1, no. 2 (2022): 193. http://dx.doi.org/10.56028/aemr.1.2.193.

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In 2016, China put forward the topic of green finance and inclusive finance at the G20 Summit in Hangzhou. Since then, more and more scholars in China have paid attention to the possibility and influence of the integrated development of green finance and inclusive finance. In recent years, China has gradually established a domestic green financial market system through a series of measures such as issuing green financial standards, disclosure requirements and encouraging green financial product innovation. According to data from the National Statistical Bulletin, by the end of 2021, the loan balance of major rural financial institutions (rural credit cooperatives, rural cooperative banks, rural commercial banks) was 2.42496 trillion yuan, an increase of 2.6607 billion yuan compared with the beginning of the year. From the perspective of Liaoning Province, at the end of 2020, the balance of loans in domestic and foreign currencies of banking financial institutions in Liaoning was 520.94 billion yuan, an increase of 262.68-billion-yuan year-on-year, an increase of 5.3% year-on-year. The growth rate slowed down, with a decrease of 4.9 percentage points compared with the previous year. Firstly, we select a series of indicators and use entropy method to calculate the weight of each indicator. According to the analysis and processing of historical data, the quantization results of core explained variables, core explained variables and control variables are obtained. Descriptive statistics are made according to China Rural Statistical Yearbook and Peking University Digital China GSP Financial Index (2011-2020). Then, four spatial econometric models, namely spatial Dubbin model, spatial autoregression model, spatial correlation model and spatial error model, were applied to analyze the development relationship between digital inclusive finance and rural revitalization. Based on the evaluation system of rural revitalization, the development level of rural revitalization was obtained by combining the relevant data of 30 provinces in China from 2011 to 2021. Finally, the application of LR inspection, LM test to verify the reliability of the regression results, and considering the effect of partial differential decomposition method is used to the calculation results are decomposed, the weights in different space under the condition of digital Pratt & Whitney directly influence to the time of rural financial revitalization policy and policy space spillover effects, further analysis Pratt &Whitney financial relationship with the revitalization of the development of the rural.

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Pan, Luyu. "Integrated development of inclusive finance and green finance promotes rural revitalization." Advances in Economics and Management Research 2, no. 1 (2022): 193. http://dx.doi.org/10.56028/aemr.2.1.193.

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In 2016, China put forward the topic of green finance and inclusive finance at the G20 Summit in Hangzhou. Since then, more and more scholars in China have paid attention to the possibility and influence of the integrated development of green finance and inclusive finance. In recent years, China has gradually established a domestic green financial market system through a series of measures such as issuing green financial standards, disclosure requirements and encouraging green financial product innovation. According to data from the National Statistical Bulletin, by the end of 2021, the loan balance of major rural financial institutions (rural credit cooperatives, rural cooperative banks, rural commercial banks) was 2.42496 trillion yuan, an increase of 2.6607 billion yuan compared with the beginning of the year. From the perspective of Liaoning Province, at the end of 2020, the balance of loans in domestic and foreign currencies of banking financial institutions in Liaoning was 520.94 billion yuan, an increase of 262.68-billion-yuan year-on-year, an increase of 5.3% year-on-year. The growth rate slowed down, with a decrease of 4.9 percentage points compared with the previous year. Firstly, we select a series of indicators and use entropy method to calculate the weight of each indicator. According to the analysis and processing of historical data, the quantization results of core explained variables, core explained variables and control variables are obtained. Descriptive statistics are made according to China Rural Statistical Yearbook and Peking University Digital China GSP Financial Index (2011-2020). Then, four spatial econometric models, namely spatial Dubbin model, spatial autoregression model, spatial correlation model and spatial error model, were applied to analyze the development relationship between digital inclusive finance and rural revitalization. Based on the evaluation system of rural revitalization, the development level of rural revitalization was obtained by combining the relevant data of 30 provinces in China from 2011 to 2021. Finally, the application of LR inspection, LM test to verify the reliability of the regression results, and considering the effect of partial differential decomposition method is used to the calculation results are decomposed, the weights in different space under the condition of digital Pratt & Whitney directly influence to the time of rural financial revitalization policy and policy space spillover effects, further analysis Pratt &Whitney financial relationship with the revitalization of the development of the rural.

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Eleje, Edward Ogbonnia, Agha Eze Okechukwu, and Eli Oyavuru Chikanele. "Debt Finance and Corporate Performance: Firm Level Empirical Evaluation." Archives of Business Research 8, no. 1 (2020): 94–106. http://dx.doi.org/10.14738/abr.81.7617.

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Debt finance relevance or financial leverage debate has continued to gain more strength in every discussion of firm capital structure locally and beyond. To some researchers, the application of debt finance could worsen performance of firms and create difficult economic scenario; to others, debt finance could induced better business performance and profitability. It is on the premise of the foregoing arguments that this study sought to investigate the effect of long and short tenured debt on return on assets (ROA) as well as return on equity (ROE) of corporate manufacturing firms in Nigeria. To achieve this, the study relied on firm level data generated from annual report of the National Salt Corporation of Nigeria (NASCON) Plc for a 12-year period (2007-2018). Data were analyzed using time series analysis while the computer-based multivariate linear regression approach aided by Special Package for Social Sciences (SPSS) version 20 was used in the test of the two stated hypotheses. Consistently, the two null hypotheses were sustained since their significant values (sig-value) were greater than 0.05 and their corresponding t-values positive. The paper thus concludes that although long and short tenured debt finances may not significantly impact positively on ROA and ROE, long tenured debt could slightly enhance corporate performance. Accordingly, the study recommends among others that, financial managers of corporate manufacturing firms should design optimum capital structure for long and short tenured debt finances considering the varied impact of both on corporate performance.

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GRUNSPAN, CYRIL, and JORIS VAN DER HOEVEN. "EFFECTIVE ASYMPTOTICS ANALYSIS FOR FINANCE." International Journal of Theoretical and Applied Finance 23, no. 02 (2020): 2050013. http://dx.doi.org/10.1142/s0219024920500132.

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It is known that an adaptation of Newton’s method allows for the computation of functional inverses of formal power series. We show that it is possible to successfully use a similar algorithm in a fairly general analytical framework. This is well suited for functions that are highly tangent to identity and that can be expanded with respect to asymptotic scales of “exp-log functions”. We next apply our algorithm to various well-known functions coming from the world of quantitative finance. In particular, we deduce asymptotic expansions for the inverses of the Gaussian and the Black–Scholes pricing functions.

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Zainal, Mirzalina, Insukindro Insukindro, and Akhmad Makhfatih. "Fiscal Cyclicality Under State Finances Law in Indonesia." Jurnal Ekonomi dan Studi Pembangunan 14, no. 1 (2022): 109. http://dx.doi.org/10.17977/um002v14i12022p109.

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This study aims to analyze the cyclicality of fiscal policy under state finances law in Indonesia. The Indonesian government officially enacted the 2003 and 2004 Laws on State Finances, and it regulates fiscal rules covering the amount of the budget deficit and balanced budget rules. This fiscal rule is expected to encourage fiscal cyclicality to become countercyclical and provide buffering to deal with various economic shocks. This study uses quarterly time-series data from 2001 to 2019. The years 2001-2004 are used as the years prior to implementing the State Finance Law. Moreover, 2005 – 2019 is the time to capture the effects of cyclicality after implementing the Law. This study uses a dynamic distributed lag model to see the effect of GDP on government spending behavior. This study indicates that fiscal cyclicality before implementing the Law on State Finance behaved acyclically. Meanwhile, after implementing the Laws, this fiscal behavior is still procyclical. It means that the fiscal rules have not been effective in changing the direction and behavior of the fiscal to be countercyclical.

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Laux, Judy. "Topics In Finance Part VIIIMergers & Acquisitio." American Journal of Business Education (AJBE) 5, no. 4 (2012): 369–76. http://dx.doi.org/10.19030/ajbe.v5i4.7114.

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In this series, three key axiomsstockholder wealth maximization, the risk-return tradeoff, and agency conflictsare applied to the major topics in financial management. The current article looks at mergers and acquisitions, reviewing the presumed motivations, the ethical challenges, and the literature dedicated to this financial activity.

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Rajeev Goyal. "Exploring the Fractal Geometry of Financial Time Series." Modern Dynamics: Mathematical Progressions 1, no. 1 (2024): 1–5. http://dx.doi.org/10.36676/mdmp.v1.i1.01.

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Fractal geometry has emerged as a powerful tool for understanding complex and irregular structures in various domains, including finance. In this paper, we delve into the application of fractal geometry to analyze financial time series data. We begin by providing an overview of fractal geometry concepts and its relevance to understanding the dynamics of financial markets. Subsequently, we explore different fractal dimensions and their implications for characterizing the self-similarity and scaling properties of financial time series. We investigate how fractal geometry can be used to detect patterns, trends, and anomalies in financial data, offering insights into market behavior and potential forecasting capabilities. Furthermore, we discuss the challenges and limitations associated with applying fractal geometry to financial time series analysis, such as data noise, non-stationarity, and parameter estimation. Finally, we conclude with future directions and potential avenues for further research in this exciting and interdisciplinary field at the intersection of mathematics and finance.

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Pham, Huong Thu, and Tuan Anh Dao. "Impacts of Fintech to Finance-Banking activities and recommendation for Finance-Banking industry in Vietnam." Journal of Mining and Earth Sciences 61, no. 5 (2020): 104–10. http://dx.doi.org/10.46326/jmes.ktqtkd2020.14.

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Impact of the 4th industrial revolution on development of finance and banking system (TCNH) is increasingly clear with the launch of a series of new and innovative banking products and services, as well as introduction of new distribution channels for banking services based on financial technology platform (Fintech). On the basis of components and products of Fintech ecosystem, the article mentioned Fintech market in Vietnam, clarifying positive and negative impacts of Fintech on operations of finance-banking industry, from there to issue some recommendations for activities of Vietnam’s Finance-Banking industry to meet new trends.

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Li, Yangyi. "Internet Finance Non-stationary Time Series Prediction Algorithm Based on Deep Learning." Information Technology and Control 53, no. 4 (2024): 1236–50. https://doi.org/10.5755/j01.itc.53.4.37053.

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Inaccurate prediction results of financial time series will lead to wrong investment decisions. Therefore, a prediction algorithm for Internet financial non-stationary time series based on deep learning is proposed. EMD (empirical mode decomposition) method is used to divide the collected historical Internet financial non-stationary time series information into high-frequency and low-frequency parts, and remove the noise in the decomposed high-frequency components to obtain the financial non-stationary time series without noise. The knowledge map method is used to mine the transaction characteristics and market characteristics of Internet finance from the financial non-stationary time series without noise, and the two are fused as the input of the improved CNN (convolutional neural network) prediction model. The prediction results of Internet financial time series are obtained through CNN. The experimental results show that after setting the CNN parameters, the predicted results are consistent with the actual market trends. The highest RSE of the predicted result is 0.551, The highest RAE is 0.443, which is relatively low, the CORR value is 0.864, which is relatively high, indicating that the relative square root error, relative absolute error, and relevant empirical coefficients of the prediction results are all good, making it a highly applicable algorithm.

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Rateiwa, Ronald, and Meshach Jesse Aziakpono. "Financial structure and economic performance in selected African countries: time series evidence." Banks and Bank Systems 11, no. 2 (2016): 45–60. http://dx.doi.org/10.21511/bbs.11(2).2016.05.

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In this paper, the authors investigate the long-debated question of whether or not a country’s financial structure matters for economic performance and, if so, how exactly it matters. The study uses the Johansen cointegration and vector error correction modelling framework within a country-specific setting to examine empirically the existence of a long-run equilibrium relationship between the financial structure of a country and per capita GDP and the causality thereof. The empirical assessment is based on evidence from selected African countries over the period 1971-2013, notably Egypt, Nigeria and South Africa. Firstly, cointegration test results reported in this paper show that there exists a strong relationship between the financial structure of Egypt and South Africa, and per capita GDP in these countries. However, such a relationship is weak in Nigeria, mainly attributable to its low level of financial development and the possibility of the natural resource curse emanating from the oil industry. Secondly, the evidence also strongly suggests that the nature of the relationship between the financial structure of Egypt and South Africa and per capita GDP is positive, albeit based on different measures of financial structure. In Egypt, financial structure is measured by the S-Size ratio, while, in South Africa, it is proxied by the S-Activity ratio. In Nigeria, there is no evidence suggesting that the country’s financial structure influences per capita GDP. Lastly, coefficients of the error correction term for all three countries are low, suggesting inefficiencies in the financial system and possible rigidities within the economies

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Gourio, François. "Time-series predictability in the disaster model." Finance Research Letters 5, no. 4 (2008): 191–203. http://dx.doi.org/10.1016/j.frl.2008.08.005.

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Power, Gabriel J. "Quantitative finance for agricultural commodities: discussion and extension." Agricultural Finance Review 76, no. 1 (2016): 27–41. http://dx.doi.org/10.1108/afr-02-2016-0013.

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Purpose – The purpose of this paper is to review three papers in this issue and contribute new results on commodity futures prices and volume using wavelet analysis. Design/methodology/approach – The paper uses time series econometrics including variance ratio tests, fractional integration estimators, and wavelet transforms. Findings – The role of time horizon is emphasized in the discussion of the three papers, and wavelet methods are shown to be a useful tool to better understand time horizon-specific risk. Moreover, changes in the time horizon of futures trading are documented and discussed. Originality/value – In addition to discussing three papers on quantitative finance for agricultural commodities, this paper also looks at how the analysis and management of short-term and long-term risk may differ. To this end, wavelet transform-based time series methods are reviewed and applied.

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Afiz Adewale Lawal, Omogbolahan Alli, Aishat Oluwatoyin Olatunji, Enuma Ezeife, and Ehizele Dean Okoduwa. "Time series analysis and forecasting in finance: A data mining approach." Open Access Research Journal of Science and Technology 9, no. 1 (2023): 075. https://doi.org/10.53022/oarjst.2023.9.1.0045.

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Time series analysis and forecasting are essential methodologies in finance, playing a pivotal role in predicting market trends, evaluating economic conditions, and supporting decision-making. These methods rely on analyzing sequential data to uncover patterns, trends, and seasonal variations that drive financial phenomena. Traditional statistical models, such as ARIMA and GARCH, have long been utilized; however, their effectiveness is often constrained by assumptions like linearity and stationarity. Recent advancements in data mining techniques, including machine learning and artificial intelligence, have transformed the landscape of time series forecasting. These innovative approaches excel at handling non-linear relationships, high-dimensional data, and noise inherent in financial markets, making them indispensable for modern financial analytics. This paper scours the concept of time series analysis and data mining, examining their integration to improve forecasting accuracy. Additionally, it evaluates challenges such as data quality and computational requirements, while highlighting emerging opportunities, such as real-time forecasting and big data applications.

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Sushanth, T. Sai, T. Sanjay Siddarda, A. Sathvika, A. Shruthi, A. Sree Lekha, and Thanish Kumar. "Time Series Forecasting using RNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem39164.

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Time series forecasting is essential across various fields such as finance, economics, meteorology, and healthcare, where accurate predictions are crucial for effective decision- making. Traditional statistical methods often fall short in capturing the intricate patterns and long- term dependencies inherent in time series data, limiting their practical applicability. This research investigates the application of Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), to enhance forecasting accuracy. LSTMs are particularly well-suited for this task due to their ability to process and retain information over extended sequences, allowing them to capture complex temporal relationships that conventional methods might overlook. This work employs a comprehensive approach that includes advanced data preprocessing, feature engineering, and model architecture design, combined with meticulous hyperparameter tuning to optimize performance. The effectiveness of the LSTM- based approach is evaluated using the M4 competition dataset, which is widely recognized for its complexity and diversity in time series data. The results demonstrate that the optimized LSTM model consistently outperforms traditional statistical methods. Specifically, it shows superior accuracy in capturing both short-term fluctuations and long- term trends. Performance metrics, including Mean Absolute Scaled Error (MASE) and Symmetric Mean Absolute Percentage Error (sMAPE), reveal significantly lower error rates with the LSTM model compared to conventional approaches. The LSTM model’s proficiency in predicting long-term trends further highlights its effectiveness in practical time series forecasting scenarios. Overall, LSTMs prove to be a robust tool for time series forecasting, offering substantial improvements over traditional methods by effectively leveraging long-term dependencies in data. These models are especially valuable in fields like finance, economics, and healthcare. Future studies could aim to further optimize LSTM models and explore their application to other complex datasets, pushing the boundaries of this field even further.

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Dhar, Vasant, Chenshuo Sun, and Puneet Batra. "Transforming Finance Into Vision: Concurrent Financial Time Series as Convolutional Nets." Big Data 7, no. 4 (2019): 276–85. http://dx.doi.org/10.1089/big.2019.0139.

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Arroyo, Javier, Rosa Espínola, and Carlos Maté. "Different Approaches to Forecast Interval Time Series: A Comparison in Finance." Computational Economics 37, no. 2 (2010): 169–91. http://dx.doi.org/10.1007/s10614-010-9230-2.

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Rousseau, Peter L., and Dadanee Vuthipadadorn. "Finance, investment, and growth: Time series evidence from 10 Asian economies." Journal of Macroeconomics 27, no. 1 (2005): 87–106. http://dx.doi.org/10.1016/j.jmacro.2003.09.004.

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Ma, Xiangkai, and Huaxiong Zhang. "Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer." Applied Sciences 15, no. 7 (2025): 3768. https://doi.org/10.3390/app15073768.

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Accurate time series forecasting is crucial in fields such as business, finance, and meteorology. To achieve more precise predictions and effectively capture the potential cycles and stochastic characteristics at different scales in time series, this paper optimizes the network structure of the Autoformer model. Based on multi-scale convolutional operations, a multi-scale feature fusion network is proposed, combined with date–time encoding to build the MD–Autoformer time series forecasting model, which enhances the model’s ability to capture information at different scales. In forecasting tasks across four fields—apparel sales, meteorology, finance, and disease—the proposed method achieved the lowest RMSE and MAE. Additionally, ablation experiments demonstrated the effectiveness and reliability of the proposed method. Combined with the TPE Bayesian optimization algorithm, the prediction error was further reduced, providing a reference for future research on time series forecasting methods.

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Journal articles on the topic 'Series " Finance'

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