Pravesh Kriplani

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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)
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)
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)
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