Search results for: Poonam Goswami

Authors: Vikrant Gupta, Amrit Goswami

Abstract:

The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Vijay Marisetty, Poonam Singh

Abstract:

Research on the effect of director's network connections on investor welfare is inconclusive. Some studies suggest that directors' connections are beneficial, in terms of, improving earnings information, firms valuation for new investors. On the other hand, adverse effects of directorial networks are also reported, in terms of higher earnings management, options back dating fraud, reduction in firm performance, lower board monitoring. From regulatory perspective, the role of directorial networks on corporate welfare is crucial. Cognizant of the possible ill effects associated with directorial networks, large investors, for better representation on the boards, are building their own database of prospective directors who are highly qualified, however, sourced from outside the highly connected directorial labor market. For instance, following Dodd-Frank Reform Act, California Public Employees' Retirement Systems (CalPERs) has initiated a database for registering aspiring and highly qualified directors to nominate them for board seats (proxy access). Our paper stems from this background and tries to explore the chances of outside directors getting directorships who lack established network connections. The paper is able to identify such aspiring directors' information by accessing a unique Indian data sourced from an online portal that aims to match the supply of registered aspirants with the growing demand for outside directors in India. The online portal's tie-up with stock exchanges ensures firms to access the new pool of directors. Such direct access to the background details of aspiring directors over a period of 10 years, allows us to examine the chances of aspiring directors without corporate network, to enter directorial network. Using this resume data of 16105 aspiring corporate directors in India, who have no prior board experience in the directorial labor market, the paper analyses the entry dynamics in corporate directors' labor market. The database also allows us to investigate the value of corporate network by comparing non-network new entrants with incumbent networked directors. The study develops measures of network centrality and network degree based on merit, i.e. network of individuals belonging to elite educational institutions, like Indian Institute of Management (IIM) or Indian Institute of Technology (IIT) and based on job or company, i.e. network of individuals serving in the same company. The paper then measures the impact of these networks on the appointment of first time directors and subsequent appointment of directors. The paper reports the following main results: 1. The likelihood of becoming a corporate director, without corporate network strength, is only 1 out 100 aspirants. This is inspite of comparable educational background and similar duration of corporate experience; 2. Aspiring non-network directors' elite educational ties help them to secure directorships. However, for post-board appointments, their newly acquired corporate network strength overtakes as their main determinant for subsequent board appointments and compensation. The results thus highlight the limitations in increasing board diversity.

Keywords: aspiring corporate directors, board diversity, director labor market, director networks

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Authors: Sajad Khaliq Dar, Dipanjan Goswami, Arshad H. Khuroo, Mohd. Akhtar, Pulak Kumar Metia, Sudershan Kumar

Abstract:

Background: Development of modified-release oral dosage formulations (OSD) like tacrolimus in narrow therapeutic categories, together with high levels of intra-individual variability, impose greater challenges. The risk assessment for bioequivalence studies requires developing a suitable design through pilot studies involving the comparison of multiple formulations of the same product with a marketed product to understand the in-vivo behaviour. These formulations could have varying coating levels and other minor quantitative differences to achieve the desired release rate for the final product. Although small-scale studies are critical before the conduct of full-scale Pharmacokinetic (PK) studies, regulatory agencies evaluate critical bioavailability attributes (CBA) before approving the submitted dossiers. Since Tacrolimus is a BCS Class II drug, therefore developing the extended-release formulation, in addition to associated challenges, provides an opportunity to present the In vitro-in vivo correlations (IVIVC) to regulatory agencies, not only to exhibit product quality but also to reduce the burden of additional human trials and cost involved to them for bringing the product to market. Objective: The objective of this study was to develop a Level-A In vitro - In vivo Correlation (IVIVC) model for Sun Pharma’s test formulation Tacrolimus ER tablet 4mg and extend its application to a widened dissolution window of 25% at 2.5 hours (critical release time) sampling time point. Experimental Procedure: Post the conduct of two in-vivo studies, a pilot study evaluating two test prototypes on 24 subjects (under fasting) and a pivotal study having 50 subjects (under fasting), the observed pharmacokinetic profile was used for IVIVC model development. The dissolution media used was 0.005% HPC + 0.25% SLS in Water 900 mL at pH 4.50 using USP II (Paddle) apparatus with alternative sinkers operated at 100 RPM. The sampling time points were chosen to mimic the drug absorption in vivo. The dissolution best fit to data was obtained using Makoid Banakar kinetics. Then deconvolution, anchoring to concepts of the single compartment by Wagner Nelson method was applied for tacrolimus slow-release formulation batch with film coating weight build-up of 5.4% (used in pilot bio study), medium release with Hypromellose (retard-release exhibit batch used in the pivotal study) and fast release formulation batch with film coating weight build-up of 5.05% (used in pilot bio study). Results and Conclusion: The results were deemed acceptable as prediction errors for internal and external validation were < 3% depicting in-vitro drug release mimics in-vivo absorption. Moreover, the prediction result for the Test/Reference ratio was <15% for all test formulations and widening dissolution (i.e., 39%-64% drug release at 2.5hrs) predictions were well within 80-125% when compared against Envarsus XR (reference drug). This IVIVC-validated model can be used in the futuristic exploration of dose titration with 1mg tacrolimus ER OSD as a surrogate for In-vivo bioequivalence trials.

Keywords: pharmacokinetics, BCS, oral dosage form, Bioavailability, intra-individual variability

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