Overreaction to growth opportunities: an explanation of the asset growth anomaly

The negative relation between asset growth and subsequent stock returns is known as the asset growth anomaly. We propose that overreaction to growth opportunities is the source of the asset growth anomaly. This suggests that growth firms as opposed to mature firms, and firms with longer series of asset growth should experience a stronger asset growth anomaly. Our evidence supports these predictions

Identifying jumps in financial assets: A comparison between nonparametric jump tests

We perform a comprehensive Monte Carlo comparison between nine alternative procedures available in the literature to detect jumps in financial assets using high-frequency data. We evaluate size and power properties of the procedures under alternative sampling frequencies, persistence in volatility, jump size and intensity, and degree of contamination with microstructure noise. The overall best performance is shown by the Andersen, Bollerslev, and Dobrev (2007) and Lee and Mykland (2008) intraday procedures (ABD-LM), provided the price process is not very volatile. We propose two extensions to the existing battery of tests. The first regards the finite sample improvements based on simulated critical values for the ABD-LM procedure. The second regards a procedure that combines frequencies and tests able to reduce the number of spurious jumps. Finally, we report an empirical analysis using real high frequency data on five stocks listed in the New York Stock Exchange

Scaling of the distribution of fluctuations of financial market indices

We study the distribution of fluctuations over a time scale $\Delta t$ (i.e., the returns) of the S&P 500 index by analyzing three distinct databases. Database (i) contains approximately 1 million records sampled at 1 min intervals for the 13-year period 1984-1996, database (ii) contains 8686 daily records for the 35-year period 1962-1996, and database (iii) contains 852 monthly records for the 71-year period 1926-1996. We compute the probability distributions of returns over a time scale $\Delta t$, where $\Delta t$ varies approximately over a factor of 10^4 - from 1 min up to more than 1 month. We find that the distributions for $\Delta t \leq$ 4 days (1560 mins) are consistent with a power-law asymptotic behavior, characterized by an exponent $\alpha \approx 3$, well outside the stable L\'evy regime $0 < \alpha < 2$. To test the robustness of the S&P result, we perform a parallel analysis on two other financial market indices. Database (iv) contains 3560 daily records of the NIKKEI index for the 14-year period 1984-97, and database (v) contains 4649 daily records of the Hang-Seng index for the 18-year period 1980-97. We find estimates of $\alpha$ consistent with those describing the distribution of S&P 500 daily-returns. One possible reason for the scaling of these distributions is the long persistence of the autocorrelation function of the volatility. For time scales longer than $(\Delta t)_{\times} \approx 4$ days, our results are consistent with slow convergence to Gaussian behavior.Comment: 12 pages in multicol LaTeX format with 27 postscript figures (Submitted to PRE May 20, 1999). See http://polymer.bu.edu/~amaral/Professional.html for more of our work on this are

What drives idiosyncratic volatility over time?

We document the patterns of market-wide and firm-specific volatility in the Portuguese stock market over the 1991–2005 period and test several explanations for the behavior of firm-level idiosyncratic volatility. Unlike previous studies we find no evidence of a statistically significant rise in firm- specific volatility. On the contrary, the ratio of firm-specific risk to total risk slightly decreases. We show that this result stems from new listings of large privatized companies that display lower firm-specific risk. Our findings are consistent with the idea that changes in idiosyncratic volatility are related to changes in the composition of the market.info:eu-repo/semantics/publishedVersio