Review of the machine learning methods in the classification of phishing attack

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail

Liquidity Constraints and Firm’s Export Activity

This paper will assess the importance of internal firm resources in overcoming sunk entry costs associated with export. When firms are not able to raise additional external funds for investments, they are credit-constrained, and in such a case, new exporters have to rely on their internal liquidity to pay sunk costs. Using a data set of small and medium size Italian enterprises (SMEs), we find that entry probability in the export market is affected by the level of cash stock for constrained firms. We propose a methodology used to identify a priori constrained firms, employing index analysis as used in business economics. The estimation of the Euler equation for investments confirms the fitness of our classification. In addition we find that exporters show higher liquidity if they raise the number of destinations. Finally, we do not find evidence that entry in the export market improves firm\'s financial health, while ex-ante new entrants are found to be relatively more leveraged.Productivity, Credit constraints, Heterogenous firms, Trade

EARLY WARNING INDICATORS OF THE GLOBAL FINANCIAL CRISIS : Focus on bank profitability and funding in the United States 2004-2008

The global financial crisis began in the United States in 2007, when the housing prices collapsed. Depositary and financial institutions noticed that the values of mortgage-backed securities in their balance sheets were dramatically less than expected. The banks’ profitability started to decrease severely. As the risks increased, the short-term liquidity dried up, and the trust in the collateral securities weakened. The institutions had to resort to more complex funding options. The complex structures of financial instruments and, excessive securitization spread the crisis to the whole banking system. This caused a panic, which paralyzed the efficient functioning of the financial markets. Private and public institutions tend to actively forecast and predict forthcoming crises and early warning indicators, that are sensitive to market changes vulnerabilities and by using econometric models. During the pre-crisis years in the U.S., the early warning indicators and early warning models failed to predict the beginning of the global financial crisis. The purpose of this study is to analyze, whether the beginning of the financial crisis could have been predicted with securitized short-term funding of the banking sector. The goal is to assess, if the volume of mortgage-backed securities, repurchase agreements and federal funds had a significant correlation and impact with bank profitability and return on equity. The question is whether these variables acted as early warning indicators. The data is from Worldscope-database and includes U.S. banks from 2004-2008. The empirical methods are linear multiple regression analysis and fixed-effects -regression analysis. Generally, in the academic literature, the studied variables have seen to be a direct impact and effect to the causes of the global financial crisis. However, the results of this study are divergent. The linear regression model only finds a significant positive correlation between the repurchase agreements and return on equity. This implies, that using repurchase agreements as early warning indicators is applicable only, if there is a noticeable large collapse or decrease in the volume of the agreements. On the contrary, the fixed-effects mode does not find any significant correlations with any of the variables. The interpretation is, that the unique bank-specific time-invariant characteristics and effects were the real fundamental reasons and causes for the severity and the start of the crisis. Equally, the underlying risks of the securities had a significant impact on the start of the crisis and the collapse of bank profitability. Based on the results, the studied variables do not act on their own as good reliable early warning indicators, and in the econometric early warning models, the time-invariant underlying unique characteristics and risks should strongly be accounted for. By doing this, crisis early warning prediction based on bank securities would obtain more efficient and usable prediction models and forecasts.Globaali finanssikriisi sai alkunsa Yhdysvalloissa 2007 asuntomarkkinoiden hintojen romahtaessa. Rahoituslaitokset huomasivat taseissaan oleviensa asuntolainavakuudellisten arvopapereiden arvojen olevan nopeasti huomattavasti oletettua alhaisempia. Pankkien kannattavuus kääntyi jyrkkään laskuun. Lyhytaikainen likviditeetti ehtyi riskien kasvaessa ja luottamus arvopaperien vakuuksiin heikkeni. Rahoituslaitokset joutuivat turvautumaan mon-imuotoisempiin rahoitusratkaisuihin. Rahoitusinstrumenttien monimutkaiset rakenteet ja liiallinen arvopaperistaminen levittivät kriisin nopeasti koko pankkijärjestelmään. Tämä aiheutti paniikin, joka lamautti rahoitusmarkkinoiden tehokkaan toiminnan. Yksityiset ja julkiset instituutiot pyrkivät aktiivisesti ennustamaan ja ennakoimaan tulevia kriisejä ja markkinoiden muutoksille herkkiä ennakoivia indikaattoreita käyttämällä ekonometrisia mallinnuk-sia. Ennen finanssikriisiä Yhdysvalloissa ennakoivat indikaattorit ja mallinnukset kuitenkin epäonnistuivat ennustamaan kriisin alun. Tämän tutkimuksen tarkoituksena on analysoida, mikäli finanssikriisin alku olisi voitu ennakoida pankkijärjestelmän arvopaperistettujen ja lyhytaikaisen rahoituksen avulla. Päämääränä on selvittää, oliko asuntolainavakuudellisilla arvopapereilla, takaisinostosopimuksilla sekä yhdysvaltain keskuspankin liikkeellelaskemien pankkien välisten markkinoiden arvopapereilla merkittävä vaikutus pankkien kannattavuuteen ja pääoman tuottoasteeseen. Tärkeänä kysymyksenä on, toimivatko kyseiset muuttujat kriisejä ennakoivina indikaattoreina. Tutkimuksen aineisto sisältää Worldscope -tietokannan yhdysvaltalaisia pankkeja vuosilta 2004-2008. Tutkimusmenetelminä ovat lineaarinen sekä kiinteiden menetelmien -regressiomalli. Yleisesti kyseisillä muuttujilla on akateemisessa kirjallisuudessa nähty olevan suora vaikutus finanssikriisin alkuun. Tämän tutkimuksen tulokset ovat kuitenkin poikkeavia. Lineaarinen regressiomalli löytää ainoastaan merkittävän positiivisen korrelaation takaisinostosopimusten ja pankkien pääoman tuottoasteen väliltä. Tämä viittaa siihen, että takasisinostosopimusten käyttö ennakoivina indikaattoreina on soveltuvaa vain, jos sopimukset kärsivät nopeasta määrän laskusta tai romahduksesta. Kiinteiden menetelmien -malli puolestaan ei löydä minkäänlaisia merkittäviä korrelaatioita. Tässä tulkintana on, että pankkikohtaiset ajassa muuttumattomat taustalla olevat ominaisuudet olivat todelliset perusteelliset syyt kriisin vakavuudelle. Vastaavasti myös arvopapereiden taustalla olevat riskit vaikuttivat merkittävästi kriisin syntyyn ja pankkien kannattavuuden romahdukseen. Tulosten perusteella tutkitut muuttujat eivät itsessään toimi hyvinä ennakoivina indikaattoreina, ja käytetyissä ekonometrisissa malleissa tulisi vahvasti huomioida taustalla olevat muuttumattomat ominaisuudet ja riskit. Tällöin kriisien ennakoiminen pankkien arvopapereiden pohjalta tuottaisi tehokkaampia ja käyttökelpoisisempia ennusteita

Essays on Risk Creation in the Banking Sector

This thesis consists of four essays exploring risk creation in the banking sector. The essays examine how conflicting interests can compromise the objectivity, judgment, and decision making of economic agents. Consequently, they may prioritize their personal or institutional interests over the best interests of others or the entire financial system. Chapter 2 delves into the conflict of interest that arises when a bank serves as an investor in the stock market. Chapter 3 revisits the discussion of the potential misalignment between sovereign incentives and the collective interests of the currency union, particularly in the bond market. Chapter 4 draws attention to a situation where regulations in the banking sector may be advantageous for a government in the sovereign bond market. Finally, Chapter 5 looks at the flip side of the coin, examining how banks may be susceptible to moral hazard concerns in their FX lending decisions, given that they do not fully bear the consequences of their actions

‘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