Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

Tracking economic growth by evolving expectations via genetic programming: A two-step approach

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents’ expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents' expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents' to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland

Evolutionary computation for macroeconomic forecasting

The final publication is available at Springer via http://dx.doi.org/10.1007/s10614-017-9767-4The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.Peer ReviewedPostprint (author's final draft

Evolutionary computation for macroeconomic forecasting

The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents' expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents' to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden

Price dynamics, informational efficiency and wealth distribution in continuous double auction markets

This paper studies the properties of the continuous double auction trading mechanishm using an artificial market populated by heterogeneous computational agents. In particular, we investigate how changes in the population of traders and in market microstructure characteristics affect price dynamics, information dissemination and distribution of wealth across agents. In our computer simulated market only a small fraction of the population observe the risky asset's fundamental value with noise, while the rest of agents try to forecast the asset's price from past transaction data. In contrast to other artificial markets, we assume that the risky asset pays no dividend, so agents cannot learn from past transaction prices and subsequent dividend payments. We find that private information can effectively disseminate in the market unless market regulation prevents informed investors from short selling or borrowing the asset, and these investors do not constitute a critical mass. In such case, not only are markets less efficient informationally, but may even experience crashes and bubbles. Finally, increased informational efficiency has a negative impact on informed agents' trading profits and a positive impact on artificial intelligent agents' profits

What drives information dissemination in continuous double auction markets?.

In this paper, we investigate further the way information disseminates from informed to uninformed traders in a market populated by heterogeneous boundedly rational agents. In order to achieve the goal, a computer simulated market where only a small fraction of the population observe the risky asset's fundamental value with noise was constructed, while the rest of agents try to forecast the asset's price from past transaction data. The paper departs from previous studies in that the risky asset does not pay a dividend every period, so agents cannot learn from past transaction prices and subsequent dividend payments. The main finding is that information can potentially disseminate in the market as long as: (1) informed investors' trades tilt transaction prices in the fundamental path direction; and (2) the median investor's expectation is very responsive to transaction prices. Otherwise, markets may display crashes or bubbles. It is found that the first condition requires a minimal amount of informed investors, and is severely limited by short selling and borrowing constraints.

Tracking economic growth by evolving expectations via genetic programming: a two-step approach

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents’ expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents’ to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland.Preprin