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This study investigates whether stock market returns are related to temperature. Research in psychology has shown that temperature significantly affects mood, and mood changes in turn cause behavioral changes. Evidence suggests th...
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This study investigates whether stock market returns are related to temperature. Research in psychology has shown that temperature significantly affects mood, and mood changes in turn cause behavioral changes. Evidence suggests that lower temperature can lead to aggression, while higher temperature can lead to both apathy and aggression. Aggression could result in more risk-taking while apathy could impede risk-taking. We therefore expect lower temperature to be related to higher stock returns and higher temperature to be related to higher or lower stock returns, depending on the trade-off between the two competing effects. We examine many stock markets world-wide and find a statistically significant, negative correlation between temperature and returns across the whole range of temperature. Apathy dominates aggression when temperature is high. The observed negative correlation is robust to alternative tests and retains its statistical significance after controlling for various known anomalies.
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In this article, we search for the evidence of intraweek and intraday anomalies on the spot foreign exchange (FOREX) market. Having in mind the international scope of this market, empirical evidence against market efficiency (i.e....
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In this article, we search for the evidence of intraweek and intraday anomalies on the spot foreign exchange (FOREX) market. Having in mind the international scope of this market, empirical evidence against market efficiency (i.e. market anomalies) will have important consequences for the substantial number of FOREX investors all around the globe. We explore intraweek, intraday and interaction between days and hour trade anomalies on the FOREX market over the period of 10 years using hourly time-series data of Euro and US Dollar (EUR/ USD) exchange rate on Swiss FOREX market from 1 January 2004 to 11 January 2014. We compare by analysis of variance test all pairs of mean returns on a daily, hourly and daily/hourly basis. /-Test is used to test whether intraday returns are significantly different from zero. We employ Tukey's honestly significant difference test to explore which intraday pairs of hourly mean returns are significantly greater than zero. We find that intraday and interaction between day and hour anomalies are present in trading EUR/USD on the spot FOREX market over the period of 10 years. The best arbitrage opportunity is evidenced on Fridays, when selling USD and buying EUR at 00:00 and selling EUR and buying USD at 03:00 the same day.
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Despite an extensive number of studies documenting evidence of seasonal anomalies in developed markets, relatively few studies have comprehensively examined these anomalies within emerging markets. Testing the robustness of season...
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Despite an extensive number of studies documenting evidence of seasonal anomalies in developed markets, relatively few studies have comprehensively examined these anomalies within emerging markets. Testing the robustness of seasonal anomalies in emerging markets would first, help to examine the theoretical explanations that have been proposed and second, provide an out-of-sample result for these seasonality anomalies. This study examines the efficiency of advanced emerging markets by testing five seasonal anomalies: the month of the year, other January, day-of-the-week, holiday, and week 44. Evidence is reported that is consistent with all of these seasonal anomalies with the exception of the other January effect; supporting the argument that advanced emerging markets are less than perfectly efficient.
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This paper examines calendar effects in Australian daily stock returns from 6 January 1958 to 30 December 2005. Three calendar effects-day-of-the-week, turn-of-the-month and month-of-the-year-are examined using parametric tests an...
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This paper examines calendar effects in Australian daily stock returns from 6 January 1958 to 30 December 2005. Three calendar effects-day-of-the-week, turn-of-the-month and month-of-the-year-are examined using parametric tests and a regression-based approach. The results indicate that the Australian market is characterised by seasonality of all three forms, with Tuesday, September and the second trading day of the month the most significant. However, there is also evidence of parameter instability and structural breaks in these relationships, with day-of-the-week effects becoming less important in the post-1987 crash period.
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In this paper, we investigate the relation between stock returns and R&D spending under different market conditions. Our empirical evidence suggests that investors' response to R&D activities varies according to stock market status. Following the conventional definitions of markets, we first categorize the market into four different states: slightly up (up by 0-20%), bull (up by more than 20%), slightly down (down by 0-20%), and bear (down by more than 20%). Using firms in high-tech industries from 1992 to 2009 as our sample, we show that investors value R&D spending consistently positively only when the market (proxied by the S&P 500) is up. R&D is valued less in the downward market and R&D response coefficients even turn negative during bear markets. However, earnings response coefficients are consistently positive regardless of market status. The results remain unchanged after we control for beta, bankruptcy risk, size, and different measuring windows. Our findings cannot be explained by risk-based hypothesis. The study advances our understanding of the relation between stock returns and R&D activities by empirically documenting its variations in market valuation across different market states; particularly, we found empirical evidence that R&D response coefficients in the down markets are negative. The study also provides additional input to the ongoing debate on finding the appropriate accounting treatment for intangible assets....
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In this paper, we investigate the relation between stock returns and R&D spending under different market conditions. Our empirical evidence suggests that investors' response to R&D activities varies according to stock market status. Following the conventional definitions of markets, we first categorize the market into four different states: slightly up (up by 0-20%), bull (up by more than 20%), slightly down (down by 0-20%), and bear (down by more than 20%). Using firms in high-tech industries from 1992 to 2009 as our sample, we show that investors value R&D spending consistently positively only when the market (proxied by the S&P 500) is up. R&D is valued less in the downward market and R&D response coefficients even turn negative during bear markets. However, earnings response coefficients are consistently positive regardless of market status. The results remain unchanged after we control for beta, bankruptcy risk, size, and different measuring windows. Our findings cannot be explained by risk-based hypothesis. The study advances our understanding of the relation between stock returns and R&D activities by empirically documenting its variations in market valuation across different market states; particularly, we found empirical evidence that R&D response coefficients in the down markets are negative. The study also provides additional input to the ongoing debate on finding the appropriate accounting treatment for intangible assets.
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Assets whose carry is trending up, namely, assets with high carry momentum, tend to have higher returns than those with low carry momentum. Using data from different asset classes, we show that portfolios with high-carry-momentum ...
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Assets whose carry is trending up, namely, assets with high carry momentum, tend to have higher returns than those with low carry momentum. Using data from different asset classes, we show that portfolios with high-carry-momentum assets delivered higher returns than portfolios with low-carry-momentum assets. The return differentials cannot be explained by exposure to traditional market risks or by such seemingly related factors as time-series momentum and carry. The results can be motivated by a model in which investors' demand for an asset depends on the market's view on the expected returns. An increase in carry raises investors' belief in the attractiveness of an asset, leading to the possibility for higher demand in the market and in turn to the potential for higher prices and positive returns.
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We evaluate the robustness of momentum returns in the US stock market over the period 1965-2012. We find that momentum profits have become insignificant since the late 1990s. Investigations of momentum profits in high and low vola...
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We evaluate the robustness of momentum returns in the US stock market over the period 1965-2012. We find that momentum profits have become insignificant since the late 1990s. Investigations of momentum profits in high and low volatility months address the concerns about unprecedented levels of market volatility in this period rendering momentum strategy unprofitable. Momentum profits remain insignificant in tests designed to control for seasonality, up or down market conditions, firm size and liquidity. Past returns, can no longer explain the cross-sectional variation in stock returns, even following up markets. Investigation of post holding period returns of momentum portfolios and risk adjusted buy and hold returns of stocks in momentum suggests that investors possibly recognize that momentum strategy is profitable and trade in ways that arbitrage away such profits. These findings are partially consistent with Schwert (Handbook of the economics of finance. Elsevier, Amsterdam, 2003) that documents two primary reasons for the disappearance of an anomaly in the behavior of asset prices, first, sample selection bias, and second, uncovering of anomaly by investors who trade in the assets to arbitrage it away. In further analyses we find evidence that suggest two other possible explanations for the declining momentum profits, besides uncovering of the anomaly by investors, that involve decline in the risk premium on a macroeconomic factor, growth rate in industrial production in particular and relative improvement in market efficiency.
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Despite their strong positive average returns across numerous asset classes, momentum strategies can experience infrequent and persistent strings of negative returns. These momentum crashes are partly forecastable. They occur in p...
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Despite their strong positive average returns across numerous asset classes, momentum strategies can experience infrequent and persistent strings of negative returns. These momentum crashes are partly forecastable. They occur in panic states, following market declines and when market volatility is high, and are contemporaneous with market rebounds. The low ex ante expected returns in panic states are consistent with a conditionally high premium attached to the option like payoffs of past losers. An implementable dynamic momentum strategy based on forecasts of momentum's mean and variance approximately doubles the alpha and Sharpe ratio of a static momentum strategy and is not explained by other factors. These results are robust across multiple time periods, international equity markets, and other asset classes. (C) 2016 The Authors. Published by Elsevier B.V.
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We investigate the relation between predictable market returns and predictable analyst forecast errors. Perfect correlation between predictable components of forecast errors and abnormal returns would lend credence to the view tha...
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We investigate the relation between predictable market returns and predictable analyst forecast errors. Perfect correlation between predictable components of forecast errors and abnormal returns would lend credence to the view that pricing anomalies are not merely an artifact of inadequately controlled risk. Our evidence implies an imperfect correlation. Moreover, we find that while the predictable component of abnormal returns is significantly associated with future forecast errors, trading strategies based directly on the predictable component of forecast errors are not profitable. Further implications of our findings are that predictable components of analysts' forecast errors are robust with respect to loss functions and analysts' earnings forecasts may significantly diverge from the market expectations.
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Research documents higher stock returns in November through April than for the rest of the year. This anomaly is known as the "Halloween effect" and results in the following trading rule: sell stocks in early May, invest in T-bill...
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Research documents higher stock returns in November through April than for the rest of the year. This anomaly is known as the "Halloween effect" and results in the following trading rule: sell stocks in early May, invest in T-bills, and re-invest in stocks on Halloween. In contrast to recent studies, we show that the Halloween effect is robust to consideration of outliers and the "January effect." Additionally, we show that investing in a "Halloween portfolio" provides risk-adjusted returns in excess of buy and hold equity returns even after consideration of transaction costs.
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