Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
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48 results for “Indian chemical industry”
DeepVARMA predicts chemical industry index trends using LSTM and VARMAX models.
problem Forecasting the chemical industry index for economic analysis.
method Combines LSTM and VARMAX models to predict nonstationary series.
result DeepVARMA achieves best prediction accuracy and adaptability.
Network structure of inter-industry flowsphysics.soc-ph
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Study examines impact of capital structure on Indian auto companies' profitability.
problem Understanding the impact of capital structure on profitability of Indian auto companies.
method Used fixed and random effect models with 10 years of data from 17 companies.
result Optimal capital structure improves company performance and maintains capital adequacy.
A new HOM model improves forecasting of Indian base metal prices.
problem Improving accuracy in predicting base metal prices in the Indian market.
method A Higher Order Markovian (HOM) model with varying order based on market delay.
result The HOM model consistently outperforms the standard Markovian model in forecasting.
Proposes SPCA to incorporate structural constraints in model identification.
problem Model identification with partial structural knowledge.
method Structural Principal Component Analysis (SPCA) that leverages structural information.
result Demonstrates improved model estimates using synthetic and industrial data.
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Propertiesstat.ML
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automat…
Study compares Indian derivatives markets and finds NSE outperforming BSE.
problem Lack of strong regulations and robust framework in Indian derivatives market.
method Comparison of performance of derivatives in BSE and NSE, analysis of derivatives with cash market and market volatility.
result NSE derivatives outperform BSE, need stronger regulations.
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that…
Study benchmarks mutual funds in India using DEA, finding efficiency metrics.
problem Benchmark mutual funds in India based on efficiency metrics.
method Data Envelopment Analysis (DEA) model incorporating risk, cost, return, and information ratio.
result DEA model identifies efficiency frontier and compares results with traditional metrics.
Bayesian Recurrent Neural Networks improve fault detection and identification in manufacturing.
problem Detect and identify faults in chemical processes to ensure optimal operations.
method Bayesian Recurrent Neural Networks (BRNNs) with variational dropout.
result BRNNs provide uncertainty estimates for fault detection and identification.
Paper presents LSTM models for short-term stock price prediction.
problem Accurately predicting short-term stock prices is challenging.
method Univariate and multivariate LSTM models using historical data.
result Multivariate LSTM model with technical indicators outperforms univariate model.
Safe offline RL for chemical reactors using input convex neural networks.
problem Safe control of exothermic polymerization reactors using historical data.
method Gymnasium-compatible simulation, behaviour cloning, implicit Q-learning, input convex neural networks (PICNNs).
result Offline RL with convex action correction outperforms traditional control approaches.
Model predicts viscosity of multicomponent systems efficiently.
problem Expensive experimental viscosity measurements in various industries.
method Artificial neural networks trained on a database of chemical systems and temperatures.
result Model Viskositas provides more accurate predictions with lower errors, variability, and outliers.
Surrogate models improve chemical process equipment design and optimization.
problem Design and optimization of complex chemical processes.
method Development and validation of CFD simulators, active learning strategies, and regression techniques.
result Gaussian process regression outperformed other techniques in benchmarking.
Paper proposes a method to identify key variables in thick data.
problem Selecting significant variables for response prediction.
method Permutation tests on candidate variables for feature selection.
result The approach outperforms Lasso in feature selection.
Study finds WACC negatively impacts firm profitability in Bangladesh's food industry.
problem Determining the impact of Weighted Average Cost of Capital (WACC) on firm profitability.
method Fixed Effects Panel Regression Model using 12 food and allied industry companies from 2005-2019.
result WACC negatively correlates with firm profitability (ROA), significant relationship.
Study shows demonetization strengthened Indian currency and stock market.
problem Impact of demonetization on Indian stock market and foreign exchange rate.
method Daily rate of return analysis of foreign exchange rate and Nifty 50 index, use of dummy variable for demonetization period.
result Demonetization led to an upward trend in Indian stock market and strengthened the Indian currency (decreased foreign exchange rate).
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
problem Survival strategies of unorganized retailers challenged by organized and e-retailers.
method Field investigation, conjoint analysis, design of experiments, agent-based simulation.
result Unorganized retailers' performance influenced by their own attribute levels and price discounts.
The paper evaluates various machine learning models for predicting industrial aging processes.
problem Accurately predicting industrial aging processes to schedule maintenance efficiently.
method Compared traditional stateless models (linear and kernel ridge regression, feed-forward neural networks) to more complex recurrent neural networks (echo state networks and LSTMs) on synthetic and real-world data.
result Recurrent models produce near perfect predictions when trained on larger datasets and maintain good performance even with domain shifts, while simpler models perform comparably on smaller datasets.
Study confirms Indian stock market is weak form inefficient.
problem Impact of stock market efficiency on investment returns.
method Runs test, Autocorrelation test, Autoregression test on daily stock indices.
result Indian stock market is weak form inefficient and can be outperformed.
Study examines downsizing impact on Indian construction firms' profitability.
problem Impact of downsizing layoffs on construction firms' profitability in India.
method Used Co-integration test, OLS, and VAR models on secondary data of 15 companies.
result Employee Expenses and Number of Employees have significant impact on profitability.
Creating an Optimal Portfolio of Crops Using Price Forecasting to Increase ROI for Indian Farmersq-fin.RM
Project forecasts crop prices to help Indian farmers choose optimal crops for better ROI.
problem Indian farmers struggle to choose crops that fetch decent profits due to lack of scientific decision-making.
method Price forecasting to create an optimal portfolio of crops for better ROI.
result Data-driven decision-making for crop selection leads to higher estimated ROI.
Deep RL applied for Indian stock trading strategies.
problem Designing profitable trading strategies for Indian stock markets.
method Applied deep reinforcement learning to ten Indian stock datasets.
result Models' performance compared and evaluated.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
problem Understanding cash conversion cycle in manufacturing firms and its impact on profitability and size.
method Empirical study of 30 manufacturing firms in Dhaka Stock Exchanges, categorizing them into six industries, analyzing industry averages and relationships with size and profitability.
result Negative relationship between cash conversion cycle and profitability, especially ROE; negative relationship with firm size in terms of net sales.
CTS machines improve screen development in printing industries, reducing costs and increasing profitability.
problem Inefficient screen development process in heat transfer printing and labeling.
method Implementation of CTS (computer-to-screen) technology for screen development.
result Reduction in material and operational costs, leading to profitability.
Analyzes Indian commercial dynamism using time series data.
problem Understanding commercial dynamism in India.
method Time series analysis of various economic indicators.
result Detailed insights into growth rate, trade balance, etc.
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
problem Improving financial competitiveness of Indian real estate companies in a competitive market.
method Financial competitiveness evaluation index system using key financial ratios and a scoring system.
result Companies with high scores have strong profitability and operational capacity, while those with lower scores struggle with solvency and working capital.
BoFire optimizes chemistry experiments using Bayesian Optimization.
problem Effective deployment of Bayesian Optimization in the chemical industry.
method Combines Bayesian Optimization with DoE strategies, providing a rich feature-set.
result BoFire enables seamless integration into RESTful APIs for real-world use.
The nature of fluctuations in the Indian financial market is analyzed in this paper. We have looked at the price returns of individual stocks, with tick-by-tick data from the National Stock Exchange (NSE) and daily closing price data from both NSE and the Bombay Stock Exchange (BSE), the two largest exchanges in India.…
Scaling Analysis on Indian Foreign Exchange Marketphysics.soc-ph
In this paper we investigate the scaling behavior of the average daily exchange rate returns of the Indian Rupee against four foreign currencies namely US Dollar, Euro, Great Britain Pound and Japanese Yen. Average daily exchange rate return of the Indian Rupee against US Dollar is found to exhibit a persistent scaling…
This study compares three portfolio optimization methods on Indian stocks.
problem Comparing portfolio optimization methods on Indian stocks.
method Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning approaches.
result Reinforcement Learning outperformed other methods in terms of Sharpe ratio.
ICP models flexible DAG structures using Bayesian nonparametrics.
problem Learning the structure of complex neural networks.
method Bayesian nonparametric prior on DAGs and orders controlled by a latent Beta Process.
result ICP supports every possible DAG structure.
The paper develops a Gaussian process model for predicting chemical efficacy.
problem Statistical methodologies for analyzing chemical databases are limited.
method Conditional Gaussian process models with Tanimoto distance and a scaling parameter.
result Predictive performance improves when accounting for chemical space correlation.
We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-param…
Is the Indian Stock Market efficient - A comprehensive study of Bombay Stock Exchange Indicesq-fin.ST
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no undervalued securities i.e. securities whose value is less than its assumed intrinsic value, which of…
FinTech framework clusters innovations for financial services.
problem Lack of comprehensive definition and analysis of FinTech.
method Narrative review of over 100 studies, clustering framework development.
result Developed a comprehensive FinTech clustering framework.
Historical daily data for eleven years of the fifty constituent stocks of the NIFTY index traded on the National Stock Exchange have been analyzed to check for the stylized facts in the Indian market. It is observed that while some stylized facts of other markets are also observed in Indian market, there are significan…
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
problem Determining abnormal returns for physical momentum portfolios in the Indian stock market.
method Constructed physical momentum portfolios for daily, weekly, monthly, and yearly timescales, evaluated historical returns and risk profiles.
result Daily time scale physical momentum portfolios showed the strongest reversal with a 16-fold profit.
Study clusters Indian stocks using polyspectral means for nuanced market insights.
problem Analyzing temporal patterns and financial relationships in Indian stock market.
method k-means clustering algorithm applied to polyspectral means of stock data.
result Identified five distinctive clusters of stocks with varying ownership structures.
Machine learning improves financial stress testing in Indian markets.
problem Conventional stress testing limitations in Indian financial markets.
method Dimensionality reduction, latent factor modeling, Variational Autoencoders, Monte Carlo simulation.
result Improved flexibility, robustness, and realism in financial stress testing.
CIBP models feature abundance in latent feature models.
problem Modeling feature abundance in latent feature models.
method Proposes a new Bayesian nonparametric prior, the CIBP, for latent feature models.
result The expected number of features is bounded even as the number of objects increases.
In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especia…
In this paper a data analytical approach featuring support vector machines (SVM) is employed to train a predictive model over an experimentaldataset, which consists of the most relevant studies for two-phase flow pattern prediction. The database for this study consists of flow patterns or flow regimes in gas-liquid two…
This review discusses challenges and solutions for AI in chemical engineering.
problem Challenges in applying classical machine learning to chemical engineering data.
method Identifying four data characteristics and discussing their applications and solutions.
result Current research extends data science and machine learning to handle chemical engineering data challenges.
Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained chemists several days to complete. Given a chemical formula, there could be over a…
GraphAF generates chemically valid molecules efficiently and accurately.
problem Generating chemically valid molecular structures while optimizing chemical properties.
method Flow-based autoregressive model combining autoregressive and flow-based approaches.
result GraphAF generates 68% chemically valid molecules without chemical knowledge rules and 100% with rules, achieving state-of-the-art performance.
A flexible calendar rebalancing approach for Indian stock portfolios.
problem Optimizing stock portfolio performance in the Indian stock market.
method Calendar rebalancing of sector-specific portfolios based on historical stock prices.
result The proposed calendar rebalancing approach improves portfolio performance over the test period.