Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Synthesis of research studies examining prediction of bankruptcy

The purpose of this study is to synthesize the findings of prior bankruptcy prediction research studies by compiling and classifying the independent variables used as predictor variables in the studies. The objective is to find out the popularity of the different types of the predictor variables by classifying the variables into the categories describing the fincancial function of the variables, and by assessing the popularity of the significant variables in the categories. This work studies elementary theories on firm failure and bankruptcy to discuss and seek justitication for what might be the reasons for using the most popular financial function measures in the bankruptcy prediction. Bankruptcy prediction research literature covers vast amount of studies in which various different predicton models are developed for predicting bankruptcy. Usually these studies use a prediction model with a set of some financial and/or non-financial variables that are presumed to be relevant proxies for financial distress and eventually business failure and bankrupcty. However, there seems to be no consensus or unified theory on how the variables predicting bankrupcty should be selected, thus the numerous bankruptcy prediction research studies include vast number and various different types of variables that are presumed to be applicable in predicting bankruptcy. This study includes a systematic literature review where 51 bankruptcy prediction research studies were collected from well-recognized scientific journals. The studies included into the review were such that included a single or multiple bankruptcy prediction models, the detailed description of the independent variables, and the information about the statistical significances of the independent variables. The variables were then classified according to their financial function and a meta-analysis were conducted on those variables which were significant in bankruptcy prediction, to find out the popularity of the different variable categories. The findings of this study suggest that the most popular predictor variables included into the banktuptcy predicton models are accounting-based financial ratios, particurarly ones measuring liquidity, profitability, and financial leverage, and that there exists also theoretical foundation for using these variables in the bankruptcy prediction

DESIGN & PROTOTYPE OF A KNEE MRI RF COIL ENCLOSURE

Recognizing the need for greater resolution images produced from Magnetic Resonance (MR) technology, we designed an adaptable, rotating radiofrequency (RF) coil enclosure system specifically intended for knees. Machined out of magnetically transparent materials in the WPI Washburn labs, we used manual and automated mini-mills to fabricate a practical RF enclosure capable of ergonomically accommodating extremities and capturing images at virtually any angle on a single plane. Our prototype can accommodate a variety knee sizes and RF coil designs

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Development of a Plasma Source with Particulate Injector

A plasma discharge chamber was designed and built to investigate the charging of dust in a plasma. The design was based on an ion thruster discharge chamber using a filament cathode. The discharge chamber consists of an aluminum cylinder with gas and electrical feedthroughs. A filament cathode is used to ionize this gas and create the plasma. Magnets are used to increase the electron residence time in the gas and hence the number of collisions. Dust is introduced using a rate-controllable dispenser and falls through the chamber where it is charged through collisions with ions and electrons. Some of these dust particles fall into an induction charge detector that measures their charge. A Langmuir probe is also used to collect data on the plasma to investigate its properties

Computer Numerical Controlled (CNC) machining for Rapid Manufacturing Processes

The trends of rapid manufacturing (RM) have influenced numerous developments of technologies mainly in additive processes. However, the material compatibility and accuracy problems of additive techniques have limited the ability to manufacture end-user products. More established manufacturing methods such as Computer Numerical Controlled (CNC) machining can be adapted for RM under some circumstances. The use of a 3-axis CNC milling machine with an indexing device increases tool accessibility and overcomes most of the process constraints. However, more work is required to enhance the application of CNC for RM, and this thesis focuses on the improvement of roughing and finishing operations and the integration of cutting tools in CNC machining to make it viable for RM applications. The purpose of this research is to further adapt CNC machining to rapid manufacturing, and it is believed that implementing the suggested approaches will speed up production, enhance part quality and make the process more suitable for RM. A feasible approach to improving roughing operations is investigated through the adoption of different cutting orientations. Simulation analyses are performed to manipulate the values of the orientations and to generate estimated cutting times. An orientations set with minimum machining time is selected to execute roughing processes. Further development is carried out to integrate different tool geometries; flat and ball nose end mill in the finishing processes. A surface classification method is formulated to assist the integration and to define the cutting regions. To realise a rapid machining system, the advancement of Computer Aided Manufacturing (CAM) is exploited. This allows CNC process planning to be handled through customised programming codes. The findings from simulation studies are supported by the machining experiment results. First, roughing through four independent orientations minimized the cutting time and prevents any susceptibility to tool failure. Secondly, the integration of end mill tools improves surface quality of the machined parts. Lastly, the process planning programs manage to control the simulation analyses and construct machining operations effectively

A fault-tolerant multiprocessor architecture for aircraft, volume 1

A fault-tolerant multiprocessor architecture is reported. This architecture, together with a comprehensive information system architecture, has important potential for future aircraft applications. A preliminary definition and assessment of a suitable multiprocessor architecture for such applications is developed

Predicting financial distress using corporate efficiency and corporate governance measures

Credit models are essential to control credit risk and accurately predicting bankruptcy and financial distress is even more necessary after the recent global financial crisis. Although accounting and financial information have been the main variables in corporate credit models for decades, academics continue searching for new attributes to model the probability of default. This thesis investigates the use of corporate efficiency and corporate governance measures in standard statistical credit models using cross-sectional and hazard models. Relative efficiency as calculated by Data Envelopment Analysis (DEA) can be used in prediction but most previous literature that has used such variables has failed to follow the assumptions of Variable Returns to Scale and sample homogeneity and hence the efficiency may not be correctly measured. This research has built industry specific models to successfully incorporate DEA efficiency scores for different industries and it is the first to decompose overall Technical Efficiency into Pure Technical Efficiency and Scale Efficiency in the context of modelling financial distress. It has been found that efficiency measures can improve the predictive accuracy and Scale Efficiency is a more important measure of efficiency than others. Furthermore, as no literature has attempted a panel analysis of DEA scores to predict distress, this research has extended the cross sectional analysis to a survival analysis by using Malmquist DEA and discrete hazard models. Results show that dynamic efficiency scores calculated with reference to the global efficiency frontier have the best discriminant power to classify distressed and non-distressed companies. Four groups of corporate governance measures, board composition, ownership structure, management compensation and director and manager characteristics, are incorporated in the hazard models to predict financial distress. It has been found that state control, institutional ownership, salaries to independent directors, the Chair’s age, the CEO’s education, the work location of independent directors and the concurrent position of the CEO have significant associations with the risk of financial distress. The best predictive accuracy is made from the model of governance measures, financial ratios and macroeconomic variables. Policy implications are advised to the regulatory commission

‘Big data analytics’ for construction firms insolvency prediction models

In a pioneering effort, this study is the first to develop a construction firms insolvency prediction model (CF-IPM) with Big Data Analytics (BDA); combine qualitative and quantitative variables; advanced artificial intelligence tools such as Random Forest and Bart Machine; and data of all sizes of construction firms (CF), ensuring wide applicabilityThe pragmatism paradigm was employed to allow the use of mixed methods. This was necessary to allow the views of the top management team (TMT) of failed and existing construction firms to be captured using a qualitative approach.TMT members of 13 existing and 14 failed CFs were interviewed. Interview result was used to create a questionnaire with over hundred qualitative variables. A total of 272 and 259 (531) usable questionnaires were returned for existing and failed CFs respectively. The data of the 531 questionnaires were oversample to get a total questionnaire sample of 1052 CFs. The original and matched sample financial data of the firms were downloaded. Using Cronbach’s alpha and factor analysis, qualitative variables were reduced to 13 (Q1 to Q13) while11 financial ratios (i.e. quantitative variables) (R1 and R11) reported by large and MSM CFs were identified for the sample CFs.The BDA system was set up with the Amazon Web Services Elastic Compute Cloud using five ‘Instances’ as Hadoop DataNodes and one as NameNode. The NameNode was configured as Spark Master. Eleven variable selection methods and three voting systems were used to select the final seven qualitative and seven quantitative variables, which were used to develop 13 BDA-CF-IPMs. The Decision Tree BDA-CF-IPM was the model of choice in this study because it had high accuracy, low Type I error and transparency. The most important variables (factors) affecting insolvency of construction firms according to the best model are returned on total assets; liquidity; solvency ratio; top management characteristics; strategic issues and external relations; finance and conflict related issues; industry contract/project knowledge