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Journal articles on the topic 'Preference for skewness'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Zaremba, Adam, and Andrzej Nowak. "Skewness preference across countries." Business and Economic Horizons 11, no. 2 (July 10, 2015): 115–30. http://dx.doi.org/10.15208/beh.2015.09.

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2

Yang and Nguyen. "Skewness Preference and Asset Pricing: Evidence from the Japanese Stock Market." Journal of Risk and Financial Management 12, no. 3 (September 12, 2019): 149. http://dx.doi.org/10.3390/jrfm12030149.

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Previous studies have shown that investor preference for positive skewness creates a potential premium on negatively skewed assets. In this paper, we attempt to explore the connection between investors’ skewness preferences and corresponding demand for a risk premium on asset returns. Using data from the Japanese stock market, we empirically study the significance of risk aversion with skewness preference that potentially delivers a premium. Compared to studies on other stock markets, our finding suggests that Japanese investors exhibit preference for positively skewed assets, but do not display dislike for ones that are negatively skewed. This implies that investors from different countries having dissimilar attitudes toward risk may possess different preferences toward positive skewness, which would result in a different magnitude of expected risk premium on negatively skewed assets.
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3

Gao, Xiang, Kees G. Koedijk, and Zhan Wang. "Volatility-Dependent Skewness Preference." Journal of Portfolio Management 48, no. 1 (October 5, 2021): 43–58. http://dx.doi.org/10.3905/jpm.2021.1.295.

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4

Wen, Fenghua, Zhifang He, and Xiaohong Chen. "Investors’ Risk Preference Characteristics and Conditional Skewness." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/814965.

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Perspective on behavioral finance, we take a new look at the characteristics of investors’ risk preference, building the D-GARCH-M model, DR-GARCH-M model, and GARCHC-M model to investigate their changes with states of gain and loss and values of return together with other time-varying characteristics of investors’ risk preference. Based on a full description of risk preference characteristic, we develop a GARCHCS-M model to study its effect on the return skewness. The top ten market value stock composite indexes from Global Stock Exchange in 2012 are adopted to make the empirical analysis. The results show that investors are risk aversion when they gain and risk seeking when they lose, which effectively explains the inconsistent risk-return relationship. Moreover, the degree of risk aversion rises with the increasing gain and that of risk seeking improves with the increasing losses. Meanwhile, we find that investors’ inherent risk preference in most countries displays risk seeking, and their current risk preference is influenced by last period’s risk preference and disturbances. At last, investors’ risk preferences affect the conditional skewness; specifically, their risk aversion makes return skewness reduce, while risk seeking makes the skewness increase.
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5

Burke, Christopher J., and Philippe N. Tobler. "Reward skewness coding in the insula independent of probability and loss." Journal of Neurophysiology 106, no. 5 (November 2011): 2415–22. http://dx.doi.org/10.1152/jn.00471.2011.

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Rewards in the natural environment are rarely predicted with complete certainty. Uncertainty relating to future rewards has typically been defined as the variance of the potential outcomes. However, the asymmetry of predicted reward distributions, known as skewness, constitutes a distinct but neuroscientifically underexplored risk term that may also have an impact on preference. By changing only reward magnitudes, we study skewness processing in equiprobable ternary lotteries involving only gains and constant probabilities, thus excluding probability distortion or loss aversion as mechanisms for skewness preference formation. We show that individual preferences are sensitive to not only the mean and variance but also to the skewness of predicted reward distributions. Using neuroimaging, we show that the insula, a structure previously implicated in the processing of reward-related uncertainty, responds to the skewness of predicted reward distributions. Some insula responses increased in a monotonic fashion with skewness (irrespective of individual skewness preferences), whereas others were similarly elevated to both negative and positive as opposed to no reward skew. These data support the notion that the asymmetry of reward distributions is processed in the brain and, taken together with replicated findings of mean coding in the striatum and variance coding in the cingulate, suggest that the brain codes distinct aspects of reward distributions in a distributed fashion.
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6

Post, Thierry, Pim van Vliet, and Haim Levy. "Risk aversion and skewness preference." Journal of Banking & Finance 32, no. 7 (July 2008): 1178–87. http://dx.doi.org/10.1016/j.jbankfin.2006.02.008.

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7

Brockett, Patrick L., and Yehuda Kahane. "Risk, Return, Skewness and Preference." Management Science 38, no. 6 (June 1992): 851–66. http://dx.doi.org/10.1287/mnsc.38.6.851.

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8

Autore, Don M., and Jared R. DeLisle. "Skewness Preference and Seasoned Equity Offers." Review of Corporate Finance Studies 5, no. 2 (January 25, 2016): 200–238. http://dx.doi.org/10.1093/rcfs/cfw001.

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We find that the degree of expected idiosyncratic skewness in seasoned equity issuers’ stock returns is an important determinant of flotation costs and subsequent abnormal stock performance. High skewness issuers incur significantly greater offer price discounts, particularly when institutional share allocation is largest, pay higher gross underwriting spreads, and exhibit poorer stock performance in the three years after issuance, all compared to low skewness issuers. These results suggest that skewness-induced overpricing increases the flotation costs of seasoned equity offers and leads to poor subsequent stock performance. Received November 18, 2014; accepted December 17, 2015 by Editor Paolo Fulghieri.
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9

Mishra, Suchismita, Richard A. DeFusco, and Arun J. Prakash. "Skewness preference, value and size effects." Applied Financial Economics 18, no. 5 (March 2008): 379–86. http://dx.doi.org/10.1080/09603100600892855.

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10

Dertwinkel-Kalt, Markus, and Mats Köster. "Salience and Skewness Preferences." Journal of the European Economic Association 18, no. 5 (July 17, 2019): 2057–107. http://dx.doi.org/10.1093/jeea/jvz035.

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Abstract Whether people seek or avoid risks on gambling, insurance, asset, or labor markets crucially depends on the skewness of the underlying probability distribution. In fact, people typically seek positively skewed risks and avoid negatively skewed risks. We show that salience theory of choice under risk can explain this preference for positive skewness, because unlikely, but outstanding payoffs attract attention. In contrast to alternative models, however, salience theory predicts that choices under risk not only depend on the absolute skewness of the available options, but also on how skewed these options appear to be relative to each other. We exploit this fact to derive novel, experimentally testable predictions that are unique to the salience model and that we find support for in two laboratory experiments. We thereby argue that skewness preferences—typically attributed to cumulative prospect theory—are more naturally accommodated by salience theory.
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11

Strait, Caleb E., and Benjamin Y. Hayden. "Preference patterns for skewed gambles in rhesus monkeys." Biology Letters 9, no. 6 (December 23, 2013): 20130902. http://dx.doi.org/10.1098/rsbl.2013.0902.

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While standard models of risky choice account for the first and second statistical moments of reward outcome distributions (mean and variance, respectively), they often ignore the third moment, skewness. Determining a decision-maker's attitude about skewness is useful because it can help constrain process models of the mental steps involved in risky choice. We measured three rhesus monkeys’ preferences for gambles whose outcome distributions had almost identical means and variances but differed in skewness. We tested five distributions of skewness: strong negative, weak negative, normal, weak positive and strong positive. Monkeys preferred positively skewed gambles to negatively skewed ones and preferred strongly skewed and normal (i.e. unskewed) gambles to weakly skewed ones. This pattern of preferences cannot be explained solely by monotonic deformations of the utility curve or any other popular single account, but can be accounted for by multiple interacting factors.
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12

Choi, Joung Hwa and Eunyoung Cho. "IPO Underpricing and the Preference for Skewness." KOREAN JOURNAL OF FINANCIAL MANAGEMENT 34, no. 2 (June 2017): 143–69. http://dx.doi.org/10.22510/kjofm.2017.34.2.006.

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13

Mitton, Todd, and Keith Vorkink. "Equilibrium Underdiversification and the Preference for Skewness." Review of Financial Studies 20, no. 4 (January 29, 2007): 1255–88. http://dx.doi.org/10.1093/revfin/hhm011.

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14

Zhang, Xiao-Jun. "Book-to-Market Ratio and Skewness of Stock Returns." Accounting Review 88, no. 6 (June 1, 2013): 2213–40. http://dx.doi.org/10.2308/accr-50524.

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ABSTRACT: This study demonstrates that stocks with low book-to-market ratios, also known as glamour stocks, have significantly more positive skewness in their return distributions compared to the return distributions of value stocks with high book-to-market ratios. The premium (discount) investors apply to these glamour (value) stocks also correlates significantly with the difference in return skewness. These findings suggest that the value/glamour-stock puzzle is partially explained by investor preference for positive skewness in stock returns. Such preference for skewness, which is consistent with investors having inverse S-shaped utility functions, is observed in such consumer behaviors as lottery purchases and gambling. This paper further documents significant predictive power of accounting-based measures, such as the book rate of return, with respect to the skewness of stock returns. Data Availability: Data are available from sources identified in the paper.
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15

Chiu, W. Henry. "Skewness Preference, Risk Taking and Expected Utility Maximisation." Geneva Risk and Insurance Review 35, no. 2 (March 23, 2010): 108–29. http://dx.doi.org/10.1057/grir.2009.9.

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16

Mishra, Suchismita, Arun J. Prakash, Gordon V. Karels, and Therese E. Pactwa. "Skewness preference and the measurement of abnormal returns." Applied Economics 39, no. 6 (April 2007): 739–57. http://dx.doi.org/10.1080/00036840500439077.

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17

Huang, James, and Richard Stapleton. "Cautiousness, Skewness Preference, and the Demand for Options." Review of Finance 18, no. 6 (November 14, 2013): 2375–95. http://dx.doi.org/10.1093/rof/rft048.

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18

Reid, Donald W., and Bernard V. Tew. "The skewness preference of a risk-averse investor." Atlantic Economic Journal 18, no. 2 (June 1990): 89–90. http://dx.doi.org/10.1007/bf02313373.

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19

Ebert, Sebastian, and Christian Hilpert. "Skewness preference and the popularity of technical analysis." Journal of Banking & Finance 109 (December 2019): 105675. http://dx.doi.org/10.1016/j.jbankfin.2019.105675.

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20

Bali, Turan G., and Scott Murray. "Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns?" Journal of Financial and Quantitative Analysis 48, no. 4 (August 2013): 1145–71. http://dx.doi.org/10.1017/s0022109013000410.

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AbstractWe investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the underlying stock price (delta), and exposure to changes in implied volatility (vega) are removed, isolating the effect of skewness. We find a strong negative relation between risk-neutral skewness and the skewness asset returns, consistent with a positive skewness preference. The returns are not explained by well-known market, size, book-to-market, momentum, short-term reversal, volatility, or option market factors.
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21

DeLisle, R. Jared, and Nathan Walcott. "The Role of Skewness in Mergers and Acquisitions." Quarterly Journal of Finance 07, no. 01 (February 21, 2017): 1740001. http://dx.doi.org/10.1142/s2010139217400018.

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Investors prefer stocks with idiosyncratic skewness in their returns, which may be evidence of behavioral biases. Previous research suggests that skewness is related to the choice of target in corporate acquisitions, which may reflect CEOs’ behavioral biases. However, if the acquiring firms’ stock returns are also skewed, then the acquirer CEOs may rationally use their stock as currency in these deals. We investigate the skewness of the acquiring firm and the method of payment to determine if takeovers involving high skewness stocks are consistent with shareholder wealth maximization. We find that firms with high levels of skewness are more likely to become takeover targets and that takeover premiums increase with skewness, but there is no relation between the target’s skewness level and acquirer announcement returns. We also find that acquirers with high skewness are more likely to pay with stock and have higher announcement returns. We conclude that acquirer CEOs often take advantage of investor preference for skewness when undertaking mergers and acquisitions activity.
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22

Hassett, Matt, R. Stephen Sears, and Gary L. Trennepohl. "Asset preference, skewness, and the measurement of expected utility." Journal of Economics and Business 37, no. 1 (February 1985): 35–47. http://dx.doi.org/10.1016/0148-6195(85)90004-9.

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23

Mu, Congming, Weidong Tian, and Jinqiang Yang. "Portfolio choice with skewness preference and wealth-dependent risk aversion." Quantitative Finance 19, no. 11 (June 11, 2019): 1905–19. http://dx.doi.org/10.1080/14697688.2019.1592214.

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24

Poitras, Geoffrey, and John Heaney. "Skewness preference, mean-variance and the demand for put options." Managerial and Decision Economics 20, no. 6 (September 1999): 327–42. http://dx.doi.org/10.1002/(sici)1099-1468(199909)20:6<327::aid-mde948>3.0.co;2-0.

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25

Chiu, W. Henry. "Skewness Preference, Risk Aversion, and the Precedence Relations on Stochastic Changes." Management Science 51, no. 12 (December 2005): 1816–28. http://dx.doi.org/10.1287/mnsc.1050.0431.

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26

Green, T. Clifton, and Byoung-Hyoun Hwang. "Initial Public Offerings as Lotteries: Skewness Preference and First-Day Returns." Management Science 58, no. 2 (February 2012): 432–44. http://dx.doi.org/10.1287/mnsc.1110.1431.

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27

Nam, Seok Goo, and Byung Jin Kang. "Information Content of Skewness Risk Premium." Journal of Derivatives and Quantitative Studies 26, no. 4 (November 30, 2018): 391–423. http://dx.doi.org/10.1108/jdqs-04-2018-b0001.

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The variance risk premium defined as the difference between risk neutral variance and physical variance is one of the most crucial information recovered from option prices. It does not, however, reflect the asymmetry in upside and downside movements of underlying asset returns, and also has limitation in reflecting asymmetric preference of investors over gains and losses. In this sense, this paper decomposes variance risk premium into downside - and upside-variance risk premium, and then derives the skewness risk premium and examines its effectiveness in predicting future underlying asset returns. Using KOSPI200 option prices, we obtained the following results. First, we found out that the estimated skewness risk premium has meaningful forecasting power for future stock returns, while the estimated variance risk premium has little forecasting power. Second, by utilizing our results of skewness risk premium, we developed a profitable investment strategy, which verifies the effectiveness of skewness risk premium in predicting future stock returns. In conclusion, the empirical results of this paper can contribute to the literature in that it helps us understand why variance risk premium, in most global markets except the US market, has not been successful in forecasting future stock returns. In addition, our results showing the profitability of investment strategies based on skewness risk premium can also give important implications to practitioners.
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28

Luchtenberg, Kimberly F., and Michael J. Seiler. "Do Institutional and Individual Investors Differ in Their Preference for Financial Skewness?" Journal of Behavioral Finance 15, no. 4 (October 2, 2014): 299–311. http://dx.doi.org/10.1080/15427560.2014.968718.

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29

Aissia, Dorsaf Ben. "IPO first-day returns: Skewness preference, investor sentiment and uncertainty underlying factors." Review of Financial Economics 23, no. 3 (September 2014): 148–54. http://dx.doi.org/10.1016/j.rfe.2014.06.001.

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30

Homaifar, Ghassem, and Duane B. Graddy. "SKEWNESS PREFERENCE AND THE RETURN BEHAVIOR OF HIGH AND LOW RISK COMMON STOCKS." Financial Review 20, no. 3 (August 1985): 61. http://dx.doi.org/10.1111/j.1540-6288.1985.tb00239.x.

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31

Yao, Shouyu, Chunfeng Wang, Xin Cui, and Zhenming Fang. "Idiosyncratic skewness, gambling preference, and cross-section of stock returns: Evidence from China." Pacific-Basin Finance Journal 53 (February 2019): 464–83. http://dx.doi.org/10.1016/j.pacfin.2019.01.002.

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32

Booth, James R., and Richard L. Smith. "AN EXAMINATION OF THE SMALL-FIRM EFFECT ON THE BASIS OF SKEWNESS PREFERENCE." Journal of Financial Research 10, no. 1 (March 1987): 77–86. http://dx.doi.org/10.1111/j.1475-6803.1987.tb00477.x.

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33

Peel, David. "Subjective Skewness of Return as an Explanation of the Optimal Choice between Gambles in Cumulative Prospect Theory." Journal of Gambling Business and Economics 2, no. 2 (January 2, 2013): 97–108. http://dx.doi.org/10.5750/jgbe.v2i2.533.

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Given that the expected return and variance of return of two gambles are equal the hypothesis that the gamble with the greater positive skewness of return will be chosen by an expected utility maximiser is appealing. However the hypothesis is not, in general, correct. Brockett and Garven (1998) and Brocket and Kahane (1992) demonstrate this both theoretically and by constructing counter examples.A particularly revealing example is the following one constructed by Brockett and Kahane. Gamble A has the two outcomes 2.45 and 7.49 with probabilities 0.5141 and 0.4859 respectively. Gamble B has the three outcomes 0, 4.947 and 10 with probabilities 0.12096, 0.750085 and 0.128955 respectively. Even though gamble A exhibits lower expected return, a higher variance and lower positive skewness than gamble B it is preferred to gamble B by an expected utility maximiser on the basis of any standard utility function such as power, log or exponential. Consequently in this example of theirs the expected utility maximiser exhibits an aversion to higher expected return and higher skewness and a preference for higher variance. As noted by Brockett and Kahane these results cannot be dismissed as decision makers “trading” variance for mean or skewness or having a strange idiosyncratic utility function.
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34

Ebert, Sebastian, and Philipp Strack. "Until the Bitter End: On Prospect Theory in a Dynamic Context." American Economic Review 105, no. 4 (April 1, 2015): 1618–33. http://dx.doi.org/10.1257/aer.20130896.

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We provide a result on prospect theory decision makers who are naïve about the time inconsistency induced by probability weighting. If a market offers a sufficiently rich set of investment strategies, investors postpone their trading decisions indefinitely due to a strong preference for skewness. We conclude that probability weighting in combination with naïveté leads to unrealistic predictions for a wide range of dynamic setups. (JEL D81, G02, G11)
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35

Peel, David, and David Law. "AN EXPLANATION OF OPTIMAL EACH-WAY BETS BASED ON NON-EXPECTED UTILITY THEORY." Journal of Gambling Business and Economics 3, no. 2 (January 2, 2013): 15–35. http://dx.doi.org/10.5750/jgbe.v3i2.584.

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The purpose in this paper is to demonstrate how the non-expected utility models of Markowitz and Kahneman and Tversky can explain why an agent, chooses to bet each way on a horse.We also show that that appeal to moments of return, such as a preference for skewness of return, ceteris paribus, to explain the choice of the each way gamble over the single win gamble is , in general, invalid.
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36

Gou, Xiaoju, and Limei Bie. "Research on Investment Preference and the MAX Effect in Chinese Stock Market." Journal of Systems Science and Information 4, no. 6 (December 25, 2016): 519–33. http://dx.doi.org/10.21078/jssi-2016-519-15.

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AbstractInvestors prefer to invest the stocks with high history returns, which results in that the return of the stock with high history maximum return is often lower than that with low history maximum return, i.e., the MAX effect. We show that the MAX effect is also significant in China stock market, that is, there is a significant negative relationship between maximum return and expected return. We then conduct portfolio analysis and Fama-Macbeth cross-sectional regression and find that range of price and turnover rate can explain the MAX effect in a certain extent, idiosyncratic volatility and idiosyncratic skewness cannot explain the negative relationship between maximum return and expected return. Moreover, maximum return explains the idiosyncratic volatility puzzle partially.
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37

Moskowitz, Tobias J., and Annette Vissing-Jørgensen. "The Returns to Entrepreneurial Investment: A Private Equity Premium Puzzle?" American Economic Review 92, no. 4 (August 1, 2002): 745–78. http://dx.doi.org/10.1257/00028280260344452.

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We document the return to investing in U.S. nonpublicly traded equity. Entrepreneurial investment is extremely concentrated, yet despite its poor diversification, we find that the returns to private equity are no higher than the returns to public equity. Given the large public equity premium, it is puzzling why households willingly invest substantial amounts in a single privately held firm with a seemingly far worse risk-return trade-off. We briefly discuss how large nonpecuniary benefits, a preference for skewness, or overestimates of the probability of survival could potentially explain investment in private equity despite these findings.
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38

Masset, Philippe, and Caroline Henderson. "Wine as an Alternative Asset Class." Journal of Wine Economics 5, no. 1 (2010): 87–118. http://dx.doi.org/10.1017/s1931436100001395.

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AbstractUsing a dataset that spans the period 1996 to 2007 and contains transaction prices for all reported auctions at the Chicago Wine Company, we analyze how the prices of high-end wines have evolved during this time period. The best wines according to characteristics like vintage, rating and ranking earn higher returns and tend to have a lower variance than poorer wines. Nevertheless, the different categories of wines seem to follow a rather similar trend over the long run. Wine returns are only slightly correlated with other assets and can consequently be used to reduce the risk of an equity portfolio. Wine looks even more attractive when the investor also has concerns about the skewness of his portfolio. However, the part to be invested in wine is reduced once the kurtosis is included into the analysis. Finally, it seems advisable to diversify across different wine categories as their short-run movements are partially independent of each other. First growths and wines rated as extraordinary by Robert Parker deliver the best tradeoff in terms of portfolio expected returns, variance, skewness and kurtosis for most investor preference settings under consideration. (JEL Classification: C60, G11, Q11)
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39

Brunnermeier, Markus K., and Jonathan A. Parker. "Optimal Expectations." American Economic Review 95, no. 4 (August 1, 2005): 1092–118. http://dx.doi.org/10.1257/0002828054825493.

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Forward-looking agents care about expected future utility flows, and hence have higher current felicity if they are optimistic. This paper studies utility-based biases in beliefs by supposing that beliefs maximize average felicity, optimally balancing this benefit of optimism against the costs of worse decision making. A small optimistic bias in beliefs typically leads to first-order gains in anticipatory utility and only second-order costs in realized outcomes. In a portfolio choice example, investors overestimate their return and exhibit a preference for skewness; in general equilibrium, investors' prior beliefs are endogenously heterogeneous. In a consumption-saving example, consumers are both overconfident and overoptimistic.
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40

Mursula, K., T. Getachew, and I. I. Virtanen. "Spatial–temporal evolution of photospheric weak-field shifts in solar cycles 21–24." Astronomy & Astrophysics 645 (January 2021): A47. http://dx.doi.org/10.1051/0004-6361/201936917.

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Context. Weak magnetic field elements make a dominant contribution to the total magnetic field on the solar surface. Even so, little is known of their long-term occurrence. Aims. We study the long-term spatial–temporal evolution of the weak-field shift and skewness of the distribution of photospheric magnetic field values during solar cycles 21−24 in order to clarify the role and relation of the weak field values to the overall magnetic field evolution. Methods. We used Wilcox Solar Observatory (WSO) and the Synoptic Optical Long-term Investigations of the Sun Vector SpectroMagnetograph synoptic maps to calculate weak-field shifts for each latitude bin of each synoptic map, and thereby constructed a time–latitude butterfly diagram for shifts. We also calculated butterfly diagrams for skewness for all field values and for weak field values only. Results. The weak-field shifts and (full-field) skewness depict a similar spatial–temporal solar cycle evolution to that of the large-scale surface magnetic field. The field distribution has a systematic non-zero weak-field shift and a large skewness already at (and after) the emergence of the active region, even at the highest resolution. We find evidence for coalescence of opposite-polarity fields during the surge evolution. This is clearly more effective at the supergranulation scale. However, a similar dependence of magnetic field coalescence on spatial resolution was not found in the unipolar regions around the poles. Conclusions. Our results give evidence for the preference of even the weakest field elements toward the prevailing magnetic polarity since the emergence of an active region, and for a systematic coalescence of stronger magnetic fields of opposite polarities to produce weak fields during surge evolution and at the poles. We also find that the supergranulation process is reduced or turned off in the unipolar regions around the poles. These observations improve the understanding not only of the development of the weakest magnetic field elements, but also of the dynamics of magnetic fields at large, and even of processes below the solar surface.
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41

Gundle, Kenneth R., Amy M. Cizik, Stephanie E. W. Punt, Ernest U. Conrad, and Darin J. Davidson. "Validation of the SF-6D Health State Utilities Measure in Lower Extremity Sarcoma." Sarcoma 2014 (2014): 1–4. http://dx.doi.org/10.1155/2014/450902.

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Aim. Health state utilities measures are preference-weighted patient-reported outcome (PRO) instruments that facilitate comparative effectiveness research. One such measure, the SF-6D, is generated from the Short Form 36 (SF-36). This report describes a psychometric evaluation of the SF-6D in a cross-sectional population of lower extremity sarcoma patients.Methods. Patients with lower extremity sarcoma from a prospective database who had completed the SF-36 and Toronto Extremity Salvage Score (TESS) were eligible for inclusion. Computed SF-6D health states were given preference weights based on a prior valuation. The primary outcome was correlation between the SF-6D and TESS.Results. In 63 pairs of surveys in a lower extremity sarcoma population, the mean preference-weighted SF-6D score was 0.59 (95% CI 0.4–0.81). The distribution of SF-6D scores approximated a normal curve (skewness = 0.11). There was a positive correlation between the SF-6D and TESS (r=0.75,P<0.01). Respondents who reported walking aid use had lower SF-6D scores (0.53 versus 0.61,P=0.03). Five respondents underwent amputation, with lower SF-6D scores that approached significance (0.48 versus 0.6,P=0.06).Conclusions. The SF-6D health state utilities measure demonstrated convergent validity without evidence of ceiling or floor effects. The SF-6D is a health state utilities measure suitable for further research in sarcoma patients.
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42

Park, Jooyoung, and Jae Young Lee. "The Impact of Network Hub on New Product Diffusion in Online Community : Focusing on the Moderating Effect of Preference Skewness." Journal of CEO and Management Studies 25, no. 4 (December 31, 2022): 215–37. http://dx.doi.org/10.37674/ceoms.25.4.11.

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43

Forrest, David, Levi Perez, and Rose Baker. "EVALUATING THE EFFECTS OF GAME DESIGN ON LOTTO SALES: A CASE STUDY FROM SPAIN." Journal of Gambling Business and Economics 4, no. 2 (January 2, 2013): 1–19. http://dx.doi.org/10.5750/jgbe.v4i2.552.

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In February, 2005 the Spanish National Lottery Agency (LAE) made several modifications to the design of one of its lotto games. The entry fee was not changed but the familiar 6/49 format was replaced by 5/54 + 1/10. This considerably lengthened the odds against winning a share of the grand prize. However, extra lower tiers of prizes were added and a guaranteed jackpot of €5m introduced. The change in rules provides an unusual opportunity to study the effect on sales of features of lotto games other than entry fee and pay-back rate. The changes in design appear in this case to have allowed the operator to achieve higher and more stable sales. Reasons for this are explored through estimation of demand models. Results indicate that gains to the operator had been achieved by better satisfying players’ preference for skewness in the distribution of returns.
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44

REMMEL, MARK, and LESLIE SMITH. "New intermediate models for rotating shallow water and an investigation of the preference for anticyclones." Journal of Fluid Mechanics 635 (September 10, 2009): 321–59. http://dx.doi.org/10.1017/s0022112009007897.

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New intermediate models for the rotating shallow water (RSW) equations are derived by considering the nonlinear interactions between subsets of the eigenmodes for the linearized equations. It is well-known that the two-dimensional quasi-geostrophic (QG) equation results when the nonlinear interactions are restricted to include only the vortical eigenmodes. Continuing past QG in a non-perturbative manner, the new models result by including subsets of interactions which include inertial-gravity wave (IG) modes. The such simplest model adds nonlinear interactions between one IG mode and two vortical modes. In sharp contrast to QG, the latter model behaves similar to the full RSW equations for decay from balanced initial conditions as well as unbalanced random initial conditions with divergence-free velocity. Quantitative agreement is observed for statistics that measure structure size, intermittency and cyclone/anticyclone asymmetry. In particular, dominance of anticyclones is observed for Rossby numbers Ro in the range 0.1 < Ro < 1 (away from the QG parameter regime Ro → 0). A hierarchy of models is explored to determine the effects of wave-vortical and wave–wave interactions on statistics such as the skewness of vorticity in decaying turbulence. Possible advantages over previously derived intermediate models include (i) the non-perturbative nature of the new models (not restricting them a priori to any particular parameter regime) and (ii) insight into the physical and mathematical consequences of vortical–wave interactions.
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45

Dinç, Sırrı Cem, and Mustafa Demircan. "Investigation of the Relationship Between Dispositional Flow State, Sensation Seeking and Ski Resort Preference of Skiing and Snowboarding Participants." Journal of Education and Learning 8, no. 5 (September 19, 2019): 57. http://dx.doi.org/10.5539/jel.v8n5p57.

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The aim of this study is to investigate the relationship between was to examine dispositional flow state, sensation seeking and ski resort preference of skiing and snowboarding participants. The sample of this study constituted 510 (126 female x̄ = 23.94 &plusmn; 5.79 years; 384 male x̄ = 27.33 &plusmn; 7.87 years) skiers and snowboarders from varied ski resort between the years 2018&ndash;2019. A demographic questionnaire, The Contextual Sensation Seeking Questionnaire for Skiing and Snowboarding (CSSQ-S) Ski Resort Preference Scale (SRPS) and Dispositional Flow Scale-2&mdash;Short Form (DFS2-SF) were used to collect data. Before the statistical analysis, coefficient of kurtosis, coefficient of skewness and test of normality (Kolmogorov-Smirnov Test) of the data were examined and the deviation from the normal distribution was determined meaningless (p &gt; .05). Pearson Product-Moment Correlation Coefficient was used to determine the relationship between variables. The mediator effect of sensation seeking in the relationship between ski resort preference and flow of participants were examined with Structural Equation Modeling (SEM). The correlation value between the variables was determined between 0.196&ndash;0.549. (P &lt; 0.01). There is causal relationship between; SRPS and DFS2-SF (&beta; = .187, p &lt; .01), SRPS and CSSQ-S (&beta; = .932, p &lt; .01), CSSQ-S and DFS2-SF (&beta; = .581, p &lt; .01) respectively. It was found that when SRPS variable was included in the model as a mediating variable, causal relationship between SRPS and DFS2-SF was eliminated (&beta; = 7.067, p &gt; .05) and causal relationship between CSSQ-S and DFS2-SF was significantly increased (&beta; = .722, p &lt; .01). When the fit indexes of the models were examined, all values in all four models indicated acceptable/perfect fit. The results show that ski resort preference has a significant causal relationship on flow state and sensation seeking in skier and snowboarders. However, when sensation seeking is included in the model as a mediating factor, effect of the ski resort preference on the flow state disappears, while the effect level of sensation seeking increases.
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46

DIGBY, DAVID, WILLIAM SEFFENS, and FISSEHA ABEBE. "RUNS OF AMINO ACIDS ARE LONGER THAN EXPECTED IN PROTEINS BASED ON A GRAPH THEORY REPRESENTATION OF THE GENETIC CODE." Journal of Biological Systems 10, no. 04 (December 2002): 319–35. http://dx.doi.org/10.1142/s0218339002000718.

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An in silico study of mRNA secondary structure has found a bias within the coding sequences of genes that favors "in-frame" pairing of nucleotides. This pairing of codons, each with its reverse-complement, partitions the 20 amino acids into three subsets. The genetic code can therefore be represented by a three-component graph. The composition of proteins in terms of amino acid membership in the three subgroups has been measured, and sequence runs of members within the same subgroup have been analyzed using a runs statistic based on Z-scores. In a GENBANK database of over 416,000 protein sequences, the distribution of this runs-test statistic is negatively skewed. To assess whether this statistical bias was due to a chance grouping of the amino acids in the real genetic code, several alternate partitions of the genetic code were examined by permuting the assignment of amino acids to groups. A metric was constructed to define the difference, or "distance", between any two such partitions, and an exhaustive search was conducted among alternate partitions maximally distant from the natural partition of the genetic code, to select sets of partitions that were also maximally distant from one another. The statistical skewness of the runs statistic distribution for native protein sequences were significantly more negative under the natural partition than they were under all of the maximally different partition of codons, although for all partitions, including the natural one, the randomized sequences had quite similar skewness. Hence under the natural graph theory partition of the genetic code there is a preference for more protein sequences to contain fewer runs of amino acids, than they do under the other partitions, meaning that the average run must be longer under the natural partition. This suggests that a corresponding bias may exist in the coding sequences of the actual genes that code for these proteins.
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47

Wang, J., J. Hu, N. Wang, M. Yao, and Z. Wang. "Multi-criteria decision-making method-based approach to determine a proper level for extrapolation of Rainflow matrix." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 226, no. 5 (September 23, 2011): 1148–61. http://dx.doi.org/10.1177/0954406211420212.

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To obtain the genuine load spectra of the components of engineering vehicles, the influencing factors in the extrapolation of load spectra should be considered because these are of paramount importance. In the present paper, a multi-criteria decision-making method (MCDM) is proposed to determine the proper upcrossing of the high and low levels (UHLL) for extrapolation of the Rainflow matrix (RFM). First, the distribution characteristics of load amplitude extracted from observed RFMs are obtained. Subsequently, five criteria, namely, the statistic of mean, statistic of variance, coefficient of skewness, coefficient of kurtosis, and the experts’ preference, are selected to describe the fitting accuracy of the Weibull probability density function. Finally, the relationship between the load threshold and the level crossing function is determined after calculating their relative importance, and consequently, the proper UHLL is obtained. The usefulness and validity of the proposed method is illustrated by two typical load-time histories. Results show that the MCDM-based method can combine the subjective and objective factors and obtain an optimal load level.
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48

Leidman, Eva, Louise Masese Mwirigi, Lucy Maina-Gathigi, Anna Wamae, Andrew Amina Imbwaga, and Oleg O. Bilukha. "Assessment of Anthropometric Data Following Investments to Ensure Quality: Kenya Demographic Health Surveys Case Study, 2008 to 2009 and 2014." Food and Nutrition Bulletin 39, no. 3 (July 23, 2018): 406–19. http://dx.doi.org/10.1177/0379572118783181.

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Background: Evidence-based nutrition programs depend on accurate estimates of malnutrition derived from data collected in population representative surveys. The feasibility of obtaining accurate anthropometric data as part of national, multisectoral surveys has been a debated issue. Objectives: The study aimed to evaluate changes in anthropometric data quality corresponding to investments by the Kenya Ministry of Health and nutrition sector partners for the 2014 Kenya Demographic Health Survey. Methods: Anthropometric data collected during the 2008 to 2009 and 2014 Kenya surveys were reanalyzed to assess standard parameters of quality: standard deviation, skewness, and kurtosis of z-score values for 3 anthropometric indicators (weight for height, height for age, and weight for age), percentage of children with missing measurements and outlier values, digit preference, and heaping of age. Results: A total of 9936 households were selected in 2008 to 2009, and 39 679 households were selected in 2014. Standard deviation of z-scores for all 3 indicators was smaller in 2014 than in 2008 to 2009. Applying original Demographic and Health Survey exclusion criteria, weight for height z-scores were 1.16 in 2014, 10.1% narrower than 2008 to 2009. The percentage of outlying values declined significantly from 2008 to 2009 to 2014 for both height for age and weight for height ( P < .001). Digit preference scores in 2014 improved for both weight ( P = .011) and height ( P < .001) suggesting less rounding of terminal digits. Conclusions: All tests of data quality suggest an improvement in 2014 relative to 2008 to 2009, despite the complexity implied by the larger sample. This improvement corresponds with efforts to enhance training and supervision of anthropometry, suggesting a positive effect of these enhancements.
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49

Sharma, Manik, Samriti Sharma, and Gurvinder Singh. "Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining." Data 3, no. 4 (November 24, 2018): 54. http://dx.doi.org/10.3390/data3040054.

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Nowadays, overwhelming stock data is available, which areonly of use if it is properly examined and mined. In this paper, the last twelve years of ICICI Bank’s stock data have been extensively examined using statistical and supervised learning techniques. This study may be of great interest for those who wish to mine or study the stock data of banks or any financial organization. Different statistical measures have been computed to explore the nature, range, distribution, and deviation of data. The different descriptive statistical measures assist in finding different valuable metrics such as mean, variance, skewness, kurtosis, p-value, a-squared, and 95% confidence mean interval level of ICICI Bank’s stock data. Moreover, daily percentage changes occurring over the last 12 years have also been recorded and examined. Additionally, the intraday stock status has been mined using ten different classifiers. The performance of different classifiers has been evaluated on the basis of various parameters such as accuracy, misclassification rate, precision, recall, specificity, and sensitivity. Based upon different parameters, the predictive results obtained using logistic regression are more acceptable than the outcomes of other classifiers, whereas naïve Bayes, C4.5, random forest, linear discriminant, and cubic support vector machine (SVM) merely act as a random guessing machine. The outstanding performance of logistic regression has been validated using TOPSIS (technique for order preference by similarity to ideal solution) and WSA (weighted sum approach).
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50

Blau, Benjamin M., Jason Hsu, and Ryan J. Whitby. "Skewness preferences and gambling cultures." Pacific-Basin Finance Journal 58 (December 2019): 101206. http://dx.doi.org/10.1016/j.pacfin.2019.101206.

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