Regulation of Financial Sector in Developing Countries: Lessons From the 2008 Financial Crisis

This chapter aims to draw some practical lessons and raises some issues from the 2008 financial crisis for regulation of financial sectors in developing countries. At the time of writing, the crisis is far from over and the aftermath is still unclear. The chapter is divided into five sections. The first section gives an overview of considerations that are important in drawing lessons from the crisis, especially from the point of view of developing economies. The second section addresses the major issues of scope for, and limits to, counter-cyclicality in regulation, in view of the widely perceived need for such an approach to avoid similar crisis in the future. The third section addresses an issue, which has been in focus since late 2008—the idea of comprehensiveness in the regulatory scope of the financial sector. The fourth section explores possible improvements in regulatory structures that are provoked by the recent crisis. The concluding section lists several broader issues that need to be kept in view while considering improvements in regulation of financial sectors for the future

Predictive Analysis of S&P BSE Greenex Index: Unlocking Insights for Sustainable Investments

The COVID-19 pandemic has led to reduced economic and industrial activities, prompting a noticeable transition towards a more sustainable way of life. This could indicate that we are on the path to reducing our carbon footprint in the long term. Consequently, analysed the performance of India\u27s sustainability index, the S&P BSE GREENEX, which assesses the sector-wise carbon performance of stocks. It comprises stocks selected based on their energy efficiency performance using publicly disclosed financial and energy data. Forecasting the stock market is critical when formulating investment strategies. Considering the profound negative impact of the COVID-19 pandemic on global stock markets, investment decisions are becoming increasingly challenging and riskier, especially when channelling funds towards green technologies and clean energy. This study analysed the predictive accuracy of the Long Short-Term Memory (LSTM) deep learning model for Indian companies that promote sustainability through their investment decisions during and after the COVID-19 period. The empirical outcomes demonstrate the ability of the LSTM model to generate fairly precise predictions for a wide spectrum of companies across diverse sectors; during and after the crisis. These findings provide valuable insights for investors seeking to make informed decisions regarding sustainability-focused investments as represented by the S&P BSE GREENEX Index

Pricing Efficiency of Exchange Traded Funds in India

Exchange traded funds (ETFs) have two prices, the market price and the net asset value (NAV) price. ETFs NAV price gets determined by the net value of the constituent assets, whereas the market price of ETFs depends upon the number of units bought or sold on the stock exchange during trading hours. As per the law of one price, the NAV and market price of the ETF should be the same. However, due to demand and supply forces, the market price may divert from its NAV. This price difference may have significant repercussions to investors, as it represents a cost if they buy overvalued ETF shares or sell undervalued ETF shares. Pricing efficiency is the speed at which the market makers correct the deviations between ETFs NAV and market price. The present study attempts to investigate the pricing efficiency of Indian equity ETFs employing an autoregression model over its price deviation, and also attempts to understand the lead-lag relationship between the price and NAV using the vector error correction model (VECM)

Sampling-Based Query Re-Optimization

Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and authors that appears in the Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2016

Logical Topology Design Using Efficient Heuristics in Wavelength Routed Networks

Wavelength Division Multiplexed (WDM) point to point networks play a vital role in the backbone transport networks. The set of light paths at optical layer forms a Logical Topology. This paper deals with the design of Logical Topology for wavelength routed WDM networks. This paper proposes new heuristics on fiber optic networks to develop efficient logical topology design and to examine the critical aspects of performance constraints like single hop traffic maximization, Average weighted hop count and number of wavelengths/Transceivers. Further two new heuristics LUMHSN and ILUMHSN are proposed, tested and compared the performances with the existing HLDA on 14-node NSFNET Model. Keywords: wavelength routed WDM, Logical Topology, single hop traffic, Average weighted hop Count, LUMHSN, ILUMHSN

Towards Benchmarking Scene Background Initialization

Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.Comment: 6 pages, SBI dataset, SBMI2015 Worksho

Weed Plant Detection

Knowledge about the distribution of weeds in the field is a prerequisite for site-specific treatment. Optical sensors make it possible to detect varying weed densities and species, which can be mapped using GPS data. The weeds are extracted from images using image processing and described by shape features. A classification based on the features reveals the type and number of weeds per image. For the classification only a maximum of 16 features out of the 81 computed ones are used. Features are used, which enable an optimal distinction of the weed classes. The selection can be done using data mining algorithms, which rate the discriminance of the features of prototypes. If no prototypes are available, clustering algorithms can be used to automatically generate clusters. In a next step weed classes can be assigned to the clusters. Such a procedure aids to select prototypes, which is done manually. Classes can be identified, that are distinct in the feature space or which are overlapping and therefore not well separable. Clustering can be used in some, less complex cases to establish an automatic procedure for the classification. Weed maps are generated using the system. These are compared to the result of a manual weed sampling

A Routing Delay Predication Based on Packet Loss and Explicit Delay Acknowledgement for Congestion Control in MANET

In Mobile Ad hoc Networks congestion control and prevention are demanding because of network node mobility and dynamic topology. Congestion occurs primarily due to the large traffic volume in the case of data flow because the rate of inflow of data traffic is higher than the rate of data packets on the node. This alteration in sending rate results in routing delays and low throughput. The Rate control is a significant concern in streaming applications, especially in wireless networks. The TCP friendly rate control method is extensively recognized as a rate control mechanism for wired networks, which is effective in minimizing packet loss (PL) in the event of congestion. In this paper, we propose a routing delay prediction based on PL and Explicit Delay Acknowledgement (EDA) mechanism for data rate and congestion control in MANET to control data rate to minimize the loss of packets and improve the throughput. The experiment is performed over a reactive routing protocol to reduce the packet loss, jitter, and improvisation of throughput

Poly-(γ-glutamic acid) Production and Optimization from Agro-Industrial Bioresources as Renewable Substrates by Bacillus sp. FBL-2 through Response Surface Methodology

We optimized culture conditions using Bacillus sp. FBL-2 as a poly-(γ-glutamic acid) (PGA) producing strain isolated from cheonggukjang. All experiments were performed under aerobic conditions using a laboratory scale 2.5 L fermentor. We investigated the effects of fermentation parameters (temperature, pH, agitation, and aeration) and medium components (glutamic acid, citric acid, and yeast extract) on poly-(γ-glutamic acid) production, viscosity, and dry cell mass. A non-optimized fermentation method (1.5 vvm, 350 rpm, and 37 °C) yielded PGA, viscosity, and dry cell mass at levels of 100.7 g/L, 483.2 cP, and 3.4 g/L, respectively. L-glutamic acid, citric acid, and yeast extract supplementation enhanced poly-(γ-glutamic acid) production to 175.9 g/L. Additionally, the production of poly-(γ-glutamic acid) from rice bran and wheat bran was assessed using response surface methodology (central composite rotatable design). Agricultural by-products (rice bran and wheat bran) and H2SO4 were selected as factors, and experiments were performed by combining various component concentrations to determine optimal component concentrations. Our experimentally-derived optimal parameters included 38.6 g/L of rice bran, 0.42% of H2SO4, 28.0 g/L of wheat bran, and 0.32% of H2SO4. Under optimum conditions, rice bran medium facilitated poly-(γ-glutamic acid) production of up to 22.64 g/L, and the use of wheat bran medium yielded up to 14.6 g/L. Based on a validity test using the optimized culture conditions, poly-(γ-glutamic acid) was produced at 47.6 g/L and 36.4 g/L from these respective mediums, and both results were higher than statistically predicted. This study suggests that rice bran can be used as a potential alternative substrate for poly-(γ-glutamic acid) production