Back propagation as a test of the efficient markets hypothesis

Tsibouris, George; Zeidenberg, Matthew

doi:10.1109/hicss.1992.183443

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…For NNs, there have been multiple studies that have shown the utility of BP algorithms in stock market prediction problems [ 20 , 21 ], and how easily BP algorithms can outperform even the best regression models for this task [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Valencia

Gómez-Espinosa

Valdés-Aguirre

2019

Entropy

191

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Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.

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Section: Introductionmentioning

confidence: 99%

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Valencia

Gómez-Espinosa

Valdés-Aguirre

2019

Entropy

191

View full text Add to dashboard Cite

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“…These interconnected neurons get activated based on the inputs. A similar study on six stocks listed on the NYSE using historical price data has been reported (Tsibouris and Zeidenberg 1995). Several other studies such as Kolarik and Rudorfer (1994) have noted that ANNs have performed better than statistical techniques such as ARIMA and regression.…”

Section: Literature Reviewmentioning

confidence: 66%

Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?

Mohapatra¹,

Mukherjee²,

Roy

et al. 2022

JRFM

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This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.

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“…The Neural Network Model used to this paper is based on a certain number of data/ information made up a time series of eighteen points and covers the period from 1991 to 2008 (Table 1). We construct a back propagation network (Widrow & Lehr, 1990;Tsibouris & Zeidenberg, 1996;Armano, Marchesi, & Murru, 2005), that consists of one input layer, one middle or hidden layer and one output layer. The input layer has twelve processing elements (PE) neurons; the middle layer has also twelve neurons.…”

Section: Neural Network Constructionmentioning

confidence: 99%

A Study on the Contribution of 12 Key-Factors to the Growth Rates of the Region of the East Macedonia-Thrace (EMTH) by Using a Neural Network Model

Hanias

Antoniades

Magafas

et al. 2012

International Journal of Productivity Management and Assessment Technologies

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This study gives a new methodological framework regarding the measuring of the contribution of some key-factors on the regional growth rate and forecasting the future development rates, based on Neural Network Models (NN Models). It’s a serious attempt to study the contribution of twelve key-factors to the change of the Regional Gross Domestic Product of the Region of East Macedonia -Thrace during a long-term of growth process, by creating and using a suitable Neural Network Model. Specifically, twelve key-factors, time functioned in the period 1991-2008, are studied for the first time, in order to be investigated, scientifically, firstly their % contribution to growth of the regional economy and secondly, to be predicted how much the (Regional Growth Domestic Product) RGDP-under certain conditions-will be changed. It’s a NN Model with inputs the twelve key-factors in order to be evaluated and measured, at the best precise, their percentage contribution to the RGDP. The model and results can be found further into the article.

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Back propagation as a test of the efficient markets hypothesis

Cited by 6 publications

References 10 publications

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?

A Study on the Contribution of 12 Key-Factors to the Growth Rates of the Region of the East Macedonia-Thrace (EMTH) by Using a Neural Network Model

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