We derive optimal portfolio weights for an investor who has specific beliefs regarding the distribution of a stock price at a future time. For example, a fundamental investor will want to take advantage of the information his analysis provides when constructing a portfolio. In this regard, we examine the optimal weights for models in which the investor believes that there is a range in which th...

The paper suggests a CUSUM-type test for time-varying parameters in a recently proposed spatial autoregressive model for stock returns and derives its asymptotic null distribution as well as local power properties. As can be seen from Euro Stoxx 50 returns, a combination of spatial modelling and change point tests allows for superior risk forecasts in portfolio management. JEL Classification: C...

Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, wherever that there are problems of prediction, classification or control, neural networks are being introduced. Neural networks are very sophisticated ...

“Matching” portfolios is a technique for generating a reasonable benchmark for determining the relative performance of a specific equity portfolio and is based on the work in Ho et al. (2005a). Consider the simplest case of a long-only mutual fund that has returned 10% in the last year. Has the portfolio done well? If the average stock in the universe has gone up 50% then, obviously, the portfo...

We study how stock return’s predictability and model uncertainty affect a rational buy-and-hold investor’s decision to allocate her wealth for different lengths of investment horizons in the UK market. We consider the FTSE All-Share Index as the risky asset, and the UK Treasury bill as the risk free asset in forming the investor’s portfolio. We identify the most powerful predictors of the stock...

This work discusses most frequently traded stocks of National stock exchange of India. A prediction based portfolio optimization model is considered to present an ideal portfolio out of the considered stocks. Neural network has been used to predict stock returns and a risk measure is derived that has the same foundation as that of mean variance model. The architecture of the network is designed...

We consider a financial market with one bond and one stock. The dynamics of the stock price process allow jumps which occur according to a Markov-modulated Poisson process. We assume that there is an investor who is only able to observe the stock price process and not the driving Markov chain. The investor’s aim is to maximize the expected utility of terminal wealth. Using a classical result fr...

there have been several efforts in the literature to extract as much information as possible from the financial networks. most of the research has been concerned about the hierarchical structures, clustering, topology and also the behavior of the market network; but not a notable work on the network filtration exists. this paper proposes a stock market filtering model using the correlation - ba...

The influence of changing economic environment leads the distribution of stock market returns to be time-varying. This requires a dynamic adjustment of the asset allocation in order to enjoy a conditionally optimal investment. In this context, we examine the improvement of the portfolio performance by simulating portfolio strategies that are conditioned on the Markov regime switching behavior o...