Forecasting futures trading volume using neural networks

Kaastra, Iebeling; Boyd, Milton S.

doi:10.1002/fut.3990150806

Cited by 106 publications

(49 citation statements)

References 10 publications

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“…To make the solution of SVM sparser, adopts the following form: (10) where has the same meaning as in (9). is the parameter to control the descending rate.…”

Section: B Modification Of Parametermentioning

confidence: 99%

“…In addition, they are more flexible and have the capability to learn dynamic systems through a retraining process using new data patterns. So neural networks are more powerful in describing the dynamics of financial time series in comparison to traditional statistical models [10]- [12]. Recently, a novel type of learning machine, called the support vector machine (SVM), has been receiving increasing attention in areas ranging from its original application in pattern recognition [13]- [15] to the extended application of regression estimation [16]- [19].…”

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confidence: 99%

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Support vector machine with adaptive parameters in financial time series forecasting

Cao

Tay

2003

IEEE Trans. Neural Netw.

861

344

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“…To make the solution of SVM sparser, adopts the following form: (10) where has the same meaning as in (9). is the parameter to control the descending rate.…”

Section: B Modification Of Parametermentioning

confidence: 99%

mentioning

confidence: 99%

Support vector machine with adaptive parameters in financial time series forecasting

Cao

Tay

2003

IEEE Trans. Neural Netw.

861

344

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“…The empirical finance literature has mainly concentrated on trying to predict returns and volatility, but has hardly paid any attention to volume: a search for pertinent contributions in the literature has brought about only one single entry, Kaastra and Boyd (1995). These authors use neural networks and ARIMA models to forecast monthly futures trading volume for the Winnipeg Commodity Exchange.…”

Section: Modelsmentioning

confidence: 99%

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Lux

Kaizoji

2007

Journal of Economic Dynamics and Control

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Abstract:We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal model) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have quite a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the naïve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models.

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“…• Commodity trading: Collard (1991), Bergerson and Wunsch (1991), Trippi and DeSieno (1992), Kaastra and Boyd (1995).…”

Section: Exhibit 1 Applications Of Neural Networkmentioning

confidence: 99%

Using neural networks for forecasting volatility of S&P 500 Index futures prices

Hamid

Iqbal

2004

Journal of Business Research

169

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Ability to forecast market variables is critical to analysts, economists and investors.Among other uses, neural networks are gaining in popularity in forecasting market variables.They are used in various disciplines and issues to map complex relationships.We present a primer for using neural networks for forecasting market variables in general, and in particular, forecasting volatility of the S&P 500 Index futures prices. We compare volatility forecasts from neural networks with implied volatility from S&P 500 Index futures options using the Barone-Adesi and Whaley (BAW) model for pricing American options on futures. Forecasts from neural networks outperform implied volatility forecasts. Volatility forecasts from neural networks are not found to be significantly different from realized volatility.Implied volatility forecasts are found to be significantly different from realized volatility in two of three cases.

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Forecasting futures trading volume using neural networks

Cited by 106 publications

References 10 publications

Support vector machine with adaptive parameters in financial time series forecasting

Support vector machine with adaptive parameters in financial time series forecasting

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Using neural networks for forecasting volatility of S&P 500 Index futures prices

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