A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
- Topic classification is ongoing.
- Please kindly let me know [mingze.gao@mq.edu.au] in case of any errors.
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Results 76 resources
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We propose a theoretical measure of income hedging demand and show that it affects asset prices. We focus on the value factor and first demonstrate that our demand estimates are correlated with the actual demands of retail and mutual fund investors. We then show that the aggregate high‐minus‐low (HML) demand predicts HML returns. Exploiting the state‐level variation in income risk, we demonstrate that state‐level hedging demands predict state‐level HML returns. A long‐short portfolio that exploits this hedging‐induced predictability earns an annualized risk‐adjusted return of 6%.
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We document a highly significant, strongly nonlinear dependence of stock and bond returns on past equity market volatility as measured by the VIX. We propose a new estimator for the shape of the nonlinear forecasting relationship that exploits variation in the cross‐section of returns. The nonlinearities are mirror images for stocks and bonds, revealing flight‐to‐safety: expected returns increase for stocks when volatility increases from moderate to high levels while they decline for Treasuries. These findings provide support for dynamic asset pricing theories in which the price of risk is a nonlinear function of market volatility.
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We present a dynamic model that links characteristic‐based return predictability to systematic factors that determine the evolution of firm fundamentals. In the model, an economy‐wide disruption process reallocates profits from existing businesses to new projects and thus generates a source of systematic risk for portfolios of firms sorted on value, profitability, and asset growth. If investors are overconfident about their ability to evaluate the disruption climate, these characteristic‐sorted portfolios exhibit persistent mispricing. The model generates predictions about the conditional predictability of characteristic‐sorted portfolio returns and illustrates how return persistence increases the likelihood of observing characteristic‐based anomalies.
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In this paper, we demonstrate that the funding value adjustments (FVAs) of major dealers are debt overhang costs to their shareholders. To maximize shareholder value, dealer quotations therefore adjust for FVAs. Our case studies include interest‐rate swap FVAs and violations of covered interest parity. Contrary to current valuation practice, FVAs are not themselves components of the market values of the positions being financed. Current dealer practice does, however, align incentives between trading desks and shareholders. We also establish a pecking order for preferred asset financing strategies and provide a new interpretation of the standard debit value adjustment.
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Barras, Scaillet, and Wermers propose the false discovery rate (FDR) to separate skill (alpha) from luck in fund performance. Using simulations with parameters informed by the data, we find that this methodology is conservative and underestimates the proportion of nonzero‐alpha funds. For example, 65% of funds with economically large alphas of ±2% are misclassified as zero alpha. This bias arises from the low signal‐to‐noise ratio in fund returns and the resulting low statistical power. Our results question FDR's applicability in performance evaluation and other domains with low power, and can materially change the conclusion that most funds have zero alpha.
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Past studies document that incentive conflicts may lead issuer‐paid credit rating agencies to provide optimistically biased ratings. In this paper, we present evidence that investors question the quality of issuer‐paid ratings and raise corporate bond yields where the issuer‐paid rating is more positive than benchmark investor‐paid ratings. We also find that some firms with favorable issuer‐paid ratings substitute public bonds with borrowings from informed intermediaries to mitigate the “lemons discount” associated with poor quality ratings. Overall, our results suggest that the quality of issuer‐paid ratings has significant effects on borrowing costs and the choice of debt.
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A growing body of evidence suggests that the benefits of international diversification via developed markets have declined dramatically. While emerging markets still offer diversification opportunities, their public equity indices capture only a fraction of emerging countries' economic activity. We propose a diversification approach that exploits the global connectedness of developed countries to gain exposure to emerging countries' overall economies rather than their shallow equity markets. In doing so, we demonstrate that developed markets still offer substantial diversification benefits beyond those available through equity indices. Our results suggest that relying on equity indices to assess diversification benefits understates diversification gains.
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Using trade‐level data, we study whether brokers play a role in spreading order flow information in the stock market. We focus on large portfolio liquidations that result in temporary price drops, and identify the brokers who intermediate these trades. These brokers’ clients are more likely to predate on the liquidating funds than to provide liquidity. Predation leads to profits of about 25 basis points over 10 days and increases the liquidation costs of the distressed fund by 40%. This evidence suggests a role of information leakage in exacerbating fire sales.
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In this paper, we investigate how globalization is reflected in asset prices. We use shipping costs to measure firms' exposure to globalization. Firms in low shipping cost industries carry a 7% risk premium, suggesting that their cash flows covary negatively with investors' marginal utility. We find that the premium emanates from the risk of displacement of least efficient firms triggered by import competition. These findings suggest that foreign productivity shocks are associated with times when consumption is dear for investors. We discuss conditions under which a standard model of trade with asset prices can rationalize this puzzle.
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This paper explores whether affine models with volatility jumps estimated on intradaily S&P 500 futures data over 1983 to 2008 can capture major daily outliers such as the 1987 stock market crash. Intradaily jumps in futures prices are typically small; self‐exciting but short‐lived volatility spikes capture intradaily and daily returns better. Multifactor models of the evolution of diffusive variance and jump intensities improve fits substantially, including out‐of‐sample over 2009 to 2016. The models capture reasonably well the conditional distributions of daily returns and realized variance outliers, but underpredict realized variance inliers. I also examine option pricing implications.