A Comprehensive Survey of Data Mining-based Fraud Detection Research

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.Comment: 14 page

Association of value and size factors with equity systematic risk:research on S&P 500 from 2013 to 2018

Abstract. The purpose of this thesis is to investigate the relation of value and size factor anomalies to the systematic risk of equities. Value and size effects are academically proven market anomalies that have existed on various markets and time periods. Value anomaly refers to the tendency of stocks trading at low price multiples, such as the price to book value of equity (P/B), to outperform stocks trading at higher price multiples. Size anomaly means the tendency of smaller market capitalization stocks to outperform larger market capitalization stocks. For example, Fama & French (1996) and Malkiel (2014) argue that these market anomalies rise from these investment types being exposed to larger than average risk, which would explain the abnormal returns. Because of this proposition, these anomalies are also called risk factors. Investment styles exploiting these anomalies are called factor investment strategies or “Smart Beta” strategies as branded by the investment industry. Factor investment strategies have become increasingly popular during recent years and there is a wide range of easily available investment vehicles such as ETF:s to employ these strategies. The goal of our research is to investigate if the value and size factor strategies carry with them a higher systematic risk than that of the market. This is done by making a set of regression analyses on the constituent stocks of the Standard & Poor’s 500-index. In the regressions we test for associations between beta and firm size and value factor proxies price-to-book, price-to-earnings, and dividend yield. Value factor proxies are investigated in separate regressions to avoid multicollinearity. The dataset is then further divided into industry sectors and separate regressions are made for each sector to explore for sector differences. The linkage of size effect into large cap S&P 500 stocks can be criticized but we find it relevant to investigate also this factor since the range of company sizes across S&P 500 is by any standards high, with 60-month average market capitalizations ranging from 3 billion USD to 642 billion USD. We aim to answer the question if and how loading an investment portfolio with value or size factor tilts influences the level of systematic risk the portfolio is exposed to. Our empirical analysis finds that overall the factor proxies do not have an association to either increasing nor decreasing systematic risk. Price-to-earnings ratio, price-to-book ratio, and market capitalization do not have statistically or practically significant relation to beta. Dividend yield has a statistically and practically significant negative association with beta across S&P 500. However, this effect is not observed within separate sector regressions, indicating that the effect across S&P 500 might be caused by sector differences. In short, value and size factor investment strategies do not influence the level of systematic risk of a portfolio except if value factor is proxied by dividend yield, in which case it has a beta decreasing effect

Applying text mining techniques to forecast the stock market fluctuations of large it companies with twitter data: descriptive and predictive approaches to enhance the research of stock market predictions with textual and semantic data

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis research project applies advanced text mining techniques as a method to predict stock market fluctuations by merging published tweets and daily stock market prices for a set of American Information Technology companies. This project executes a systematical approach to investigate and further analyze, by using mainly R code, two main objectives: i) which are the descriptive criteria, patterns, and variables, which are correlated with the stock fluctuation and ii) does the single usage of tweets indicate moderate signal to predict with high accuracy the stock market fluctuations. The main supposition and expected output of the research work is to deliver findings about the twitter text significance and predictability power to indicate the importance of social media content in terms of stock market fluctuations by using descriptive and predictive data mining approaches, as natural language processing, topic modelling, sentiment analysis and binary classification with neural networks

Prediction in Financial Markets: The Case for Small Disjuncts

Predictive models in regression and classification problems typically have a single model that covers most, if not all, cases in the data. At the opposite end of the spectrum is a collection of models each of which covers a very small subset of the decision space. These are referred to as “small disjuncts.” The tradeoffs between the two types of models have been well documented. Single models, especially linear ones, are easy to interpret and explain. In contrast, small disjuncts do not provide as clean or as simple an interpretation of the data, and have been shown by several researchers to be responsible for a disproportionately large number of errors when applied to out of sample data. This research provides a counterpoint, demonstrating that “simple” small disjuncts provide a credible model for financial market prediction, a problem with a high degree of noise. A related novel contribution of this paper is a simple method for measuring the “yield” of a learning system, which is the percentage of in sample performance that the learned model can be expected to realize on out-of-sample data. Curiously, such a measure is missing from the literature on regression learning algorithms.NYU Stern School of Busines

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Sector level cost of equity in African financial markets

This paper assesses the effectiveness of Liu (2006) metrics in measuring illiquidity within a multifactor CAPM pricing model. Costs of equity are estimated using this model for the major sectors within Africa’s larger equity markets: Morocco, Tunisia, Egypt, Kenya, Nigeria, Zambia, Botswana and South Africa. In all countries, the cost of equity is found to be highest in the financial sector and lowest in the blue chip stocks of Tunisia, Morocco, Namibia and South Africa. At an aggregate level, Nigeria and Zambia have the highest cost of capital