71 research outputs found
Forecasting Financial Distress With Machine Learning – A Review
Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic
Analysis of Bankruptcy using Data Mining Approach
This study involves the development of neural network prediction model to predict the stage of bankruptcy of a company. A total of 367 data was attained from the
Registrar of Business and Companies, Kuala Lumpur Stock Exchange (KLSE) and Bank Negara Malaysia (Central Bank of Malaysia). The data was then analyzed by considering the basic statistics, frequency and cross tabulation in order to get more information about the data. Initially, the data was classified using logistic regression.In addition, it was also trained using neural network in order to obtain the bankruptcy model. The findings show that the most suitable prediction model consist of 12 nodes of input , hidden layer 6 node and one output layer. The generalization performance of the selected model is100%. This methodology should be able to provide some new insight into the type of pattern that exists in the data. Thus, neural network has a great potential in supporting for predicting bankruptcy
Soft Computing Techniques for Stock Market Prediction: A Literature Survey
Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Soft Computing Techniques for Stock Market Prediction: A Literature Survey
Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market
Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress
This chapter aims to dynamically improve the method of predicting financial distress based on Kalman filtering. Financial distress prediction (FDP) is an important study area of corporate finance. The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the state-space method, we establish two models that are used to describe the dynamic process and discriminant rules of financial distress, respectively, that is, a process model and a discriminant model. These two models collectively are called dynamic prediction models for financial distress. The operation of the dynamic prediction is achieved by Kalman filtering algorithm, and further, a general n-step-ahead prediction algorithm based on Kalman filtering is derived for prospective prediction. We also conduct an empirical study for China’s manufacturing industry, and the results have proved the accuracy and advance of predicting financial distress in such case
Corporate Credit Rating: A Survey
Corporate credit rating (CCR) plays a very important role in the process of
contemporary economic and social development. How to use credit rating methods
for enterprises has always been a problem worthy of discussion. Through reading
and studying the relevant literature at home and abroad, this paper makes a
systematic survey of CCR. This paper combs the context of the development of
CCR methods from the three levels: statistical models, machine learning models
and neural network models, summarizes the common databases of CCR, and deeply
compares the advantages and disadvantages of the models. Finally, this paper
summarizes the problems existing in the current research and prospects the
future of CCR. Compared with the existing review of CCR, this paper expounds
and analyzes the progress of neural network model in this field in recent
years.Comment: 11 page
Gene expression programming for Efficient Time-series Financial Forecasting
Stock market prediction is of immense interest to trading companies and buyers due to
high profit margins. The majority of successful buying or selling activities occur close
to stock price turning trends. This makes the prediction of stock indices and analysis a
crucial factor in the determination that whether the stocks will increase or decrease the
next day. Additionally, precise prediction of the measure of increase or decrease of
stock prices also plays an important role in buying/selling activities. This research
presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased
Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of
neural networks with that of fuzzy logic. A specialised extension to this technique is
known as the genetic programming (GP) and gene expression programming (GEP) to
explore and investigate the outcome of the GEP criteria on the stock market price
prediction.
The research presented in this thesis aims at the modelling and prediction of short-tomedium
term stock value fluctuations in the market via genetically tuned stock market
parameters. The technique uses hierarchically defined GP and gene-expressionprogramming
(GEP) techniques to tune algebraic functions representing the fittest
equation for stock market activities. The technology achieves novelty by proposing a
fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance
between varied mutation rates between varied-fitness chromosomes thereby improving
prediction accuracy and fitness improvement rate. The methodology is evaluated
against five stock market companies with each having its own trading circumstances
during the past 20+ years. The proposed GEP/GP methodologies were evaluated based
on variable window/population sizes, selection methods, and Elitism, Rank and Roulette
selection methods. The Elitism-based approach showed promising results with a low
error-rate in the resultant pattern matching with an overall accuracy of 95.96% for
short-term 5-day and 95.35% for medium-term 56-day trading periods. The
contribution of this research to theory is that it presented a novel evolutionary
methodology with modified selection operators for the prediction of stock exchange
data via Gene expression programming. The methodology dynamically adapts the
mutation rate of different fitness groups in each generation to ensure a diversification
II
balance between high and low fitness solutions. The GEP-FAMR approach was
preferred to Neural and Fuzzy approaches because it can address well-reported
problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP
and GEP algorithmsSaudi Cultural Burea
A Comparative Analysis of Machine Learning Techniques For Foreclosure Prediction
The current decline in the U.S. economy was accompanied by an increase in foreclosure rates starting in 2007. Though the earliest figures for 2009 - 2010 indicate a significant decrease, foreclosure of homes in the U.S. is still at an alarming level (Gutierrez, 2009a). Recent research at the University of Michigan suggested that many foreclosures could have been averted had there been a predictive system that did not only rely on credit scores and loan-to-value ratios (DeGroat, 2009). Furthermore, Grover, Smith & Todd (2008) contend that foreclosure prediction can enhance the efficiency of foreclosure mitigation by facilitating the allocation of resources to areas where predicted foreclosure rates will be high.
The primary goal of this dissertation was to develop a foreclosure prediction model that builds upon established bankruptcy and credit scoring models. The study utilized and compared the predictive accuracy of three supervised machine learning (ML) techniques when applied to mortgage data. The selected ML techniques were:
ML1. Classification Trees
ML2. Support Vector Machines (SVM)
ML3. Genetic Programming
The data used for the study is comprised of mortgage data, demographic metrics and certain macro-economic indicators that are available at the time of the inception of the loan.
The hypothesis of the study was based on the assumption that foreclosure rates, and associated actions, are dependent on critical demographic (age, gender), economic (per capita income, inflation) and regional variables (predatory lending, unemployment index). The task of the machine learning techniques was to identify a function that well approximates the relationship between these explanatory variables and the binary outcome of interest (mortgage status in +3 years from inception).
The predictive accuracy of ML1 through ML3 was significantly better than expected given the size of the recordset (1000) and the number of input variables (~110). Each ML technique achieved classification accuracy better than 75%, with ML3 scoring in the upper 90s. Given such high scores, it was concluded that the hypothesis was satisfied and that ML techniques are suitable for prediction tasks in this problem domain
Quantitative Methods for Economics and Finance
This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice
- …