An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Ordinal Convolutional Neural Networks for Predicting RDoC Positive Valence Psychiatric Symptom Severity Scores

Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task. Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are on the ordinal scale. Specifically, we present our entries (methods and results) in the N-GRID shared task in predicting research domain criteria (RDoC) positive valence ordinal symptom severity scores (absent, mild, moderate, and severe) from psychiatric notes. Methods—We propose a novel convolutional neural network (CNN) model designed to handle ordinal regression tasks on psychiatric notes. Broadly speaking, our model combines an ordinal loss function, a CNN, and conventional feature engineering (wide features) into a single model which is learned end-to-end. Given interpretability is an important concern with nonlinear models, we apply a recent approach called locally interpretable model-agnostic explanation (LIME) to identify important words that lead to instance specific predictions. Results—Our best model entered into the shared task placed third among 24 teams and scored a macro mean absolute error (MMAE) based normalized score (100 · (1 − M M AE)) of 83.86. Since the competition, we improved our score (using basic ensembling) to 85.55, comparable with the winning shared task entry. Applying LIME to model predictions, we demonstrate the feasibility of instance specific prediction interpretation by identifying words that led to a particular decision

ExplainabilityAudit: An Automated Evaluation of Local Explainability in Rooftop Image Classification

Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of each validation rooftop image during the prediction. Then a bag of image patches is extracted from the superpixels to determine their texture and evaluate the local explanations. Our results show that 95.7% of the cases to be audited are detected by the proposed system

Predicting Credit Default among Micro Borrowers in Ghana

Microfinance institutions play a major role in economic development in many developing countries. However many of these microfinance institutions are faced with the problem of default because of the non-formal nature of the business and individuals they lend money to. This study seeks to find the determinants of credit default in microfinance institutions. With data on 2631 successful loan applicants from a microfinance institution with braches all over the country we proposed a Binary logistic regression model to predict the probability of default. We found the following variables significant in determining default: Age, Gender, Marital Status, Income Level, Residential Status, Number of Dependents, Loan Amount, and Tenure. We also found default to be more among the younger generation and in males. We however found Loan Purpose not to be significant in determining credit default. Microfinance institutions could use this model to screen prospective loan applicants in order to reduce the level of default. Keywords: Microfinance, Loan Default, Default Prediction, Logistic Regressio

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Appearance of Corporate Innovation in Financial Reports : A Text-Based Analysis

Innovations are important drivers of economic growth and firm profitability. Firms need funding to generate profitable innovations, which is why it is important to reliably distinguish innovative firms. Innovation indicators are used to measure this innovativeness, and consequently, it is important that the used indicator is reliable and measures innovation as desired. Patents, research and development expenditure and innovation surveys are examples of popular innovation indicators in research literature. However, these indicators have weaknesses, which is why new innovation indicators have been developed. This thesis studies the text-based innovation indicator developed by Bellstam et al. (2019) with a new type of data. Bellstam et al. (2019) created a new text-based innovation indicator that compares corporations’ analyst reports with an innovation textbook as the basis for the indicator. The similarity between these texts created the measurement for innovativeness. Analyst reports are usu-ally subject to charge. However, the 10-K reports used as data for this study are publicly available, and their functionality as the basis of the innovation indicator would mean good availability for the indicator. The study begins by training a Latent Dirichlet allocation (LDA) model with a sample of 10-K documents from 2008-2018. LDA-model is an unsupervised machine learning method, it finds topics in the text documents based on the probabilities of different words. The LDA-model was trained to find 15 topic allocations in the data and the output of the model is the distribution of these topics for each document. The same topic distributions were also allocated for eight samples from innovation textbooks. When the topic distributions were allocated, a Kullback-Leibler-divergence (KL-divergence) was calculated between each text sample and 10-K document. Thus, the KL-divergence calculated is the lowest for those reports that are the most similar to the innovation text and works as the text-based innovation indicator. Finally, the text-based innovation indicator was validated with regression analysis, in other words, it was confirmed that the indicator measures innovation. The text-based indicator was compared with research and development costs and the balance sheet value of brands and patents in different linear regressions. Out of the eight innovation measurements, most had a statistically significant correlation with one or both of the other innovation indicators. The ability of the text-based indicator to predict the development of sales in the next year was studied with regression analysis as well and all of the measurements had a significant effect on this. The most significant findings of this thesis are the relationship of the text-based innovation indicator and other indicators and its ability to predict firms’ sales.Innovaatiot ovat tärkeitä talouskasvun ja yritysten kannattavuuden ajureita. Tuottavien innovaatioiden syntymiseksi yritykset tarvitsevat rahoitusta, minkä takia onkin tärkeää, että innovatiiviset yritykset pystytään tunnistamaan luotettavasti. Innovaatioindikaattoreita käytetään tähän innovatiivisuuden mittaamiseen ja on siksi tärkeää, että käytetty indikaattori on luotettava ja mittaa innovatiivisuutta oikealla tavalla. Kirjallisuudessa paljon käytettyjä innovaatioindikaattoreita ovat esimerkiksi patentit, tutkimus- ja kehitysmenot sekä innovaatiokyselyt. Näissä indikaattoreissa on kuitenkin myös heikkouksia, joiden takia uusia indikaattoreita on alettu kehittää. Tässä tutkielmassa tutkitaan Bellstamin ja muiden (2019) luomaa tekstipohjaista innovaatioindikaattoria erilaisella datalla. Bellstam ja muut (2019) loivat uuden innovaatioindikaattorin, jonka pohjana oli yritysten ana-lyytikkoraporttien vertailu innovaatio-oppikirjan tekstin kanssa, näiden samankaltaisuusver-tailusta saatiin innovaatiomittari. Analyytikkoraportit ovat usein maksullisia. Tässä tutkimuk-sessa aineistona on käytetty lakisääteisiä tilinpäätösraportteja, jotka ovat julkisia tiedostoja, joten niiden toimivuus innovaatioindikaattorin pohjana tarkoittaisi hyvää saatavuutta indi-kaattorille. Tutkimus alkaa Latent Dirichlet allocation (LDA) –mallin harjoittamisella Yhdysvaltalaisten yritysten 10-K, eli tilinpäätösraporteilla vuosilta 2008-2018. LDA-malli on valvomaton koneoppimismenetelmä, eli se etsii datasta itse aihepiirejä sanojen todennäköisyyksien perusteella. LDA-malli asetettiin etsimään datasta 15 eri aihepiiriä raporteissa käytettyjen aiheiden perusteella ja mallin tuloksena on näiden aihepiirien jakautuminen jokaisessa dokumentissa. Samat aihepiirijakaumat haettiin myös kahdeksalle tekstiotokselle innovaatio-oppikirjoista. Aihepiirijakaumien ollessa valmiit, laskettiin Kullback-Leibler-divergenssi (KL-divergenssi) tilinpäätösraporttien ja innovaatio-oppikirjojen tekstiotosten aihepiirijakaumien välille. Laskettu KL-divergenssi on siten matalin niille tilinpäätösraporteille, joiden teksti on lähimpänä kunkin innovaatio-oppikirjan tekstiä ja toimii tekstipohjaisena innovaatioindikaattorina. Lopuksi indikaattorin toimivuus vahvistetaan regressioanalyysillä, eli tutkitaan, että se mittaa innovatiivisuuta. Regressioanalyysillä tutkitaan innovaatiomittarien yhteyttä yritysten tutkimus- ja kehitystoiminnan kuluihin sekä patenttien ja brändien tasearvoon. Kahdeksasta innovaatiomittarista suurimmalla osalla oli tilastollisesti merkitsevä yhteys muuttujista toiseen tai molempiin. Myös uuden innovaatiomittarin kykyä ennustaa yritysten seuraavan vuoden myyntiä tutkittiin regressioanalyysillä ja jokaisella mittarilla oli tilastollisesti merkitsevä yhteys yritysten liikevaihdon muutokseen. Tutkimuksen merkittävin löydös oli tekstipohjaisen innovaatiomittarin yhteys muihin innovaatiomittareihin ja yritysten liikevaihdon kehitykseen

Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?

This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation

Emotional Design: An Overview

Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.http://deepblue.lib.umich.edu/bitstream/2027.42/163319/1/Emotional_Design_Manuscript_Final.pdfSEL