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Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI
16 references·1 citing·57 second hop
Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI (2021)Introspecting predictability of market fe…Technical analysis strategy optimization using a machine learning approach in stock market indices (2021)Technical analysis strategy o…International stock market risk contagion during the COVID-19 pandemic (2021)International stock market ri…Impact of COVID-19 on stock market efficiency: Evidence from developed countries (2021)Impact of COVID-19 on stock m…Fake news detection: A hybrid CNN-RNN based deep learning approach (2021)Fake news detection: A hybrid…Effects of the COVID-19 pandemic on the US stock market and uncertainty: A comparative assessment between the first and second waves (2021)Effects of the COVID-19 pande…Which sentiment index is more informative to forecast stock market volatility? Evidence from China (2020)Which sentiment index is more…Theory building with big data-driven research – Moving away from the “What” towards the “Why” (2020)Theory building with big data…Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis: Evidence from the Markov switching approach (2020)Stock market returns, volatil…Gold volatility prediction using a CNN-LSTM approach (2020)Gold volatility prediction us…COVID-19 lockdowns, stimulus packages, travel bans, and stock returns (2020)COVID-19 lockdowns, stimulus …Social-media and intraday stock returns: The pricing power of sentiment (2019)Social-media and intraday sto…Novel volatility forecasting using deep learning–Long Short Term Memory Recurrent Neural Networks (2019)Novel volatility forecasting …A local and global event sentiment based efficient stock exchange forecasting using deep learning (2019)A local and global event sent…Deep learning with long short-term memory networks for financial market predictions (2017)Deep learning with long short…The VIX, the variance premium and stock market volatility (2014)The VIX, the variance premium…Is VIX an investor fear gauge in BRIC equity markets? (2012)Is VIX an investor fear gauge…A deep learning approach for estimation of price determinants (2022)A deep learning approach for …CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets (1965)THE VALUATION OF RISK ASSETS AND THE SELECTION OF RISKY INVESTMENTS IN STOCK PORTFOLIOS AND CAPITAL BUDGETS**This paper is another in a series of interrelated theoretical and statistical studies of corporate financial and investment policies being made under grants from the Rockefeller Foundation, and more recently the Ford Foundation, to the Harvard Business School. The generous support for this work is most gratefully acknowledged. The author is also much indebted to his colleagues Professors Bishop, Christenson, Kahr, Raiffa, and (especially) Schlaifer, for extensive discussion and commentary on an earlier draft of this paper; but responsibility for any errors or imperfections remains strictly his own.[Professor Sharpe's paper, “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk” (Journal of Finance, September 1964) appeared after this paper was in final form and on its way to the printers. My first section, which parallels the first half of his paper (with corresponding conclusions), sets the algebraic framework for sections II, III and VI, (which have no counterpart in his paper) and for section IV on the equilibrium prices of risk assets, concerning which our results differ significantly for reasons which will be explored elsewhere. Sharpe does not take up the capital budgeting problem developed in section V below.] (1975)Dynamic network of implied volatility transmission among US equities, strategic commodities, and BRICS equities (2018)Stock Market Forecasting Using Computational Intelligence: A Survey (2020)A closer look into the global determinants of oil price volatility (2021)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)On Persistence in Mutual Fund Performance (1997)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)The Adjustment of Stock Prices to New Information (1969)Efficient Capital Markets: II (1991)Twitter mood predicts the stock market (2011)A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction (2021)Stock Prediction Based on Technical Indicators Using Deep Learning Model (2021)Stock-Price Forecasting Based on XGBoost and LSTM (2021)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold (2010)An Advanced CNN-LSTM Model for Cryptocurrency Forecasting (2021)Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)Stock Price Prediction Using CNN-BiLSTM-Attention Model (2023)Structural Equation Modeling and Regression: Guidelines for Research Practice (2000)Design science in information systems research (2004)Design Science in Information Systems Research1 (2004)From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets (2021)Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector (2021)User-Generated Content behavior and digital tourism services: A SEM-neural network model for information trust in social networking sites (2021)Investor Sentiment and the Cross‐Section of Stock Returns (2006)Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns (2020)Financial markets under the global pandemic of COVID-19 (2020)Japanese currency and stock market—What happened during the COVID-19 pandemic? (2020)COVID-19 pandemic and firm performance: Cross-country evidence (2021)The nexus between COVID-19 fear and stock market volatility (2021)"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection (2017)The spread of true and false news online (2018)Systematic Literature Review on the Spread of Health-related Misinformation on Social Media (2019)A Comprehensive Review on Fake News Detection With Deep Learning (2021)Time-series forecasting of seasonal items sales using machine learning – A comparative analysis (2022)A Deep Learning-based Fast Fake News Detection Model for Cyber-Physical Social Services (2023)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)The Impact of Uncertainty Shocks (2009)Exploiting the errors: A simple approach for improved volatility forecasting (2015)The Asset Redeployability Channel: How Uncertainty Affects Corporate Investment (2016)The Time Variation in Risk Appetite and Uncertainty (2021)Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices (2001)Introduction to Time Series and Forecasting (2002)The Adaptive Markets Hypothesis (2004)Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period (2022)A hybrid decision support system for adaptive trading strategies: Combining a rule-based expert system with a deep reinforcement learning strategy (2023)Stock Market Prediction Using Machine Learning and Deep Learning Techniques: A Review (2025)Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder‐LSTM Techniques (2021)Predicting stock market volatility based on textual sentiment: A nonlinear analysis (2021)What is the best RNN-cell structure to forecast each time series behavior? 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References

  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 2014The VIX, the variance premium and stock market volatility812 cited
  • 2021Fake news detection: A hybrid CNN-RNN based deep learning approach567 cited
  • 2020COVID-19 lockdowns, stimulus packages, travel bans, and stock returns401 cited
  • 2020Theory building with big data-driven research – Moving away from the “What” towards the “Why”301 cited
  • 2020Gold volatility prediction using a CNN-LSTM approach274 cited
  • 2019Novel volatility forecasting using deep learning–Long Short Term Memory Recurrent Neural Networks208 cited
  • 2012Is VIX an investor fear gauge in BRIC equity markets?193 cited
  • 2019A local and global event sentiment based efficient stock exchange forecasting using deep learning178 cited
  • 2021Technical analysis strategy optimization using a machine learning approach in stock market indices171 cited
  • 2020Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis: Evidence from the Markov switching approach170 cited
  • 2019Social-media and intraday stock returns: The pricing power of sentiment169 cited
  • 2021Effects of the COVID-19 pandemic on the US stock market and uncertainty: A comparative assessment between the first and second waves169 cited
  • 2021Impact of COVID-19 on stock market efficiency: Evidence from developed countries

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167 cited
  • 2021International stock market risk contagion during the COVID-19 pandemic166 cited
  • 2020Which sentiment index is more informative to forecast stock market volatility? Evidence from China163 cited
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    • 2022A deep learning approach for estimation of price determinants11 cited