Affect Asset Returns Research Articles

Asset pricing with long-run disaster risk.

Traditional disaster models with time-varying disaster risk are not perfect in explaining asset returns. We redefine rare economic disasters and develop a novel disaster model with long-run disaster risk to match the asset return moments observed in the U.S. data. The difference from traditional disaster models is that our model contains the long-run disaster risk by treating the long-run ingredient of consumption growth as a function of time-varying disaster probability. Our model matches the U.S. data better than the traditional disaster model with time-varying disaster risk. This study uncovers an additional channel through which disaster risk affects asset returns and bridges the gap between long-run risk models and rare disaster models.

A Sustainable Capital Asset Pricing Model (S-CAPM): Evidence from Environmental Integration and Sin Stock Exclusion

Abstract This article shows how sustainable investing—through the joint practice of exclusionary screening and environmental, social, and governance (ESG) integration—affects asset returns. I develop an asset pricing model with partial segmentation and heterogeneous preferences. I characterize two exclusion premia generalizing Merton’s (1987) premium on neglected stocks and a taste premium that clarifies the relationship between ESG and financial performance. Focusing on US stocks, I estimate the model by applying it to sin stocks as excluded assets and using the holdings of green funds to proxy for environmental integration. The average annual exclusion effect is 2.79% for the period 1999–2019. Although the annual taste effect ranges from –1.12% to + 0.14% across industries for 2007–19, the taste effect spread between the top and bottom terciles of companies within each industry can exceed 2% per year. Finally, I estimate and explain the dynamics of these premia.

Is Farmland a Common Risk Factor in Asset Pricing Models?

Farmland represents the largest share of the U.S. agricultural balance sheet, accounting for nearly 80% of U.S. farm assets. Motivated by the well-documented real estate risk factor and the similarities between farmland and real estate investing, this paper examines whether farmland has a risk factor, like real estate, that is affecting asset returns. The proposed farmland risk factor is proxied by the National Council of Real Estate Investment Fiduciaries farmland property index (Farmland NCREIF). Relying on quarterly data from 1991-Q1 to 2016-Q2, we employed the Generalized Method of Moments (GMM) to provide empirical evidence that even though farmland exhibits diversification benefits, it fails to be a risk factor. Instead, market frictions and/or non-risk explanations might provide a more plausible description of farmland’s high risk-adjusted return.

Analisis Pengaruh Manajemen Risiko Terhadap Kinerja Keuangan Perbankan yang Terdaftar di BEI

Banks have a strategic role in national economic development. The large number of funds managed by banks causes the risks faced are also very large. Very supportive if the risk affects the bank's performance, therefore banks are required to implement risk management. After being selected using the purposive sampling method, the sample banks were 21 banks. Tests carried out with multiple regression analysis show that bad loans and operating expenses on operating income negatively affect asset returns. In contrast to the loan to deposit ratio which does not affect the return on assets. Based on the results of this study, it is necessary to optimize credit and operational risks which are considered capable of maintaining the stability of bank profitability.

Climate Change and Asset Allocation: A Distinction That Makes a Difference

It has become generally accepted that climate change affects economic variables such as growth and inflation. Via these economic channels, it is likely that climate change will also affect asset returns and risks. The authors provide evidence-based estimates of what these effects are and discuss how to incorporate climate change to build portfolios that are robust to a variety of climate change scenarios. <b>TOPICS:</b>Portfolio construction, ESG investing, risk management, tail risks <b>Key Findings</b> ▪ Climate change will likely lower economic growth and equity returns while increasing equity market volatility. ▪ A simple example is used to show how to incorporate various climate change scenarios into asset allocation decisions. ▪ Based on empirical risk and returns for traditional and highly rated environment, social, and governance indexes, it is a simple matter of using constrained optimization to achieve investment outcomes with sustainability goals or constraints.

Local investor horizon clientele and IPO underpricing

We study how the heterogeneity in investment horizons of local institutional investors affects the IPO market. Because short-term investors prefer more liquid stocks than long-term investors do and IPO stocks have high liquidity in the aftermarket, a higher presence of local short-term investors should be related to higher IPO underpricing. We test this hypothesis by constructing a geography-based measure of “local short-term presence”, which captures the cross-sectional variations in regional investor horizon clienteles. We find that local short-term presence is strongly positively related to IPO underpricing. Our results are consistent with the view that investor-base heterogeneity affects asset returns.

Pricing of time-varying illiquidity within the Eurozone: Evidence using a Markov switching liquidity-adjusted capital asset pricing model

This paper investigates time-varying characteristics of illiquidity and the pricing of its risk using a liquidity-adjusted capital asset pricing model (L-CAPM). Collecting data from a pool of Eurozone countries between 1990 and 2018, we employ Markov switching models to assess the degree of persistence of illiquidity shocks. Contrary to prior research, which largely makes use of autoregressive (AR) processes, we provide strong evidence that illiquidity is time-varying and the persistence of shocks determines two distinct regimes characterised by high and low illiquidity. We assess pricing of illiquidity risk by developing and empirically testing a conditional L-CAPM model, where different regimes constitute priced risk factors for the cross-section of stock returns. We extend previous unconditional versions of L-CAPM models and we show that the various channels through which illiquidity affects asset returns and price of risks are time-varying. We find strong support for our conditional L-CAPM and our results are robust to alternative specifications and estimation techniques. These findings have important implications for portfolio management practices and are relevant to portfolio and risk managers and regulatory institutions.

Effect of the Political Risk on Capital Asset Valuation in Financial Markets: The Case of Turkey

Recent political events and the subsequent movements in financial markets both in the developing and the developed countries raise the instincts regarding the interaction of political risks and financial markets and increase the importance of understanding the behavior of financial markets against political risks. Therefore the purpose of this study is to explain the impact of political risks on the returns of securities traded on capital markets in Turkey in the framework of Capital Asset Pricing Model (CAPM). The relation between the monthly returns of 47 companies’ assets being traded on Borsa Istanbul and the political risks in Turkey between the years of 1997 and 2013 is examined in the study. Results of the analysis held by two-step regression method represent that there exists a positive and linear relation between beta and expected return. On the other hand, it is found that 4 out of 12 political risk factors, in other words internal conflict, external conflict, government stability and military in politics have a significant impact on expected return. Contrary to the other three risk factors, only the risk of government stability is found to be positively affecting asset returns.

Investor sentiment, trading behavior and stock returns

ABSTRACTThis article examines how investor sentiment and trading behaviour affect asset returns. By analysing the unique stock trading dataset of the Korean market, we find that high investor sentiment induces higher stock market returns. We also find that institutional (individual) trades are positively (negatively) associated with stock returns, suggesting the information superiority (inferiority) of institutional (individual) investors. Investor sentiment generally plays a more important role in explaining stock market returns than investor trading behaviour.

The Asset Pricing Implications of Disclosed Risk Factors

We show theoretically that differences in priors, which are key inputs for Bayesian investors who face time-series uncertainty about parameters, have implications for the differential pricing of risk in the cross-section. Specifically, different priors for the same asset can generate differences in perceived (vs. actual) risk exposures, and consequently, generate differences in required rates of return. The main testable implication is that the Security Market Line is steeper when the prior is low risk exposure than when the prior is high risk exposure. Using novel proxies for investors' priors about firms' exposure to risk factors, we find strong empirical support for our main prediction. Our results have important implications for understanding how parameter uncertainty affects asset returns.

The pricing of systematic liquidity risk in stock markets

The question whether liquidity affects asset returns or not remains unresolved thus far. The absence of conclusive results in previous research suggests that asset pricing and liquidity have not been properly addressed in the standard literature. We consider that systematic liquidity shocks affect the optimal behavior of agents in financial markets. Indeed, fluctuations in various measures of liquidity are significantly correlated across common stocks. Accordingly, we propose the construction of a liquidity risk factor based on the ratio of absolute stock returns on euro volume suggested by Amihud (2002) and the approximately orthogonalizing procedure of Fama and French (1993), using it as an augmenting variable in their three-factor model.

Investor Sentiment, Stock Returns, and Firm Characteristics: Evidence from a Leading Emerging Market

This study examines how investor sentiment affects asset returns in the Korean stock market, a representative leading emerging market, in different ways depending on firm characteristics. By analyzing the unique stock trading dataset containing rich information on investor types and sentiment, we construct the firm-level investor sentiment index and investor-type trading behavior index. We confirm that high investor sentiment induces higher stock market returns. The positive association between investor sentiment and stock returns is highly significant after controlling trading behavior and other risk and firm characteristics. Investor sentiment is shown to have stronger effects for small-firm, low-priced, high book-to-market ratio, high excess return, and highly volatile stocks, and stocks heavily traded by individual investors. The diverse degree and intensity of the sentiment effect across firm and stock characteristics are attributable to individual investors’ trading.

Nominal rigidities, asset returns, and monetary policy

Asset-return implications of nominal price and wage rigidities are analyzed in general equilibrium. Nominal rigidities, combined with permanent productivity shocks, increase expected excess returns on production claims. This is mainly explained by consumption dynamics driven by rigidity-induced changes in employment and markups. An interest-rate monetary policy rule affects asset returns. Stronger (weaker) rule responses to inflation (output) increase expected excess returns. Policy shocks substantially increase asset-return volatility. Price rigidity heterogeneity produces cross-sectoral differences in expected returns. The model matches important macroeconomic moments and the Sharpe ratio of stock returns, but only captures a small fraction of the observed equity premium.

How does a pay-as-you-go system affect asset returns and the equity premium?

When applying a differences-in-differences approach, equity returns and the equity premium are both estimated to be more than four percentage points higher after the introduction of a pay-as-you-go (PAYGO) system. In a realistically calibrated model, the PAYGO system is also found to increase the returns and the premium, although the effects are smaller than in the data. Intuitively, the system lowers asset prices, which in turn increases the importance of dividend risk. Since only equity is subject to dividend risk, equity returns become more volatile relative to bond returns.

In the Shadow of the United States: The International Transmission Effect of Asset Returns

AbstractWe examine how fluctuations in financial and housing markets in the United States affect asset returns and GDP in Hong Kong. In contrast to studies using linear specifications, which find that the United States and Hong Kong are virtually delinked in terms of the asset markets, our regime‐switching models indicate that an unexpected change in US stock returns, followed by the TED spread, has the most significant effect on Hong Kong asset returns and GDP, typically in a regime of high return and low volatility. For in‐sample one‐step‐ahead forecasting, the US term spread is the best predictor.

寿险业资产与保险配置对资产报酬率与损失率之纵横面资料分析

本文利用纵横面数据(Panel Data)欲探讨资产配置与保险配置对于资产报酬率及损失率之回归分析。首先，本文以各观察值之资产报酬率及损失率为分群变量，利用集群分析将研究样本区分为两集群的观察值，并对两集群的观察值进行主成分分析建立其资产配置与保险配置之模型，最后藉由纵横面数据分析较适当之回归模型，观察资产与保险配置对资产报酬率及损失率之影响因素。研究结果发现资产报酬率较适用随机效果模型，损失率较适用固定效果模型。再者，资产配置与保险配置间存有交互影响，而不同集群的观察值与资产配置及保险配置也存有交互影响，且影响资产报酬率及损失率之变项也有差异。 By collecting Panel Data, this study investigates how life insurance company’s asset and insurance allocations affect asset returns and loss ratio. First, the asset returns and loss ratio of each life insurance company each year are treated as response variables and cluster analysis is used to grouping the companies into observations of two groups. Then principle component analysis is applied to construct the model of asset and insurance allocations of observations of two groups. Finally, find the effects of asset and insurance allocations on asset of returns and loss ratio by regression model for panel data. The findings show that ROA is applicable to the random model and the loss ratio is applicable to the fixed effect model. Furthermore, there exist the interaction effects between asset allocation and insurance allocation. The two groups of companies also exist interaction effects on asset and insurance allocation. Finally, there exist different asset and insurance allocation effect on asset returns and loss ratio.

The characteristics of informed trading: Implications for asset pricing

Abstract This paper investigates the linkage of microstructure, accounting, and asset pricing. We determine the relationship between firm characteristics as captured by accounting and market data and a firm's probability of private information-based trade (PIN) as estimated from trade data. This allows us to determine what types of firms have high information risk. We then use these data to create an instrument for PIN, the PPIN, which we can estimate from firm-specific data. We show that PPINs have explanatory power for the cross-section of asset returns in long sample tests. We also investigate whether information risk vitiates the influence of other variables on asset returns. We develop a PPIN factor and show that it dominates the Amihud factor in asset returns. Our results provide strong support for information risk affecting asset returns in long sample tests.

Using the Black and Litterman framework for stress test analysis in asset management

In the classic Black and Litterman approach, it is possible, using reverse engineering, to obtain the expected asset equilibrium returns implied by the weights of the market portfolio, that is, the benchmark. However, given that analysts may have different views on some of the expected returns implied by the benchmark weights, it is possible to obtain the posterior distribution by combining analysts’ views and prior market information. In this article, we propose a methodology for the stress test analysis of a current managed portfolio, in which two different shock types are combined. More precisely: To this end, we introduce a number of macroeconomic factors that affect asset returns, such as volatilities, interest rates, oil price and so on. At this stage, a multi-factor analysis is not carried out, although we include the information in the covariance matrix. We assume that a mixture of normal distributions is well suited to describing and representing the presence of high- and low-volatility periods, taking into account extreme movements in the market. We derive the conditional moments of the posterior distribution by combining views on factors and market information.

Empirical Tests on Turnover Information

The objective of this study is to investigate turnover information and to test whether this information affects asset returnｓ. We explore the relationship between turnover and price momentum (reversalｓ) for Japanese stocks. In the Japanese stock market, the return reversal effect is known to be stronger than in other developed capital markets and most preceding studies show that price momentum cannot be found in the Japanese stock market. In this study, we investigate the relationship between price momentum and turnover, particularly, conditional upon the level of turnover; we find that high turnover tends to induce momentum in short-term periods. The study also investigates whether the coskewness premium affects price momentum effects, like the findings in previous studies of U.S. data, which demonstrated that coskewness helps explain some of the profitability of momentum-based trading strategies. Our results show that the coskewness of past losers is larger than that of past winners. This indicates that negative coskewness stock has a coskewness premium and that investors require a premium for negative coskewness stocks. Furthermore, the coskewness premium helps enhance price momentum as the turnover level increases and depresses return reversal effects for the highest turnover stock group.

Using Black & Litterman framework for stress testing analysis in asset management

In the classical Black and Litterman approach, by using reverse engineering, it is possible to obtain the expected assets equilibrium returns implied in the weights of the market portfolio, i.e. the benchmark. However, analysts may have different views on some of the expected returns implied in the benchmark’s weights and it is possible to obtain the posterior distribution by combining analysts’ views and prior market information. In this paper we propose a methodology for a stress test analysis of the current managed portfolio, where two different shock types are combined. More precisely: 1. we shock a set of factors which affect asset returns, imposing the analysts’ views on their variation from the expected level; 2. we assume that a mixture of normal distributions can describe the presence of hectic periods and quiet period. The asset correlation breakdown effect is well known i.e., “.. joint distributions estimated over periods without panics will misestimate the degree of correlation between asset returns during panics. “ (Alan Greenspan). For this purpose, we introduce a number of macroeconomic factors which affect asset returns such as volatilities, interest rates, oil price etc. , At this stage, we do not perform a multi factor analysis, but we include the information in the covariance matrix. We assume that a mixture of normal distributions can describe the presence of high volatility periods and low volatility periods, taking into account extreme movements in the market. We derive the conditional moments of the posterior distribution by combining views on factors and market information.