Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models

Leung, Mark T.; Daouk, Hazem; Chen, An-Sing

doi:10.2139/ssrn.200429

Cited by 97 publications

(134 citation statements)

References 39 publications

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“…Relevant literature includes Breen, Glosten and Jagannathan (1989), Leitch and Tanner (1991), Wagner, Shellans and Paul (1992), Pesaran and Timmermann (1995), Kuan and Liu (1995), Larsen and Wozniak (1995), Womack (1996), Gencay (1998), Leung, Daouk and Chen (1999), Elliott and Ito (1999), White (2000), Pesaran and Timmermann (2001), and Cheung, Chinn and Pascual (2003).…”

Section: Second Consider Dependence and Hence Forecastability Of Marmentioning

confidence: 99%

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Christoffersen¹,

Diebold²

2003

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We consider three sets of phenomena that feature prominently -and separately -in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very lowfrequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with timevarying conditional skewness and/or kurtosis.

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Section: Second Consider Dependence and Hence Forecastability Of Marmentioning

confidence: 99%

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Christoffersen¹,

Diebold²

2003

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“…Kimoto et al (1990) presented a technique using neural network based on historical accounting data and various macroeconomic parameters to forecast variations in stock returns. Leigh et al (2005) Leung et al (2000) and Kim (2004) proposed applications of hybrid systems in stock price prediction.…”

Section: Summary Of Forecasting Resultsmentioning

confidence: 99%

A Predictive Analysis of the Indian FMCG Sector Using Time Series Decomposition-Based Approach

Sen

Chaudhuri

2017

SSRN Journal

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Abstract. Stock price movements being random in its nature, prediction of stock prices using time series analysis presents a very difficult and challenging problem to the research community. However, over the last decade, due to rapid development and evolution of sophisticated algorithms for complex statistical analysis of large volume of time series data, and availability of high-performance hardware and parallel computing architecture, it has become possible to efficiently process and effectively analyze voluminous and highly diverse stock market time series data effectively, in real-time. Robust predictive models are being built for accurate forecasting of values of highly random variables such as stock price movements. This paper has presented a highly reliable and accurate forecasting framework for predicting the time series index values of the fast moving consumer goods (FMCG) sector in India. A time series decomposition approach is followed to understand the behavior of the FMCG sector time series for the period January 2010 till December 2016. Based on the structural analysis of the time series, six methods of forecast are designed. These methods are applied to predict the time series index values for the months of 2016. Extensive results are presented to demonstrate the effectiveness ofthe proposed decomposition approaches of time series and the efficiency of the six forecasting methods.

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“…Our near concentrate likewise measures the relative quality of these models as for the exchanging benefit created by their conjectures. To encourage more successful exchanging, we build up an arrangement of limit exchanging rules driven by the probabilities assessed by the order models [12,13].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Sun¹,

Tang²,

Guo³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

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Abstract. Financial time arrangement, for example, stock cost and trade rates are, regularly, non-straight and non-stationary. Beforehand, numerous scientists have endeavored to conjecture those utilizing factual models and machine learning models. Measurable models expect the time arrangement to be stationary and straight, accordingly bringing about expansive factual mistakes. Examination and expectation of securities exchange time arrangement information has pulled in impressive enthusiasm from the exploration group in the course of the most recent decade. Quick advancement and development of modern calculations for measurable examination of time arrangement information, and accessibility of elite equipment has made it conceivable to prepare and dissect high volume securities exchange time arrangement information successfully, continuously. In monetary field, exceptions speak to unpredictability of securities exchange, which assumes an imperative part in administration, portfolio choice and subordinate evaluating. Along these lines, anticipating anomalies of securities exchange is of the immense significance in principle and application. In this paper, the issue of anticipating anomalies in light of versatile gathering models of Intense Erudition Machines (IEMs) is considered. We discovered that the prescribed novel model is material for exception estimating and beats the strategies in light of auto relapse (AR) Intense Erudition Machines (IEMs) models.

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Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models

Cited by 97 publications

References 39 publications

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

A Predictive Analysis of the Indian FMCG Sector Using Time Series Decomposition-Based Approach

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

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