مقاله انگلیسی رایگان در مورد نسخه بتا و اسطوره بی طرفی بازار

elsevier

 

مشخصات مقاله
عنوان مقاله

 Betas and the myth of market neutrality

ترجمه عنوان مقاله  نسخه های بتا و اسطوره بی طرفی بازار
فرمت مقاله  PDF
نوع مقاله  ISI
نوع نگارش مقاله مقاله پژوهشی (Research article)
سال انتشار

مقاله سال ۲۰۱۶

تعداد صفحات مقاله  ۱۱ صفحه
رشته های مرتبط  مدیریت و حسابداری
گرایش های مرتبط  مدیریت کسب و کار MBA
مجله  مجله بین المللی پیش بینی – International Journal of Forecasting
دانشگاه  بخش مالیه، کانادا
کلمات کلیدی  ارزیابی پیش بینی، نسخه های بتا تحقق یافته، ریسک سیستماتیک، سری زمانی، اوراق بهادار صفر بتا
کد محصول  E4029
نشریه  نشریه الزویر
لینک مقاله در سایت مرجع  لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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بخشی از متن مقاله:
۱٫ Introduction

Hedge funds are often portrayed as investments which are market (beta) neutral, in that they have little systematic exposure to market risk. However, work by Asness, Krail, and Liew (2001), Bali, Brown, and Caglayan (2011, 2012), and Patton (2009) has shown hedge funds to have substantial market exposure. The recent financial crisis provided further evidence of significant beta exposure by equity market neutral hedge fund managers, as over 70% of funds reporting to Hedge Fund Research (HFR) finished 2008 in the red. Brown, Gregoriou, and Pascalau (2012) further highlight these concerns over the risk characteristics of hedge funds, and in reference to hedge funds during the recent financial crisis, remark ‘‘we all fall down together’’. The successful construction of a market neutral portfolio depends inherently on the ability of the manager to measure and forecast the beta exposure of his long and short portfolios accurately. The greater the forecast errorof the betas, the more likely the fund is to have a significant residual beta exposure, and therefore, the greater the potential exposure to systematic risk factors. This, of course, is precisely what investors hope to avoid when investing in market neutral funds. They do not want to be paying alpha fees (averaging 1.5% in management fees and with 20% in incentive fees) for beta returns (which can be obtained easily through index exchange traded funds for a fee of typically less than 0.2%). In response to these concerns, this study investigates the accuracy of the methods commonly employed for constructing market neutral portfolios.

In general, daily equity returns is the highest frequency that is available reliably for the construction of equity market neutral portfolios, though it is also very common to see beta forecasts generated from monthly equity returns. The overwhelming majority of beta forecasts are generated from a constant beta model with a typical estimation period of between one and five years. This dates back to the work of Fama and MacBeth (1973), who proposed an estimation period of five years of monthly returns, and was further justified by Ghysels (1998), who showed that constant beta models have outperformed more sophisticated models of the time-varying beta. Recentlyproposed time-varying realized beta models for quarterly beta forecasting were studied by Andersen, Bollerslev, Diebold, andWu (2005, 2006), Ghysels and Jacquier (2006), and Hooper, Ng, and Reeves (2008), among others, though Reeves and Wu (2013) showed that these time-varying realized beta models did not outperform the constant beta model estimated on daily returns over the prior year.

In this paper, we evaluate these competing beta forecasting approaches in the setting of the construction of an equity market neutral portfolio. Equity momentum portfolios are constructed, as this is a common portfolio construction technique; see Carhart (1997), Grundy and Martin (2001), Jegadeesh and Titman (1993) and NovyMarx (2012), among others. However, we are not evaluating the return generating abilities of momentum strategies, but focus instead on evaluating the beta neutrality of portfolios by applying a commonly used and widely studied trading strategy. In addition, as a robustness check for portfolio construction based on momentum, we also construct portfolios by selecting stocks randomly and assessing the bootstrap distribution of statistics. We obtain similar results for both portfolio construction approaches, which provides an indication of the general applicability of the results beyond momentum-based strategies

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