Empirical studies have suggested that stock returns can be predicted by financial variables such as the dividend-price ratio. However, these studies typically ignore the high persistence of predictor variables, which can make first-order asymptotics a poor approximation in finite samples. Using a more accurate asymptotic approximation, we propose two methods to deal with the persistence problem...

We comprehensively examine the effects of stock return volatility on firms’ financial and investment decisions. Consistent with theories of investment with financing frictions, firms with high volatility actively reduce their leverage, cut investment, increase cash holding, cut non-cash current assets such as inventories and account receivables, and cut dividend. The effects of volatility are s...

The results for the dynamic normal linear regression models fit in the previous chapter suggest that there are significant autocorrelation effects in the returns for all eighteen series examined and significant day-of-the-week effects, including a negative Monday effect, for four of the indices and two of the individual stocks. However, misspecification tests for these models reveal a number of...

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive international dataset, we find that interest-rate related variables are usually among the most prominent predict...

I study how growth affects liquidity of global stock exchanges and how liquidity determines cross-sectional returns on those stock exchange index portfolios. I measure portfolio liquidity by turnover ratio computed as value of shares traded over the market capitalization. I obtain data from FIBV, an association of global stock exchanges. In a multiple regression model for turnover ratio, I find...

The regression of stock returns on predictive variables, such as dividend yield, has proven useful in optimal portfolio selection when investment opportunities are timevarying. Conditional versions of factor models impose a restriction on that regression, thereby implying a particular portfolio choice. The study examines several pricing models from a perspective of conditional mean-variance opt...

The results of the previous chapter demonstrate that the serial dependencies within Taiwanese stock returns that are reflected in the significant autocorrelation and nonlinearity test results of Chapter Four are not constant; rather, they shift in direction and magnitude over time, with a number of brief episodes of very strong dependencies accounting for much of the magnitude of the full sampl...

Artificial neural networks are, again, on the rise. The decreasing costs of computing power and the availability of big data together with advancements of neural network theory have made this possible. In this thesis, LSTM (long short-term memory) recurrent neural networks are used in order to perform financial time series forecasting on return data of three stock indices. The indices are S&P 5...

In an economy where agents hold money, the short interest rate determines the trade-off between money holdings and consumption. Building on this idea, we develop a theoretical model that shows the transmission mechanism through which the short rate finds its way to stock-return predictability regressions. We construct a cointegration relation that links share prices and dividends to the short i...