Parallel prediction of stock volatility

Priscilla Jenq, John Jenq

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

The financial industry is an industry that requires multidisciplinary expertise. To be a good financial engineer, one should possess skills in math, finance, economics, and coding. Volatility is a measurement of the risk of financial products. A stock will hit new highs and lows over time and if these highs and lows fluctuate wildly, then it is considered a high volatile stock. Such a stock is considered riskier than a stock whose volatility is low. High tech stocks usually have high volatility. Although these stocks are riskier, the returns that they generate for investors can be quite high. Of course, with a riskier stock also comes the chance of losing money and yielding negative returns. In this project, we will use historic stock data to help us forecast volatility. The financial industry usually uses S&P 500 as the indictor of the market. Therefore, S&P 500 would be a benchmark to compute the risk. We will use artificial neural networks as a tool to predict volatilities for a period of time frame that will be set when we configure this neural network. There have been reports that neural networks with different numbers of layers and different numbers of hidden nodes may generate varying results. As a matter of fact, we may be able to find the best configuration of a neural network to compute volatilities. We will implement this system using the parallel approach. The system can be used as a tool for investors to allocating and hedging assets.

Original languageEnglish
Title of host publicationWMSCI 2017 - 21st World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
EditorsNagib C. Callaos, Michael Savoie, Andres Tremante, Belkis Sanchez
PublisherInternational Institute of Informatics and Systemics, IIIS
Pages26-29
Number of pages4
Volume1
ISBN (Electronic)9781941763599
StatePublished - 1 Jan 2017
Event21st World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2017 - Orlando, United States
Duration: 8 Jul 201711 Jul 2017

Other

Other21st World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2017
CountryUnited States
CityOrlando
Period8/07/1711/07/17

Fingerprint

Neural networks
Industry
Finance
Engineers
Economics

Keywords

  • Artificial neural network
  • Parallel processing
  • Volatility

Cite this

Jenq, P., & Jenq, J. (2017). Parallel prediction of stock volatility. In N. C. Callaos, M. Savoie, A. Tremante, & B. Sanchez (Eds.), WMSCI 2017 - 21st World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings (Vol. 1, pp. 26-29). International Institute of Informatics and Systemics, IIIS.
Jenq, Priscilla ; Jenq, John. / Parallel prediction of stock volatility. WMSCI 2017 - 21st World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. editor / Nagib C. Callaos ; Michael Savoie ; Andres Tremante ; Belkis Sanchez. Vol. 1 International Institute of Informatics and Systemics, IIIS, 2017. pp. 26-29
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abstract = "The financial industry is an industry that requires multidisciplinary expertise. To be a good financial engineer, one should possess skills in math, finance, economics, and coding. Volatility is a measurement of the risk of financial products. A stock will hit new highs and lows over time and if these highs and lows fluctuate wildly, then it is considered a high volatile stock. Such a stock is considered riskier than a stock whose volatility is low. High tech stocks usually have high volatility. Although these stocks are riskier, the returns that they generate for investors can be quite high. Of course, with a riskier stock also comes the chance of losing money and yielding negative returns. In this project, we will use historic stock data to help us forecast volatility. The financial industry usually uses S&P 500 as the indictor of the market. Therefore, S&P 500 would be a benchmark to compute the risk. We will use artificial neural networks as a tool to predict volatilities for a period of time frame that will be set when we configure this neural network. There have been reports that neural networks with different numbers of layers and different numbers of hidden nodes may generate varying results. As a matter of fact, we may be able to find the best configuration of a neural network to compute volatilities. We will implement this system using the parallel approach. The system can be used as a tool for investors to allocating and hedging assets.",
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Jenq, P & Jenq, J 2017, Parallel prediction of stock volatility. in NC Callaos, M Savoie, A Tremante & B Sanchez (eds), WMSCI 2017 - 21st World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. vol. 1, International Institute of Informatics and Systemics, IIIS, pp. 26-29, 21st World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2017, Orlando, United States, 8/07/17.

Parallel prediction of stock volatility. / Jenq, Priscilla; Jenq, John.

WMSCI 2017 - 21st World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. ed. / Nagib C. Callaos; Michael Savoie; Andres Tremante; Belkis Sanchez. Vol. 1 International Institute of Informatics and Systemics, IIIS, 2017. p. 26-29.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AU - Jenq, John

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A2 - Callaos, Nagib C.

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PB - International Institute of Informatics and Systemics, IIIS

ER -

Jenq P, Jenq J. Parallel prediction of stock volatility. In Callaos NC, Savoie M, Tremante A, Sanchez B, editors, WMSCI 2017 - 21st World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. Vol. 1. International Institute of Informatics and Systemics, IIIS. 2017. p. 26-29