Hedge fund vs. non-hedge fund institutional demand and the book-to-market effect

Caglayan, Mustafa; Celiker, Umut; Sonaer, Gokhan

doi:10.1016/j.jbankfin.2018.04.021

Cited by 19 publications

(7 citation statements)

References 75 publications

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“…Dechow et al (2010) indicate that systematic inaccuracies in market expectations about long-term earnings growth can partially explain the success of contrarian investment techniques and the book-tomarket effect. Companies with high book-to-market ratios provide a unique opportunity to test the capacity of simple fundamental analysis heuristics to distinguish between them (Caglayan et al, 2018;Pätäri et al, 2022;Piotroski, 2000). Ball et al (2020), Papadamou et al (2017), and Piotroski (2000) argued that high BM value enterprises employ accounting indicators of financial soundness to distinguish really distressed firms from out-of-favor but financially strong firms.…”

Section: High Book-to-market and Growth Of Earnings Per Share Strategymentioning

confidence: 99%

“…This effect is particularly evident for earnings announcements from small businesses, firms with high volatility, growth/value firms, and firms with low return on assets, implying that comparability is more informative for more speculative stocks. Caglayan et al (2018) compare the characteristics of growth stocks bought by hedge funds with those of growth stocks heavily bought by non-hedge funds and find some minor differences in characteristics such as book-to-market ratio, size, price, demand, idiosyncratic volatility, illiquidity, intangible returns, and standardized earnings surprises. Controlling for these stock features in Fama and MacBeth's (1973) multivariate regressions does not diminish the predictive power of hedge fund trading (demand) over the cross-sectional variance of future stock returns (Hanauer et al, 2022).…”

Section: Financial Performance Signals Used To Differentiate High Bm ...mentioning

confidence: 99%

“…Wieland (2011) finds that in 28.9% of firmyear observations, consensus analyst forecasts incorrectly Raúl Daniel Navas / Sónia Margarida Ricardo Bentes indicate an increase in one-year-ahead earnings, and that correct (incorrect) businesses achieve 14.8% (25.7%) abnormal returns over the next year, on average. See also Amira and Hafssa (2021), Bradbury et al (2021), andCaglayan et al (2018). We use estimated growth of EPS for the next 5 years: GEPS 5y (FW) > 10%, estimated growth of EPS for next year: GEPS (FW) > 10%, past GEPS 5y > 10% and past sales 5y > 10%.…”

Section: Financial Performance Signals Used To Differentiate High Bm ...mentioning

confidence: 99%

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Section: High Book-to-market and Growth Of Earnings Per Share Strategymentioning

confidence: 99%

Section: Financial Performance Signals Used To Differentiate High Bm ...mentioning

confidence: 99%

Section: Financial Performance Signals Used To Differentiate High Bm ...mentioning

confidence: 99%

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Navas¹,

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2023

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“…Due to decreasing yields on financial instruments, combined with persistently low yields of fixed income instruments, and the volatility of the property markets, investors continue to seek alternative sources of yield outside traditional strategies (Caglayan et al, 2018). Hedge funds have been an increasingly popular form of alternative investments since the turn of the millennium.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Investment Fraud Using the Bias Ratio

Vuuren

2022

Sage Open

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Hedge funds play an important role in investment markets, but high-profile frauds and market manipulation have necessitated increased scrutiny for the detection and prevention of such activities. Market metrics which signal fraudulent activity quickly, early, and reliably are scarce and sometimes provide fallacious results. The bias ratio in conjunction with other metrics could provide a solution as it compares the real asset return distribution to that of unbiased returns. High bias ratios combined with statistical moments which differ considerably from those expected from normal distributions provide compelling evidence for possible return manipulation. Application of these metrics to known fraud cases shows that—used historically and in tandem—they would have provided powerful early indicators of suspicious investment activity. Uncovering these frauds earlier could have potentially saved investors considerable resources. The bias ratio also shows promising results in detecting other possible market scams such as insider trading.

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“…Caglayan et al in (Caglayan, Celiker, & Sonaer, 2018) leveraged a study on comparing hedge fund and non-hedge fund and involved research on how these two kinds of funds affect the related stock price. Stock prices and returns data were obtained from the Center for Research in Security Prices (CRSP) Monthly Stock File.…”

Section: Survey Of Existing Work In Financial Domainmentioning

confidence: 99%

Short-term Stock Market Price Trend Prediction Using a Customized Deep Learning System

Shen¹

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I want to extend thanks to many people, all over the world, who so generously helped me during my Master's degree and contributed to this thesis work. Special mention goes to my supervisors, Dr. Omair Shafiq. During my Master's degree, Dr. Shafiq has always been providing his tremendous academic support. He is enthusiastic in academic, and his attitude encourages me a lot as a researcher working in data science. He also generously provided me funding help to support my trip to CBDCom2019 in Japan, which is the first time I present at an international conference. I would also like to express my very profound gratitude to Dr. Anthony Whitehead, who was supposed to be my supervisor but unfortunately passed away before I entered Carleton University. Without Dr. Whitehead, I would never get the opportunity to study in Canada. Thanks for giving me the chance to become a student in CLUE; it is not about the funding support only, but also the precious internship opportunity to work in You. i TV. I am also hugely appreciative to Dr. Audrey Girouard, the director of CLUE, who has provided me countless opportunities to participate in the HCI activities, and inspired me to exploit deep learning technique to contribute to UX research domain. Also, profound gratitude goes to, Dr. Olga Baysal, Dr. Frank Dehne. Thanks for introducing me to the world of big data, and always being patient when I asked the fundamental questions. Last but not least, thanks to my family and friends, especially my mom. Thanks for supporting me to complete my Master's degree abroad. This thesis work will never exist iv without her, not only the financial and mental support, but also her passion in the stock market. I wish my thesis work can help her as a return, even a little. Thanks to all her generous support given to me all the time.

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Hedge fund vs. non-hedge fund institutional demand and the book-to-market effect

Cited by 19 publications

References 75 publications

Value investing: a new SCORE model

Value investing: a new SCORE model

Detecting Investment Fraud Using the Bias Ratio

Short-term Stock Market Price Trend Prediction Using a Customized Deep Learning System

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