Although recurrent neural networks (RNNs) are effective in handling sequential data, they are poor in capturing the long‐term dependencies in the data due to a problem known as vanishing and exploding gradients. A variant of RNNs known as Long‐and‐Short‐Term Memory (LSTM) networks effectively gets rid of the problem, and hence these networks are proved to be very efficient and accurate in handling sequential data. This chapter presents the basic design of LSTM networks and highlights their working principles. Six different variants of LSTM models are also presented with a particular focus on stock price forecasting. The models are trained and tested on the historical NIFTY 50 index of the National Stock Exchange (NSE) of India from December 29, 2014 to July 31, 2020. The performances of the models are compared on the basis of their execution speeds and prediction accuracies. It is observed that while the univariate LSTMs with the basic architecture are more accurate than their encoder‐decoder counterparts, the opposite is the case for the execution speed.
Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses three ratios, the Sharpe ratio, the Sortino ratio, and the Calmar ratio, for designing the mean-variance optimized portfolios for six important sectors listed in the National Stock Exchange (NSE) of India. Three portfolios are designed for each sector maximizing the ratios based on the historical prices of the ten most important stocks of each sector from Jan 1, 2017 to Dec 31, 2020. The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021. The ratio that yields the maximum cumulative returns for both the training and the test periods for the majority of the sectors is identified. Additionally, the sectors which exhibit the maximum cumulative returns for the same ratio are also identified. The results provide useful insights for the investors in the stock market in making their investment decisions based on the current return and risks associated with the six sectors and their stocks.
Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past prices of the stocks are extracted from the web from January 1, 2016, to December 31, 2020. Optimum portfolios are designed on the selected seven sectors. An LSTM regression model is also designed for predicting future stock prices. Five months after the construction of the portfolios, i.e., on June 1, 2021, the actual and predicted returns and risks of each portfolio are computed. The predicted and the actual returns indicate the very high accuracy of the LSTM model.
Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week’s open value of the NIFTY 50 time series is the most accurate model.
Competency-based performance management system (CBPMS) has become the key tool for every firm to be in a strategically sustained advantageous position. The objective is to identify relevant and important competencies for successful accomplishment of desired tasks. In this chapter a holistic approach is proposed to review the competency-based approach that is based on the framework of relevant work-related and behavioral competencies. CBPMS is explained in terms of sequential steps of framing the competencies which provide better performance advantages with their expected values and thereby measuring the levels of these work related and behavioral competencies present in the job incumbents with the help of IT interventions. Using this approach, an organization will be able to more effectively use their limited resources to reap more benefits from their investments in both people and technology.
Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible to effectively and efficiently extract, store, process and analyze high volume of stock market data with diversity in its contents. Availability of complex algorithms which can execute very fast on parallel architecture over the cloud has made it possible to achieve higher accuracy in forecasting results while reducing the time required for computation. In this paper, we use the time series data of the healthcare sector of India for the period January 2010 till December 2016. We first demonstrate a decomposition approach of the time series and then illustrate how the decomposition results provide us with useful insights into the behavior and properties exhibited by the time series. Further, based on the structural analysis of the time series, we propose six different methods of forecasting for predicting the time series index of the healthcare sector. Extensive results are provided on the performance of the forecasting methods to demonstrate their effectiveness.
Tourism is increasingly becoming an extremely important sector with its rapidly increasing contribution to GDP of any state or country as a whole. Analyzing and predicting tourist inflow not only enables us to make an accurate estimate of the number of tourists that is likely to visit a destination, but it also provides us with an opportunity to gear up the capacities of that place in terms of logistics, hospitality etc. in order to cater to the tourists leading to an overall socio-economic development of the place. This paper presents a study on the tourism demand for two very popular beaches of the state of West Bengal in India. In this work, time series values of the domestic tourist inflow to Digha and Mandarmoni beaches in West Bengal are used for the period of January 2008-December 2014. The time series is decomposed into its components – trend, seasonal, and random – in order to make further analysis. Based on the structural analysis, five different approaches of forecasting are formulated and the forecast accuracy is computed for each of the methods. Using R statistical tool, extensive results have been presented that provide very meaningful insights to the tourists' inflow time series. The results also demonstrate the effectiveness of our proposed forecasting framework.
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