scholarly journals Finding Nemo: Predicting Movie Performances by Machine Learning Methods

2020 ◽  
Vol 13 (5) ◽  
pp. 93
Author(s):  
Jong-Min Kim ◽  
Leixin Xia ◽  
Iksuk Kim ◽  
Seungjoo Lee ◽  
Keon-Hyung Lee
Keyword(s):  
Machine Learning ◽  
Return On Investment ◽  
Film Industry ◽  
Bayesian Variable Selection ◽  
Movie Industry ◽  
Financial Success ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Quantile Regression Model ◽  
Out Of Sample

Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined Bayesian variable selection and machine learning methods for forecasting the return on investment (ROI). We also attempt to compare machine learning methods including the quantile regression model with movie performance data in terms of in-sample and out of sample forecasting.

scholarly journals Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

2019 ◽  
Vol 5 (52) ◽  
pp. 206-229
Author(s):  
Przemysław Ryś ◽  
Robert Ślepaczuk
Keyword(s):  
Machine Learning ◽  
Optimization Design ◽  
Solution Space ◽  
Computation Time ◽  
Parameters Optimization ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
First Case ◽  
Out Of Sample ◽  
Brute Force Method

Abstract The main aim of this paper was to formulate and analyse the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The problem was presented for three sets of two assets’ portfolios. In the first case, a strategy was trading on the SPX and DAX index futures; in the second, on the AAPL and MSFT stocks; and finally, in the third case, on the HGF and CBF commodities futures. The methods operated on the in-sample data, containing 16 years of daily prices between 1998 and 2013 and was validated on the out-of-sample period between 2014 and 2017. The major hypothesis verified in this paper is that machine learning methods select strategies with evaluation criterion near the highest one, but in significantly lower execution time than the brute force method (Exhaustive Search).

scholarly journals Supervised machine learning methods in psychology: A practical introduction with annotated R code

2019 ◽  
Author(s):  
Hannes Rosenbusch ◽  
Felix Soldner ◽  
Anthony M Evans ◽  
Marcel Zeelenberg
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Psychological Research ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Comprehensive Overview ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Out Of Sample

Machine learning methods for pattern detection and prediction are increasingly prevalent in psychological research. We provide a comprehensive overview of machine learning, its applications, and how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-of-sample evaluation, and summarize four standard prediction algorithms: linear regressions, ridge regressions, decision trees, and random forests (plus k-nearest neighbors, Naïve Bayes classifiers, and support vector machines in the supplementary material). This selection provides a set of powerful models that are implemented regularly in machine learning projects. We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology’s status as a predictive science.

A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection

2020 ◽  
Author(s):  
Wenbo Wu ◽  
Jiaqi Chen ◽  
Zhibin (Ben) Yang ◽  
Michael L. Tindall
Keyword(s):  
Machine Learning ◽  
Hedge Fund ◽  
Forecast Model ◽  
Specific Information ◽  
Forecast Method ◽  
Learning Methods ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Machine Learning Methods ◽  
Out Of Sample

We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the out-of-sample performance, we find that our forecast method significantly outperforms the four styled Hedge Fund Research indices in almost all situations. Among the four machine learning methods, we find that deep neural network appears to be overall most effective. Investigating the source of methodological advantage of our method using a case study, we find that cross-sectional forecast outperforms forecast based on time series regression in most cases. Advanced modeling capabilities of machine learning further enhance these advantages. We find that the return-based features lead to higher returns than the benchmark of a set of macroderivative features, and our forecast method yields best performance when the two sets of features are combined. This paper was accepted by David Simchi‐Levi, finance.

scholarly journals Neural Network Models for Empirical Finance

2020 ◽  
Vol 13 (11) ◽  
pp. 265
Author(s):  
Hector F. Calvo-Pardo ◽  
Tullio Mancini ◽  
Jose Olmo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Ensemble Methods ◽  
Network Models ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Out Of Sample

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context.

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