With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. Kalman filters are recursive and use a feedback mechanism to perform error correction. This correction makes them best suited for making accurate predictions as they can factor in the market volatility, whereas XGBoost is a promising technique for datasets that are nonlinear and can gather knowledge by detecting patterns and relationships in the data. XGBoost is also capable of capturing the time dependency of features efficiently. ARIMA refers to an Auto Regressive Integrated Moving Average model that has become very popular in recent times. It is mostly used on time series data and works by eliminating its stationarity. Finally, a hybrid model combining Kalman filters and XGBoostis discussed and a comparison of the results of each of the four models, are made to provide a better clarity for making investments by forecasting the price of a stock.
A Risk Assessment Model (RAM) is necessary to avoid the limitations associated with a simplistic and broad classification of applicants into a "good" or "bad" category. The absence of appropriate weights in the current evaluation system triggers the need for the development of the comprehensive model based on proven statistical application. Literature survey undertaken brought to surface 28 parameters that need to be taken into account while evaluating a prospect. These parameters were classified under four heads namely credit, operations, liquidity and market risks. Weights developed in this study were based on a conceptual understanding and the importance attached by people proficient in this area. A questionnaire was developed and a judgmental survey was conducted for this purpose amongst various credit officers extending commercial vehicle and construction equipment financing. The sample size was 117 small and medium corporate clients.The existing model was able to classify 28 records correctly. So the predictive power of the original/existing model was about 80%. The proposed/new model is able to classify 30 records correctly. So the predictive power of the propose/new model is 85.71%.
Abstract-Non-banking financial companies (NBFCs) form an integral part of the Indian financial system. The history of the NBFC Industry in India is a story of under-regulation followed by over-regulation. Policy makers have swung from one extreme position to another in their attempt to set controls and then restrain them so that they do not curb the growth of the industry. This report covers the industry. Most of this NBFCs' are operating with high risk of lending and more often NBFCs' lend credit to Small and Medium size enterprises, which are categorized as high risk class of Assets. To assess such high risk assets we need to have a comprehensive model. This paper aim is to build Risk Assessment Model for NBFCs' based on both qualitative and quantitative aspects of the client.
This paper ‘Demonetization Impact on Liquidity of Large Corporates in India’ focuses on the changes in the Liquidity pattern of the companies’ in-order to cope up with the Business Risk that emerged due to the unforeseen Demonetization Policy of the Government of India. The study aimed at finding the performance of various sectors on liquidity parameters during pre and post demonetization period. The study covered the companies listed in NIFTY 50 over the period of 6 years from 2014-2019. The methodology used for analysis is the longitudinal study under descriptive analysis as it involves the repeated observations of the same variables over a period of time. As the paper focuses on the top companies listed, though companies faced the uncertainty during that particular period of demonetization, they bounced back at the earlier than the MSME companies. Some sectors like FMCG, Pharmaceutical were immune to demonetization as it has an inelastic demand in the market and demonetization created giant opportunities for the software sector as the country has been shifted towards digitalization. Found short-term implications for the cash-intensive sectors but in the long run it helps in the growth of the economy, which in turn will have a positive correlation with the growth of companies.
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