دانلود مقاله تکانه و نوسانات بازار
خلاصه
1. مقدمه
2. حرکت در بازار سهام انگلستان
3. ساخت مدل سوئیچینگ دو رژیمی برای دینامیک حرکت
4. یک مدل سوئیچینگ دو رژیمی با هتروسکداستیکیته
5. بررسی استحکام
6. بحث
7. نتیجه گیری
یادداشت
یادداشت هایی در مورد مشارکت کنندگان
منابع
Abstract
1. Introduction
2. Momentum in the UK stock market
3. Construction of two-regime switching model for momentum dynamics
4. A two-regime switching model with heteroskedasticity
5. Robustness check
6. Discussion
7. Conclusion
Notes
Notes on contributors
References
چکیده
مطالعه ما نشان می‌دهد که مومنتوم یک پدیده پایدار است که تنوع زیادی در قدرت خود در بازار سهام بریتانیا نشان می‌دهد. با الهام از شواهد روان‌شناختی مبنی بر اینکه سوگیری‌های شناختی می‌توانند در طول زمان تغییر کنند، حدس می‌زنیم که ممکن است دو حالت بازار سهام وجود داشته باشد، یعنی وضعیت آرام و آشفته بازار، و اینکه تغییر بین این دو حالت بازار توسط نوسانات بازار کنترل می‌شود. با استفاده از روش‌های تخمین بیزی، نتایج ما نقش نوسانات بازار را به‌عنوان متغیر کلیدی کلیدی تأیید می‌کند، که همچنین مشخص شده است که قدرت پیش‌بینی بیشتری برای بازده حرکت در وضعیت آشفته بازار دارد. تا حدودی با یافته‌های مطالعات مقطعی متناقض است، متوجه می‌شویم که بازده گذشته تأثیر منفی بر سود حرکتی دارد. همچنین متوجه شدیم که هم برنده‌ها و هم بازنده‌ها در وضعیت آشفته بازار نسبت به وضعیت آرام بازار بهتر عمل می‌کنند و عملکرد بهتر بازنده‌ها مسئول زیان حرکت بزرگ در وضعیت آشفته بازار است. استراتژی‌های سرمایه‌گذاری که از قابلیت پیش‌بینی دینامیک حرکت سود می‌برند، از استراتژی‌های حرکت بهتر عمل می‌کنند. یافته‌های ما به‌راحتی با توضیحات مبتنی بر ریسک تطبیق داده نمی‌شوند، اما می‌توانند به راحتی در یک چارچوب رفتاری توضیح داده شوند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Our study finds that momentum is a persistent phenomenon that exhibits great variability in its strength in the UK stock market. Inspired by psychological evidence that cognitive biases can shift overtime, we conjecture that there may be two stock market states, namely, the calm and the turbulent market state, and that the switch between these two market states is governed by market volatility. Using Bayesian estimation methods, our results confirm the role of market volatility as the critical switching variable, which is also found to have additional predictive power for momentum returns in the turbulent market state. Somewhat contradictory to the findings in cross-sectional studies, we find that past returns have a negative impact on momentum profits. We also find that both winners and losers tend to perform better in the turbulent market state than in the calm market state and that losers’ outperformance is responsible for large momentum losses in the turbulent market state. Investment strategies that take advantage of the predictability of momentum dynamics outperform momentum strategies. Our findings are not readily reconciled with risk-based explanations but can be loosely explained in a behavioural framework.
Introduction
Momentum, first documented by Jegadeesh and Titman (1993), is a well-established phenomenon in the stock market. In the rational framework, momentum profits reflect variations in expected returns and are compensations for risks (Johnson 2002; Sagi and Seasholes 2007; Vayanos and Woolley 2013; Albuquerque and Miao 2014); whereas in the behavioural framework, momentum profits are the outcome of exploiting mispriced stocks, thanks to cognitive biases or bounded rationality (Barberis, Shleifer, and Vishny 1998; Daniel, Hirshleifer, and Subrahmanyam 1998; Hong and Stein 1999). Despite more than two decades of extensive studies, empirical findings on the sources of momentum profits are mixed and the question ‘what drives momentum?’ remains.
Recent research work, including Barroso and Santa-Clara (2015), Daniel and Moskowitz (2016), and Dobrynskaya (2019), finds that momentum strategies that are profitable on average over time suffer occasional substantial losses and that these losses are predictable to some extent.1 These empirical findings on momentum dynamics and its predictability are intriguing and offer a new perspective for us to examine the causes of momentum and its time-varying characteristics. Daniel and Moskowitz (2016) conclude that none of the crash risk, volatility risk, and Fama and French (1993) factors, can fully explain their findings. They also point out that the existence of the same phenomena and option-like features for momentum strategies in other financial asset markets challenges explanations such as Merton (1974) story. Dobrynskaya (2019) investigates the explanatory power of 13 risk factors for the profitability of investment strategies based on the predictability of momentum crashes, and all factor betas except one are found to be close to zero and statistically insignificant.
Conclusion
This study extends the investigation of momentum dynamics conditional on market state defined by market volatility. We investigate the momentum effect in the UK stock market and find that it is a significant phenomenon with great variability over time. In line with the recent studies on momentum effect in the US stock, the most striking features of its dynamics are the occasional sharp reversal and its association with market volatility. Informed by the patterns in momentum time series data and psychological evidence of time-varying cognitive biases and heuristics, we construct a two-regime switching model with lagged market volatility as the switching variable to capture the dominance of momentum effect and occasional reversals in the stock market in the short run and show that it performs well in explaining the facts both within and out of sample.
We find a significant negative nonlinear relationship between the lagged market volatility and the momentum return, and a significant negative relationship between the past return and the momentum return in the calm market state. These relationships are robust across momentum strategies and over time. Momentum dynamics, especially the occasional sharp reversals, are highly predictable. Investment strategies that follow the prediction based on the two-regime switching model significantly outperform momentum strategies. The superior performance of the model-guided investment strategy in combination with the more favourable-to-investors distributional characteristics of its monthly holding period returns, particularly, the lower standard deviation and positive skewness, represents an anomaly which challenges the efficient market hypothesis.