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Forecasting stock market volatility in India - Using linear and non - Linear models
Volatility models and their forecasting performance attracted the interest of many economic agents, especially for financial risk management. The role of economic agents is to decide which one will be best model for forecasting volatility. This paper examines the modeling and forecasting performance of BSE Sensex daily stock market returns over the period from 1 July 1997 to 31 October 2008, by using simple Random Walk, GARCH, EGARCH and TGARCH models. The out-of-sample forecasts are evaluated by using MAE, RMSE, MAPE and Theil - U Statistics. The result suggests the standardized residual of white noise series strongly rejects the null hypothesis for GARCH model and capture the serial dependence and inherent nonlinearity series. Moreover, Random walk model dominates the forecasting performance and it is considered as the best model followed by the TGARCH model. International Economic Society. -
Forecasting Stock Market Indexes Through Machine Learning Using Technical Analysis Indicators and DWT
In recent years, the stock market prices have become more volatile due to refinement in technology and a rise in trading volume. As these seemingly unpredictable price trends continue, the stock market investors and consumers refer to the security indices to assess these financial markets. To maximise their return on investment, the investors could employ appropriate methods to forecast the stock market trends, taking into account the nonlinearity and nonstationarity of the stock market data. This research aims to assess the predictive capability of supervised machine learning models for the stock market regression analysis. The dataset utilised in this research includes the daily prices and additional technical indicator data of S&P 500 Index of US stock exchange and Nifty50 Index of Indian stock exchange from January 2008 to June 2016; both the indexes are weighted measurements of the top companies listed on respective stock exchanges. The model proposed in this research combines the discrete wavelet transform and support vector regression (SVR) with various kernels such as Linear, Poly and Radial basis function kernel (RBF) of the support vector machine. The results show that using the RBF kernel on Nifty 50 index data, the proposed model achieves the lowest MSE and RMSE error during testing are 0.0019 and 0.0431, respectively, and on S&P 500 index data, it achieves 0.0027 and 0.0523, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Forecasting Prices of Black Pepper in Kerala and Karnataka using Univariate and Multivariate Recurrent Neural Networks
Our country has a high level of agricultural employment. Price swings harm the economy of our country. To combat this impact, forecasting the selling price of agricultural products has become a need. Forecasts of agricultural prices assist farmers, government officials, businesses, central banks, policymakers, and consumers. Price prediction can then assist in making better selections in this area. Black pepper, sometimes known as the "King of Spices, " is a popular spice farmed and exported in India. The largest producers of black pepper are Karnataka and Kerala. For black pepper in Kerala and Karnataka, this study provides a univariate and multivariate price prediction model using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data is denoised using Singular Spectral Analysis (SSA). The most accurate method is the multivariate variate LSTM technique, which uses macroeconomic variables. It has a Mean Absolute Percentage Error (MAPE) of 0.012 and 0.040 for Kerala and Karnataka, respectively. Grenze Scientific Society, 2022. -
Forecasting of foreign currency exchange rate using neural network
Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different training algorithms. The model was trained using historical data to predict four foreign currency exchange rates against Indian Rupee. The forecasting performance of the proposed system is evaluated by using statistical metric and compared. From the results, it is confirmed that the new approach provided an improve technique to forecast foreign exchange rate. It is also an effective tool and significantly close prediction can be made using simple structure. Among the five models, Levenberg-Marquardt based model outperforms than other models and attains comparable results. It also demonstrates the power of the proposed approach and produces more accurate prediction. In conclusion, the proposed scheme can improve the forecasting performance significantly when measured on three commonly used metrics. -
Forecasting of Environmental Sustainability through Green Innovation of E-Vehicle Industry
E-mobility sustainability forecasting is getting more detailed with study, taking into account social cost in addition to technological, economic, or environmental factors. One solution for reducing greenhouse gas emissions is to implement green innovation in the transportation sector. The citizenrys view and acceptance of electric cars must be improved, more research into the social cost of these innovations is required. Consequently, the transportation industry might decarbonize more quickly. Another approach to do it is to advocate for more all-encompassing green innovations that can enhance sustainable development. Using Our Common Future, published in 1987 by the World Commission on Environment and Development [1], the commission emphasized the importance of sustainability while integrating social and economic development. Additionally, it recommended that governments take environmental factors into account while making decisions. The significance of sustainability was then increased and institutionalized, which meant that nations began passing laws that promoted sustainability. Consumer awareness of sustainability is rising largely from an economic and environmental standpoint. This also has an impact on the transportation industry and poses significant environmental, social, and economic difficulties. However, given that it generates close to 5% of the GDP and employs almost 11 million people, transportation is crucial from an economic standpoint. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic
The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute. -
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd. -
Forecasting gold prices based on extreme learning machine /
International Journal Of Computers Communications & Control, Vol.11, Issue 3, pp.372-380, ISSN: 1841-9836. -
Forecasting gold prices based on extreme learning machine
In recent years, the investors pay major attention to invest in gold market because of huge profits in the future. Gold is the only commodity which maintains its value even in the economic and financial crisis. Also, the gold prices are closely related with other commodities. The future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. Hence, an accurate gold price forecasting is required to foresee the business trends. This paper concentrates on forecasting the future gold prices from four commodities like historical data's of gold prices, silver prices, Crude oil prices, Standard and Poor's 500 stock index (S & P500) index and foreign exchange rate. The period used for the study is from 1st January 2000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered Feed forward neural networks called Extreme Learning Machine (ELM) is used which has good learning ability. Also, this study compares the five models namely Feed forward networks without feedback, Feed forward back propagation networks, Radial basis function, ELMAN networks and ELM learning model. The results prove that the ELM learning performs better than the other methods. 2006-2016 by CCC Publications. -
Forecasting Demand for Paddy and Cotton in India: Empirical Analysis Using Machine Learning Models
India has a thriving and varied agricultural sector, which has long served as the foundation of the economy. Agriculture contributes significantly to Indias economy and is essential to the nations food security because a sizable percentage of the countrys agricultural population works in farming and associated industries. Indian farmers have managed to successfully produce a variety of commodities, including cash crops like cotton and sugarcane as well as staples like rice and wheat, despite confronting numerous obstacles like small landholdings, poor infrastructure, and unpredictable weather. In this context, it is crucial to examine the status of Indian agriculture at the moment, its advantages and disadvantages, and the possibilities and difficulties confronting farmers and policymakers. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research
Bitcoin and other cryptocurrencies are the alternative and speculative digital financial assets in today's growing fintech economy. Blockchain technology is essential for ensuring ownership of bitcoin, a decentralized technology. These coins display high volatility and bubble-like behavior. The widespread acceptance of cryptocurrencies poses new challenges to the corporate community and the general public. Currency market traders and fintech researchers have classified cryptocurrencies as speculative bubbles. The study has identified the bitcoin bubble and its breaks during the COVID-19 pandemic. From 1st April 2018 to 31st March 2021, we used high-frequency data to calculate the daily closing price of bitcoin. The prophet model and Arima forecasting methods have both been taken. We also examined the explosive bubble and found structural cracks in the bitcoin using the ADF, RADF, and SADF tests. It found five multiple breaks detected from 2018 to 2021 in bitcoin prices. ARIMA(1,1,0) fitted the best model for price prediction. The ARIMA and Facebook Prophet model is applied in the forecasting, and found that the Prophet model is best in forecasting prices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Forecasting a Fast-Moving Consumer Goods (FMCG) Company's Customer Repurchase Behavior via Classification Machine Learning Models
With numerous businesses offering clients equivalent products, the FMCG (Fast Moving Consumer Goods) industry is very competitive. Retaining client loyalty and encouraging them to return to make product purchases is a big concern for businesses in this sector. One of the main issues this bleak business needs to overcome is customer retention. Failure to repurchase by customers is a sign that they do not trust the brand, which will increase attrition rates and have an adverse effect on the company's revenue. These issues were addressed by attempting to predict the customer repurchase rate and approaching the target segments in accordance with that prediction, but this was done entirely from the perspective of the consumer and not from the retailer, and it ignores other factors like location, the salespeople they work with, the wholesaler they are affiliated with, and the customer programme they have chosen. The retailer's repurchase pattern must be predicted using a more accurate and effective model that considers all the variables. Retailers play a significant role in the supply chain for FMCG businesses. Different models like KNN, Nae Bayes and Logistic Regression was explored to find the best fit. By keeping them, the business can forge enduring connections that are crucial for preserving stabilityand dependability in the distribution network and having the resources necessary to serve its clients. 2023 ACM. -
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Forcing Parameters and Propagation Time of Certain Graph Classes
A branch of mathematics that treats vertices and edges of a graph is called graph theory. This theory is used to replicate many real-life situations and physical problems. Graph coloring problem is one of the prominent studies in extremal graph theory. Suppose information has to be communicated in a network or some product has to be marketed to all the people in a cluster then there are two types of cost that needs to be encountered, one the cost of selecting the initial set of vertices in a network and the second is, time it takes to propagate the information through the entire network. The sum of these two parameters is known as the total cost. Optimizing the total cost, which is the sum of vertices and the time it takes to propagate information through the entire network, is a challenging problem for any newlinegraph. Such an interesting and well-studied problem is called the dynamic coloring newlineproblem. The forcing problem also known as infecting or spreading problem is one newlinesuch dynamic coloring problem where two colors- white and black are used. Assume that a fxed set of vertices in a graph G are initially black and that the remaining vertices are considered white vertices. The aim of the forcing process is to obtain, fully black-colored vertices of the graph G by progressively applying the color change law, making sure that at least one white vertex is forced black in every discrete time interval. The forcing index is the minimum cardinality of the forcing sets. Diand#64256;erent types of forcing, such as one forcing, connected one forcing, k-forcing connected k-forcing etc., are defned based on the color change law. The one forcing is the basic form of forcing. A generalised form of one forcing is known as k-forcing where k lt V (G). The time taken by a forcing set to force the entire vertices of the graph G black is the propagation time or iteration index. The subject of study aims to fnd the one forcing number and k-forcing number of diand#64256;erent types of graph classes and derived graph classes. -
Forced Labour, Global Supply Chain and TNCs: Recent Trends and Practices
The abolition of forced labour is a fundamental element of contemporary international human rights law, but the idea has undergone a protracted and complex history, and the scope of the various international mechanisms that handle different aspects of it is not always precisely defined. Slavery, forced labour, and related practices are strictly prohibited under international law. Forced labour is a longstanding and complex obstacle in global supply chains, frequently associated with the desire for inexpensive products and the outsourcing of manufacturing processes to nations with lax labour regulations. The growing power of transnational corporations (TNCs) poses significant challenges to workers at the bottom of supply chains. However, disagreements have made it unclear how to deal with new forms of forced labour, or modern forms of slavery. This confusion highlights the need for a comprehensive approach to combating these issues. Efforts to stop or restrict forced labour will be made easier with a clear legal definition at both the national and international levels, particularly with an emphasis on the human rights perspective. 2024 Kluwer Law International BV, The Netherlands -
Forbidden Cravings: Exploring socio-cultural ramifications of food practices in Aamis
Food choices represent conscious affirmation and expression of personal, group, ethnic or national identity. Due to its multidimensional role, food that we rely on sustenance is often politicised and used as a tool to create conflict amongst and within diverse social groups. Assamese cuisine includes a rich platter of authentic food varieties, often limited to the north-eastern region. Although food consumption is a subjective experience, cultural taboos within a community might be acceptable practices in another culture, creating conflicting notions of food practices. The balance between the twin axis of culture and politics regarding food is disrupted when heterogeneous cultural patterns and opposing political notions are in discord. Similarly, the solidarity within a cultural group becomes hostile when the authority of the individual concerning food choices is not aligned with the authority of the social structure. This discord from a political and cultural standpoint is evident in the Assamese socio-cultural scenario. Taking Bhaskar Hazarika's Ravening/Aamis (2019) as a case study, this paper proposes to analyse the representational troupe of food, through a structuralist anthropological lens, with respect to food politics to understand socio-cultural ramifications of Assamese food patterns. 2022 Aesthetics Media Services. All rights reserved. -
FOPID controller tuning: A comparative study of optimization techniques for an automatic voltage regulator
This study evaluated a fractional order proportional-integral-derivative (FOPID) controller optimization with a fractional filter for an automated voltage regulator (AVR) system. For the suggested controller, a variety of different parameters can be changed. For the purpose of creating the optimum PID controller for an automated voltage regulator system, comparative analysis using multiple optimization methodologies is carried out. The Salp Swarm Algorithm (SSA), Ant Lion Optimization (ALO), and Particle Swarm Optimization algorithm (PSO) are the techniques that are being examined in this study. The settling time, rising time, and overshoot performance indices is being used. The transient responsiveness of the AVR system was increased by each of the recommended optimization techniques in a different way, and early results were optimistic. The comparison with the most ideally tuned FOPID controllers for the AVR system also serves to support the superiority of the suggested controller. 2023 Author(s). -
Footloose Culture: Migrant Workers and Cultural Meanings of Labour
[No abstract available] -
Football Player Substitution Analysis using NLP and Survival Analysis
Football player substitution is extremely significant in situations where the team is down by goals or attempting to retain a lead that can add value to the team's performance. However, substituting players based on their prior performance would not assist the squad in making good decisions. In one of the papers, they used an inverse gaussian hazard model to determine the survival rate of players. However, the main issue arises when players do not give their all due to their mental state, which plays a critical role during the game. Furthermore, most of the research papers relied solely on past performance of players and various analyses, which was insufficient. This study discovered that the player's mindset should be mentally stable and competitive which is also very crucial during the match by reading various research articles. Hence, this study proposes a framework which comprises of two models, namely Survival Analysis (Kaplan-Meier Fitter) and Natural Language Processing (Sentimental Analysis). Sentimental Analysis would hel p in determining a player's mindset before the match and Kaplan-Meier Fitter is used to find out the survival rate of player's performance based on several factors like goal scored, passing accuracy etc. which would allow the team to make better informed decisions. Comparison of these two models would yield the best results for substitute players on the bench on the basis of their past performance and their mental health which will allow them to make team management to make better judgments. 2023 IEEE. -
Food wastage and consumerism in circular economy: a review and research directions
Purpose: Considering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerisms impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability. Design/methodology/approach: A systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023. Findings: The findings reveal four prominent factors behavioural, environmental, socioeconomic and technological in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways. Research limitations/implications: Consumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour. Originality/value: Addressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends. 2024, Emerald Publishing Limited.