Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News

Kumar, B. Shravan; Ravi, Vadlamani; Miglani, Rishabh

doi:10.1007/s40745-020-00272-2

Cited by 14 publications

(7 citation statements)

References 55 publications

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“…6 shows most preferred course by the student is Master of Science because of reason- Grad school may give an individual more self-awareness and responsibility. In today's competitive employment environment, having an international credential might provide a student with an edge [ 19 – 21 ]. …”

Section: Resultsmentioning

confidence: 99%

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Student Trend Analysis for Foreign Education Employing Machine Learning: A Case Study from ‘Disha Consultants’, Gujarat, India

2022

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Section: Resultsmentioning

confidence: 99%

“…In today's competitive employment environment, having an international credential might provide a student with an edge [ 19 – 21 ].…”

Section: Resultsmentioning

confidence: 99%

Student Trend Analysis for Foreign Education Employing Machine Learning: A Case Study from ‘Disha Consultants’, Gujarat, India

2022

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“…In the literature [ 10 ], the stability of recurrent neural networks with time-varying time lags is analyzed using FTM (Flexible Edge Method), and several new stability criteria are proposed to describe the stability of this neural network by constructing a new type of Lyapunov function. In the literature [ 11 ], a novel design idea for CVHAMs (complex-valued multistate Hopfield associative memory) is proposed to analyze the stability of CVHAM systems utilizing the energy function method and finally obtain that the network can converge to an immobile point for an arbitrary input value, and the projection geometry of the GPR (generalized projection rule) is discussed. According to the literature [ 12 ], Internet enterprises have the financial characteristics of the “flow economy” model, asset-light operation model, cost-light and expense-light, fast product replacement, and equity financing.…”

Section: Status Of Researchmentioning

confidence: 99%

An Empirical Analysis of Corporate Financial Management Risk Prediction Based on Associative Memory Neural Network

Chen

2021

Computational Intelligence and Neuroscience

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As a human brain-like computational model that can reflect the cognitive function of the brain, the problem of dynamic analysis of associative memory neural networks has attracted the attention of scholars. This paper combines associative memory neural networks with enterprise financial management risks, studies the synchronization control and stability analysis problems of unidirectional associative memory-like human brain amnestic neural networks with perturbation and mixed time-varying time lags, proposes a bidirectional associative memory-like brain stochastic amnestic neural network model with mixed time-varying time lags, designs a discrete-time sampling control strategy based on the model, and studies various types of recent financial risks. Based on the early warning research, based on the associative memory neural network method, we propose to reconstruct the risk categories, including improving the enterprise risk management system, enhancing the awareness of financial risk management from top to bottom, and strengthening the core competitiveness of the enterprise itself and control measures for financing, investment, operation, and cash flow risks.

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“…They trained the model on the stock prices of Google (NASDAQ: Google) and also collected financial news articles from Dow Jones to predict the upward and downward movement of the stock. Another work [ 24 ] proposed a hybrid model that utilizes news articles for text mining and the corresponding stock prices of the selected companies. They extracted the textual psycholinguistic features using LIWC and TAALES software from the collected news articles related to Indian companies.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Directional Movement of Stock Prices using Deep Learning

et al. 2022

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Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation.

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Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News

Cited by 14 publications

References 55 publications

Student Trend Analysis for Foreign Education Employing Machine Learning: A Case Study from ‘Disha Consultants’, Gujarat, India

Student Trend Analysis for Foreign Education Employing Machine Learning: A Case Study from ‘Disha Consultants’, Gujarat, India

An Empirical Analysis of Corporate Financial Management Risk Prediction Based on Associative Memory Neural Network

Forecasting Directional Movement of Stock Prices using Deep Learning

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