Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

Operational Risk: Emerging Markets, Sectors and Measurement

The role of decision support systems in mitigating operational risks in firms is well established. However, there is a lack of investment in decision support systems in emerging markets, even though inadequate operational risk management is a key cause of discouraging external investment. This has also been exacerbated by insufficient understanding of operational risk in emerging markets, which can be attributed to past operational risk measurement techniques, limited studies on emerging markets and inadequate data. In this paper, using current operational risk techniques, the operational risk of developed and emerging market firms is measured for 100 different companies, for 4 different industry sectors and 5 different countries. Firstly, it is found that operational risk is consistently higher in emerging market firms than in the developed markets. Secondly, it is found that operational risk is not only dependent upon the industry sector but also that market development is the more dominant factor. Thirdly, it is found that the market development and the sector influence the shape of the operational risk distribution, in particular tail and skewness risk. Furthermore, an operational risk measurement method is provided that is applicable to emerging markets. Our results are consistent with under investment in decision support systems in emerging markets and imply operational risk management can be improved by increased investment

Modelling and trading the Greek stock market with mixed neural network models

In this paper, a mixed methodology that combines both the ARMA and NNR models is proposed to take advantage of the unique strength of ARMA and NNR models in linear and nonlinear modelling. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately. The motivation for this paper is to investigate the use of alternative novel neural network architectures when applied to the task of forecasting and trading the ASE 20 Greek Index using only autoregressive terms as inputs. This is done by benchmarking the forecasting performance of six different neural network designs representing a Higher Order Neural Network (HONN), a Recurrent Network (RNN), a classic Multilayer Percepton (MLP), a Mixed Higher Order Neural Network, a Mixed Recurrent Neural Network and a Mixed Multilayer Percepton Neural Network with some traditional techniques, either statistical such as a an autoregressive moving average model (ARMA), or technical such as a moving average convergence/divergence model (MACD), plus a naïve trading strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on ASE 20 fixing time series over the period 2001-2008 using the last one and a half year for out-of-sample testing. We use the ASE 20 daily fixing as many financial institutions are ready to trade at this level and it is therefore possible to leave orders with a bank for business to be transacted on that basis

The implied-realized volatility relation in foreign exchange options markets

Almost all relevant literature has characterized implied volatility as a biased predictor of realized volatility. In this paper we provide new time series techniques to investigate the validity of this finding in several foreign exchange options markets, including the Euro market. First, we develop a new fractional cointegration test that is shown to be robust to both stationary and nonstationary regions. Second, we employ both intra-day and daily data to measure realized volatility in order to assess the relevance of data frequency in resolving the bias. Third, we use data on implied volatility traded on the market. In contrast to previous studies, we show that the frequency of data used for measuring realized volatility within a fractionally cointegrating framework is important for the results of unbiasedness tests. Significantly, for many popular exchange rates, the use of intra-day rather than daily data affects the emergence of a different bias, as the possibility of a fractionally integrated risk premium admits itself

Advanced frequency and time domain filters for currency portfolio management

The first motivation of this paper is to study the existence of cyclical properties among FX markets with the use of spectral analysis. Secondly, we study the economic value of a trading model based on spectral analysis compared with technical trending models replicating the performance of typical currency managers as in Lequeux and Acar (1998). We find that both spectral models and Moving Average Convergence Divergence (henceforth MACD) technical trending models fail to perform satisfactorily when markets display cyclical properties. We then propose spectral filters to take alternative trading strategies during such times: our results show that in the 3 periods considered, trading performances are significantly enhanced by the addition of the two spectral filters proposed, either to close all market positions when markets are in cyclical mode (a “no-trade ” filter), or to reverse the original MACD trading signals during such times (a “reverse ” filter)