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The prediction of stock market movements represents a key component in developing winning trading strategies. Forecasting can have different time horizon. In this paper we focus our attention to very short term, and we develop a model able to predict market trends with a horizon of few days ahead. Based on Hierarchical Hidden Markov Model, our approach has(More)
This paper develops a stock market price model, which is based on a detrending time series by iterating the application of fuzzy trasform and computing residuls over a given lookback period. The model is used to define a mean-reverting strategy with stationary and gaussian residuals. A preliminary experimention is aimed at comparing the proposed strategy to(More)
—One of the major advantages in using Deep Learning for Finance is to embed a large collection of information into investment decisions. A way to do that is by means of compression, that lead us to consider a smaller feature space. Several studies are proving that non-linear feature reduction performed by Deep Learning tools is effective in price trend(More)
Searching for portfolios co-integrated with an index offers new opportunities in designing robust investment strategies. The problem of finding optimal index co-integrated portfolios that are maximally stationary is combinatorial. Indeed, given a basket of equities, the portfolio/index co-integration cannot be simply expressed in terms of equity/index(More)
In this paper we propose to extend the definition of fuzzy transform in order to consider an interpolation of models that are richer than the standard fuzzy transform. We focus on polynomial models, linear in particular, although the approach can be easily applied to other classes of models. As an example of application, we consider the smoothing of time(More)
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