Portfolio daily returns

This paper will discuss portfolio optimization, Quadratic Programming (QP) and Second Order Cone Programming (SOCP). We will use simulated and empirical data to compare the two optimization routines. Daily data for SP500 stocks from 2005 to 2010 was used to show that a 20-days rebalanced portfolio strategy with an expected portfolio return of 60 percent of the maximum expected return for all stocks produced an 8.4 percent return premium on an annual basis if we used QP and 11.2 percent return premium on an annual basis if we used SOCP.

15p nguyenminhlong19 21-04-2020 17 3   Download

This paper investigates the volatility transmission effect and conditional correlations among crude oil, stock market and sector stock indexes in Saudi Arabia. Using daily data from January 3, 2009 to March 21, 2012 and VAR-BEKK specification, we find significant volatility transmission between oil prices and Saudi stock market. Furthermore, our findings show that sector stock returns significantly react to oil prices changes. In addition, except telecom sector, the results show the presence of volatility transmission between stock market and sector stock market returns.

17p 035522894 13-04-2020 49 3   Download

This paper describes a new method to generate unbiased equal-weighted portfolio daily returns by removing the impacts of bid-ask bounce and non-synchronous trading. For example, for the CRSP daily equal-weighted market index over 1964 to 1993 (EWRETD), the annual bias of the time series generated by our method is 0.05%, considerably smaller than 6% as reported by Canina et al. (1998). In addition, we also discuss the research impact by using both biased and unbiased daily EWRETD on beta, alpha, volatility and event study.

18p 035522894 13-04-2020 24 2   Download

In any case, we offer one additional combined specification that completely avoids concerns about cross-sectional dependence in returns. Specifically, we provide results for a portfolio, where each daily return is an equally weighted average of the corresponding daily returns for the G-7 stock indexes. Not surprisingly, the mean daily returns are very similar to those for the pooled results, with a nearly identical difference in returns of 0.033 percent. The t-statistic for the difference is 2.18, which is significant at the 0.03 level.

46p bocapchetnguoi 06-12-2012 42 1   Download

While some studies focus on the aggregated market price indices, others apply data of rms dual-listed on the A and B, or A and H markets (Chan et al. 2007; Qiao 2007). Most deploy data of daily (Chui and Kwok 1998; Kim and Shin 2000; Qiao et al. 2008) or weekly returns (Chiang et al. 2008; Qiao et al. 2007). Some authors (Chan et al. 2007) apply high frequency intra-day transaction data to circumvent the simultaneity problem.

12p quaivattim 04-12-2012 71 2   Download

Other Applications In this chapter, we consider some further applications of independent component analysis (ICA), including analysis of financial time series and audio signal separation. 24.1 24.1.1 FINANCIAL APPLICATIONS Finding hidden factors in financial data It is tempting to try ICA on financial data. There are many situations in which parallel financial time series are available, such as currency exchange rates or daily returns of stocks, that may have some common underlying factors. ICA might reveal some driving mechanisms that otherwise remain hidden.

8p khinhkha 29-07-2010 94 4   Download

It is tempting to try ICA on financial data. There are many situations in which parallel financial time series are available, such as currency exchange rates or daily returns of stocks, that may have some common underlying factors. ICA might reveal some driving mechanisms that otherwise remain hidden. In a study of a stock portfolio [22], it was found that ICA is a complementary tool to principal component analysis (PCA), allowing the underlying structure of the data to be more readily observed....

8p duongph05 09-06-2010 92 10   Download