Financial markets: very noisy information processing

Magdon‐Ismail, Malik; Nicholson, Alexander; Abu‐Mostafa, Yaser S.

doi:10.1109/5.726786

Cited by 30 publications

(16 citation statements)

References 11 publications

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“…It means that under this condition the prediction is impossible in a theoretical point of view [10]. Secondly, stock time series are usually very noisy [15]. Thirdly, the distribution of Stock time series is changing over the time.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Kernel Function for Stock Index Forecast

Tang

2009

2009 Second International Symposium on Electronic Commerce and Security

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Stock index forecast plays an important role in finance.One of the challenging problems in forecasting the stock index is that general kernel functions in support vector machine (SVM) can't capture nonstationary characteristic of stock time series accurately. While wavelet function yields features that describe of the stock time series both at various locations and at varying time granularities, so this letter constructed a multidimensional wavelet kernel function and proved it meeting the mercer condition to address this problem. The applicability and validity of wavelet support vector machine (WSVM) for stock index forecasting were analyzed through experiments on real-world stock data. It appeared that the wavelet kernel is more accurate and performs better than the Gaussian kernel.

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Section: Introductionmentioning

confidence: 99%

Wavelet Kernel Function for Stock Index Forecast

Tang

2009

2009 Second International Symposium on Electronic Commerce and Security

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“…Therefore, a simple model with maximum diversification that spreads the risk and invests equally in all assets yields better returns than a complex model that aims to select stocks by active analysis. Stock markets are highly chaotic systems with very high levels of noise (Magdon-Ismail et al 1998;Ghosn and Bengio 1997). Therefore, the price movement of companies on the market are fundamentally unpredictable (Magdon-Ismail et al 1998).…”

Section: Risk-free Profitsmentioning

confidence: 99%

“…Stock markets are highly chaotic systems with very high levels of noise (Magdon-Ismail et al 1998;Ghosn and Bengio 1997). Therefore, the price movement of companies on the market are fundamentally unpredictable (Magdon-Ismail et al 1998). The Efficient Market Hypothesis states that the price of a stock already contains all the available information about the asset, therefore the market is informationally efficient (Malkiel 2003).…”

Section: Risk-free Profitsmentioning

confidence: 99%

Day trading profit maximization with multi-task learning and technical analysis

Bitvai

Cohn

2014

Mach Learn

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Stock price movements are claimed to be chaotic and unpredictable, and mainstream theories of finance refute the possibility of realizing risk-free profit through predictive modelling. Despite this, a large body of technical analysis work maintains that price movements can be predicted solely from historical market data, i.e., markets are not completely efficient. In this paper we seek to test this claim empirically by developing a novel stochastic trading algorithm in the form of a linear model with a profit maximization objective. Using this method we show improvements over the competitive buy-and-hold baseline over a decade of stock market data for several companies. We further extend the approach to allow for non-stationarity in time, and using multi-task learning to modulate between individual companies and the overall market. Both approaches further improve the predictive profit. Overall this work shows that market movements do exhibit predictable patterns as captured through technical analysis.

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“…To achieve maximized fitness, the GA selected 18 technicals;1, 2, 4,5,6,13,19,21,28,30,32,36,38,40,41,42, 46 and 47 from table 4.1. While a 61.1% classification percentage seems like a vast improvement from previous techniques, it needs to be remembered that this is with in-sample data.…”

mentioning

confidence: 99%

“…Other properties are used to set variable values: Appendix C: Data Filtering Stock market data is generally considered noisy data [38]. Minimizing this noise could be an effective tool to simplify and improve the data being classified.…”

mentioning

confidence: 99%

A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers

Caley¹

2000

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Financial markets: very noisy information processing

Cited by 30 publications

References 11 publications

Wavelet Kernel Function for Stock Index Forecast

Wavelet Kernel Function for Stock Index Forecast

Day trading profit maximization with multi-task learning and technical analysis

A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers

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