Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of “internal model”. The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982)

Stock Market Tendency Prediction Methods

Investing in stock market is not an easy task, it could be a high-risk investment. Stock price depends on several factors that can be internal factors, such as, the company itself which is down-grade his worth or external factor such as politics, or even the financial environment of the country of that specific market. Investors need careful decision-making skills to maintain profit in the long run. Usually they use techniques that can help them to make a better decision, or at least facilitate that “job”. In the past years, many machine learning models have been investigated for this purpose. Being an area, which is very influenced by uncontrollable factors, it´s not easy to get a perfect solution. This thesis demonstrates a possible methodology to produce a good price prediction of the stock market and make it available to the general public.Investir no mercado de ações não é uma tarefa fácil, pode tornar-se um investimento de algo risco. O preço de stock depende de vários fatores que podem ser internos, como por exemplo, uma empresa que reduza o seu valor, ou externos como política, ambiente financeiro do país. Investidores precisam de adquirir experiência em planeamento de modo a manter ou aumentar o lucro. Normalmente, estes usam técnicas que lhes facilita a tomar uma melhor decisão. Desde á uns anos atrás que machine learning tem sido estudado, mas como é uma área que tem fatores incontroláveis, faz com que não seja possível obter uma solução perfeita. Esta Tese demonstra uma possível metodologia que produz uma boa previsão de preço do mercado de ações e o tornar disponível para o publico

Empirical models, rules, and optimization

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.Decision-making. ,econometric models ,TMD ,

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Projections of Inflation Dynamics for Pakistan: GMDH Approach

Abstract. This study is focused on identifying, based on various forecast accuracy criteria, best inflation forecasting model for Pakistan using the in sample projections for Pakistan inflation from 2006II to 2009II. To resolve the important issue of degree of contribution in forecasting performance of the two monetary aggregates in forecasting inflation, three main predictors: real GDP, interest rate and one out of the two monetary aggregate have been used, thus constructing two models; one with Divisia Monetary Index (DMI) and other with Simple sum monetary aggregate (SSMA). It is revealed that, though both of the monetary aggregates are important predictors in forecasting inflation, but DMAs provide better fit and improved forecasts as compared to their simple sum counterpart. Hence, the evidence is established that monetary aggregates still play a dominant role in predicting inflation for Pakistan economy. The study recommends the construction, publication, and use of high frequency DMAs by the State Bank of Pakistan (SBP) for forecasting inflation in Pakistan instead of SSMAs. Finally, to identify the improvement in forecast accuracy w.r.t. different forecasts combination, these forecasts have been combined and compared. It is revealed that when the structure of an empirically observed underlying series has complex nonlinear structure then forecasts based on single nonlinear model may fail to capture these diverse complexities. The best strategy is then to use various nonlinear models and combine these forecasts. Further the study concluded that if the complex nonlinear structure of an observed series is, a priory, unknown then universal approximators like Group Method of Data Handling (GMDH)- Polynomial Neural Networks (PNNs) and GMDH-Combinatorially Optimized (CO)  could provide outstandingly accurate forecasts yet avoiding ‘overfitting’ even for small sample size. Specifically, it recommends the use of nonlinear non-parametric universal approximators for forecasting inflation in Pakistan by the SBP.Keywords. Monetary aggregate, Nonparametric nonlinear models, Universal approximators, Forecasting performance, Forecasts combination.JEL. E31, E47, E51, E52

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Merger Remedies at the European Commission: A Multinomial Logit Analysis

This paper aims to build and empirically evaluate a discrete choice model of merger remedies as a basis for policy analysis. The database consists of 229 merger cases accepted in Phase I or Phase II of the European merger process between 1990 and 2005. We focus on the following question: Which merging firms' characteristics lead the European Commission to decide whether to require conditional acceptance? Although a lot of empirical studies have been carried out these last years, ours is distinguished by at least two original features. First, we explore determinant factors of the Commission's decisions with a neural network model differentiating cases accepted with or without remedies (either structural or behavioral). Secondly, we implement three multinomial logit models. We find that variables related to high market power lead more frequently to a remedy outcome, whatever the phase. Innovative industries such as energy, transportation and communications positively affect the probability of a behavioral remedy. Lastly, former Competition Commissioner Mario Monti's policy appears to be pro-remedy, i.e. seeking concessions from merging parties.Merger Remedies ; Antitrust ; European Commission ; Discrete Choice Models ; Self-Organizing Maps

Merger Remedies at the European Commission: A Multinomial Logit Analysis

This paper aims to build and empirically evaluate a discrete choice model of merger remedies as a basis for policy analysis. The database consists of 229 merger cases accepted in Phase I or Phase II of the European merger process between 1990 and 2005. We focus on the following question: Which merging firms’ characteristics lead the European Commission to decide whether to require conditional acceptance? Although a lot of empirical studies have been carried out these last years, ours is distinguished by at least two original features. First, we explore determinanting factors of the Commission’s decisions with a neural network model differentiating cases accepted with or without remedies (either structural or behavioral). Secondly, we implement three multinomial logit models. We find that variables related to high market power lead more frequently to a remedy outcome, whatever the phase. Innovative industries such as energy, transportation and communications positively affect the probability of a behavioral remedy. Lastly, former Competition Commissioner Mario Monti’s policy appears to be pro-remedy, i.e. seeking concessions from merging parties.

Portfolio Optimization: A Comparative Study

Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.Comment: This is the preprint of the book chapter accepted for publication in the book titled "Deep Learning - Recent Finding and Researches" edited by Manuel Dom\'inguez-Morales. The book is scheduled to be be published by IntechOpen, London, UK in January 2024. This is not the final version of the chapte

Non Expectations and Adaptive Behaviours: the Missing Trade-off in Models of Innovation

We explore the modelling of the determination of the level of R&D investment of firms. This means that we do not tackle the decision of being an innovator or not, nor the adoption of a new technology. We exclude these decisions and focus on the situations where firms invest in internal R&D in order to produce an innovation. In that case the problem is to determine the level of R&D investment. Our interest is to analyse how expectation and adaptation can be combined in the modelling of R&D investment rules. In the literature both dimensions are generally split up: rational expectations are assumed in neoclassical models whereas alternative approaches (institutional and/or evolutionary) generally adopt a purely adaptive representation.Bounded rationality, learning, expectations, innovation dynamics.