The MAX effect: Lottery stocks with price limits and limits to arbitrage

Hung, Weifeng; Yang, Jun

doi:10.1016/j.finmar.2018.07.003

Cited by 31 publications

(18 citation statements)

References 27 publications

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“…1 we observe that the cumulative returns virtually linearly decrease across time. Moreover, we learn from Table 1 that the average predicted return linearly decreases as we move from lowest to highest maximum daily return portfolio, which confirms the corresponding literature in equity market research (Bali et al, 2011(Bali et al, , 2017Walkshäusl, 2014;Chan and Chui, 2016;Hung and Yang, 2018;Asness et al, 2020). The zero-cost portfolio generates À1.54% weekly average returns with a HAC-robust t-statistic of À2.68 indicating statistical significance even on a 1% level.…”

Section: Sorting the Portfoliossupporting

confidence: 82%

“…The results show that the difference between returns on cryptocurrency portfolios with the highest and lowest maximum daily returns is À1.54% per week. While the negative relationship confirms the results from the previous equity market studies (Bali et al, 2011(Bali et al, , 2017Walkshäusl, 2014;Chan and Chui, 2016;Hung and Yang, 2018;Asness et al, 2020), the economic magnitude is considerably higher in the case of cryptocurrency markets. The return difference in raw returns is statistically significant at a 1% risk level.…”

Section: Introductionsupporting

confidence: 87%

“…5 Using weekly data instead of monthly data used in earlier studies (e.g., Bali et al, 2011;Bali et al, 2017;Asness et al, 2020;Hung and Yang, 2018) also meets the requirements of a scientific replication as asked for by Hou et al, 2020, who investigated 452 asset pricing anomalies and found that most anomalies fail to meet currently acceptable standards for empirical finance. They emphasize that the crux is that unlike natural sciences finance is mostly observational in nature.…”

Section: Statistical Inference and Blocks Bootstrapsmentioning

confidence: 99%

“…The authors show that after controlling for the MAXeffect, the negative effect of idiosyncratic volatility on stock returns vanishes. Hung and Yang (2018) argue that many stock markets are subject to daily price limits and therefore propose a modified MAX measure. Their findings confirm earlier studies in that stocks with highest (lowest) modified MAX experience lower (higher) future returns, indicating that stocks with highest (lowest) modified MAX tend to be overpriced (underpriced).…”

Section: Introductionmentioning

confidence: 99%

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Speculation and Lottery-Like Demand in Cryptocurrency Markets

Grobys

Junttila

2020

SSRN Journal

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This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns appear to be short-memory processes, we modify Bali, Cakici and Whitelaw's (2011) and Bali, Brown, Murray, and Tang's (2017) MAX measure and employ a weekly forecast horizon and daily log-returns from the previous week to calculate the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk-adjusted return differences between cryptocurrencies in the lowest and highest MAX quintiles exceed 1.50% per week. These results are robust after controlling for Bitcoin risk or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.

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Section: Sorting the Portfoliossupporting

confidence: 82%

Section: Introductionsupporting

confidence: 87%

Section: Statistical Inference and Blocks Bootstrapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speculation and Lottery-Like Demand in Cryptocurrency Markets

Grobys

Junttila

2020

SSRN Journal

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“…In their seminal paper, Bali et al (2011) introduce the MAX effect showing that stocks with extreme historical positive returns are inclined to exhibit lower returns in the future. This effect in asset pricing has received empirical support by many studies in the stock market literature, including Fong and Toh (2014), Barinov (2018), andHung andYang (2018). In this study, following Bali et al ( 2011), we investigate the role of lagged extreme positive returns in the cross-sectional pricing of cryptocurrencies.…”

mentioning

confidence: 83%

Lottery-like preferences and the MAX effect in the cryptocurrency market

2021

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We investigate the significance of extreme positive returns in the cross-sectional pricing of cryptocurrencies. Through portfolio-level analyses and weekly cross-sectional regressions on all cryptocurrencies in our sample period, we provide evidence for a positive and statistically significant relationship between the maximum daily return within the previous month (MAX) and the expected returns on cryptocurrencies. In particular, the univariate portfolio analysis shows that weekly average raw and risk-adjusted return differences between portfolios of cryptocurrencies with the highest and lowest MAX deciles are 3.03% and 1.99%, respectively. The results are robust with respect to the differences in size, price, momentum, short-term reversal, liquidity, volatility, skewness, and investor sentiment.

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How do intangible assets and financial constraints affect stock returns in Vietnam before and during the COVID‐19 pandemic?

Duong,

Huynh,

Truong

2023

Int. J Fin Econ

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We are the first to determine the effect of intangible intensity (INTANG) on cross‐sectional stock returns after controlling financial constraints in the Vietnam stock market. Our sample includes 37,938 firm‐month observations from 488 non‐financial firms from October 2008 to February 2021. We employ Fama and MacBeth regressions and portfolio analysis methodologies to estimate the impact of intangible assets and financial constraints on stock returns. Our findings show that a percentage increase in INTANG empowers stock returns by 0.922%. Meanwhile, the cross‐sectional stock returns decrease by 0.506% when the financial constraints index increases by a percentage point. Moreover, the results suggest that intangible assets in the entire sample and before COVID‐19 empower the stock return cross‐sectionally. Our findings are robust after employing alternative INTANG proxies. Our findings support the risk‐based explanation, the pecking order theory, and prior literature. Our findings suggest governments should promote intellectual property and copyright regulations to encourage Small and Medium Enterprises (SMEs) to expand intangible assets. Furthermore, investors can utilize our suggested models to construct their portfolios efficiently.

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The MAX effect: Lottery stocks with price limits and limits to arbitrage

Cited by 31 publications

References 27 publications

Speculation and Lottery-Like Demand in Cryptocurrency Markets

Speculation and Lottery-Like Demand in Cryptocurrency Markets

Lottery-like preferences and the MAX effect in the cryptocurrency market

How do intangible assets and financial constraints affect stock returns in Vietnam before and during the COVID‐19 pandemic?

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