In most cases, especially when data are acquired in a continuous period of time like time series analysis, as is true with financial data, the correlation of data will be probable (Field and Zhou, 2003). The correlation will make errors interdependent in the regression model and reject the assumption of independent errors. The rejection of the assumption will lead to the inflation of the R square (R2) and erroneous significance of the developed regressio n model (Rousseeuw, 1984). This situation indispensably necessitates using robust regression models (Field and Zhou, 2003). Hence, in order to cope with financial data as a time series analysis, it is necessary to use a regression model that is not vulnerable to outliers and prevent bias of outcomes. The robust regression is a good substit ution for the least square regression concerning this issue. According to above, this study aims to introduce the strength of robust regression in financial time series analysis, in order to encourage scholars and practitioners to deploy this technique as a mean of improving the quality of their analysis. Although there are a lot of state of the art techniques for forecasting such as Artificial Neural Networks (ANNs) with high rate of accuracy but the ration of statistical techniques and their analyzability make them in practice up to know.
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In Banking Sectors and other such leading organization the accurate assessment of consumer is of uttermost importance. Credit loans and finances have risk of being defaulted. These loans involve large amounts of capital and their non-retrieval can lead to major loss for the financial institution. Therefore, the accurate assessment of the risk involved is a crucial matter for banks and other such organizations. Not only is it important to minimize risk in granting credit but also the errors in declining any valid customer. This is to save the banks from lawsuits. For this purpose we should know which algorithm has highest accuracy.
data mining algorithms has been adopted for FFD. For instance, using a logit regression analysis, Beasley  found that no-fraud firms have boards with significantly higher percentages of outside members than fraud firms. Hansen et al.  used a powerful generalized qualitative response model to predict management fraud based on a set of data developed by an international public accounting firm. An experiment was conducted to examine the use of expert systems to enhance the performance of auditors . Green and Choi  presented a neural network fraud classification model employing endogenous financial data. A classification model created from the learned behavi or pattern is then applied to a test sample. Fanning and Cogger  also used an artificial neural network to predict management fraud. Using publicly available predictors of fraudulent financial statements, they found a model of eight variables with a high probability of detection. Beneish  investigated the incentives and the penalties related to earnings overstatements primarily in firms that are subject to accounting enforcement actions by the Securities and Exchange Commission. Abbott et al.  exa mined and measured the audit committee independence and activity in mitigating the likelihood of fraud.
The main problem of this paper can be summarized as follows: on the basis of processing the characterized candlestick of financial data, and then combining the cooperative co-evolution algorithm with SVM, the prediction of financial time series is studied. Firstly, based on the candlestick of financial time series, the collection of characterized candlesticks with single root, double root and multiple roots is selected. Secondly, a financial time series prediction model is constructed based on the optimization framework constructed by cooperative collaborative evolution algorithm. The optimization framework mainly consists of three parts: feature subset, window length, optimization algorithm. Feature selection and Window length together determine the data set. The dataset has an impact on SVM optimization parameters c and g.It is simply not enough to optimize a single component individually without considering the interaction between them. However, all three problems are judged by the final classification accuracy as the only criterion. It is precisely because these three parts are closely related, this paper uses the cooperative coevolution method based on genetic algorithm (CCGA) to optimize the above three components. The validity of the proposed algorithm is verified by real stock data.
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Abstract. With the rapid economic development, it is important for China’s current economy to grasp the core technology of internet of things and build the internationally competitive industrial system. In this paper, the financial data of sixteen representative enterprises’ annual report is used to analyze the financial position. This paper chooses major financial factors and builds financial index system. The comprehensive score of each enterprise can be shown after the principal component analysis. The result shows that enterprise comprehensive score and competitiveness have high correlation.
Four main business reference sources were used: Scopus, ABI/INFORM and Web of Science and EBSCOhost. The search was limited to peer-reviewed and scholarly journals, and limited to papers published in the ten years between 2007 and 2016, this resulted in 3082 results. Then a filter was applied to only retrieve journals on the ABDC list, resulting in 529 journal articles. However over 300 of these were from IS journals that addressed non-financial topics such as using Hadoop, social media attitudes and analysis of big data in other domains such as health. A practical screen followed to remove book reviews and literature reviews and to focus on papers that explicitly described applications of financial data. The remaining articles were assessed for quality. Thus the search was limited to papers representing field of research codes for accounting of finance, and papers in IS journals were only selected if they explicitly addressed this domain. This resulted in 75 papers which are shown in appendix 1. Although the search encompassed 10 years, all references except one in the data set fall between 2011 and 2016.
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The rise of economic globalization and evolution of information technology, financial data are being generated and accumulated at an extraordinary speed. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision- making. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. This paper describes data mining in the context of financial application from both technical and application perspective by comparing different data mining techniques.
In recent times, the growth of world financial markets has been exponential both in size and exposure. With increasing globalization, market and company developments require rapid, accurate, and sophisticated in-depth analysis. In order to enhance the analysis capacity of financial data and to work on live data feed, a Financial Lab is required. The main objectives of Finance lab is to give efficient tools to interpret the financial market, to enable rigorous and efficient research with the financial data as well as to provide a basis for validating trading and investment strategies, ‘without involving real money, but using real, live data’. In Indian Context, getting live data (without any kind of lag) from five financial markets in India ‘NSE Cash, NSE F&O, MCX, BSE Cash and NCDEX’ is an essential component of finance lab. All the features and options provided in these terminals are exact replicas of what the brokers use.
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price shock in 1973 and also between 1985 and 1990. Shiratsuka (1999) relaxes the assumption of constant real returns and addresses the question of whether a DCOLI should be used when setting monetary policy. His answer to this question is negative: he suggests that a DCOLI is considerably more volatile than the GDP deflator; that the reliability of the measurement of certain assets used to construct the DCOLI in the previous literature, such as land and house prices that receive large weight in wealth, is low; and that asset prices may respond to variations in spurious variables (e.g., sunspots). Reis (2005) constructs a DCOLI using U.S. data and also finds that it is much more volatile than the COLI. These problems have led Bryan et al. (2001) to adopt an empirical approach for measuring the dynamic cost of life that combines some restrictions from theory with an econometric approach for identifying good indicators of future prices. One feature common to all this previous research is that human wealth is not used when constructing the measure of the DCOLI. Shiratsuka (1999) points out that the human wealth component is large but argues that it is hard to measure and only reports results for a DCOLI that uses financial wealth.
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Amenity Analytics claims users can integrate their text AI service into their existing software network. Employees at financial institutions might sign into a web portal to see customer sentiment insights (such as positive responses for an online banking system) generated by the software from real-time and historical social media data. Then, the software parses through social media mentions for the finance company and uses NLP and machine learning to extract key customer sentiments by scoring each post.
of external data or by offering a small discount to customers to persuade them not to leave. Certain tools and skills are necessary in order to perform these activities; However, above all, there must be a business culture that promotes the use of data as a basis for decision-making. It has not been demonstrated yet that data-driven companies benefit more from being oriented both to customers and to the internal organization, nevertheless it seems quite obvious that the decisions that are based on data will always have a better result. Therefore is suggested a CBR architecture to propose an alternative. Presumably, customer- or user-related data have a direct impact on the company sales and revenues. The data related to the main activities of a company, such as feedback from the users on its products, is important in order to be able to calculate the costs and profits of a company. The aim of this work is to propose an alternative methodology for the measurement of financial impact that the collection, processing and storage of data generated by users has on a company. In the last decade, news reports have stressed the effectiveness of data-driven companies, however a real financial demonstrative or conclusive valuation method has not been proposed yet. However, there are some
quickly extending field of cyber crime. IC3 acknowledges online Internet crime grumblings from either the individual who trusts they were defrauded or from an outsider to the complainant. Amid 2008, non-conveyance of stock as well as installment was by a wide margin the most revealed offense, involving 32.9% of alluded crime dissensions. This speaks to a 32.1% expansion from the 2007 levels of non-conveyance of stock as well as installment answered to IC3. Moreover, amid 2008, closeout fraud spoke to 25.5% of grumblings (down 28.6% from 2007), and credit and check card fraud made up an extra 9.0% of objections. Certainty fraud, for example, Ponzi plans, PC fraud, and check fraud dissensions spoke to 19.5% of all alluded grumblings. Other protest classes, for example, Nigerian letter fraud, data fraud, money related establishments’ fraud, and risk protestations together spoke to under 9.7% of all objections (See Figure 2).
A very important area of financial risk management is systemic risk modelling, which concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them are more central and, therefore, more conta- gious/subject to contagion. The aim of this paper is to develop a novel systemic risk model. A model that, differently from existing ones, employs not only the information contained in financial market prices, but also big data coming from financial tweets. From a methodological viewpoint, the novelty of our paper is the estimation of systemic risk models using two different data sources: financial markets and financial tweets, and a proposal to combine them, using a Bayesian approach. From an applied viewpoint, we present the first systemic risk model based on big data, and show that such a model can shed further light on the interrelationships between financial institutions.
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There is no loss of generality in assuming that the underlying values being measured are a set of continuous phenomena. Such assumptions are widely found in consumer and behavioral research where Likert scaled survey responses typically serve as the data fed into classical statistical summarization and reporting models such as regression, ANOVA, factor analysis and summarization models. More recently, it has become popular to analyze Likert survey items with path analysis structural equation model software such as AMOS, LISREL and PLS path analysis software where there is an im- plicit assumption that multiple measurements need to be taken to, in effect, ‘triangulate’ an underlying latent or unobserved phenomenon. The classical approaches, in particu- lar, were designed around measurements from astronomy, agriculture and physics, and were not initially formulated for highly subjective, indirectly measured constructs such as human behavioral, performance and opinion constructs. In these cases, it is common to make implicit assumptions that the underlying opinions or beliefs of subjects – the ones that are mapped into the Likert item data – are Gaussian distributed. Clearly a
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Big Data is also a wrapper for different types of granular data. The five key sources of Big Data are public data, private data, data exhaust, community data, and self-quantification data. “Public data” are data typically held by governments, governmental organizations, and local communities that can potentially be harnessed for wide- ranging business and management applications. “Private data” are data held by private firms, non-profit organizations, and individuals that reflect private information that cannot readily be imputed from public sources. “Data exhaust” refers to ambient data that are passively collected, non-core data with limited or zero value to the original data-collection partner. “Community data” are a distillation of unstructured data—especially text—into dynamic networks that capture social trends. “Self-quantification data” are types of data that are revealed by the individual through quantifying personal actions and behaviors .
Many of these missing data imputation procedures work on a variable-by-variable basis. A procedure first ‘learns’ how to make predictions for the correct value of a certain variable using a part of the dataset that does not contain missing values. The variable in question is called the ‘target variable’. The procedure then uses that knowledge to estimate the correct values of that variable to replace the missing values in the dataset. In fact, missing data imputation procedures can also be used to cleanse datasets with other types of incorrect values than missing values. For instance, a common problem with survey datasets is that some values in them are obviously incorrect, such as an age of 250 or a non-existent address. Missing data imputation procedures can replace such incorrect values by correct values estimates as if they were missing values. This makes missing data imputation procedures a good candidate for inclusion in the model. Since the procedures are used to cre- ate estimates of the correct value of variables that have been effected by a data quality issue, these procedures are from now on referred to as ‘correct value estimation procedures’. A study on suitable procedures resulted in the following list. These procedures are described into more detail in the course of this chapter.
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“Big data” are data sets that are too big to be handled using the existing database management tools and are emerging in many important applications, such as Internet search, business informatics, social networks, social media, genomics, and meteorology . Oracle  pointed out that the financial services industry is amongst the most data driven of industries as the regulatory environment that commercial banks and insurance companies operate within requires these institutions to store and analyze many years of transaction data, and pervasiveness of electronic trading. Hence, the need to diversify the means of storing, managing these data as bulky and important they can be in the financial system. Russom  hinted that big data is forcing numerous changes in businesses and other organizations. Many struggle just to manage the massive data sets and non-traditional data structures that are typical of big data. Others are managing big data by extending their data management skills and their portfolios of data management software. This empowers them to automate more business processes, operate closer to real time, and through analytics, learn valuable new facts about business operations, customers, partners, and so on. Big Data now has the power to help businesses succeed; but this can only be achieved through appropriate and proper analysis, through the use of what is called „analytics‟ of these big volumes of data . The impact or potential impact of this has been (or could be) widespread in many different sectors across the business including Supply Chain, Information Technology, Human Resource as well as Sales and Marketing. Kaoutaret al.  estimated that by the year twenty twenty (2020), forty three (43) trillion gigabytes of data will have been generated. This will be one thousand (1000) times more than what we currently have. This data is mostly unstructured but represents an immense potential source of information that can be utilized to unlock the competitive edge of an organization if exploited. According to Press  the turning point on Big Data is the Internet and the coming into the market improved and more powerful Personal Computers and mobile phones. He had estimated that by the year 2002, data on the internet would overlap voice data. The internet and improved accessibility o f the Internet on Personal Computers and mobile phones spurned the use of social media such as Facebook, WhatsApp,Twitter,Instagram and other web platforms that have played a big role in information growth explosion currently being experienced .
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Samiloglu and Demirgunes (2008) investigated the relationship among Istanbul firms and found that growth in sales affects firm profitability positively. This result invariably support the view that liquidity and profitability are directly associated since liquidity is enhanced by sale’s growth. Lamberg andValming, (2009) studied the impact of liquidity management on profitability during financial crises with a sample of companies listed on stockholm stock Exchange’s small and mid-capitalist with some restrictions. Adopting a quantitative methodology and regression analysis, they found out that the adaptation of liquidity strategies do not have a significant impact on profitability measured by ROA. However, that increased use of liquidity forecasting and short-term financing during the financial crisis had a positive impact on ROA. In other word frequent monitoring and forecasting on liquidity levels and making more short- term investments can provide gains in profitability.
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The overall survey results show that the level of financial inclusion in Burundi is low, consistent with the policymakers’ prior belief. The survey found that only 12.5% of Burundi’s adult population has an account in a formal financial institution. This is partly due to several socio-economic factors. In fact, the monthly income of more than 60% of the population is less than 25,000 BIF (about 20 USD), over 87.6% of the rural population depends on agriculture and livestock and about 40% are illiterate. Other factors are related to the aspect of the financial system such as account opening, geographical coverage of service points, fees, guarantees and financial services and products that do not meet the needs of certain categories of the population.
The banks, including innovative smaller banks, also intensified the exploration of new business models. There were some major initiatives in the mobile space, exploring different versions of a branchless banking model, with some of those initiatives still on-going. There were also a number of initiatives involving co-operation between retailers and banks, with some essentially offering a banking service through a retailer, whilst others focussed on more limited services, e.g. store-to- store remittances enabled through the banking infrastructure. Some of the initiatives were not successful, whilst others (the remittance service being one) continue to be successful to this day. It is fair to characterise this period as one where institutions were using their newly-found appetite for the lower-income market to explore new territories with new partners, but the impact on improving financial inclusion in terms of the percentage of banked adults was negligible (see figure 5 below). The effect of the global economic situation and the reduction in growth rates was a further exacerbating factor in this muted impact.
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