A holistic auto-configurable ensemble machine learning strategy for financial trading

Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions

A Hybrid Neural Network for Stock Price Direction Forecasting

The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock price movements by using a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) neural network, a standalone LSTM neural network, a random forest model, and support vectors machines (SVM) model. Specifically, the study seeks to explore the predictive ability using stock price data, technical indicators, and foreignexchange (FX) rates transformed into deterministic trend signals as features for a hybrid CNN-LSTM neural network. This paper additionally considered including news article sentiment scores relating to stocks as part of the training dataset, but significant correlation was not found. In this study, the predictive ability is the accuracy of predicting the direction a stock price moves not the actual price. The experiment results suggest that a hybrid CNN-LSTM model can achieve around 60% accuracy trained with deterministic trend signals for stock trend prediction. This accuracy has higher than the accuracy of LSTM, random forest, and SVM. On this basis, one can conclude that the hybrid neural network model is superior to standalone LSTM, random forest, and SVM for stock price trend prediction

Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha

Machine-learning methods exploit fund characteristics to select tradable long-only portfolios of mutual funds that earn significant out-of-sample annual alphas of 2.4% net of all costs. The methods unveil interactions in the relation between fund characteristics and future performance. For instance, past performance is a particularly strong predictor of future performance for more active funds. Machine learning identifies managers whose skill is not sufficiently offset by diseconomies of scale, consistent with informational frictions preventing investors from identifying the outperforming funds. Our findings demonstrate that investors can benefit from active management, but only if they have access to sophisticated prediction methods

Data envelopment analysis and data mining to efficiency estimation and evaluation

Purpose: This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance. Design/methodology/approach: Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression. Findings: The results showed that random forests and bagging outperform other methods in terms of predictive power. Originality/value: This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.Scopu

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

Predicting individual stock returns using optimized neural networks

I investigate individual monthly U.S. stock return predictability through a comparative study on neural networks and ordinary least squares benchmarks, using a predictor set of 102 lagged firm characteristics and the market return from 1980 to 2018. I find monthly out-of-sample (OOS) R2 of 0.80% for the best neural network, confirming similar findings of marginal predictability from existing literature applying machine learning to empirical finance. OOS R2 increases to 7.12% for the best neural network, when considering average market return predictability using market return predictions constructed bottom-up from equal-weighting individual stock predictions. I also find significant monthly four-factor alphas of 1.55% and annualized Sharpe ratios of 2.62 on long-short top-bottom decile portfolios sorted on predicted returns – not taking into account trading costs. Investigating variable importances within neural networks reveals that networks using Rectifier as their activation function focus on momentum and liquidity variables, similar to existing findings, but networks using Maxout focus on firm fundamentals and risk measures instead – a new observation for the anomalies literature. Lastly, my findings confirm that return anomalies are stronger in small stocks, and prediction performance is generally stronger during market turbulence