This paper analyzes the asset pricing and portfolio implications of an important barrier to sustainable investing: uncertainty about the corporate ESG profile. In equilibrium, the market premium ...increases and demand for stocks declines under ESG uncertainty. In addition, the CAPM alpha and effective beta both rise with ESG uncertainty and the negative ESG-alpha relation weakens. Employing the standard deviation of ESG ratings from six major providers as a proxy for ESG uncertainty, we provide supporting evidence for the model predictions. Our findings help reconcile the mixed evidence on the cross-sectional ESG-alpha relation and suggest that ESG uncertainty affects the risk-return trade-off, social impact, and economic welfare.
The world price of liquidity risk Lee, Kuan-Hui
Journal of financial economics,
2011, 2011-1-00, 20110101, Letnik:
99, Številka:
1
Journal Article
Recenzirano
This paper empirically tests the liquidity-adjusted capital asset pricing model of
Acharya and Pedersen (2005) on a global level. Consistent with the model, I find evidence that liquidity risks are ...priced independently of market risk in international financial markets. That is, a security’s required rate of return depends on the covariance of its own liquidity with aggregate local market liquidity, as well as the covariance of its own liquidity with local and global market returns. I also show that the US market is an important driving force of global liquidity risk. Furthermore, I find that the pricing of liquidity risk varies across countries according to geographic, economic, and political environments. The findings show that the systematic dimension of liquidity provides implications for international portfolio diversification.
In recent years, deep reinforcement learning (DRL) algorithm has been widely used in algorithmic trading. Many fully automated trading systems or strategies have been built using DRL agents, which ...integrate price prediction and trading signal generation in one system. However, the previous agents extract the current state from the market data without considering the long-term market historical trend when making decisions. Besides, plenty of related and useful information has not been considered. To address these two problems, we propose a novel model named Parallel Multi-Module Deep Reinforcement Learning (PMMRL) algorithm. Here, two parallel modules are used to extract and encode the feature: one module employing Fully Connected (FC) layers is used to learn the current state from the market data of the traded stock and the fundamental data of the issuing company; another module using Long Short-Term Memory (LSTM) layers aims to detect the long-term historical trend of the market. The proposed model can extract features from the whole environment by the above two modules simultaneously, taking the advantages of both LSTM and FC layers. Extensive experiments on China stock market illustrate that the proposed PMMRL algorithm achieves a higher profit and a lower drawdown than several state-of-the-art algorithms.
This study examines the role of global oil price uncertainty in the cross-sectional pricing of individual stocks in the Chinese stock market, motivated by the intertemporal capital asset pricing ...model. Using both Fama-Macbeth regressions and multifactor time-series regressions, we find that stocks with higher oil price uncertainty betas have significantly lower expected returns. This finding is consistent with the asset pricing implications of the demand of investors for stocks with high potential to hedge against oil price uncertainty. We further conduct an industry analysis and find that the negative association between the oil price uncertainty beta and expected returns is pronounced, primarily in the Manufacturing and Transportation & Communication industries. Moreover, we demonstrate that the return predictability of the oil price uncertainty beta cannot be subsumed by those of other oil-related betas and well-known macro-uncertainty betas, thus supporting that global oil price uncertainty is an independent source of systematic risk in the Chinese stock market.
•Oil price uncertainty beta negatively and prominently predicts future stock returns.•The negative beta-return relation is consistent with intertemporal hedging demand.•Oil price uncertainty is an independent source of systematic risk in China.•Oil price uncertainty has a prominent pricing implication in the Chinese market.
In the conventional capital asset pricing model (CAPM), standard deviation as a measure of risk penalizes both upward and downward movements. However, while downward movements in investments are ...risky, upward movements are favourable. Therefore, standard semideviation that treats only those negative differences in returns over the benchmark as risky was proposed as a measure of downside risk. Attempts have been made to obtain the formulation of beta in this context, called downside beta. In this work, CAPM in the mean‐semivariance framework (D‐CAPM), with semivariance measured in terms of negative deviation around expected return, is derived and established that the model proposed earlier with a version of downside beta with respect to this definition of semivaraince is invalid. The appropriate version of downside beta is obtained, and under the rectified formulation, the validity of the true D‐CAPM is empirically tested during July 2013–July 2018. The solution to the tangent point is also obtained. The proportion of investment in the stocks in the tangent portfolio and the downside beta for the stocks in the tangent portfolio are determined. We find that there is no evidence to disprove that D‐CAPM holds true considering either Nifty 50 or S&P BSE Sensex as market portfolio.
•Decomposing the uncertainty of a typical forecaster into common and idiosyncratic uncertainty.•Developing monthly measures of economic uncertainty covering 45 countries.•Constructing a measure of ...global uncertainty.•Common uncertainty shocks have large and persistent effects on economic activity.•Idiosyncratic uncertainty shocks have short-lived and negligible effects.
Motivated by the literature on the capital asset pricing model, we decompose the uncertainty of a typical forecaster into common and idiosyncratic uncertainty. Using individual survey data from the Consensus Forecasts over the period of 1989–2014, we develop monthly measures of macroeconomic uncertainty covering 45 countries and construct a measure of global uncertainty as the weighted average of country-specific uncertainties. Our measure captures perceived uncertainty of market participants and derives from two components that are shown to exhibit strikingly different behavior. Common uncertainty shocks produce the large and persistent negative response in real economic activity, whereas the contributions of idiosyncratic uncertainty shocks are negligible.
This paper presents evidence for a significantly positive link between the dynamic conditional beta and the cross section of daily stock returns. An investment strategy that takes a long position in ...stocks in the highest conditional beta decile and a short position in stocks in the lowest conditional beta decile produces average returns and alphas in the range of 0.60%–0.80% per month. We provide an investor attention-based explanation of this finding. We show that stocks with high conditional beta have strong attention-grabbing characteristics, leading to a higher fraction of buyer-initiated trades for these stocks. We also find that stocks recently bought perform significantly better than stocks recently sold. Hence, the high beta stocks that investors are more likely to buy have higher expected returns than the low beta stocks that investors are more likely to sell.
This paper was accepted by Lauren Cohen, finance
.
Traditional asset pricing models have extensively been used; however, determining asset pricing in isolation in the current global environment demands for the inclusion of certain extraneous events. ...The goal of this study is to measure the impact of disintegrated structural oil shocks, proposed by Kilian (2009) on disaggregated US sectoral returns. Our work is motivated by the influence of global oil shocks on asset returns that are widely documented in the literature. Equity pricing data for nine major US sectors is used on monthly frequency. We apply non-linear Markov Regime Switching framework and report clear regime differences across all sectors. Commonality in behavior for majority of the sectoral returns is observed however consumer discretionary, materials and automobile sector remains sensitive in both regimes contrary to other sectors exhibiting sensitivity in one regime or the other. Results also present robust evidence of oil specific demand shocks implying more uncertainty for US sectoral returns with important investment and policy implication.
•We integrate disintegrated structural oil shocks (through SVAR) into the capital asset pricing model.•The study analyzes the effect of asset pricing model with structural shocks to US sectoral returns.•To observe the non-linearity of model, we apply Markov Regime Switching framework with robustness analysis.•Oil specific demand shocks induce more uncertainty for US sectoral returns compared to oil supply shocks and aggregate demand shocks.
Asset pricing and energy consumption risk Lim, Ashley; Lan, Yihui; Treepongkaruna, Sirimon
Accounting and finance (Parkville),
December 2020, 2020-12-00, 20201201, Letnik:
60, Številka:
4
Journal Article
Recenzirano
This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the ...relative risk aversion coefficient and the implied risk‐free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross‐sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three‐factor model in the cross‐sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.
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