Deep Learning Stock Volatility with Google Domestic Trends

By Ruoxuan Xiong, Eric Paul Nichols, Yuan Shen. 2015

97 citations recorded by the source corpus.

We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors. In a held-out test set, our Long Short-Term Memory model gives a mean absolute percentage error of 24.2%, outperforming linear Ridge/Lasso and autoregressive GARCH benchmarks by at least 31%. This evaluation is based on an optimal observation and normalization scheme which maximizes the mutual information between domestic trends and daily volatility in the training set. Our preliminary investigation shows strong promise for better predicting stock behavior via deep learning and neural network models.

Subfields: Economics and Econometrics, Management Science and Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [161] used the price data and different types of Google Domestic trends with LSTM.”
Financial Time Series Prediction Using Deep Learning (2017, methodology): “Xiong [29] applied Long-Short-Term Memory (LSTM) neural networks to model the S&P500 volatility, using the Google stock domestic trends as an indicators of the market volatility.”
Using deep learning to detect price change indications in financial markets (2017, methodology): “The daily data of the S&P 500 market fund prices and Google domestic trends of 25 terms like “bankruptcy” and “insurance” are used as the input to a recurrent neural network that it is trained to predict the volatility of the market fund’s price [3].”
Blockchain for AI: Review and Open Research Challenges (2019, background): “[126] developed a neural network model that harnesses the potential of deep learning financial time series in the presence of strong noise which proved that, with huge volumes of datasets, AI techniques perform better”
Machine learning in energy economics and finance: A review (2019, methodology): “Examples include SVM (Tang et al., 2009) and DL (Xiong et al., 2015, Chatzis et al., 2018).”
Sentiment-aware stock market prediction: A deep learning method (2017, background): “Their work shows the potential of deep learning financial time series in the presence of strong noise [34].”
Deep learning with long short-term memory networks for financial market predictions (2017, background): “Selected applications, as in Xiong et al. (2015), focus on predicting the volatility of the S&P 500, on selected foreign exchange rates (Giles et al., 2001), or on the impact of incorporating news for selected companies (Siah and Myers, n.d.).”
An innovative neural network approach for stock market prediction (2018, result): “The result showed that the map of LAPM was smaller than the linear model and the autoregressive model [24].”
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017, methodology): “The daily data of the S&P 500 market fund prices and Google domestic trends of 25 terms like “bankruptcy” and “insurance” are used as the input to a recurrent neural network that it is trained to predict the volatility of the market fund’s price [3].”