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Journal articles on the topic 'Forecasting Profitability'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Maris, Brian A. "Forecasting Profitability and Earnings." CFA Digest 30, no. 4 (2000): 21–22. http://dx.doi.org/10.2469/dig.v30.n4.759.

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2

Fama, Eugene F., and Kenneth R. French. "Forecasting Profitability and Earnings." Journal of Business 73, no. 2 (2000): 161–75. http://dx.doi.org/10.1086/209638.

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Shirin, Tursunkhodjayeva. "Medium-Term Forecasting Of Investment Portfolio Profitability." International Journal Of Management And Economics Fundamental 5, no. 6 (2025): 108–16. https://doi.org/10.37547/ijmef/volume05issue06-22.

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This study explores medium-term forecasting of investment portfolio profitability by analyzing the stock prices of six Uzbek joint-stock companies using time series models. The research compares classical statistical models such as ARIMA with nonlinear models like GARCH and LSTM to determine their accuracy in volatile market conditions. Over 848 ARIMA model combinations were tested, and the most optimal models were selected based on statistical indicators such as AIC, BIC, and significance of parameters. Findings revealed that combining ARIMA with GARCH models improves forecast precision due t

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4

P. Bauman, Mark. "Forecasting operating profitability with DuPont analysis." Review of Accounting and Finance 13, no. 2 (2014): 191–205. http://dx.doi.org/10.1108/raf-11-2012-0115.

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Purpose – The purpose of this study is to re-examine the relation between changes in profit margin (ΔPM) and changes in return on net operating assets (ΔRNOA) by partitioning on the direction of the change in PM. DuPont analysis provides a means of disaggregating a firm’s return on net operating assets (RNOA) into asset turnover (ATO) and profit margin (PM) components to gain insights into the underlying drivers of operating profitability. Prior research finds that changes in ATO are informative about one-year-ahead changes in RNOA, while changes in PM are not. Design/methodology/approach – Co

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Krahmalova, Nina, and Volodymyr Slonopas. "ASSESSMENT AND FORECASTING OF BUSINESS PROFITABILITY IN THE FORMATION OF ENTREPRENEURIAL POTENTIAL IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT." Actual Problems of Economics 1, no. 276 (2024): 72–84. http://dx.doi.org/10.32752/1993-6788-2024-1-276-72-84.

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The article examines the problems of assessing and forecasting business profitability in the formation of entrepreneurial potential in the context of sustainable development. It was determined that in the formation of entrepreneurial potential, considerable attention should be paid to the analysis of market conditions and the competitive environment, which helps to identify the strengths and weaknesses of the business, as well as opportunities for growth and threats that can affect its profitability. For research, analysis, modeling and forecasting of the level of profitability of production o

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Skvarciany, Viktorija, and Justina Simanavičiūtė. "Bank Profitability Analysis and Forecasting: Lithuania Case." Trends Economics and Management 12, no. 32 (2018): 101. http://dx.doi.org/10.13164/trends.2018.32.101.

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7

Van Lear, W. "Profitability in business cycle theory and forecasting." Review of Radical Political Economics 31, no. 2 (1999): 46–60. http://dx.doi.org/10.1016/s0486-6134(99)80003-5.

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Van Lear, William. "Profitability in Business Cycle Theory and Forecasting." Review of Radical Political Economics 31, no. 2 (1999): 46–60. http://dx.doi.org/10.1177/048661349903100203.

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Hrytsiuk, Petro, and Tetyana Babych. "MODELING OF GRAIN PRODUCTION PROFITABILITY BY FUZZY LOGIC." International Journal of New Economics and Social Sciences 4, no. 2 (2016): 42–52. http://dx.doi.org/10.5604/01.3001.0010.4538.

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Ukraine is an agrarian state. One of the most important brunches of agriculture sector is grain production. High yield of grain is a basis of Ukrainian food security. Therefore the task of developing a reliable mathematical model forecasting the grain production profitability is actually. Regression analysis and fuzzy simulation principles have been used for building of the grain production profitability depending model. The values profitability forecasting for 2015 obtained by three different methods are convergent to each other.

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10

Vorst, Patrick, and Teri Lombardi Yohn. "Life Cycle Models and Forecasting Growth and Profitability." Accounting Review 93, no. 6 (2018): 357–81. http://dx.doi.org/10.2308/accr-52091.

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ABSTRACT Mean reversion in profitability and growth is a well-documented phenomenon; however, comparatively less is known about the level of analysis that best captures mean reversion. In this study, we find that analyzing firms by life cycle stage improves the out-of-sample accuracy of profitability and growth forecasts over analyzing firms pooled across the economy and analyzing firms by industry. The improved accuracy is robust to both short-term and long-term forecasts of profitability and growth. We also find that the improvement in accuracy of forecasts from life cycle analyses is greate

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11

Schröder, David, and Andrew Yim. "Industry Effects in Firm and Segment Profitability Forecasting." Contemporary Accounting Research 35, no. 4 (2018): 2106–30. http://dx.doi.org/10.1111/1911-3846.12361.

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12

Tripathi, Astha. "Forecasting Agricultural Crop Profitability and Yield in India." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 308–15. http://dx.doi.org/10.22214/ijraset.2024.58316.

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Abstract: "India's heavy reliance on agriculture underscores the importance of accurately estimating agricultural production, considering the interplay of organic, economic, and seasonal variables, particularly amid a growing population. Predicting crop yields is crucial for farmers' planning, covering storage and marketing strategies. However, this task is complex and requires foresight. Data mining techniques emerge as a potent tool, leveraging extensive datasets to extract invaluable insights. By employing methods like Random Forest, this research offers a swift yet comprehensive examinatio

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Santamaria, Daniel. "EVALUATING THE PREDICTIVENESS AND PROFITABILITY OF FOREIGN EXCHANGE RATE FORECASTING MODELS." Journal of Prediction Markets 6, no. 1 (2012): 56–75. http://dx.doi.org/10.5750/jpm.v6i1.497.

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This paper evaluates the performance of two competing currency models as a forecasting and trading tool in fund management. A dynamic vector error correction model is utilized to construct a currency forecasting and fair value forecasting model for the Euro-Dollar exchange rate. Emphasis is placed on robustness testing model performance by changing its specification and how both models perform across different time periods. Based on the accuracy of the forecasts the fair value model outperforms the currency forecasting model; a finding that is not supported using directional forecasts. This is

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14

Sajal, Ashadujjaman, Md Sajedul Karim Chy, Sakib Salam Jamee, et al. "Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study." International Interdisciplinary Business Economics Advancement Journal 06, no. 06 (2025): 08–20. https://doi.org/10.55640/business/volume06issue06-02.

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This study proposes a deep learning framework for forecasting bank profitability by integrating macroeconomic indicators with firm-level financial data. Using models such as LSTM, GRU, Transformer, and TCN—compared against traditional approaches like Linear Regression, Random Forest, and XGBoost—we evaluated predictive performance across multiple metrics. Results show the Transformer model achieved the best performance, with an R² score of 0.95 and 91% directional accuracy, outperforming all other models. The inclusion of macroeconomic variables significantly enhanced prediction accuracy. Thes

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15

Saar, Dan, and Yossi Yagil. "Forecasting sectorial profitability and credit spreads using bond yields." International Review of Economics & Finance 38 (July 2015): 29–43. http://dx.doi.org/10.1016/j.iref.2014.12.009.

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Xu, Xu, Clare Anne McGrory, You-Gan Wang, and Jinran Wu. "Influential factors on Chinese airlines’ profitability and forecasting methods." Journal of Air Transport Management 91 (March 2021): 101969. http://dx.doi.org/10.1016/j.jairtraman.2020.101969.

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17

Shiela, Fadia Jasmine Has, Indah Karimatul Hasanah, Teguh Herlambang, Rizky Ramadhan, M. Yusuf Hamdani, and Mulya Nafa. "Analisis Peramalan Profitabilitas PT. Samudera Indonesia Tbk Menggunakan Metode Regresi Linear." Zeta - Math Journal 9, no. 1 (2024): 41–49. http://dx.doi.org/10.31102/zeta.2024.9.1.41-49.

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Logistics companies contribute significantly to economic growth and play an important role in global supply chains. Profitability is the main indicator of business success. The profit forecasting model helps managers improve financial performance efficiently and effectively. This research optimizes the financial performance of PT Samudera Indonesia Tbk by forecasting net profit using the linear regression method. Financial data from 2018 to 2023 was collected from the IDX website. Simulation analysis using Python shows that the independent variables in linear regression have a significant effe

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Aswad, Yazan, Amer Ibrahim, Aghiad Kh .., and Mahmoud Mahfuri. "Using Social Media Data To Forecast Telecom Companies Revenues With Machine Learning." American Journal of Business and Operations Research 7, no. 1 (2022): 34–43. http://dx.doi.org/10.54216/ajbor.070103.

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Traditional models for predicting future sales of a product or service are based on previous, not updated data, resulting in unsatisfactory and inaccurate forecasting results, meaning that the data used as inputs to the forecasting process is stable and not dynamic during the forecasting process.The research aims to leverage social media data by extracting features from Facebook platform (features are reactions to posts) and using them as input to the automated forecasting system to try to predict corporate revenues.Machine learning algorithms have been trained to predict returns according to

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19

Smolina, V. S., and T. G. Volkova. "COMPARATIVE ANALYSIS OF METHODS FOR FORECASTING FINANCIAL RESULTS OF AN ENTERPRISE USING THE EXAMPLE OF AN AGRO-INDUSTRIAL HOLDING." Social’no-ekonomiceskoe upravlenie: teoria i praktika 19, no. 4 (2023): 41–47. http://dx.doi.org/10.22213/2618-9763-2023-4-41-47.

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The article is devoted to a theoretical analysis of approaches to forecasting the financial results of a company, since high-quality financial forecasting allows for the growth of companies and their financial results. To improve the quality of the forecast, various methods for forecasting the main financial indicators, on which the final result of the company depends, were analyzed. It is noted that for the growth of a company and strengthening its position in the market, it is important to provide reliable and accurate forecasts of profit and return on sales. The authors come to the conclusi

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20

Martyushev, Nikita V., Vladislav Spitsin, Roman V. Klyuev, et al. "Predicting Firm’s Performance Based on Panel Data: Using Hybrid Methods to Improve Forecast Accuracy." Mathematics 13, no. 8 (2025): 1247. https://doi.org/10.3390/math13081247.

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The problem of predicting profitability is exceptionally relevant for investors and company owners making decisions about investment and business development. The global literature contains a number of studies where researchers predict the profitability of firms using various methods, including modern machine learning. However, these works hardly take advantage of panel data. This paper takes advantage of additional capabilities offered by panel data and proposes hybrid forecasting methods based on panel data, which allow significantly improving the accuracy of predicting the profitability. Ou

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21

Iqbal, Zahid, and Saadatullah Khan. "Determinants and Forecasting of Islamic and conventional Banks Profitability in Pakistan by using Logistic Regression." VAWKUM Transactions on Computer Sciences 13, no. 1 (2025): 82–94. https://doi.org/10.21015/vtcs.v13i1.1822.

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Banks’ profitability has been an essential consideration for economists, investors, and researchers. It serves as a benchmark for the banking industry economy. In the previous literature, it was observed that an internal factor of Pakistan banks’ profitability i.e. non-interest income was not included. This study examined in addition to other micro and macro factors, the non-interest income of banks’ profitability in Pakistan. Data for the period 2007 to 2016 was obtained from State bank Pakistan and the Finance Division of Pakistan. Bank’s profitability was measured by the return on assets ta

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22

ParkSangKyu and Woen Park. "Industry Adjusted Financial Ratio Analysis and the Forecasting of Profitability." Korea International Accounting Review ll, no. 43 (2012): 21–40. http://dx.doi.org/10.21073/kiar.2012..43.002.

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23

Stádník, Bohumil. "Market Price Forecasting and Profitability – How to Tame Mrandom Walk?" Verslas: teorija ir praktika 14, no. 2 (2013): 166–76. http://dx.doi.org/10.3846/btp.2013.18.

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24

Rinne, Heikki, and Michael Geurts. "A forecasting model to evaluate the profitability of price promotions." European Journal of Operational Research 33, no. 3 (1988): 279–89. http://dx.doi.org/10.1016/0377-2217(88)90171-3.

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25

Stádník, Bohumil. "Market Price Forecasting and Profitability – How to Tame Mrandom Walk?" Business: Theory and Practice 14, no. (2) (2013): 166–76. https://doi.org/10.3846/btp.2013.18.

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Directional forecasting of a future market price development of liquid investment instruments is the focus of interest of investment companies, individual investors, banks and other financial market participants. This problematic has still not been fully answered because the market price development is a process which is very close to a random walk and appropriate models are still under the discussion. The opportunities can be used for the better prediction, their usage for profit making, quantification and also their discussion according to the current financial market models (models with the

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26

MUBARAK LUBIS, M. ZAKY. "ANALISIS PERAMALAN RASIO PROFITABILITAS BANK TABUNGAN NEGARA SYARIAH MENGGUNAKAN METODE ANALISIS TREND KUADRATIK." Al-Masraf: Jurnal Lembaga Keuangan dan Perbankan 7, no. 2 (2022): 51. http://dx.doi.org/10.15548/al-masraf.v7i2.305.

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The primary factor in the investment that investors will issue is profitability. Although a company's prospects can be determined, it is required to predict the production that will be generated in order to identify potential future markets. By utilizing the Return on Assets (ROA) and Return on Equity (ROE) ratios, this study aims to examine forecasting the profitability ratios at the Islamic State Savings Bank (BTN). The data ranges from 2016 to 2021. Quadratic trend analysis is used to process the data. The forecasting of ROA and ROE using the quadratic trend approach may be summarized based

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27

Maharanie, Dhea Sinta, and Ani Wilujeng Suryani. "PERAMALAN PERUBAHAN PROFITABILITAS PERUSAHAAN MENGGUNAKAN PERUBAHAN ASSET TURNOVER DAN PROFIT MARGIN." Ekonomi Bisnis 28, no. 2 (2023): 86. https://doi.org/10.17977/um042v28i2p22-33.

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Financial analysis is an important component of a business because it enables forecasting of future performances. Return on net operating assets (RNOA) is commonly used measurement to forecast changes in company profitability. As such, the purpose of this study is to forecast profitability through the use of RNOA. The study was carried out on companies listed on the IDX30 from 2012 to 2018. Weighted least squared regression panel data is used to examine the effect of RNOA on company profitability forecast. The results show that ΔRNOA disaggregation has a positive effect on company profitabilit

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28

Oey, Elia, and Gabriella Karolina Ayrine. "PENERAPAN PROSES DAN TEKNIK PERAMALAN – STUDI KASUS DI MANUFAKTUR TRANSFORMER." Jurnal Manajemen Industri dan Logistik 2, no. 2 (2018): 106–15. http://dx.doi.org/10.30988/jmil.v2i2.31.

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Demand forecasting is essential for business processes and firm’s profitability. A Good demand forecasting requires the expertise and reliability of the planning staff. The research is a case study in a transformer manufacturer in Indonesia, which planned to apply forecasting process and suitable tools for their products. The study demonstrated a systematic step required in forecasting the four studied products. For each product, 6 forecast models were evaluated and the best model was selected by optimizing forecast parameters which give the least forecast errors. The chosen forecasting model

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29

Frum, Olha, Serhii Didukh, and Lyudmila Lobotska. "Methodological approaches to effective planning of enterprise profitability strategies based on scenario simulation method." Ukrainian Journal of Applied Economics and Technology 2024, no. 3 (2024): 418–22. https://doi.org/10.36887/2415-8453-2024-3-75.

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In the new economic conditions formed under the influence of the global pandemic and military aggression, a new vision of the essence and sequence of specific actions is necessary to manage Ukrainian enterprises to make sound economic decisions and determine their further behavior in the market environment. The article considers the scenario planning method as part of the strategic planning of enterprises, which expands the possibilities of enterprise management in conditions of high uncertainty based on the construction of various future models. Theoretical approaches to the scenario method,

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30

González-Rodríguez, Miguel A., Miguel E. Vázquez-Méndez, and Ulises Diéguez-Aranda. "Forecasting Variations in Profitability and Silviculture under Climate Change of Radiata Pine Plantations through Differentiable Optimization." Forests 12, no. 7 (2021): 899. http://dx.doi.org/10.3390/f12070899.

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Climate change might entail significant alterations in future forest productivity, profitability and management. In this work, we estimated the financial profitability (Soil Expectation Value, SEV) of a set of radiata pine plantations in the northwest of Spain under climate change. We optimized silvicultural interventions using a differentiable approach and projected future productivity using a machine learning model basing on the climatic predictions of 11 Global Climate Models (GCMs) and two Representative Concentration Pathways (RCPs). The forecasted mean SEV for future climate was lower th

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Kharitonova, Anna Evgen'evna. "Forecasting the tax burden of agricultural enterprises by machine learning methods." Налоги и налогообложение, no. 4 (April 2023): 28–38. http://dx.doi.org/10.7256/2454-065x.2023.4.43917.

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The article analyzes the data of a set of agricultural enterprises and builds machine learning models to predict the tax burden. The subject of this study is a system of statistical indicators of agricultural enterprises that characterize the level of tax burden. The purpose of the study is to predict the tax burden using machine learning methods. The introduction of modern artificial intelligence tools is an integral and inevitable process in all spheres, including in the tax environment. Machine learning methods were used to build models: regression analysis, decision tree, random forest, gr

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32

Carpenter, Chris. "An Efficient Work Flow for Planning Well Completions and Forecasting Profitability." Journal of Petroleum Technology 67, no. 07 (2015): 110–13. http://dx.doi.org/10.2118/0715-0110-jpt.

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33

Yohn, Teri Lombardi. "Research on the use of financial statement information for forecasting profitability." Accounting & Finance 60, no. 3 (2018): 3163–81. http://dx.doi.org/10.1111/acfi.12394.

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34

Konchitchki, Yaniv, and Panos N. Patatoukas. "Taking the Pulse of the Real Economy Using Financial Statement Analysis: Implications for Macro Forecasting and Stock Valuation." Accounting Review 89, no. 2 (2013): 669–94. http://dx.doi.org/10.2308/accr-50632.

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ABSTRACT In this study, we hypothesize and find that financial statement analysis of firm profitability drivers applied at the aggregate level yields timely insights that are relevant for forecasting real economic activity. We first show that focusing on the 100 largest firms offers a cost-effective way to extract information embedded in accounting profitability data of the entire stock market portfolio. We then show that accounting profitability data aggregated across the 100 largest firms have predictive content for subsequent real Gross Domestic Product (GDP) growth. We also show that stock

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35

Savel’eva, Lyudmila Yu, and Dar’ya N. Stoeva. "SOLVING THE ISSUE OF FORECASTING SALES VOLUME ON MARKETPLACES BASED ON THE USE OF TIME SERIES ANALYSIS METHODS AND TOOLS." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 3 (2024): 24–38. https://doi.org/10.28995/2686-679x-2024-3-24-38.

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The article deals with solving the problem of forecasting sales volume on marketplaces based on the use of time series analysis methods and tools. It presents a detailed analysis of time series analysis methods and tools in the context of sales volume forecasting. and considers various approaches to time series analysis, including statistical methods, machine learning and deep learning. The article also describes in detail the use of methods such as ARIMA, GARCH to predict sales volume on marketplaces. The features of these marketplaces and their impact on the choice of time series analysis me

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Zaremba, Adam. "QUALITY INVESTING IN CEE EMERGING MARKETS." Business, Management and Education 12, no. 2 (2014): 159–80. http://dx.doi.org/10.3846/bme.2014.241.

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Using sorting, cross-sectional tests, regression, and tests of a monotonic relation, the study examines the return patterns related to seven distinct quality characteristics: accruals, bid-ask spread, balance sheet liquidity, profitability, leverage, payout ratio and turnover. The investigation of more than 1.300 stocks from 11 Central and Eastern European countries for the period 2002–2014 documents a strong gross-profitability premium and an inverted liquidity premium. Profitable and not heavily leveraged companies provide a partial hedge against market distress. Finally, the paper proposes

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37

Smagin, R. S., and T. V. Lebedeva. "ANALYSIS AND FORECASTING FINANCIAL RISKS OF RETAIL TRADING NETWORKS." Intelligence. Innovations. Investment, no. 1 (2021): 54–64. http://dx.doi.org/10.25198/2077-7175-2021-1-54.

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The relevance of the problem under study is due to the fact that, despite the many works devoted to the theoretical and applied aspects of risk research, the specifics of the formation of financial risks in the activities of retail trade networks have not been sufficiently studied. In particular, there is no methodology and practical recommendations for quantitative risk assessment using statistical methods that allow to identify trends and patterns in the dynamics of risk indicators, to consider external and internal risk factors, the results of which should be used as a basis for developing

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Zrelli, Iman, and Salah Ali Salma Mohamed. "Overview of Yield management taxonomy: Efficiency and profitability for hospitality field." RESEARCH REVIEW International Journal of Multidisciplinary 4, no. 4 (2019): 244–49. https://doi.org/10.5281/zenodo.2649313.

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This paper aims to highlight the dimensions of yield management. It is a relatively new concept which, since its appearance a few decades ago, has been enriched by research conducted here and there. Custom pricing, segmentation and demand forecasting are all new elements that need to redefine the content of the concept in depth.

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Dutta, Deep. "ARIMA-Based Forecasting of S&P BSE SENSEX Returns." Management Journal for Advanced Research 3, no. 6 (2023): 1–8. http://dx.doi.org/10.54741/mjar.3.6.1.

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Investment in the stock market requires a delicate balance between profitability and risk management, with risk aversion playing a vital role. This study explores the ARIMA forecasting method to predict S&P BSE SENSEX returns, providing valuable insights for investors and financial experts. Using a 3-year dataset, the ARIMA (3,1,1) model was identified as the optimal choice. Diagnostic checks confirmed its reliability, ensuring unbiased and accurate forecasts. In static forecasting, the model exhibited high-quality performance with low error rates. Dynamic forecasting further revealed prec

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Wang, Yutong, and Can Cui. "Forecasting superstore fruit and vegetable sales based on ARIMA-LSTM." Journal of Education, Humanities and Social Sciences 25 (January 26, 2024): 105–10. http://dx.doi.org/10.54097/bpheft04.

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In the context of a fresh food supermarket, managing perishable vegetables, which have a notably limited shelf life, maintaining optimal stock levels becomes imperative for safeguarding profitability. This research paper intricately explores the analysis of numerous datasets using Power Query and Power BI, with a pivotal focus on devising a succinct coding system. To ensure data quality, outliers are meticulously eliminated using advanced techniques like linear interpolation and Z-Score processing. The ARIMA time series model is employed to predict the sales volume for each category in July, h

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41

aal, Samah I. Abdel. "An Intelligent Approach for Demand Forecasting in E-commerce." American Journal of Business and Operations Research 1, no. 2 (2020): 77–83. http://dx.doi.org/10.54216/ajbor.010203.

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With the growth of e-commerce, accurate demand forecasting has become a critical aspect of successful business operations. Traditional demand forecasting techniques such as time-series analysis, moving averages, and exponential smoothing have been used for years, but they have limitations in capturing the complex and dynamic nature of e-commerce demand. In this paper, we explore innovative approaches to demand forecasting in e-commerce. Specifically, we discuss the use of tree-based Machine Learning (ML) techniques as well as advanced statistical models such as Bayesian networks and hierarchic

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Koskela, Juha, Antti Mutanen, and Pertti Järventausta. "Using Load Forecasting to Control Domestic Battery Energy Storage Systems." Energies 13, no. 15 (2020): 3946. http://dx.doi.org/10.3390/en13153946.

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The profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of surplus photovoltaic energy make it possible to have multiple control targets in domestic use. The battery control system needs accurate load forecasting so that its capacity can be utilized in an optimally economical way. This study shows how the accuracy of short-term load forecasting affects cos

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43

Kunin, V. A., and S. I. Peshko. "Forecasting of financial risks of decline in profit and profitability of companies in the retail industry on the basis of correlation and regression analysis of macroeconomic factors." Economics and Management 29, no. 6 (2023): 690–708. http://dx.doi.org/10.35854/1998-1627-2023-6-690-708.

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Aim. Development and realization of the innovative approach to forecasting of financial risks of profit and profitability decrease of retail industry companies on the basis of correlation and regression analysis of macroeconomic factors.Tasks. To identify and substantiate the macroeconomic risk factors of decrease in the retail trade turnover of the Russian companies of the retail industry; to develop a methodology of construction and build regression dependence of the total turnover of the companies of the retail trade industry in the Russian Federation (RF) on the macroeconomic factors; to d

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Farizal Mohammed, Nor, Sazalina Ahmad Puat, Mira Susanti Amirrudin, and Afizah Hashim. "LEVERAGE, LIQUIDITY AND PROFITABILITY RATIOS: ACCOUNTABILITY OF MALAYSIAN LISTED OIL AND GAS FIRMS." Humanities & Social Sciences Reviews 8, no. 2 (2020): 941–47. http://dx.doi.org/10.18510/hssr.2020.82104.

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Purpose: This study examines the impact of leverage and liquidity on the profitability among the listed O&G firms in Malaysia. Methodology: Data were gained from the audited financial statements of 22listed O&G firms for a period of ten years (2008 – 2017) and a quantitative data methodology was utilized to analyze the study. Main Findings: The findings demonstrated that leverage in terms of debt-equity ratio has a significant negative association on a firm's profitability. Nevertheless, liquidity ratios are found to be insignificantly related to the profitability of the O&

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Ahn, Kyung-Hwan, Jong-Soon Park, Jong-Sik Lee, Dae-Jung Kwon, and Jae-Youl Chun. "A Profitability Forecasting Model available in Planning Stage of Housing Redevelopment Project." Korean Journal of Construction Engineering and Management 14, no. 1 (2013): 63–70. http://dx.doi.org/10.6106/kjcem.2013.14.1.063.

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Lee, Hwi-Young. "A Study on the Profitability of Airlines by Using Demand Forecasting Models." Journal of Tourism Enhancement 7, no. 1 (2019): 79–97. http://dx.doi.org/10.35498/kotes.2019.7.1.079.

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Allen *, D. E., and H. M. Salim. "Forecasting profitability and earnings: a study of the UK market (1982–2000)." Applied Economics 37, no. 17 (2005): 2009–18. http://dx.doi.org/10.1080/00036840500244634.

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Audzeyeva, Alena, Barbara Summers, and Klaus Reiner Schenk-Hoppé. "Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective." International Journal of Forecasting 28, no. 2 (2012): 507–18. http://dx.doi.org/10.1016/j.ijforecast.2011.05.005.

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Dautel, Alexander Jakob, Wolfgang Karl Härdle, Stefan Lessmann, and Hsin-Vonn Seow. "Forex exchange rate forecasting using deep recurrent neural networks." Digital Finance 2, no. 1-2 (2020): 69–96. http://dx.doi.org/10.1007/s42521-020-00019-x.

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Abstract Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evide

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Bernardes, Fernando, Douglas Vieira, Vasile Palade, and Rodney Saldanha. "Winds of Change: How Up-To-Date Forecasting Methods Could Help Change Brazilian Wind Energy Policy and Save Billions of US$." Energies 11, no. 11 (2018): 2952. http://dx.doi.org/10.3390/en11112952.

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This paper proposes a revaluation of the Brazilian wind energy policy framework and the energy auction requirements. The proposed model deals with the four major issues associated with the wind policy framework that are: long-term wind speed sampling, wind speed forecasting reliability, energy commercialization, and the wind farm profitability. Brazilian wind policy, cross-checked against other countries policies, showed to be too restrictive and outdated. This paper proposes its renewal, through the adoption of international standards by Brazilian policy-makers, reducing the wind time samplin

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