Mergers and Acquisitions: A pre-post analysis for the Indian financial services sector

This paper examines the Mergers & Acquisitions scenario of the Indian Financial Services Sector. The data for eighty cases of M&A in the period from March 1993- Feb 2010 is collected for a set of ten financial parameters representing the various characteristics of a firm. All the cases have been analyzed individually and collectively to determine the overall effects of M&A in the industry. The results of the study indicate that PAT and PBDITA have been positively affected after the merger but the liquidity condition represented by Current Ratio has deteriorated. Also Cost Efficiency and Interest Coverage have improved and deteriorated in equal number of cases. Interest Coverage remains an important factor in determining the return on shareholders’ funds both before and after the merger but Profit Margin also becomes important after the merger. And looking at the diversification effects of merger, in two out of the three cases there has been a reduction in total and systematic risk.Mergers & Acquisitions,Financial Services Sector,liquidity

Analysis of WIMAX/BWA Licensing in India: A real option approach

Indian Internet and broadband market has experienced very slow growth and limited penetration till now. The introduction of Broadband Wireless Access (BWA) is expected to aid in increasing the penetration of internet and broadband in India. The report sheds light on the guidelines and procedure used in 4G/BWA spectrum auction and presents comparative analysis of the competing technologies, providing the information about suitability of each technology available. Recently held 4G/ BWA spectrum auction saw enthusiastic participation by the industry and even saw some new entrants in Indian broadband market. Government benefited by Rs, 385bn that it earned as revenue from the auction of the spectrum and projected it as successful auction. However, the question remains if the auctions were efficient and whether they led to creation of value or will it prove to be burden to the telecom operators and will depress their balance sheet for years to come. The report uses both traditional valuation methods such as Discounted Cash Flow as well as Real Option approach to answer such questions. Using DCF analysis, the broadband subscribers have been forecasted to grow from present 13.77mn to 544mn by the end of 2025. The wireless subscribers are forecasted to be 70% of the total broadband subscribers after 5 years of roll out as it will be difficult to replace all wireline subscribers with wireless subscribers in India due to the high cost of wireless broadband and new technology. WiMAX is expected to increase its presence with time and reach 90mn subscribers from meager 0.35mn subscribers by 2025. Using industry wide cost of capital as 12.05%, the Net Present Value has been found Rs 221bn aggregate with an IRR of 17.1%. Using Real option approach, the value of license has been calculated as Rs 437bn which is 13.5% more than the spectrum fees paid by the operators. This mismatch, between the auction value and the correct value that should have been discovered by supply-demand dynamics, can be due to limited participants in BWA spectrum auctions and companies such as TATA and Reliance opting out of the auction process midway as well as uncertainty about acceptance of new technology with Indian subscribers.WiMAX, broadband, 3G spectrum, 4G,broadband wireless access, valuation, licensing, real option

Synthesis, structure and properties of nanolayered DLC/DLC films

Diamondlike carbon (DLC) films have been explored extensively in the past due to their highly attractive properties. However, the high level of internal stress developed during growth prevents deposition of thick films. Synthesis of DLC/DLC multilayers (DDM) presents a venue to overcome this drawback. In the present study, DLC films and DDM were deposited on Si substrate using dc plasma of CH4 and Ar gas mixture. FTIR was used to analyze the structure of the DLC films. Mechanical properties of the films were characterized by microhardness testing and nanoindentation. The tribological properties were studied by conducting pin-on-disc experiments in the laboratory environment (relative humidity 40-60%). Optical profilometry was used to analyze Intrinsic stress in the films and the wear profiles. A preliminary study was conducted utilizing different processing parameter (bias voltage, chamber pressure and ratio of Ar to CH4) to select the constituents of the DDM. Subsequently, DDM were synthesized consisting of alternating nanolayers of “soft” (high sp2content) and “hard” (low sp2 content) DLC by varying: (i) individual layer thickness while keeping the thickness ratio of soft/ hard DLC film, λ = 1 and; (ii) λ. The multilayered films found to exhibit low intrinsic stress ranging mostly below the average values of the two individual components. Nanoindentation behavior of DDM was comparable to the parent films and no significant variation was observed in different DDM films. DDM films with λ=1 exhibited better tribological properties compared to the films with λ other than unity. The 50 nm/50 nm DDM film exhibited the best tribological properties. It combined the low friction coefficient of the soft DLC component and low wear rate of the harder DLC component. The stress was found to be the average of the parent DLC films; hence it possesses the promise to be deposited as a thick coating, while maintaining desirable mechanical and tribological properties

Development of aliphatic polyketone fibers by melt spinning and drawing

Aliphatic polyketones are a class of perfectly alternating co- and ter- polymers that have ethylene and carbon monoxide as the main repeat units. In the terpolymer, a small amount of propylene is added to improve the chain flexibility and to reduce the melting point. Melt spinning followed by drawing of polyketones yields filaments that have high tensile strength that could be used for industrial applications such as tire cords. Polyketones as resins have extremely good chemical, hydrolytic, barrier and mechanical properties. In this study an attempt is made to investigate whether these polymers could be formed into a high-tenacity industrial grade fiber Spinning and post spinning operations like drawing and heat setting have been optimized so that the resulting fibers have good mechanical properties Characterization of the filaments was done using a number of techniques to study the structure development of the fibers with drawing The purpose of this study was, therefore, to optimize the processing conditions and thereafter investigate the structure-property relationship of both the copolymer and the terpolymer fibers The effects of molecular weight, propylene content, draw ratio and draw temperatures on the structure were also investigated Four different grades of aliphatic polyketones were investigated in this study. The melt spun as-spun copolymer fibers have both α and β phases at room temperature. The high draw ratio copolymer fibers have predominantly α phase. The terpolymer fibers have β phase at all temperatures and preparation conditions The lattice parameters were measured for one of the terpolymer grades and they are in agreement with those obtained by other authors. The melting behavior of the as spun samples of both the co and terpolymer samples show a bi-modal peak distribution. Birefringence and WAXS analysis indicate an increase in the orientation with draw ratio for all the sample grades. Microfibrillar structure develops with increasing draw ratio. This was also corroborated by the two-point small angle x-ray scattering contour plots. However, at high draw ratios the meridian peaks in the SAXS contour plots weaken indicating an increase in the order of the non-crystalline regions. A strong correlation was found between the orientation and tensile strength and modulus of the fibers. Fibers having tenacity in the range of 9-11 gpd were obtained. Another notable feature is the retention of their strength at elevated temperatures. It was observed that these fibers retain 85-90% of their original strength till a temperature of 125 °C The terpolymer fibers were comparatively stronger than the copolymer fibers both in terms of strength and modulus

Active Hedging Greeks of an Options Portfolio integrating churning and minimization of cost of hedging using Quadratic & Linear Programing

This paper proposes a methodology for active hedging Greeks of an option portfolio integrating churning and minimization of cost of hedging. In the first section, hedging strategy is implemented by taking positions in other available options, while simultaneously minimizing the net premium paid for the hedging and then churning the portfolio to take into account the changed value of Greeks in the new portfolio. In the second section, the paper extends the model to incorporate the transaction cost while hedging the portfolio and churning it in Indian Scenario. Both constant and nonlinear shape of transaction cost has been considered as per the Security Transaction Tax and Brokerage charges in India. A quadratic programming has been presented which has been approximated by a linear programming solution. The prototype software has been developed in MS Excel using Visual Basic.Options Portfolio, Hedging Greeks, Churning of Portfolio, Linear Programing, Transaction Cost

Neural information extraction from natural language text

Natural language processing (NLP) deals with building computational techniques that allow computers to automatically analyze and meaningfully represent human language. With an exponential growth of data in this digital era, the advent of NLP-based systems has enabled us to easily access relevant information via a wide range of applications, such as web search engines, voice assistants, etc. To achieve it, a long-standing research for decades has been focusing on techniques at the intersection of NLP and machine learning. In recent years, deep learning techniques have exploited the expressive power of Artificial Neural Networks (ANNs) and achieved state-of-the-art performance in a wide range of NLP tasks. Being one of the vital properties, Deep Neural Networks (DNNs) can automatically extract complex features from the input data and thus, provide an alternative to the manual process of handcrafted feature engineering. Besides ANNs, Probabilistic Graphical Models (PGMs), a coupling of graph theory and probabilistic methods have the ability to describe causal structure between random variables of the system and capture a principled notion of uncertainty. Given the characteristics of DNNs and PGMs, they are advantageously combined to build powerful neural models in order to understand the underlying complexity of data. Traditional machine learning based NLP systems employed shallow computational methods (e.g., SVM or logistic regression) and relied on handcrafting features which is time-consuming, complex and often incomplete. However, deep learning and neural network based methods have recently shown superior results on various NLP tasks, such as machine translation, text classification, namedentity recognition, relation extraction, textual similarity, etc. These neural models can automatically extract an effective feature representation from training data. This dissertation focuses on two NLP tasks: relation extraction and topic modeling. The former aims at identifying semantic relationships between entities or nominals within a sentence or document. Successfully extracting the semantic relationships greatly contributes in building structured knowledge bases, useful in downstream NLP application areas of web search, question-answering, recommendation engines, etc. On other hand, the task of topic modeling aims at understanding the thematic structures underlying in a collection of documents. Topic modeling is a popular text-mining tool to automatically analyze a large collection of documents and understand topical semantics without actually reading them. In doing so, it generates word clusters (i.e., topics) and document representations useful in document understanding and information retrieval, respectively. Essentially, the tasks of relation extraction and topic modeling are built upon the quality of representations learned from text. In this dissertation, we have developed task-specific neural models for learning representations, coupled with relation extraction and topic modeling tasks in the realms of supervised and unsupervised machine learning paradigms, respectively. More specifically, we make the following contributions in developing neural models for NLP tasks: 1. Neural Relation Extraction: Firstly, we have proposed a novel recurrent neural network based architecture for table-filling in order to jointly perform entity and relation extraction within sentences. Then, we have further extended our scope of extracting relationships between entities across sentence boundaries, and presented a novel dependency-based neural network architecture. The two contributions lie in the supervised paradigm of machine learning. Moreover, we have contributed in building a robust relation extractor constrained by the lack of labeled data, where we have proposed a novel weakly-supervised bootstrapping technique. Given the contributions, we have further explored interpretability of the recurrent neural networks to explain their predictions for the relation extraction task. 2. Neural Topic Modeling: Besides the supervised neural architectures, we have also developed unsupervised neural models to learn meaningful document representations within topic modeling frameworks. Firstly, we have proposed a novel dynamic topic model that captures topics over time. Next, we have contributed in building static topic models without considering temporal dependencies, where we have presented neural topic modeling architectures that also exploit external knowledge, i.e., word embeddings to address data sparsity. Moreover, we have developed neural topic models that incorporate knowledge transfers using both the word embeddings and latent topics from many sources. Finally, we have shown improving neural topic modeling by introducing language structures (e.g., word ordering, local syntactic and semantic information, etc.) that deals with bag-of-words issues in traditional topic models. The class of proposed neural NLP models in this section are based on techniques at the intersection of PGMs, deep learning and ANNs. Here, the task of neural relation extraction employs neural networks to learn representations typically at the sentence level, without access to the broader document context. However, topic models have access to statistical information across documents. Therefore, we advantageously combine the two complementary learning paradigms in a neural composite model, consisting of a neural topic and a neural language model that enables us to jointly learn thematic structures in a document collection via the topic model, and word relations within a sentence via the language model. Overall, our research contributions in this dissertation extend NLP-based systems for relation extraction and topic modeling tasks with state-of-the-art performances

Radiative and Seesaw Threshold Corrections to the S3S_3 Symmetric Neutrino Mass Matrix

We systematically analyze the radiative corrections to the $S_3$ symmetric neutrino mass matrix at high energy scale, say the GUT scale, in the charged lepton basis. There are significant corrections to the neutrino parameters both in the Standard Model (SM) and Minimal Supersymmetric Standard Model (MSSM) with large tan$\beta$, when the renormalization group evolution (RGE) and seesaw threshold effects are taken into consideration. We find that in the SM all three mixing angles and atmospheric mass squared difference are simultaneously obtained in their current 3$\sigma$ ranges at the electroweak scale. However, the solar mass squared difference is found to be larger than its allowed 3$\sigma$ range at the low scale in this case. There are significant contributions to neutrino masses and mixing angles in the MSSM with large tan$\beta$ from the RGEs even in the absence of seesaw threshold corrections. However, we find that the mass squared differences and the mixing angles are obtained in their current 3$\sigma$ ranges at low energy when the seesaw threshold effects are also taken into account in the MSSM with large tan$\beta$.Comment: 20 Pages, 2 Figures and 2 Table

MODELING & FORECASTING OF MACRO-ECONOMIC VARIABLES OF INDIA: BEFORE, DURING & AFTER RECESSION

This paper examines the state of the Indian economy pre, during and post-recession by analysing various macro-economic factors such as GDP, exchange rate, inflation, capital markets and fiscal deficit. We forecast some of the major economic variables using ARIMA modelling and present a picture of the Indian economy in the coming years. The findings indicate that Indian economy is reviving after a slowdown during the period of global recession. It is forecasted that GDP, foreign investments, fiscal deficit and capital markets will rise in 2010-11. Furthermore, the rupee-dollar exchange rates will not change much during the same period.ARIMA, Box-Jenkins, Indian economy, forecasting

Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time

Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018