Systematic Equity Alphas Library
Find out our latest insights in systematic equity alphas

Financial News to Predict Stock Market

Financial news provides information to the general public. Consumers rely on information they read or hear before buying a product.

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Support Vector Machine for Stock Market Prediction

Predicting direction of stock price index movement using artificial neural networks and support vector machines

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Target price forecasts Fundamentals and behavioral factors

Group Normalization provides a more stable data normalization methodology that can ease optimization and enable very deep networks to converge.

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Accrual Reliability, Earnings Persistence and Stock Prices

Latest ResNet and their variations (ResNeXt, Wide ResNet, PyramdNet) have broken the records of lowest error rates but they often suffer from problems such as vanishing gradients.

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A Survival Analysis Method for Stock Market Prediction

The trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.

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Intraday volatility interaction between the crude oil and equity markets

The trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.

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Forecasting stock market short –term trends using a neuro-fuzzy based methodology

Paper proposes a new architecture that reformulate the layer inputs and provides comprehensive empirical evidence showing that these residual networks are easier to optimize.

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Technical Architectures
Insights to quantitative methods applied to investment strategies, focused on machine learning and advanced computing.  

Convolutional Neural Networks

CNN replaces fully connected multiperceptron layers with convolutional layers and pooling layers so that the model requires a minimal number of parameters.

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RLPG Report

Inspired by the idea of learning through interaction with environment, reinforcement learning (RL) is an area of machine learning concerned with what to do – how to map situations to actions in an environment so as to maximize some notion of cumulative reward.

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Group Normalization

Group Normalization provides a more stable data normalization methodology that can ease optimization and enable very deep networks to converge.

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ShakeDrop Regularization

Latest ResNet and their variations (ResNeXt, Wide ResNet, PyramdNet) have broken the records of lowest error rates but they often suffer from problems such as vanishing gradients.

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Deep Residual Learning for Image Recognition

Paper proposes a new architecture that reformulate the layer inputs and provides comprehensive empirical evidence showing that these residual networks are easier to optimize.

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Generative Adversarial Nets

GAN is at least competitive with the better generative models in the literature and highlight the potential of the adversarial framework.

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

In Deep Neural Networks, inputs of each layers usually changes during training, which therefore requires lower learning rates and careful parameter initialization.

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Deep Extreme Learning Machines

The emerging algorithm, Extreme Learning Machine, is known to be fast to train in comparison with iterative training methods, and performs with similar accuracy with Support Vector Machines(SVM).

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Proximal Policy Optimization

The purpose of this research is to introduce a new family of policy gradient methods for reinforcement learning called PPO.

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Ladder Variational Autoencoders

VAE, consisting of hierarchies of conditional stochastic variables, are highly expressive models retaining the computational efficiency of fully factorized models.

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Algo Depth Research Blog

Quantitative Investment Topics

WHITE PAPER

Application of Reinforcement Learning For Order Execution

 

We present a large scale application of reinforcement learning to optimize trading execution. The experiment is based on Apple Inc. The results generate a 1.2% annual cost savings, and show the promise of applying reinforcement learning methods to solve market microstructure problems. Our algorithm can further improve by including tick data, and market order books. It can be tested on any stock.

2018

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