Relevant bibliographies by topics / Financial Prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Financial Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Financial Prediction"

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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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Dissertations / Theses on the topic "Financial Prediction"

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Fletcher, T. S. B. "Machine learning for financial market prediction." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1338146/.

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The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide range of asset classes. Committees of discriminative techniques (Support Vector Machines (SVM), Relevance Vector Machines and Neural Networks) are found to perform well when incorporating sophisticated exoge
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Alhnaity, Bashar. "Financial engineering modelling using computational intelligent techniques : financial time series prediction." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/13652.

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Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and dynamic nature. In any investment activity, having an accurate prediction system will significantly benefit investors by guiding decision making, especially in trading, asset management and risk management. Thus, the attempts to build such systems have attracted the attention of practitioners in the market and also researchers for many decades. Furthermore, the purpose of this thesis is to investigate and develop a new approach to predicting financial
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Stulpinienė, Vaida. "Financial distress prediction model of family farms." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140123_133545-56537.

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Designed financial distress prediction model is intended directly for the farmer (decision-maker) in order to diagnose the farm’s financial condition and predict the likelihood of financial distress, by using financial information of his farm. There are identified family farm characteristics in which family farms have higher risks to run in financial distress and are guidelines for the family farms that intend to more carefully monitor and control their financial condition. The aim of the research: after analysing the conception of financial distress and identifying the factors determining the
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Albanis, George T. "Financial prediction using non linear classification techniques." Thesis, City University London, 2001. http://openaccess.city.ac.uk/8289/.

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In this thesis, we explore the ability of statistical classification methods to predict financial events in the bond and stock markets. Our classification methods include conventional Linear Dicriminant Analysis (LDA), and a number of less familiar non-linear techniques such as Probabilistic Neural Network (PNN), Learning Vector Quanization (LVQ), Oblique Classifer (OCI), and Ripper Rule Induction (RRI).
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Avaritsioti, Eleni. "Financial time series prediction in the wavelet domain." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502386.

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Salina, Aigul Pazenovna. "Financial soundness of Kazakhstan banks : analysis and prediction." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3128.

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Purpose – The financial systems in many emerging countries are still impacted by the devastating effect of the 2008 financial crisis which created a massive disaster in the global economy. The banking sector needs appropriate quantitative techniques to assess its financial soundness, strengths and weaknesses. This research aims to explore, empirically assess and analyze the financial soundness of the banking sector in Kazakhstan. It also examines the prediction of financial unsoundness at an individual bank level using PCA, cluster, MDA, logit and probit analyses. Design/Methodology/Approach –
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Gottschling, Andreas Peter. "Three essays in neural networks and financial prediction /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9728773.

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Cutugno, Carmen. "Statistical models for the corporate financial distress prediction." Thesis, Università degli Studi di Catania, 2011. http://hdl.handle.net/10761/283.

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Chan, Ho-cheong, and 陳浩昌. "Financial ratios, discriminant analysis and the prediction of corporate financial distress in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1985. http://hub.hku.hk/bib/B31263100.

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Pun, Pou Kao. "Some applications of support vector machines in financial prediction." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1943000.

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Books on the topic "Financial Prediction"

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Zirilli, Joseph S. Financial prediction using neural networks. International Thomson Computer Press, 1997.

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McGrath, M. Neural networks for financial performance prediction. University CollegeDublin, 1995.

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Ramaswamy, Srichander. One-step prediction of financial time series. Bank for International Settlements, Monetary and Economic Dept., 1998.

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Yadav, R. A. Financial ratios and the prediction of corporate failure. Concept Pub. Co., 1986.

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Punsalan, Romeleo N. Bankruptcy prediction in the construction industry: Financial ratio analysis. Available from the National Technical Information Service, 1989.

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Moses, O. Douglas. Analysts earnings forecasts: An alternative data source for failure prediction. Naval Postgraduate School, 1986.

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Lin, Feng Yu. A data mining approach to the prediction of financial distress. The Author], 2004.

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Kasanen, Eero. The prediction of international accounting standards profits from the financial statements of Finnish firms. European Institute for Advanced Studies in Management, 1991.

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Munir, Qaiser. Handbook of research on financial and banking crisis prediction through early warning systems. Business Science Reference, 2016.

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1979-, Hasanhodzic Jasmina, ed. The evolution of technical analysis: Financial prediction from Babylonian tablets to Bloomberg terminals. John Wiley & Sons, 2010.

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Book chapters on the topic "Financial Prediction"

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Amaniyah, Evaliati, Abdul Mongid, Nadia Asandimitra Haryono, and Hariyati Hariyati. "Financial Distress Prediction Model." In Advances in Social Science, Education and Humanities Research. Atlantis Press SARL, 2025. https://doi.org/10.2991/978-2-38476-317-7_160.

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Pham, Duc Truong, and Xing Liu. "Financial Prediction Using Neural Networks." In Neural Networks for Identification, Prediction and Control. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3244-8_5.

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Han, Ying, and Colin Fyfe. "Complexity Pursuit for Financial Prediction." In Intelligent Data Engineering and Automated Learning — IDEAL 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_59.

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Creamer, Germán G. "Financial Data and Trend Prediction." In Encyclopedia of Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_95.

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Creamer, Germán G. "Financial Data and Trend Prediction." In Encyclopedia of Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_95-1.

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Kugiumtzis, D. "State Space Local Linear Prediction." In Modelling and Forecasting Financial Data. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0931-8_5.

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Bentov, Iddo, Alex Mizrahi, and Meni Rosenfeld. "Decentralized Prediction Market Without Arbiters." In Financial Cryptography and Data Security. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70278-0_13.

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Briggs, William M. "Testing, Prediction, and Cause in Econometric Models." In Econometrics for Financial Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73150-6_1.

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Simić, Dragan, Zoran Budimac, Vladimir Kurbalija, and Mirjana Ivanović. "Case-Based Reasoning for Financial Prediction." In Innovations in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11504894_114.

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Ribeiro, Bernardete, and Noel Lopes. "Deep Belief Networks for Financial Prediction." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_86.

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Conference papers on the topic "Financial Prediction"

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Kuncoro, Bangkit Mulyo, Reza Fuad Rachmadi, and Diah Puspito Wulandari. "Prediction of Company Financial Performance From Financial Statements and Stock Price Using LSTM." In 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE, 2024. https://doi.org/10.1109/icimcis63449.2024.10956214.

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Peng, Xiangyu. "Financial Evaluation Algorithm Based on Enhanced Time Series Prediction." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733072.

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Wang, Mengyu, Shay B. Cohen, and Tiejun Ma. "Modeling News Interactions and Influence for Financial Market Prediction." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.189.

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Lan, Ming. "Financial Market Risk Prediction Systems Based on Data Analysis." In 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS). IEEE, 2024. http://dx.doi.org/10.1109/aiars63200.2024.00084.

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He, Qiong. "Risk Prediction of Financial Management by Macro Indicator Method." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10775103.

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Basu, Uzzal, Yanjun Yan, Paul Yanik, and Chaitanya Borra. "Classifier Comparison for Recession Prediction Using Imbalanced Financial Data." In SoutheastCon 2025. IEEE, 2025. https://doi.org/10.1109/southeastcon56624.2025.10971682.

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Senarathna, Supun, and Rasika Rajapaksha. "Forex Market Prediction Through Text Mining of Financial News." In 2024 International Conference on Advances in Technology and Computing (ICATC). IEEE, 2024. https://doi.org/10.1109/icatc64549.2024.11025246.

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Uppal, Mudita, Deepali Gupta, Deepam Goyal, Sukhvinder Singh Bamber, and Rajeev Kumar Dang. "Financial Market Forecasting: Enhancing RNNs for Improved Stock Prediction." In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2025. https://doi.org/10.1109/ispcc66872.2025.11039535.

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Ramzan, Sana. "Comparison of Financial Distress Prediction Models Using Financial Variables." In 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2023. http://dx.doi.org/10.1109/icecet58911.2023.10389294.

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Unadkat, Vyom, Parth Sayani, Pratik Kanani, and Prachi Doshi. "Deep Learning for Financial Prediction." In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). IEEE, 2018. http://dx.doi.org/10.1109/iccsdet.2018.8821178.

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Reports on the topic "Financial Prediction"

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Соловйов, Володимир Миколайович, V. Saptsin, and D. Chabanenko. Markov chains applications to the financial-economic time series predictions. Transport and Telecommunication Institute, 2011. http://dx.doi.org/10.31812/0564/1189.

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In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different freque
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Соловйов, Володимир Миколайович, and D. N. Chabanenko. Financial crisis phenomena: analysis, simulation and prediction. Econophysic’s approach. Гумбольдт-Клуб Україна, 2009. http://dx.doi.org/10.31812/0564/1138.

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With the beginning of the global financial crisis, which attracts the attention of the international community, the inability of existing methods to predict the events became obvious. Creation, testing, adaptation of the models to the concrete financial market segments for the purpose of monitoring, early prediction, prevention and notification of financial crises is gaining currency nowadays. Econophysics is an interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stoch
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Соловйов, Володимир Миколайович, Vladimir Saptsin, and Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, 2009. http://dx.doi.org/10.31812/0564/1131.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of tim
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Del Negro, Marco, Raiden Hasegawa, and Frank Schorfheide. Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20575.

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Soloviev, V., V. Saptsin, and D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Брама-Україна, 2014. http://dx.doi.org/10.31812/0564/1305.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov Chains and simple first-ord yer ones is the existing of aftereffect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the «generalized state» as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the
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Соловйов, Володимир Миколайович, V. Saptsin, and D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Transport and Telecommunication Institute, 2010. http://dx.doi.org/10.31812/0564/1145.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov chains [1] and simple first-order ones is the existing of after effect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the “generalized state” as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with
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Bleakley, Hoyt, and Kevin Cowan. Maturity Mismatch and Financial Crises: Evidence from Emerging Market Corporations. Inter-American Development Bank, 2005. http://dx.doi.org/10.18235/0010956.

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Substantial attention has been paid in recent years to the risk of maturity mismatch in emerging markets. Although this risk is microeconomic in nature, the evidence advanced thus far has taken the form of macro correlations. This paper empirically evaluates this mechanism at the micro level by using a database of over 3,000 publicly traded firms from fifteen emerging markets. The paper measures the risk of short-term exposure by estimating, at the firm level, the effect on investment of the interaction of short-term exposure and aggregate capital flows. This effect is (statistically) zero, co
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Tirapat, Sunti. An investigation of default probability in Thailand. Chulalongkorn University, 2001. https://doi.org/10.58837/chula.res.2001.21.

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Using the sample of 100 most liquid companies listed in the Stock Exchange of Thailand during 1992-1999, the default probabilities from two approaches, the logit model and the KMV model, are calculated and compared. The results from the KMV model suggest that the default probabilities of financial institutions are higher than the probabilities of industrial companies. Moreover, the results from the KMV model confirm that the average default probabilities of the financial distressed firms in the 1997 financial crisis are higher than the average default probabilities of non-distressed firms. Com
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Blanco, Roberto, Elena Fernández, Miguel García-Posada, and Sergio Mayordomo. An estimation of the default probabilities of Spanish non-financial corporations and their application to evaluate public policies. Banco de España, 2023. http://dx.doi.org/10.53479/33512.

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We model the one-year ahead probability for default of Spanish non-financial corporations using data for the period 1996-2019. While most previous literature considers that a firm is in default if it files for bankruptcy, we define default as having non-performing loans during at least three months of a given year. This broader definition allows us to predict firms’ financial distress at an earlier stage that cannot generally be observed by researchers, before their financial conditions become too severe and they have to file for bankruptcy or engage in private workouts with their creditors. W
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Soloviev, Vladimir, Andrii Bielinskyi, Oleksandr Serdyuk, Victoria Solovieva, and Serhiy Semerikov. Lyapunov Exponents as Indicators of the Stock Market Crashes. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4131.

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Abstract:
The frequent financial critical states that occur in our world, during many centuries have attracted scientists from different areas. The impact of similar fluctuations continues to have a huge impact on the world economy, causing instability in it concerning normal and natural disturbances [1]. The an- ticipation, prediction, and identification of such phenomena remain a huge chal- lenge. To be able to prevent such critical events, we focus our research on the chaotic properties of the stock market indices. During the discussion of the re- cent papers that have been devoted to the chaotic beh
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