A Closer Look at Value Premium: Literature Review and Synthesis

Pätäri, Eero; Leivo, Timo H.

doi:10.1111/joes.12133

Cited by 20 publications

(9 citation statements)

References 219 publications

(306 reference statements)

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“…The model's explanation of the predicted P/BV ratio is far from complete. For one thing, value premium, the greater risk-adjusted return, should be considered when estimating P/BV [27,28], while the return predictability of China is weak [14]. For another, ATS, which are not used frequently as a trading tool in the Chinese stock market of the past decade, would influence the decision-making of investors [18].…”

Section: P/bv Ratiomentioning

confidence: 99%

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

et al. 2019

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Predicted price-to-book value ratios (P/BV) are widely used for the valuation of listed common stocks. However, with the application of automated trading system (ATS), the existing indicators that are applied in the method are losing their effectiveness in the Chinese market. Combining qualitative research with the text mining method, this study explores and validates those ignored factors to improve the accuracy of the stock valuation. On the basis of the principal of the existing valuation method, we clarify the scope of the factors that affects the P/BV ratio prediction. Through semi-structured interviews that are designed with six first-level factors which are taken from the literature, we then excavate some second-level factors. After that, with three corpuses including samples form Sina.com.cn, Xueqiu.com, and CSDN.net, four first-level factors and thirteen second-level factors have been verified step by step through the Latent Dirichlet Allocation (LDA) model. In the process, two other new factors and three sub-factors are also found. Furthermore, based on the factor correlation that was found in a data analysis, a factor relationship model was built. The results can be used in a stock valuation in future work as the basis of the indicator system for the prediction of P/BV ratio.

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Section: P/bv Ratiomentioning

confidence: 99%

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

et al. 2019

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“…In our empirical analysis, we use data on value and momentum factor portfolios of U.S. stocks over the period December 1926 through September 2015. The value factor 1 Pätäri and Leivo (2017) provide a rigorous survey of the value premium literature. 2 The literature on the momentum effect is reviewed in Swinkels (2004) and Galariotis (2014).…”

Section: Introductionmentioning

confidence: 99%

Another look at value and momentum: volatility spillovers

Grobys

Vähämaa

2020

Rev Quant Finan Acc

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This paper examines volatility interdependencies between value and momentum returns. Using U.S. data over the period 1926-2015, we document persistent periods of low and high volatility spillovers between value and momentum strategies. Moreover, we find that the intensity of the volatility spillovers may change substantially in very short periods of time and that these shifts in spillover intensity can be linked to prominent economic events and financial market turmoil. Our results further demonstrate that value returns increase and momentum returns decrease monotonically with increasing volatility spillovers between the two strategies. Given this linkage between spillover intensity and returns, we propose a simple trading strategy which utilizes a volatility spillover index for allocating funds between value and momentum portfolios. The proposed trading strategy outperforms value and momentum strategies and generates payoffs that are not subject to option-like behavior.Keywords Asset pricing · Value effect · Momentum effect · Volatility spillovers · Volatility spillover index JEL Classification G11 · G12 · G14

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“…This review omits the voluminous studies on the empirical evidence of size and value effect and their various risk-based and behavioural explanations because preceding articles (van Dijk, 2011;Pätäri and Leivo, 2017) already provide extensive reviews.…”

Section: Introductionmentioning

confidence: 99%

Q‐factors in Empirical Asset Pricing: A Review and Synthesis

Datta

Chakraborty

2019

Journal of Economic Surveys

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The latest development in the asset pricing literature is the emergence of empirical asset pricing models comprising q‐factors (profitability and investment factors) in conjunction with other factors. However, as in the case of the older empirical models, there is scepticism regarding the application of these newer factor models consisting of q‐factors because of the debate surrounding the explanatory power of these empirically inspired asset pricing models. This review attempts to synthesize studies pertaining to the four alternative explanations of the asset pricing models comprising the q‐factors (profitability and investment) – the data snooping hypothesis, the risk‐based explanation, the irrational investor behaviour explanation and the interpretation that suggest that the combination of the risk‐free asset and the factors comprising the model span the mean‐variance efficient tangency portfolio that prices the universe of assets.

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A Closer Look at Value Premium: Literature Review and Synthesis

Cited by 20 publications

References 219 publications

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

Another look at value and momentum: volatility spillovers

Q‐factors in Empirical Asset Pricing: A Review and Synthesis

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