Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

Towards a Macroprudential Surveillance and Remedial Policy Formulation System for Monitoring Financial Crisis

Several developing economies witnessed a large number of systemic financial and currency crises since the 1980s which resulted in severe economic, social, and political problems. The devastating impact of the 1982 and 1994-95 Mexican crises, the 1997-98 Asian financial crisis, the 1998 Russian crisis and the ongoing financial crisis of 2008-2009 suggest that maintaining financial sector stability through reduction of vulnerability is highly crucial. The world is now witnessing an unprecedented systemic financial crisis originated from USA in September 2008 together with a deep worldwide economic recession, particularly in developed countries of Europe and North America. This calls for devising and using on a regular basis an appropriate and effective monitoring and policy formulation system for detecting and addressing vulnerabilities leading to crisis. This paper proposes a macroprudential/financial soundness monitoring, analysis and remedial policy formulation system that can be used by most developing countries with or without crisis experience as well as developed countries with limited data. It also discusses a process for identifying, and compiling a set of leading macroprudential indicators/financial soundness indicators. An empirical illustration using Philippines data is presented.economic and financial vulnerability, macroprudential indicators and financial soundness indicators analysis, macroprudential surveillance and policy, developing countries, financial sector, currency and financial crises, Early Warning Models, Stress Test

Market dynamics associated with credit ratings: a literature review.

Credit ratings produced by the major credit rating agencies (CRAs) aim to measure the creditworthiness, or more specifically the relative creditworthiness of companies, i.e. their ability to meet their debt servicing obligations. In principle, the rating process focuses on the fundamental long-term credit strength of a company. It is typically based on both public and private information, except for unsolicited ratings, which focus only on public information. The basic rationale for using ratings is to achieve information economies of scale and solve principal-agent problems. Partly for the same reasons, the role of credit ratings has expanded significantly over time. Regulators, banks and bondholders, pension fund trustees and other fiduciary agents have increasingly used ratings-based criteria to constrain behaviour. As a result, the influence of the opinions of CRAs on markets appears to have grown considerably in recent years. One aspect of this development is its potential impact on market dynamics (i.e. the timing and path of asset price adjustments, credit spreads, etc.), either directly, as a consequence of the information content of ratings themselves, or indirectly, as a consequence of the “hardwiring” of ratings into regulatory rules, fund management mandates, bond covenants, etc. When considering the impact of ratings and rating changes, two conclusions are worth highlighting. – First, ratings correlate moderately well with observed credit spreads, and rating changes with changes in spreads. However, other factors, such as liquidity, taxation and historical volatility clearly also enter into the determination of spreads. Recent research suggests that reactions to rating changes may also extend beyond the immediately-affected company to its peers, and from bond to equity prices. Furthermore, this price reaction to rating changes seems to be asymmetrical, i.e. more pronounced for downgrades than for upgrades, and may be more significant for equity prices than for bond prices. – Second, the hardwiring of regulatory and market rules, bond covenants, investment guidelines, etc., to ratings may influence market dynamics, and potentially lead to or magnify threshold effects. The more that different market participants adopt identical ratings-linked rules, or are subject to similar ratings-linked regulations, the more “spiky” the reaction to a credit event is likely to be. This reaction may include, in some cases, the emergence of severe liquidity pressures. Efforts have recently been made, notably with support from the rating agencies themselves, to encourage a more systematic disclosure of rating triggers and to renegotiate and smooth the possibly more destabilising forms of rating triggers. However, the lack of a clear disclosure regime makes it difficult to assess how far this process has evolved. Questions also remain as to the extent to which ratings-based criteria introduce a fundamentally new element into market behaviour, or, conversely, the extent to which they are simply a va riant of more traditional contractual covenants. Rating agencies strive to provide credit assessments that remain broadly stable through the course of the business cycle (rating “through the cycle”). Agencies and other analysts frequently contrast the fundamental credit analysis on which ratings are based with market sentiment — measured for example by bond spreads — which is arguably subject to more short-term influences. Agencies are adamant that they do not directly incorporate market sentiment into ratings (although they may use market prices as a diagnostic tool). On the contrary, they make every effort to exclude transient market sentiment. However, as reliance on ratings grows, CRAs are being increasingly expected to satisfy a widening range of constituencies, with different, and even sometimes conflicting, interests: issuers and “traditional” asset managers will look for more than a simple statement of near-term probability of loss, and will stress the need for ratings to exhibit some degree of stability over time. On the other hand, mark-to-market traders, active investors and risk managers may seek more frequent indications of credit changes. Hence, in the wake of major bankruptcies with heightened credit stress, rating agencies have been under considerable pressure to provide higher-frequency readings of credit status, without loss of quality. So far, they have responded to this challenge largely by adding more products to their traditional range, but also through modifications in the rating process. The rating process and the range of products offered by rating agencies have thus evolved over time, with, for instance, an increasing emphasis on the analysis of liquidity risks, a new focus on the hidden liabilities of companies and an increased use of market-based tools. It is too early, however, to judge whether these changes should simply be regarded as a refinement of the agencies’ traditional methodology or whether they suggest a more fundamental shift in the approach to credit risk measurement. For the same reason, it is not possible to draw any firm conclusions about changes in the effects of credit ratings on market dynamics.

Volatility co-movements and spillover effects within the Eurozone economies: A multivariate GARCH approach using the financial stress index

The Eurozone crisis is one the most important economic event in recent years. At its peak, the effects of the crisis have put at serious risk the outcome of the euro project, exposing the inherent weaknesses and vulnerabilities of the monetary union. As the degree of economic and financial integration of these countries is significant, we aim to investigate in details the potential cross-covariance and spillover effects between the Eurozone economies and financial markets. In order to do this, we employ financial stress indexes, as systemic risk metrics in a multivariate GARCH model. This method is able to capture markets’ dependencies and volatility spillovers and is employed on a single market level as well as on the full spectrum of Eurozone markets. The empirical results have shown the important and intensive stress transmission on banking and money markets. Moreover, the role of peripheral countries as stress transmitter is verified, but only for particular periods. The significant spillover effects from core countries are also evident, indicating their important role in the Euro Area and its overall financial stability. The “decoupling” hypothesis is empirically verified, underling the gradually decreasing intensity of spillovers between Euro Area countries. Overall, this paper exhibits the complex structure of spillover effects for Eurozone, along with a clustering effect in the most recent times

NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation

Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and timevarying characteristics, resulting in a high percentage of false positives. Therefore, analysts1 are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decisionmaking. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30% time and cost compared to their previous manual approaches of AML investigation