Stock Selection via Nonlinear Multi-Factor Models
نویسنده
چکیده
This paper discusses the use of multilayer feed forward neural networks for predicting a stock's excess return based on its exposure to various technical and fundamental factors. To demonstrate the effectiveness of the approach a hedged portfolio which consists of equally capitalized long and short positions is constructed and its historical returns are benchmarked against T-bill returns and the S&P500 index.
منابع مشابه
The Integration of Multi-Factor Model of Capital Asset Pricing and Penalty Function for Stock Return Evaluation
One of the main concerns of investors is the evaluation of the return on investment, which is conducted using various models such as the CAPM (single-factor model), Fama-French three/five-factor models, and Roy and Shijin’s six-factor model and other models known as multi-factor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, ...
متن کاملAn Improved Hybrid Model with Automated Lag Selection to Forecast Stock Market
Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic A...
متن کاملPresenting a Multi Objective Model for Supplier Selection in Order to Reduce Green House Gas Emission under Uncertion Demand
Recently, much attention has been given to Stochastic demand due to uncertainty in the real -world. In the literature, decision-making models and suppliers' selection do not often consider inventory management as part of shopping problems. On the other hand, the environmental sustainability of a supply chain depends on the shopping strategy of the supply chain members. The supplier selection pl...
متن کاملStock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models
Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting finan...
متن کاملSECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کامل