We use machine learning for designing a medium frequency trading strategy for a portfolio of 5 year and 10 year US Treasury note futures. We formulate this as a classification problem where we predict the weekly direction of movement of the portfolio using features extracted from a deep belief network trained on technical indicators of the portfolio constituents. The experimentation shows that the resulting pipeline is effective in making a profitable trade.
Machine learning application in finance is a challenging problem owing to low signal to noise ratio. Moreover, domain expertise is essential for engineering features which assist in solving an appropriate classification or regression problem. Most prior work in this area concentrates on using popular ML techniques like SVM @cite_4 , @cite_7 , @cite_0 and neural networks @cite_1 , @cite_9 , @cite_6 coupled with rigorously designed features, and the general area of focus is financial time series forecasting. With deep learning techniques, we can learn the latent representation of the raw features and use this representation for further analysis @cite_10 . In this paper, we construct a minimal risk portfolio of 5 year and 10 year T-note futures and use a machine learning pipeline to predict weekly direction of movement of the portfolio using features derived from a deep belief network. The prediction from the pipeline is then used in a day trading strategy. Using derivatives instead of the underlying entities themselves leads to a more feasible problem, since derivatives are less volatile and hence have clearer patterns.
@cite_4 @cite_7 @cite_9 @cite_1 @cite_6 @cite_0 @cite_10
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A novel financial time-series analysis method based on deep learning technique is proposed in this paper. In recent years, the explosive growth of deep learning researches have led to several successful applications in various artificial intelligence and multimedia fields, such as visual recognition, robot vision, and natural language processing. In this paper, we focus on the time-series data processing and prediction in financial markets. Traditional feature extraction approaches in intelligent trading decision support system are used to applying several technical indicators and expert rules to extract numerical features. The major contribution of this paper is to improve the algorithmic trading framework with the proposed planar feature representation methods and deep convolutional neural networks (CNN). The proposed system is implemented and benchmarked in the historical datasets of Taiwan Stock Index Futures. The experimental results show that the deep learning technique is effective in our trading simulation application, and may have greater potentialities to model the noisy financial data and complex social science problems. In the future, we expected that the proposed methods and deep learning framework could be applied to more innovative applications in the next financial technology (FinTech) generation. In recent years, machine learning research has gained momentum: new developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods can be applied to various fields, including finance. This paper implements and analyzes the effectiveness of deep neural networks (DNN), gradient-boosted-trees (GBT), random forests (RAF), and several ensembles of these methods in the context of statistical arbitrage. Each model is trained on lagged returns of all stocks in the S&P 500, after elimination of survivor bias. From 1992 to 2015, daily one-day-ahead trading signals are generated based on the probability forecast of a stock to outperform the general market. The highest k probabilities are converted into long and the lowest k probabilities into short positions, thus censoring the less certain middle part of the ranking. Empirical findings are promising. A simple, equal-weighted ensemble (ENS1) consisting of one deep neural network, one gradient-boosted tree, and one random forest produces out-of-sample returns exceeding 0.45 percent per day for k=10, prior to transaction costs. Irrespective of the fact that profits are declining in recent years, our findings pose a severe challenge to the semi-strong form of market efficiency. Application of pattern recognition and machine learning in highly dynamic and data intensive financial markets is a popular research area amongst researchers and financial analysts. With evolving social dynamics of millions across the globe, it provides opportunity to make use of patterns in investor sentiment comprising of large scale microblog discussions to understand market movements and make an effective application in making hedging decisions. We apply sentiment analysis and machine learning principles to study causation between public collective sentiment and market movements. In this work we have used 0.6 million tweets for a period of November 2010 to June 2011, to run a practical simulation of hedging model for Dow Jones Industrial Average-DJIA Index. We have elaborated on how a simple hedging strategy like married-put can exercise use of weekly directional forecasts for DJIA to make portfolio adjustments from risky to high market conditions and vice versa. We have found the maximum of 91 SVM based binary classifier accuracy, towards direction (up and down prediction) estimations of DJIA. It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled instances. Unlike previous approaches that use labeled features to create labeled pseudo-instances, we use labeled features directly to constrain the model's predictions on unlabeled instances. We express these soft constraints using generalized expectation (GE) criteria --- terms in a parameter estimation objective function that express preferences on values of a model expectation. In this paper we train multinomial logistic regression models using GE criteria, but the method we develop is applicable to other discriminative probabilistic models. The complete objective function also includes a Gaussian prior on parameters, which encourages generalization by spreading parameter weight to unlabeled features. Experimental results on text classification data sets show that this method outperforms heuristic approaches to training classifiers with labeled features. Experiments with human annotators show that it is more beneficial to spend limited annotation time labeling features rather than labeling instances. For example, after only one minute of labeling features, we can achieve 80 accuracy on the ibm vs. mac text classification problem using GE-FL, whereas ten minutes labeling documents results in an accuracy of only 77