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: 1 February 2022

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Journal articles on the topic "Financial Prediction"

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Tjiptono, Fandy. "KEWIRAUSAHAAN, KINERJA KEUANGAN, DAN KELANGGENGAN BISNIS." Jurnal Manajemen Indonesia 15, no. 1 (April 3, 2017): 17. http://dx.doi.org/10.25124/jmi.v15i1.389.

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Predicting business longevity using financial performance is one of the interesting topics in accounting and financial management studies. Incorrect prediction of a distressed firm may cause losses to investors, management, creditors and bankers, and inaccurate prediction of a non-distressed company may result in the loss of opportunities. This paper aims to review previous studies using financial performance as the predictor of business longevity in a number of countries. The sources of data include published articles in top international journals. The results indicate that the most dominant approach was bankruptcy prediction models using single and multiple financial ratios. The current paper also identified three main problems in using financial performance as the predictor of business longevity: inconsistent definitions of 'business failure', inconsistent predictive power of financial ratios, and an emphasis on financial symptoms rather than on the more fundamental causes of failure. Managerial implications and research agenda were formulated at the end of this paper
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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 conditions of a particular country or even just for a specific sector of a national economy. Field of financial predictions regarding to international view consists of elementary used models, for example, such as Altman´s Z-score or Beerman´s index, which are globally know and used as basic of many other modificated models. Following article deals with selected Slovak prediction models designed to Slovak conditions, states how these models stand in this global world, what is their international connection to the worldwide economies, and also states verification of their prediction ability in a specific sector. The verification of predictive ability of the models is defined by ROC analysis and through results the paper demonstrates the most suitable prediction models to use in the selected sector.
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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 (June 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 (July 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 reports. Multiple discriminant analysis was used to derive quarterly bankruptcy prediction models for each of the three quarters before and after the last annual period preceding bankruptcy and for the last annual period preceding bankruptcy. Twenty-four financial ratios that were identified in previous studies as being useful for bankruptcy prediction were selected as the independent variables in the stepwise discriminant process. The classification accuracy, using alternative assumptions regarding prior probability of bankruptcy and cost of misclassification and the statistical significance of the quarterly models for each of the six quarters tested, indicated that quarterly data are useful for predicting bankruptcy. There was no statistical evidence to suggest that the classification accuracy of the annual model was superior to that of the quarterly model. This finding suggests that more timely bankruptcy predictions can be provided to investors, creditors, and auditors by quarterly models without the loss of accuracy provided by annual models.
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Ouenniche, Jamal, Kais Bouslah, Blanca Perez-Gladish, and Bing Xu. "A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction." Annals of Operations Research 296, no. 1-2 (April 9, 2019): 495–512. http://dx.doi.org/10.1007/s10479-019-03223-0.

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AbstractNowadays, business analytics has become a common buzzword in a range of industries, as companies are increasingly aware of the importance of high quality predictions to guide their pro-active planning exercises. The financial industry is amongst those industries where predictive analytics techniques are widely used to predict both continuous and discrete variables. Conceptually, the prediction of discrete variables comes down to addressing sorting problems, classification problems, or clustering problems. The focus of this paper is on classification problems as they are the most relevant in risk-class prediction in the financial industry. The contribution of this paper lies in proposing a new classifier that performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new VIKOR-based classifier and out-of-sample predictions are devised with a CBR-based classifier trained on the risk class predictions provided by the proposed VIKOR-based classifier. The performance of this new non-parametric classification framework is tested on a dataset of firms in predicting bankruptcy. Our findings conclude that the proposed new classifier can deliver a very high predictive performance, which makes it a real contender in industry applications in finance and investment.
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Ashraf, Sumaira, Elisabete G. S. Félix, and Zélia Serrasqueiro. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?" Journal of Risk and Financial Management 12, no. 2 (April 4, 2019): 55. http://dx.doi.org/10.3390/jrfm12020055.

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Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.
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Valaskova, Katarina, Pavol Durana, Peter Adamko, and Jaroslav Jaros. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities." Journal of Risk and Financial Management 13, no. 5 (May 7, 2020): 92. http://dx.doi.org/10.3390/jrfm13050092.

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The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.
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Gunawardana, Kennedy Degaulle. "An Analysis of Financial Distress Prediction of Selected Listed Companies in Colombo Stock Exchange." International Journal of Sociotechnology and Knowledge Development 13, no. 2 (April 2021): 48–70. http://dx.doi.org/10.4018/ijskd.2021040104.

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The main objective of the study is to predict financial distress and developing a prediction model using accounting related variables in selected listed firms in Sri Lanka. Decision criteria for financial distress has been selected based on the existing literature on financial distress prediction applicable to the Sri Lankan firms. A sample of 22 financially distressed firms along with 33 financially non-distressed firms have been used to conduct this study. Artificial neural network was used as the basic approach to the study in predicting financial distress. A neural network to predict financial distress was developed with an accuracy of 85.7% one year prior to its occurrence. The second analysis conducted was the panel regression considering five years of cross-sectional data for the sample of companies selected. This analysis was able to identify a significant relationship of leverage, price-to-book ratio and Tobin's Q ratio to the prediction of financial distress of a firm.
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Balasubramanian, Senthil Arasu, Radhakrishna G.S., Sridevi P., and Thamaraiselvan Natarajan. "Modeling corporate financial distress using financial and non-financial variables." International Journal of Law and Management 61, no. 3/4 (October 23, 2019): 457–84. http://dx.doi.org/10.1108/ijlma-04-2018-0078.

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Purpose This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique. Design/methodology/approach This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error. Findings The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress. Originality/value This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.
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Dhar, Vasant. "Prediction in financial markets." ACM Transactions on Intelligent Systems and Technology 2, no. 3 (April 2011): 1–22. http://dx.doi.org/10.1145/1961189.1961191.

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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 exogenous financial information in order to predict daily FX carry basket returns. The higher dimensionality that Electronic Communication Networks make available through order book data is transformed into simple features. These volume-based features, along with other price-based ones motivated by common trading rules, are used by Multiple Kernel Learning (MKL) to classify the direction of price movement for a currency over a range of time horizons. Outperformance relative to both individual SVM and benchmarks is found, along with an indication of which features are the most informative for financial prediction tasks. Fisher kernels based on three popular market microstructural models are added to the MKL set. Two subsets of this full set, constructed from the most frequently selected and highest performing individual kernels are also investigated. Furthermore, kernel learning is employed - optimising hyperparameter and Fisher feature parameters with the aim of improving predictive performance. Significant improvements in out-of-sample predictive accuracy relative to both individual SVM and standard MKL is found using these various novel enhancements to the MKL algorithm.
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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 time series with consideration given to their dynamic nature. In this thesis, the prediction procedures will be carried out in three phases. The first phase proposes a new hybrid dynamic model based on Ensemble Empirical Mode Decomposition (EEMD), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Support Vector Regression (SVR) and EEMD-Genetic Algorithm (GA)-Weighted Average (WA) to predict stock index closing price. EEMD in this phase is introduced as a preprocessing step to historical observation for the first time in the literature. The experimental results show that the EEMDD-GA-WA model performance is a notch above the other methods utilised in this phase. The second phase proposes a new hybrid static model based on Wavelet Transform (WT), RNN, Support Vector Machine (SVM), Nave Bayes and WT-GA-WA to predict the exact change of the stock index closing price. In this phase, the experimental results showed that the proposed WT-GA-WA model outperformed the rest of the models utilised in this phase. Moreover, the input data that are fed into the hybrid model in this phase are technical indicators. The third phase in this research introduces a new Hybrid Heuristic-Rules-based System (HHRS) for stock price prediction. This phase intends to combine the output of the hybrid models in phase one and two in order to enhance the final prediction results. Thus,to the best of our knowledge, this study is the only one to have carried out and tested this approach with a real data set. The results show that the HHRS outperformed all suggested models over all the data sets. Thus, this indicates that combining di↵erent techniques with diverse types of information could enhance prediction accuracy.
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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 financial condition as well as related indicators and prediction models, to methodologically justify and design financial distress prediction model of family farms.
Parengtas finansinio išsekimo prognozavimo modelis tiesiogiai skirtas ūkininkui, kuris panaudodamas savo ūkio finansinę informaciją, galėtų diagnozuoti ūkio finansinę būklę ir iš anksto numatyti finansinio išsekimo grėsmę. Disertacijoje nustatytos ir įvardintos ūkininkų ūkių charakteristikos, kurioms esant ūkiai turi didesnes grėsmes finansiškai išsekti, yra gairės ūkininkų ūkiams, kurie ketina atidžiau stebėti savo veiklą ir kontroliuoti finansinę būklę. Tyrimo tikslas – ištyrus finansinio išsekimo sampratą, identifikavus finansinę būklę sąlygojančius veiksnius, indikatorius ir prognozavimo modelius, metodologiškai pagrįsti ir parengti ūkininkų ūkių finansinio išsekimo prognozavimo modelį.
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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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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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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 – A cluster analysis, in combination with principal component analysis (PCA), was utilized as a classification technique. It groups sound and unsound banks in Kazakhstan's banking sector by examining various financial ratios. Cluster analysis was run on a sample of 34 commercial banks on 1st January, 2008 and 37 commercial banks on 1st January, 2014 to test the ability of this technique to detect unsound banks before they fail. Then, Altman Z” and EM Score models were tested and re-estimated and the MDA, logit and probit models were constructed on a sample of 12 Kazakhstan banks during the period between 1st January, 2008 and 1st January, 2014. The sample consists of 6 sound and 6 unsound banks and accounts for 81.3% of the total assets of the Kazakhstan banking sector in 2014. These statistical methods used various financial variables to represent capital adequacy, asset quality, management, earnings and liquidity. Last but not least, the MDA, logit and probit models were systematically combined together to construct an integrated model to predict bank financial unsoundness. Findings – First of all, results from Chapter 3 indicate that cluster analysis is able to identify the structure of the Kazakh banking sector by the degree of financial soundness. Secondly, based on the findings in the second empirical chapter, the tested and re-estimated Altman models show a modest ability to predict bank financial unsoundness in Kazakhstan. Thirdly, the MDA, logit and probit models show high predictive accuracy in excess of 80%. Finally, the model that integrated the MDA, logit and probit types presents superior predictability with lower Type I errors. Practical Implications – The results of this research are of interest to supervisory and regulatory bodies. The models can be used as a reliable and effective tool, particularly the cluster based methodology for assessing the degree of financial soundness in the banking sector and the integrated model for predicting the financial unsoundness of banks. Originality/Value – This study is the first to employ a cluster-based methodology to assess financial soundness in the Kazakh banking sector. In addition, the integrated model can be used as a promising technique for evaluating the financial unsoundness of banks in terms of predictive accuracy and robustness. Importance – Assessing the financial soundness of the Kazakh banking system is of particular importance as the World Bank has ranked Kazakhstan as leading the world for the volume of non-performing credits in the total number of loans granted in 2012. It is one of the first academic studies carried out on Kazakhstan banks which comprehensively evaluate the financial soundness of banks. It is anticipated that the findings of the current study will provide useful lessons for developing and transition countries during periods of financial turmoil.
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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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Punsalan, Romeleo N. "Bankruptcy prediction in the construction industry: financial ratio analysis." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25711.

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Books on the topic "Financial Prediction"

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Financial prediction using neural networks. New York: International Thomson Computer Press, 1996.

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

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

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

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

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Punsalan, Romeleo N. Bankruptcy prediction in the construction industry: Financial ratio analysis. Springfield, Va: 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. Monterey, California: Naval Postgraduate School, 1986.

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

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

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

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Book chapters on the topic "Financial Prediction"

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

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Han, Ying, and Colin Fyfe. "Complexity Pursuit for Financial Prediction." In Intelligent Data Engineering and Automated Learning — IDEAL 2002, 397–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_59.

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Pham, Duc Truong, and Xing Liu. "Financial Prediction Using Neural Networks." In Neural Networks for Identification, Prediction and Control, 83–110. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3244-8_5.

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Kugiumtzis, D. "State Space Local Linear Prediction." In Modelling and Forecasting Financial Data, 95–113. Boston, MA: 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, 199–217. Cham: 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, 3–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73150-6_1.

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Ribeiro, Bernardete, and Noel Lopes. "Deep Belief Networks for Financial Prediction." In Neural Information Processing, 766–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_86.

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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, 839–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11504894_114.

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Aliaj, Tesi, Aris Anagnostopoulos, and Stefano Piersanti. "Firms Default Prediction with Machine Learning." In Mining Data for Financial Applications, 47–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37720-5_4.

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Burke, John J. A. "Impact of FinTech: A Prediction." In Financial Services in the Twenty-First Century, 199–212. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63967-9_15.

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Conference papers on the topic "Financial Prediction"

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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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"Prediction of N/LAB Financial Product." In 2020 International Conference on Social Sciences and Social Phenomena. Scholar Publishing Group, 2020. http://dx.doi.org/10.38007/proceedings.0001117.

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Yeh, Hu-Hsiang, and Min-Te Sun. "Coal Price Prediction Using Financial Indices." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959901.

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Wang, Jia, Tong Sun, Benyuan Liu, Yu Cao, and Degang Wang. "Financial Markets Prediction with Deep Learning." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00022.

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Ribeiro, Bernardete, Catarina Silva, Armando Vieira, A. Gaspar-Cunha, and Joao C. das Neves. "Financial distress model prediction using SVM+." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596729.

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CHENG, B., and H. TONG. "INTERVAL PREDICTION OF FINANCIAL TIME SERIES." In Proceedings of the Hong Kong International Workshop on Statistics in Finance. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160156_0014.

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Tang, Lingbing, and Huanye Sheng. "Financial Prediction Using Manifold Wavelet Kernel." In 2009 Second Pacific-Asia Conference on Web Mining and Web-based Application (WMWA). IEEE, 2009. http://dx.doi.org/10.1109/wmwa.2009.77.

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Kwon, Yung-Keun, Sung-Soon Choi, and Byung-Ro Moon. "Stock prediction based on financial correlation." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068351.

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Guo, Xinwei, and Xiao Hu. "Corporate governance and financial distress prediction." In 2013 International Conference on Services Science and Services Information Technology. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/sssit130181.

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Amoudi, Ghada. "Popularity Prediction in Twitter During Financial Events." In 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, 2018. http://dx.doi.org/10.1109/ncg.2018.8593027.

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Reports on the topic "Financial Prediction"

1

Del Negro, Marco, Raiden Hasegawa, and Frank Schorfheide. Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance. Cambridge, MA: National Bureau of Economic Research, October 2014. http://dx.doi.org/10.3386/w20575.

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Estrella, Arturo, and Frederic Mishkin. Predicting U.S. Recessions: Financial Variables as Leading Indicators. Cambridge, MA: National Bureau of Economic Research, December 1995. http://dx.doi.org/10.3386/w5379.

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Osipov, Gennadij Sergeevich, Natella Semenovna Vashakidze, and Galina Viktorovna Filippova. Basics of forecasting financial time series based on NeuroXL Predictor. Постулат, 2017. http://dx.doi.org/10.18411/postulat-2017-7.

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Angrisani, Marco, Jeremy Burke, Annamaria Lusardi, and Gary Mottola. The Stability and Predictive Power of Financial Literacy: Evidence from Longitudinal Data. Cambridge, MA: National Bureau of Economic Research, November 2020. http://dx.doi.org/10.3386/w28125.

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