Quantpedia introduces Quantconferences portal Tuesday, 2 February, 2016

We have launched a new portal Quantconferences.com - a brand new directory for quantitative finance and algorithmic trading conferences.

It contains links to majority of quant finance events in one place. Portal offers opportunity to search a detailed list of events:
http://quantconferences.com/ScreenByEvent

or screen our database for keynote speakers to see which conferences they will attend:
http://quantconferences.com/ScreenBySpeaker
http://quantconferences.com/ScreenBySpeaker/Detail/3346 (Emanuel Derman as an example)

You are most welcome to visit our new project. Let us know if you are missing any event in our list and we will add it there.

The QUANTPEDIA & QUANTCONFERENCES Team

FX Liquidity Risk and Carry Trade Returns Thursday, 28 January, 2016

A new related paper has been added to:

#5 - FX Carry Trade

Authors: Abankwa, Blenman

Title: FX Liquidity Risk and Carry Trade Returns

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2662955

Abstract:

We study the effects of FX liquidity risk on carry trade returns using a low-frequency market-wide liquidity measure. We show that a liquidity-based ranking of currency pairs can be used to construct a mimicking liquidity risk factor, which helps in explaining the variation of carry trade returns across exchange rate regimes. In a liquidity-adjusted asset pricing framework, we show that the vast majority of variation in carry trade returns during any exchange rate regime can be explained by two risk factors (market and liquidity risk) in the FX market. Our results are further corroborated when the hedge liquidity risk factor is replaced with a non-tradable innovations risk factor.

Notable quotations from the academic research paper:

"Academic research used to ignore liquidity. The theory assumed frictionless markets which are perfectly liquid all of the time. This paper takes the opposite view. We argue that illiquidity is a central feature of the securities and financial markets. This paper provides a comprehensive study that links liquidity risk to carry trade returns and provides an explanation of why currency investors should consider and manage FX liquidity risk.  The paper contributes to the international fi nance and empirical asset pricing literature in three major perspectives.

This is the first study to investigate the e ffects of liquidity risk on carry trade returns across exchange rate regimes, using a low-frequency market-wide liquidity measure constructed from daily transaction prices. The possibility of using a low-frequency (LF) liquidity measure circumvents the restricted and costly access of intraday high-frequency (HF) data. Not only is access to HF data limited and costly, it is also subjected to time-consuming handling, cleaning, and fi ltering techniques.

Second, we show that FX liquidity risk can be gleaned from the low-frequency market-wide liquidity measure, which helps in explaining the variation of carry trade returns in an asset pricing framework.

Third, we fi nd that liquid and illiquid G10 currencies behave di erently toward liquidity risk for all regimes. Whereas liquid currencies such as the JPY and EUR are not that sensitive to liquidity risk, illiquid currencies such as the AUD and NZD are highly sensitive to liquidity risk. Liquid currencies have negative liquidity betas whereas illiquid currencies show positive liquidity betas. This also substantiates the finding by Mancini, Ranaldo, and Wrampelmeyer (2013) that negative liquidity beta currencies act as insurance or liquidity hedge, whereas positive liquidity beta currencies expose currency investors to liquidity risk."


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The Betting Against Beta Anomaly: Fact or Fiction? Wednesday, 20 January, 2016

A new related paper has been added to:

#77 - Beta Factor in Stocks

Authors: Buchner, Wagner

Title: The Betting Against Beta Anomaly: Fact or Fiction?

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2703752

Abstract:

This paper suggests an alternative explanation for the recently documented betting against beta anomaly. Given that the equity of a levered firm is equivalent to a call option on firm assets and option returns are non-linearly related to underlying stock returns, linear CAPM-type regressions are generally misspecified. We derive theoretical expressions for the pricing error and analyze its magnitude using numerical examples. Consistent with the empirical findings of Frazzini and Pedersen (2014), our pricing errors are negative, increase with leverage, and become economically significant for higher levels of firm leverage.

Notable quotations from the academic research paper:

"In this paper, we suggest a possible alternative explanation for the betting against beta phenomenon. We propose that the betting against beta phenomenon is due to pricing errors, which arise given that the CAPM does not take non-linearities in stock returns into account. Our rationale is as follows. As highlighted by the classic Black-Scholes-Merton model of corporate debt and equity valuation, the equity of a levered firm is equivalent to a call option written on the underlying value of the firm’s assets. As is known, option returns are highly skewed and non-linearly related to the returns of the underlying. Therefore, linear CAPM-type regressions of equity returns may suffer from model misspecification. Using the Black-Scholes-Merton model, we derive expressions for the model pricing error under the standard CAPM and analyze its magnitude using numerical examples.

Our analysis highlights that the pricing error is negative and becomes economically large as firm leverage increases. That is, consistent with the empirical findings of Frazzini and Pedersen (2014), our theoretical analysis predicts that a portfolio that is long low-beta stocks and short high-beta stocks generates a positive CAPM alpha. However, since the equity is correctly priced under our Black-Scholes-Merton framework, the observed positive alpha is due to the pricing error that is induced by the inadequate linearity assumption of the CAPM. This result questions whether the betting against beta phenomenon is indeed an asset pricing anomaly or whether it is due to the fact that the standard CAPM is an inappropriate setting for analyzing the equity returns of highly levered firms. As the analysis presented in this paper is purely theoretical, our aim here is not to assert that the documented betting against beta phenomenon can fully be attributed to the pricing error that we point out. Such detailed empirical tests are beyond the scope of the present paper. Nonetheless, our findings highlight that care must be taken when we interpret the negative alphas of high-beta stocks as an asset pricing anomaly"


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A Multiples-Based Decomposition of the Value Premium Thursday, 14 January, 2016

A new related paper has been added to:

#26 - Value (Book-to-Market) Anomaly

Authors: Golubov, Konstantinidi

Title: A Closer Look at the Value Premium: Evidence from a Multiples-Based Decomposition

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2702822

Abstract:

We use industry multiples-based market-to-book decomposition of Rhodes-Kropf, Robinson and Viswanathan (2005) to study the value premium. The market-to-value component drives all of the value strategy return, while the value-to-book component exhibits no return predictability in both portfolio sorts and firm-level return regressions controlling for other stock characteristics. Prior results in the literature linking value/glamor to expectational errors and limits to arbitrage hold due to the market-to-value component, whereas the results linking market-to-book to cashflow risk, exposure to investment-specific technology shocks, and analyst’s risk ratings hold only for the unpriced value-to-book. Overall, our evidence points towards the mispricing explanation for the value premium.

Notable quotations from the academic research paper:

"In this paper, we shed further light on the origins of the “value premium” using a market-to-book decomposition proposed by Rhodes–Kropf, Robinson, and Viswanathan (2005) (RRV hereafter) in their study of misvaluation and merger waves. In particular, the market-to-book ratio is decomposed into market-to-value and value-to-book components, where value is an estimate of fundamental value based on industry multiples conditional on a set of observable characteristics. The market-to-value component represents deviation of the stock price from fundamental value implied by long-run industry valuations, and the value-to-book component represents the “expected” industry-specific valuation of the firm’s net assets. The market-to-value component can be further decomposed into firm-specific deviation of the stock price from contemporaneous industry-level valuation, and the deviation of industry-level valuation from its long-run average.

We find that all of the return predictability of the market-to-book ratio is concentrated in the market-to-value component. Over the 1975-2013 period, a long-short portfolio strategy based on the conventionally used market-to-book ratio produces an average return of 1.42% per month (17.04% annualized). The same strategy based on the market-to-value component produces an average return of 1.56% (18.72% annualized), while going long low value-to-book and short high value-to-book stocks produces an average return of 0.27% per month – statistically insignificant. The Sharpe ratios of the market-to-value strategies are also superior to that of the conventional value strategy. Further decomposition of the market-to-value component into firm-level deviations from contemporaneous industry-level valuations and industry-level deviations from long-run averages shows that it is the firm-specific component that drives return predictability.

If high (low) market-to-value stocks are over (under)priced, we should find that investors are negatively (positively) surprised by their earnings announcements following portfolio formation. This is exactly what we find. We also find that high (low) market-to-value stocks experience positive (negative) earnings surprises in the quarters prior to portfolio formation, suggesting that the mechanism by which these stocks become mispriced is investor overextrapolation of positive (negative) news, leading to over (under)valuation that gets corrected over subsequent quarters as the true fundamentals are revealed. These patterns are not there for the value-to-book component.

We then examine the role of limits to arbitrage, such as short sale constraints and noise trader risk, in the sustaining of mispricing (De Long et al. (1990), Shleifer and Vishny (1997), Pontiff (2006)). In the absence of limits to arbitrage, overvaluation should not persist for long periods of time as the rational arbitrageurs trade against the mispricing. We find that the performance of the market-to-value strategy is concentrated in portfolios characterized by short sale constraints, as captured by institutional ownership and the existence of exchange-traded options. Moreover, these differences are largely due to high market-to-value stocks – the ones going into the short leg of the strategy – where short sale constraints are really binding. We also find that the market-to-value strategy returns are largely due to stocks characterized by noise trader risk as captured by idiosyncratic return volatility. Again, these effects are not there for value-to-book: the mispricing-based explanations of the market-to-book effect hold only for the component designed to capture mispricing."


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Are Size and Book-Value Factors Really Significant ? Tuesday, 5 January, 2016

A new related paper has been added to:

#25 - Small Capitalization Stocks Premium Anomaly

#26 - Value (Book-to-Market) Anomaly

Authors: Lambert, Hubner

Title: Size Matters, Book Value Does Not! The Fama-French Empirical CAPM Revisited

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2506690

Abstract:

The Fama and French (F&F) factors do not reliably estimate the size and book-to-market effects. Our paper shows that the former has been underestimated in the US market while the latter overestimated. We do so by replacing F&F's independent rankings by the conditional ones introduced by Lambert and Hubner (2013), over which we improve the sorting procedure. This new specification better reflects the properties of the individual risk premiums. We emphasize a much stronger size effect than conventionally documented. As a major related outcome, the alternative risk factors deliver less specification errors when used to price passive investment indices..

Notable quotations from the academic research paper:

"The paper revisits the size and book-to-market effect in the US market over the 1980-2007 sample period. It demonstrates a strong size but an insignificant book-to-market effect over the sample period. Our result challenges the Fama and French evidence of the presence of a stronger book-to-market than size effect in the US market. Fama and French’s size and book-to-market premiums are indeed shown to be respectively insignificant and positively significant over the analyzed period. Their evidence is partly supported in the standard construction methodology itself as more weight is attributed to the ranking according to the book-to-market dimensions (Fama and French, 1993).

We propose an alternative way to construct the empirical risk factors of Fama and French (1993) that avoids the contamination of the premiums from the correlation structure of the data. Our paper aims indeed at addressing some of the drawbacks identified in this heuristic approach to construct risk factors. Some attention has been drawn to the potential misevaluation of the size and book-to-market effect implied by the way the Fama and French methodology was constructed (Cremers et al., 2010; Huij and Verbeek, 2009; Brooks et al., 2008). The original Fama and French (F&F) method performs a 2x3 sort of US stocks on market capitalization and on book-to-market and forms six two-dimensional portfolios at the intersections of the two independent rankings. The premiums are defined as the spread between the average low- and high-scoring portfolios. Our main argument motivating the modifications brought to the original F&F method is that the independent sorting procedure underlying the formation of the six F&F two-dimensional portfolios distorts the way stocks are ranked into portfolios by placing disproportionate weights between the portfolios.

We follow the methodology of Lambert and Hübner (2013) and apply a generalized Fama and French technique to infer the size, book-to-market and momentum factors from the US stock market over the sample period of 1980-2007. The main innovations of our premiums reside in a monthly rebalancing of the portfolios (underlying the construction of the risk premiums) in order to capture the time-varying dimensions of risk, in a finer size classification and in a conditional sorting of stocks into portfolios. We consider three risk dimensions. The conditional sorting procedure answers the question whether there is still return variation related to the third risk criterion after having controlled for two other risk dimensions. It consists in performing a sequential sort in three stages. The first two sorts are performed on control risks, while we end by the risk dimension to be priced. As in Cremers, Petajusto and Zitzewitz (2010) and in Huij and Verbeek (2009), our paper demonstrates that the book-to-market premium of F&F is overvalued. We perform several asset pricing tests to check the validity and pricing power of our alternative premium specification. Compared to the Fama and French method, our factor construction method better captures the return spread associated with the source of risk to be priced. It maximizes the dispersion in the related source of risk while keeping minimal dispersion in correlated sources of risk. The conditional sorting and the finer size classification contribute to better balance the weights placed on the small/large value/growth portfolios. The great improvement of the new method lies in the reduction of the specification errors when pricing passive benchmark investment portfolios. Besides, without losing in significance, the modified technique is neater and leads to risk premiums that may not necessarily be used jointly in a regression-based model, unlike the original Fama and French factors whose risk exposures are highly sensitive to the inclusion of the other Fama and French risk factors in the regression.

Our paper more generally supports Lambert and Hübner’s (2013) previous evidence that a sequential sorting procedure could be more appropriate to take into consideration the contamination effects between the premiums. We show that the premiums constructed along this way deliver more consistent risk properties while reaching at least the same specification level as the F&F premiums. Given the critical stance of our paper, we have to go quite in depth into the origins of the improvements of the proposed sequential procedure, assorted with various methodological variations, over the original F&F method. The robustness checks deliver clear insights vis-à-vis the key drivers of alternative approach’s pricing performance. It is the replacement of an independent sort by a sequential one that seems to make the largest difference as expected."


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