Predicting bitcoin returns using high-dimensional technical indicators

Predicting Abnormal Returns From News Using Text Classification

We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features in order to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. This method exhib...

Predicting Stock Returns Using a Variable Order Markov Tree Model

The weak form of the Efficient Market Hypothesis (EMH) states that the current market price fully reflects the information of past prices and rules out predictions based on price data alone. In an efficient market, consistent prediction of the next outcome of a financial time series is problematic because there are no reoccurring patterns that can be used for a reliable prediction. This researc...

Predicting Short Term Stock Returns

As the capital markets evolve and expand, more and more data is being created daily. This explosion of data has made the flow of information much more efficient. As market participants act on this information flow, it drives market prices to more efficient values, . One of the driving forces in this march to efficiency, is the application of various algorithmic learning techniques on both marke...

Predicting Stock and Portfolio Returns Using Mixtures of Truncated Exponentials

Parsimonious description for predicting high-dimensional dynamics

When we observe a system, we often cannot observe all its variables and may have some of its limited measurements. Under such a circumstance, delay coordinates, vectors made of successive measurements, are useful to reconstruct the states of the whole system. Although the method of delay coordinates is theoretically supported for high-dimensional dynamical systems, practically there is a limita...