3,939 research outputs found

    DistancePPG: Robust non-contact vital signs monitoring using a camera

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    Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.Comment: 24 pages, 11 figure

    Idiosyncratic Volatility: Evidence from Asia

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    The traditional Capital Asset Pricing Model states that assets can earn only higher returns if they have a high beta. However, evidence shows that the single risk factor is not quite adequate for describing the cross-section of stock returns. The current consensus is that firm size and book-to-market equity factors are pervasive risk factors besides the overall market factor. Malkiel and Xu (1997 and 2000) further the debate in empirical asset pricing by stating that idiosyncratic volatility is useful in explaining the cross-sectional expected returns. In this paper we provide international evidence on the relationship between expected stock returns, overall market factor, firm size and idiosyncratic volatility. Our findings suggest that size and idiosyncratic volatility premium are real and pervasive. We find that small and high idiosyncratic volatility stocks generate superior returns and hence suggest that such firms carry risk premia. Our findings also suggest that idiosyncratic volatility is more powerful than the CAPM beta and the firm size effect. Our findings challenge the portfolio theory of Markowitz (1952) and the CAPM of Sharpe (1964), which advances the notion that it is rational for a utility maximizing investor to hold a well-diversified portfolio of investments to eliminate idiosyncratic risks.Idiosyncratic risk, Portfolio Theory, Capital Asset Pricing Model, Size effect and Beta.

    ASSET PRICING IN THE ASIAN REGION

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    In this asset pricing study, three questions are addressed. First, does the multifactor model of Fama and French (1993) capture returns in Asian stock markets in a meaningful manner? Second, do small firms and high book-to-market equity firms carry a risk premia? Third, can competing hypotheses (such as survivorship bias, data-snooping and irrationality) explain the multifactor model results? The answers from this study are as follows: The multifactor model of Fama and French (1993) provides a parsimonious description of the cross-section of returns, with the relationship between firm size, book-to-market equity and average stock returns being robust for Asian markets over the 1990s. We find that small firms and high book-to-market equity firms carry a risk premia, providing opportunities for mean-variance efficient investors. Finally, our findings reject the claim that the results of multifactor model can be explained by competing hypotheses for the Asian experience.Multifactor asset pricing models, Asian region, size effect, book-to-market equity effect.

    On the Value Premium in Malaysia

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    Davis, Fama and French (2000) report that the value premium in United States’ stocks is robust. Herein, we present out-of-sample evidence for Malaysia, finding that value stocks outperform growth stocks and document an arbitrage opportunity. We observe that the mean monthly returns are substantially higher for the two mimic portfolios (SMB and HML) when compared with the market portfolio. For the period 1991 through 1999, an investor generated 1.92% (annually) holding the market portfolio in Malaysia, compared with the two mimic portfolios, SMB and HML with returns of 17.70% and 17.69% respectively. We also observe that the standard deviations for the two mimic portfolios are significantly lower than the standard deviation of the market portfolio. Moreover, the findings presented in this study reject the notion of survivorship bias advanced by Kothari, Shanken and Sloan (1995) and the data-snooping hypothesis attributed to Black (1993) and Mackinlay (1995) as an explanation for the value premium.Asset pricing, multifactor models, value premium, arbitrage

    Multifactor Models are Alive and Well

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    A large number of studies have investigated the cross-section of average returns on common stocks in the United States and have found little relationship with the estimated beta of the single-factor model. This paper tests the joint roles of an overall market factor, and factors related to firm size (market equity) and style (book equity to market equity) in the cross-section of average stock returns in Australia, as there is little evidence available on the asset pricing theory in markets outside the United States. This paper also tests the claim that the size and style effect is the result of seasonal phenomena. We report that the three-factor model largely explains the variation in stock returns in a meaningful pattern. We also observe that size and style factors do a good job throughout the sample period and reject the claim that these effects are due to seasonal phenomena. Our results document that the explanatory power of the three-factor model is not restricted to a limited set of portfolios. Moreover, our findings do not support the data-snooping hypothesis.Asset Pricing, Multifactor Models, Seasonality Premium, Size and Book-to-Market
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