Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optim...

in this thesis we predict asymmetric risk premium in bothvalue and growth stock portfolios. there are two competingapproaches to explain value premium: market over-reactionhypothesis based on which agents overstate future returns on growthstock, and rational market risk hypothesis that says value stocks areinherently riskier than growth stocks. rational market riskhypothesis has two different e...

return and volatility spillovers are important for portfolio selection, asset valuation and market efficiency investigation. using a var-bekk framework model, this paper investigates return and volatility spillover effects between three size-sorted equity indices in tehran stock exchange (tse). although daily return of large stocks leads small stocks (lead-lag effect), there wasn’t any spillove...

Stock market prediction has been a challenging task due to the nature of the data which is very noisy and time varying. However, this theory has been faced by many empirical studies and a number of researchers have successfully applied machine learning approaches to predict stock market. The problem studied here is about stock prediction for the use of investors. It is true investors usually ge...

We consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using a real data and show that we are able to solve industr...

This section describes a simplified version of the dynamic IS − LM model in Blanchard (1981), that extends this elementary macroeconomic scheme along two dimensions: (i) by considering dynamics, and (ii) by enlarging the set of financial markets beyond the traditional "money" and "bonds". In the model below, the latter extension is limited to the stock market. Stock prices have a forward-lookin...

Improving out-of-sample forecasting is one of the main issues in financial research. Previous studies have achieved this objective by increasing the number of input variables or changing the kind of input variables. Changing the forecasting model is another possible approach to improve out-of-sample forecasting. Most researches have focused on linear models, while few have studied nonlinear mod...

inventory models play an important role in determining the optimal ordering and pricing policies. much work has been reported in literature regarding inventory models with finite or infinite replenishment. but in many practical situations the replenishment is governed by random factors like procurement, transportation, environmental condition, availability of raw material etc., hence, it is nee...

The paper presents results of computer-assisted portfolio management simulation based on using a DEACascor mathematical model. The model uses the Data Envelopment Analysis (DEA) ratio as a neuron with memory and combines it with Cascade Correlation Neural Network (Cascor) to forecast stock prices. The model is designed for using in high-frequency stock trading. It utilizes ability of DEA to con...

Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the c...