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Journal articles on the topic 'Financial Prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Harahap, Rahma Sari, Iskandar Muda, and Rina Br Bukit. "Analisis penggunaan metode Altman Z-Score dan Springate untuk mengetahui potensi terjadinya Financial Distress pada perusahaan manufaktur sektor industri dasar dan kimia Sub Sektor semen yang terdaftar di Bursa Efek Indonesia 2000-2020." Owner 6, no. 4 (2022): 4315–25. http://dx.doi.org/10.33395/owner.v6i4.1576.

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The objective of the research is to find out the result of predicting bankruptcy, using Altman Z-Score and Springate methods in the manufacturing companies of basic industrial and chemistry sectors, cement sub-sector listed on BEI (Indonesia Stock Exchange) in the period of 2000-2020 and to determine the most accurate predicting method of bankruptcy to be applied in the manufacturing companies in basic industrial and chemistry sectors, cement sub-sector. The research employs descriptive quantitative method. The samples are taken by using purposive sampling method with three manufacture compani

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Jeong, Jiseok, and Changwan Kim. "Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry." Buildings 12, no. 10 (2022): 1759. http://dx.doi.org/10.3390/buildings12101759.

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A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions

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Chen, Jianguo, Ben R. Marshall, Jenny Zhang, and Siva Ganesh. "Financial Distress Prediction in China." Review of Pacific Basin Financial Markets and Policies 09, no. 02 (2006): 317–36. http://dx.doi.org/10.1142/s0219091506000744.

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We use four alternative prediction models to examine the usefulness of financial ratios in predicting business failure in China. China has unique legislation regarding business failure so it is an interesting laboratory for such a study. Earnings Before Interest and Tax to Total Assets (EBITTA), Earning Per Share (EPS), Total Debt to Total Assets (TDTA), Price to Book (PB), and the Current Ratio (CR), are shown to be significant predictors. Prediction accuracy achieves a range from 78% to 93%. Logit and Neural Network models are shown to be the optimal prediction models.

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Baldwin, Jane, and G. William Glezen. "Bankruptcy Prediction Using Quarterly Financial Statement Data." Journal of Accounting, Auditing & Finance 7, no. 3 (1992): 269–85. http://dx.doi.org/10.1177/0148558x9200700301.

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The purposes of this study were to assess the usefulness of quarterly data for predicting bankruptcy and to determine if the earlier prediction by quarterly bankruptcy models can be obtained without the sacrifice of accuracy achieved by annual bankruptcy models. A sample of 40 public firms entering bankruptcy from 1977 to 1983 was matched on the basis of fiscal year, industry, and asset size with 40 nonbankrupt firms. Quarterly financial data were obtained from the firms' 10-Q reports filed with the Securities and Exchange Commission (SEC), whereas annual data were obtained from the 10-K repor

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Karpac, Dusan, and Viera Bartosova. "The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization." SHS Web of Conferences 74 (2020): 06010. http://dx.doi.org/10.1051/shsconf/20207406010.

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Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the

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Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clus

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Asmin, Erny Amriani, Ismartaya -, Dwi Gemina, and Istiyanah -. "PERBANDINGAN MODEL ALTMAN, SPRINGATE, ZMIJEWSKI DAN GROVER SEBAGAI PREDIKSI FINANCIAL DISTRESS (Studi Pada Perusahaan Sektor Infrastruktur yang Terdaftar Di Bursa Efek Indonesia (BEI) Periode 2020-2022)." PROMOSI (Jurnal Pendidikan Ekonomi) 12, no. 2 (2024): 164. https://doi.org/10.24127/jp.v12i2.10463.

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The financial performance of a company is the prospect or future, the potential growth and development for the company. The economic situation can change, this affects the activities and performance of companies, both small and large companies. One of the company's responsibilities is to produce good performance to avoid financial distress. This research aims to find out whether there are differences in predictions between the Altman, Springate, Zmijewski and Grover models as predictions of financial distress and to find out whether the Springate model is the most accurate prediction of financ

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Jin, Jing, and Yongqing Zhang. "Innovation in Financial Enterprise Risk Prediction Model." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–26. http://dx.doi.org/10.4018/joeuc.361650.

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In the context of predicting financial risks for enterprises, traditional methods are inadequate in capturing complex multidimensional data features, resulting in suboptimal prediction performance. Although existing deep learning techniques have shown some improvements, they still face challenges in processing time series data and detecting extended dependencies. To address these issues, this paper proposes an integrated deep learning framework utilizing Convolutional Neural Network (CNN), Transformer model, and Wavelet Transform (WT). The proposed model leverages CNN to derive local features

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Durairaj, M., Ch Suneetha, and BH Krishna Mohan. "Financial time series prediction using deep computing approaches." Journal of Autonomous Intelligence 6, no. 1 (2023): 558. http://dx.doi.org/10.32629/jai.v6i1.558.

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<p class="Abstracttitle">A financial time series is chaotic and non-stationary in nature, and predicting it outcomes is a very complex and challenging task. In this research, the theory of chaos, Long Short-Term Memory (LSTM), and Polynomial Regression (PR) are used in tandem to create a novel financial time series prediction hybrid, Chaos+LSTM+PR. The first step in this hybrid will determine whether or not a financial time series contains chaos. Following that, the chaos in the time series is modeled using Chaos Theory. The modeled time series is fed into the LSTM to obtain initial pred

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Aiyegbeni, Gifty, Yang Li, Joseph Annan, and Funminiyi Adebayo. "Credit Rating Prediction Using Different Machine Learning Techniques." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 219–38. http://dx.doi.org/10.69511/ijdsaa.v5i5.193.

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Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction. In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accur

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Mohammed, Mohammed Ali. "Investigation of financial applications with blockchain technology." Journal of Computer & Electrical and Electronics Engineering Sciences 1, no. 1 (2023): 10–14. http://dx.doi.org/10.51271/jceees-0003.

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Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices. Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices. Results: The research evaluated various machi

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Gaur, Varun, Sharad Bhardwaj, Utsav Gaur, and Sushant Gupta. "Stock Market Prediction & Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4404–8. http://dx.doi.org/10.22214/ijraset.2022.43403.

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Abstract: Stock trading is one of the most essential activities in the financial sector. The act of attempting to anticipate the future value of a stock or other financial instrument is known as stock market prediction. A financial exchange-traded instrument. This document illustrates how Machine Learning is used to predict a stock. The time series analysis or technical and fundamental analysis is used most stockbrokers use when deciding on a stock predictions. To forecast the outcome, the computer language is employed. Python is a stock market that uses machine learning. This paper is about W

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Dong, Peilin, Xiaoyu Wang, and Zhouhao Shi. "Financial market trend prediction model based on LSTM neural network algorithm." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (2024): 745–55. http://dx.doi.org/10.3233/jcm-237097.

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The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training s

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Kassidy, Carissa Lorenza, and Jesica Handoko. "Prediksi Financial Distress Sebelum dan Selama Masa Pandemi Covid-19." E-Jurnal Akuntansi 32, no. 10 (2022): 3005. http://dx.doi.org/10.24843/eja.2022.v32.i10.p08.

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This study aims to determine the differences in the predictions of financial distress before and during the pandemic. A single variable is used in the study from the calculation of the financial distress prediction model. The prediction model used in this study is the prediction model of Grover, Springate, Taffler and Zmijewski. The object of research is a manufacturing company listed on the Indonesia Stock Exchange in the 2018-2020 period. Hypothesis testing using Paired Sample T-Test. The results showed differences in the predictions of financial distress using the Springate and Taffler pred

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Uddin, Aftab, Md Amran Hossen Pabel, Md Imdadul Alam, et al. "Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques." American Journal of Management and Economics Innovations 07, no. 01 (2025): 5–20. https://doi.org/10.37547/tajmei/volume07issue01-02.

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This study explores the application of machine learning models for predicting financial risk and optimizing portfolio management. We compare various machine learning algorithms, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Transformer networks, to assess their effectiveness in forecasting asset returns, managing risk, and enhancing portfolio performance. The results demonstrate that machine learning models significantly outperform traditional financial models in terms of prediction accuracy and risk-adjusted returns. Notably, LSTM and Transformer models excel

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16

Xiao, Yang. "Individual Stock Price Prediction Using Stacking Method." Advances in Economics, Management and Political Sciences 99, no. 1 (2024): 17–22. http://dx.doi.org/10.54254/2754-1169/99/2024ox0212.

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This paper explores the application of stacking in machine learning to predict the price of a single stock. The complexity of financial markets and the high noise in data make stock price prediction a challenging task. To improve prediction accuracy, this paper combines multiple machine learning models, including linear regression, decision trees, and random forests, using stacking to integrate the predictions of these base models. Experimental results indicate that the stacking model performs exceptionally well in predicting the stock price of Apple Inc. (AAPL), significantly outperforming in

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Patel, Ananya (Ph D. Candidate). "ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i02-01.

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The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application

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18

Khong, Yeen Lai, Sin Yee Lee, Suet Cheng Low, Peck Ling Tee, and Wan Leng Lim. "Corporate Failure Prediction in Malaysia." Journal of Research in Business, Economics and Management 4, no. 2 (2015): 343–75. https://doi.org/10.5281/zenodo.3966013.

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This study is to develop a financial prediction equation that based on public listed companies in Malaysia. Logistic regression analysis was employed to develop the equation. Eleven financial ratios were found useful in developing the financial distress prediction models. The sample consists forty eight public listed companies in Malaysia and the data covers the period from 2010 to 2014. SPSS software was used to perform the statistical analysis. The result indicated that the selected financial ratios were significant for corporate failure prediction in Malaysia. The developed equation is able

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19

Durica, Marek, Jaroslav Frnda, and Lucia Svabova. "Decision tree based model of business failure prediction for Polish companies." Oeconomia Copernicana 10, no. 3 (2019): 453–69. http://dx.doi.org/10.24136/oc.2019.022.

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Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders. Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generat

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Kristanti, Farida Titik, Sri Rahayu, and Deannes Isynuwardhana. "Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs." GATR Accounting and Finance Review 4, no. 2 (2019): 56–63. http://dx.doi.org/10.35609/afr.2019.4.2(4).

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Objective – The growth of SMEs in Indonesia is rising from year to year. As an anticipation of bankruptcy, predictions can be made in an integrated means from the perspective of capital structure, financial, and non-financial performance. Methodology/Technique – A sample of 39 companies were selected using purposive sampling during the research period of 2013-2017. The results of the statistical logistic regression show that profitability is an important factor in predicting financial distress of the SMEs in Indonesia. Findings – The operating income to total assets has a negative and signific

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Dhar, Vasant. "Prediction in financial markets." ACM Transactions on Intelligent Systems and Technology 2, no. 3 (2011): 1–22. http://dx.doi.org/10.1145/1961189.1961191.

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Mucaj, Roneda, and Alketa Hyso. "ANN in Financial Prediction." International Journal of Business & Technology 2, no. 2 (2014): 6–12. http://dx.doi.org/10.33107/ijbte.2014.2.2.02.

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This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better pr

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Josey, Aaron, and Amrutha N. "Stock Market Prediction." Indian Journal of Data Mining 4, no. 1 (2024): 34–37. http://dx.doi.org/10.54105/ijdm.a1641.04010524.

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The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional m

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Aaron, Josey. "Stock Market Prediction." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 34–37. https://doi.org/10.54105/ijdm.A1641.04010524.

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<strong>Abstract:</strong> The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its perfor

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Lin, Yifan, and Sen Wang. "Enhancing Stock Market Prediction with Sentiment Analysis Using a BERT-based Model." Transactions on Computer Science and Intelligent Systems Research 7 (November 25, 2024): 309–15. https://doi.org/10.62051/tq61jb84.

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This study explores the prediction of stock price trends in the financial market, emphasizing the impact of investor sentiment and macroeconomic policies. Traditional research often uses mathematical, statistical, or deep learning methods to predict stock prices but overlooks the emotional factors in vast unstructured text data, such as financial news. This paper proposes a Bidirectional Encoder Representations from Transformers (BERT)-Transformer model that integrates sentiment analysis to enhance stock market prediction. Using financial news text data from the Oriental Fortune Network, the B

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Zhuang, Qian, and Lianghua Chen. "Dynamic Prediction of Financial Distress Based on Kalman Filtering." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/370280.

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The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study

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Gajdosikova, Dominika, and Katarina Valaskova. "Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia." Economics and Culture 20, no. 1 (2023): 30–42. http://dx.doi.org/10.2478/jec-2023-0003.

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Abstract Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 202

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Zulqarnain, Muhammad, Rozaida Ghazali, Muhammad Ghulam Ghouse, Yana Mazwin Mohmad Hassim, and Irfan Javid. "Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks." International Journal of Intelligent Systems and Applications 12, no. 6 (2020): 21–32. http://dx.doi.org/10.5815/ijisa.2020.06.02.

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Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentia

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El-ansari, Osama, and Lina Bassam. "Predicting Financial Distress for Listed MENA Firms." International Journal of Accounting and Financial Reporting 9, no. 2 (2019): 51. http://dx.doi.org/10.5296/ijafr.v9i2.14542.

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Financial distress prediction gives an early warning about defaulting risk for firms; thus, it is a real concern of the entire economy.Purpose: To examine the determinants of financial distress across MENA region countries, by using definitions of distress and historical data from active listed firms in the region.Methodology: logistic regression is run on firm-specific variables and a set of macroeconomic variables to develop a prediction model to examine the effect of these predictors on the probability of financial distress.Findings: it has been found that after controlling for country effe

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Zhong, Junhao, and Zhenzhen Wang. "Artificial intelligence techniques for financial distress prediction." AIMS Mathematics 7, no. 12 (2022): 20891–908. http://dx.doi.org/10.3934/math.20221145.

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<abstract> <p>Artificial intelligence (AI) models can effectively identify the financial risks existing in Chinese manufacturing enterprises. We use the financial ratios of 1668 Chinese A-share listed manufacturing enterprises from 2016 to 2021 for our empirical analysis. An AI model is used to obtain the financial distress prediction value for the listed manufacturing enterprises. Our results show that the random forest model has high accuracy in terms of the empirical prediction of the financial distress of Chinese manufacturing enterprises, which reflects the effectiveness of th

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Kim, Hyeongjun, Hoon Cho, and Doojin Ryu. "Corporate Default Predictions Using Machine Learning: Literature Review." Sustainability 12, no. 16 (2020): 6325. http://dx.doi.org/10.3390/su12166325.

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Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical m

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Li, Jingyao. "Comparison of Different Machine Learning Approaches for Forecasting Stock Prices." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 17–23. http://dx.doi.org/10.54097/2re5n809.

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Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a f

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Dharmaputra Juliyan, Hadhi, and Bertilia Lina Kusrina. "FINANCIAL DISTRESS PREDICTION AND SEEING THE INFLUENCE OF THE ALTMAN RATIO ON COMPANY FINANCIAL DISTRESS." Journal of Business Economics 23, no. 3 (2018): 236–43. http://dx.doi.org/10.35760/eb.2018.v23i3.1832.

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This research aims to determine the level of the bankruptcy of the company and to see if the Altman ratio can predict the condition of corporate bankruptcy in mining companies on the Indonesia Stock Exchange because mining companies have a large role in the Indonesian economy. This study uses the Altman Z-Score model analysis to see how much the company's bankruptcy prediction and uses logistic regression to see how much the influence of the Altman ratio in predicting corporate bankruptcy. Keywords: financial distress, the Altman z–score, bankruptcy prediction

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Merza Radhi, Deena Saleh, and Adel Sarea. "Evaluating Financial Performance of Saudi Listed Firms: Using Statistical Failure Prediction Models." International Journal of Business Ethics and Governance 2, no. 1 (2019): 1–18. http://dx.doi.org/10.51325/ijbeg.v2i1.20.

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The study aims to compare the classification power of three statistical failure prediction models for evaluating financial performance of Saudi Listed Firms. The study sample consisted of 122 listed industrial companies in the Saudi Stock Exchange for the period from 2014 to 2016. Altman model 1968, Kida model and Zmijewski are used as examples of statistical failure prediction models to evaluate the classification power of the given models to assess the financial performance of firms listed on Saudi Stock Exchange. The results showed that Zmijewski model was more powerful in predicting the fi

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Hertina, Dede, and Farida Wulan Dari. "Comparative Analysis of Financial Distress Models in Predicting Bankruptcy during Covid-19 Pandemic." Wiga : Jurnal Penelitian Ilmu Ekonomi 12, no. 4 (2022): 272–82. http://dx.doi.org/10.30741/wiga.v12i4.900.

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This study aims to determine whether there is a difference between the financial distress models in predicting bankruptcy, and the most accurate financial distress models bankruptcy in construction and building companies listed on Indonesia’s Stock Exchange for the 2020-2021 period. The financial distress models used in this research are Springate, Taffler, and Zmijewski. The research population consisted of 18 companies and the sample was taken as many as 16 companies using purposive sampling method. The data research was collected from the company’s financial statement. This research uses a

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Rehman, Muhammad, Muhammad Fuzail, Muhammad Kamran Abid, and Naeem Aslam. "Financial Prices Prediction of Stock Market using Supervised Machine Learning Models." VFAST Transactions on Software Engineering 11, no. 2 (2023): 1–10. http://dx.doi.org/10.21015/vtse.v11i2.1439.

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The process of predicting stock market movements may initially appear to be non-statistical due to the multitude of factors involved. However, machine learning techniques can be utilized to establish connections between past and present data, enabling the training of machines to make accurate assumptions based on the information. By effectively linking historical data to current data using machine learning, it becomes possible to make precise predictions regarding stock performance. These predictions can lead to substantial profits for individuals and their brokers. Traditionally, stock market

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Rahman, Abdul, Deliana Deliana, Riswanto Riswanto, and Raya Puspita Sari Hasibuan. "Prediction of Financial Distress With Financial Ratio Analysis." Kajian Akuntansi 23, no. 2 (2022): 162–74. http://dx.doi.org/10.29313/ka.v23i2.9317.

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This study aims to prove the effect of liquidity, profitability and leverage on financial distress. The study was conducted in mining companies listed on the Indonesia Stock Exchange. The population of this study was the financial statements of mining companies listed on the Indonesia Stock Exchange in 2014-2019. The sample was determined using purposive sampling using certain criteria. The test was carried out using multiple regression analysis with SPSS software assistance. The results of this study indicate that the liquidity variable has no effect on financial distress, while profitability

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AYVAZ, Rabia Nazmiye, and Mustafa Kenan ERKAN. "A review on financial failure models- The case of manufacturing industry." Business & Management Studies: An International Journal 11, no. 1 (2023): 375–99. http://dx.doi.org/10.15295/bmij.v11i1.2187.

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Increasingly globalized economic and financial dynamics create extensive complexity and uncertainty for national economies and businesses. As a result of this financial stress experienced by firms, researchers have developed models using financial ratios to measure the financial health of firms. One of the implications of this situation for academic research is the continued importance of predicting and modelling financial failure for businesses. This study aims to apply existing financial failure and bankruptcy prediction models to the financial data of 45 manufacturing enterprises traded in

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Gao, Ruiqi. "Research on the construction of financial market volatility prediction model in digital economy environment based on machine learning algorithm." Journal of Combinatorial Mathematics and Combinatorial Computing 124 (March 17, 2025): 259–76. https://doi.org/10.61091/jcmcc124-18.

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Achieving accurate prediction of financial market fluctuations is beneficial for investors to make decisions, while machine learning algorithms can utilize a large amount of data for training and learning, which has good effect on predicting financial market fluctuations. The article first analyzes the financial dataset, and then constructs a feature selection model by combining Boruta and SHAP to screen the financial data features. Based on the LSTM model, a new Dropout layer and fully connected layer are designed to construct the AMP-LSTM model to realize the prediction of financial market f

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Yin, Congyan. "Stock Price Prediction of GM Company: Comparison Based on KNN, Linear Regression and LSTM." Advances in Economics, Management and Political Sciences 60, no. 1 (2024): 128–34. http://dx.doi.org/10.54254/2754-1169/60/20231182.

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Stock price prediction holds significant importance in the financial sector. It not only aids investors in making informed buy and sell decisions to achieve potential profits but also supports enterprises and financial institutions in risk assessment and management. This study utilized the stock prices of GM stocks spanning from 2013 to 2023 as dataset, with the goal of predicting their closing prices. This paper carefully identifies the parameters in the three model and one indicator of RMSE outcomes were computed. Visualizations of the results of the three model predictions are also provided

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Srhoj, Stjepan. "Can we predict high growth firms with financial ratios?" Financial Internet Quarterly 18, no. 1 (2022): 66–73. http://dx.doi.org/10.2478/fiqf-2022-0006.

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Abstract This study attempts to predict high growth firm (HGF) status with financial ratios. Measures related to the firm’s effectiveness in using assets to generate profits, EBITDA margin, debt ratio, equity-to-debt ratio and return on assets are associated with HGF status. While the financial ratios improve HGF prediction, prediction remains modest (AUC = 0.627). This study suggests it is difficult to assume a very good HGF forecast from only financial ratios; therefore, the recommendation for researchers and policymakers building models for predicting HGFs is to incorporate non-financial ra

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Van Der Colff, Francois, and Frans Vermaak. "Predicting financial distress using financial and non-financial variables." Journal of Economic and Financial Sciences 8, no. 1 (2015): 243–60. http://dx.doi.org/10.4102/jef.v8i1.93.

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This study attempts to clarify whether using a hybrid model based on non-financial variables and financial variables is able to provide a more accurate company financial distress prediction model than using a model based on financial variables only. The relationship between the model test results and the De la Rey K-Score for the subject companies is tested, employing Cramer’s V statistical test. A movement towards a Cramer’s V value of one indicates a strengthening relationship, and a movement towards zero is an indication of a weakening relationship. Against this background, further empirica

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Listyarini, Fitri. "ANALISIS PERBANDINGAN PREDIKSI KONDISI FINANCIAL DISTRESS DENGAN MENGGUNAKAN METODE ALTMAN, SPRINGATE, DAN ZMIJEWSKI." Jurnal Bina Akuntansi 7, no. 1 (2020): 1–20. http://dx.doi.org/10.52859/jba.v7i1.71.

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This study aims to: 1) Determine the accuracy of the Altman model, the springate model and the zmijewski model in predicting financial distress conditions in manufacturing companies in Indonesia, 2) To find out the most accurate prediction models in predicting financial distress conditions in manufacturing companies in Indonesia. This study compares three financial distress prediction models, the Altman, Springate and Zmijewski models. The population of this study is the financial statements of manufacturing companies listed on the Indonesia Stock Exchange for the period 2011-2014. The samplin

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Syaharuddin, Syaharuddin. "Time-Series Analysis in Financial Prediction: A Literature Review." Sainstek : Jurnal Sains dan Teknologi 16, no. 2 (2024): 58. https://doi.org/10.31958/js.v16i2.13117.

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AbstractTime-series analysis in financial prediction has become a primary focus for many researchers and practitioners in the fields of economics and finance, particularly due to the complexity and dynamism of the evolving market. This research aims to identify the challenges and recent advancements in the application of time-series analysis for financial predictions, encompassing market volatility, non-stationary data, and unpredictable external factors such as geopolitical events and economic policy changes. The research methodology is qualitative, employing a Systematic Literature Review ap

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Researcher. "LEVERAGING AI TO TACKLE FINANCIAL DISTRESS: A COMPREHENSIVE APPROACH." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 358–69. https://doi.org/10.5281/zenodo.13255200.

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Artificial Intelligence (AI) has emerged as a powerful tool in predicting and mitigating financial distress for individuals and businesses. This article explores various AI techniques employed in financial management, including early warning systems, liquidity management, debt restructuring, personalized financial planning, and continuous monitoring strategies. AI-powered models have demonstrated remarkable accuracy in predicting financial distress, with some achieving up to 86.4% accuracy in corporate financial distress prediction. These systems utilize advanced algorithms, such as Long Short

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Cheruvu, Sai Manoj. "Stock Price Prediction Using Time Series." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 375–81. http://dx.doi.org/10.22214/ijraset.2021.39296.

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Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant

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Lestari, Fuji. "Prediksi Finansial Distress pada Salah Satu Bank Konvensional Menggunakan Machine Learning." Indonesian Journal of Applied Mathematics 3, no. 1 (2023): 21. http://dx.doi.org/10.35472/indojam.v3i1.1284.

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Financial distress is when a company experiences a shortage or insufficient funds to run the company. Prediction of financial distress is needed to prevent bankruptcy. In this study, financial distress predictions were made based on financial ratios obtained from monthly financial reports from a bank convention, after which the proportion that had the most influence on financial distress was determined. The models used in this study are several machine learning models, namely, Logistic Regression, Support Vector Machine, and Random Forest. Based on the analysis results, the best model for pred

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Podhorska, Ivana, and Maria Misankova. "Success of Prediction Models in Slovak Companies." GATR Global Journal of Business Social Sciences Review 4, no. 4 (2016): 54–59. http://dx.doi.org/10.35609/gjbssr.2016.4.4(6).

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Objective The issue of bankrupt of company is very actual topic not only in Slovakia but also in abroad. The reason is that many companies have problem with the question of their probability of default or bankrupt and also with their financial health as a whole. This paper deals with the issue of prediction models and captures the applicability of these models in the Slovak conditions. Methodology/Technique In this paper are applied eight selected prediction models in the sample of 74 companies from Slovak Republic. In addition, this paper calculated one financial ratio from the category of co

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Priambodo, Bagus, Ruci Meiyanti, Samidi Samidi, Gushelmi Gushelmi, Rabiah Abdul Kadir, and Azlina Ahmad. "Integrating Fibonacci Retracement To Improve Accuracy of Time Series Prediction of Gold Prices." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 2 (2025): 1268–79. https://doi.org/10.37385/jaets.v6i2.6073.

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The prediction of gold prices is crucial for investors and policymakers due to its significant impact on global financial markets. Machine learning and deep learning have been used for predicting gold prices on time series data. This study employs MLR, SVM and CNN LSTM with Fibonacci retracement levels to forecast gold prices based on time series data. The experiment results demonstrate that combining Fibonacci retracement with model prediction significantly enhances predictive performance compared to prediction without Fibonacci. The use of Fibonacci levels has resulted in a higher R² score a

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Abdelgalal, Abdelgalal. "Neutrosophic MOOSRA with Whale Optimization Algorithm for Unraveling Financial Futures through Inverse Problem Solving." International Journal of Neutrosophic Science 24, no. 1 (2024): 136–47. http://dx.doi.org/10.54216/ijns.240113.

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Resolving financial futures through inverse problem-solving delves into the complicated process of deciphering the difficulties subjective in the financial market to forecast behaviours and future trends. Inverse problem-solving involves working backwards from observed outcomes to uncover the underlying conditions or parameters, unlike prediction models, which often rely on past information to predict future outcomes. This method in the finance sector includes untangling the numberless factors influencing the market dynamics, like technological advancements, economic indicators, investor senti

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