Behavioral Finance and Capital Markets

Szyszka, Adam

doi:10.1057/9781137366290

Cited by 29 publications

(10 citation statements)

References 569 publications

(901 reference statements)

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“…works on behavioral finance (Szyszka, 2013) found much evidence to this question. For example, researchers argue that there are noise traders in the market who tend to make irrational actions so the price moves away from its value (Black, 1986;De Long et al, 1990;Hommes, 2013).…”

Section: Strategymentioning

confidence: 99%

“…Firstly, we propose to apply the machine learning framework to formalize the PE ratio valuation process which somehow rarely gets attention from academic re-searchers. In contrast to existing machine learning frameworks mentioned above on price pattern discovery where the discovered patterns have no meaning in finance, the interpretation of our model is well justified according to behavioral finance (Szyszka, 2013) as explained in the Section 2. Secondly, as our proposed dynamic Bayesian network having non-standard structure compared to literatures (Bishop, 2006;Murphy, 2012), we have derived the new inference formulas by applying the forward-backward methodology and the new parameter estimation algorithm according to the concept of Expectation-Maximization (Bishop, 2006;Murphy, 2012), see Section 3.…”

Section: Introductionmentioning

confidence: 99%

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Stock Trading Using PE Ratio Based on Bayesian Inference

Wang¹,

Chatpatanasiri²,

Sattayatham³

2017

Journal of Mathematics and Statistics

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Abstract:The Price Earnings (PE) ratio is one of the most widely applied tool for the firm valuation in a security market. Unfortunately, recent academic developments in financial econometrics and machine learning have rarely looked at this tool. In the paper, we propose to formalize a process of fundamental PE ratio estimation by employing Dynamic Bayesian Network (DBN) methodology. Forward-backward inference and Expectation Maximization (EM) parameter estimation algorithms are derived with respect to our proposed DBN structure. A simple but practical trading strategy is invented based on the result of Bayesian inference. We make stock trading experiments using Thai stocks and American stocks, respectively. Extensive experiments show that our trading strategy statistically outperforms the buy-and-hold strategy.

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Section: Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stock Trading Using PE Ratio Based on Bayesian Inference

Wang¹,

Chatpatanasiri²,

Sattayatham³

2017

Journal of Mathematics and Statistics

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show abstract

“…We apply the powerful framework of dynamic Bayesian network [7,15] to model the valuation process. In contrast to existing machine learning frameworks mentioned above on price pattern discovery where the discovered patterns have no meaning in finance, the interpretation of our model is well justified according to behavioral finance [16] as explained in the Section 2. The main contributions of our work are threefold.…”

Section: Common Philosophy Of Applying Machine Learning To Financial ...mentioning

confidence: 99%

“…Why does stock price deviates from its fundamental price? Works on behavioral finance [16] found many evidences for this question. For example, researchers argue that there are noise traders in the market who tend to make irrational actions so the price can move away from its value [18,19,20].…”

Section: Motivation Of Statistical Modelling: Behavioral Volatilitymentioning

confidence: 99%

Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment

Wang¹,

Chatpatanasiri²,

Sattayatham³

2017

Preprint

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On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard

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“…Skala reakcji na poszczególne informacje może jednak odbiegać od przyjętej w ekonomii definicji efektywności rynku kapitałowego (Fama, 1970). Z jednej strony, opisem tych odstępstw zajmują się finanse behawioralne (Szyszka, 2013), z drugiej zaś -konieczne jest dostarczenie inwestorom narzędzi pozwalających na określenie wpływu poszczególnych informacji na zachowanie rynku. Z uwagi na róż-nice w stopniu rozwoju poszczególnych rynków, uwzględnienie w przyjętej strategii inwestycyjnej wpływu informacji może mieć duże znaczenie dla osiąganych przez zarządzających portfelami stóp zwrotu.…”

Section: Wprowadzenieunclassified

Rynek kapitałowy- efektywność i ryzyko

Czerwińska¹,

Nowak²

2016

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Behavioral Finance and Capital Markets

Cited by 29 publications

References 569 publications

Stock Trading Using PE Ratio Based on Bayesian Inference

Stock Trading Using PE Ratio Based on Bayesian Inference

Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment

Rynek kapitałowy- efektywność i ryzyko

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