Machine Learning in Futures Markets

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

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Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer

Journal of Applied Physiology ◽

10.1152/japplphysiol.00760.2017 ◽

2018 ◽

Vol 124 (5) ◽

pp. 1284-1293 ◽

Cited By ~ 9

Author(s):

Alexander H. K. Montoye ◽

Bradford S. Westgate ◽

Morgan R. Fonley ◽

Karin A. Pfeiffer

Keyword(s):

Physical Activity ◽

Machine Learning ◽

Cross Validation ◽

Learning Models ◽

Data Set ◽

Feature Sets ◽

Activity Intensity ◽

Out Of Sample ◽

Sample Testing ◽

Machine Learning Models

Wrist-worn accelerometers are gaining popularity for measurement of physical activity. However, few methods for predicting physical activity intensity from wrist-worn accelerometer data have been tested on data not used to create the methods (out-of-sample data). This study utilized two previously collected data sets [Ball State University (BSU) and Michigan State University (MSU)] in which participants wore a GENEActiv accelerometer on the left wrist while performing sedentary, lifestyle, ambulatory, and exercise activities in simulated free-living settings. Activity intensity was determined via direct observation. Four machine learning models (plus 2 combination methods) and six feature sets were used to predict activity intensity (30-s intervals) with the accelerometer data. Leave-one-out cross-validation and out-of-sample testing were performed to evaluate accuracy in activity intensity prediction, and classification accuracies were used to determine differences among feature sets and machine learning models. In out-of-sample testing, the random forest model (77.3–78.5%) had higher accuracy than other machine learning models (70.9–76.4%) and accuracy similar to combination methods (77.0–77.9%). Feature sets utilizing frequency-domain features had improved accuracy over other feature sets in leave-one-out cross-validation (92.6–92.8% vs. 87.8–91.9% in MSU data set; 79.3–80.2% vs. 76.7–78.4% in BSU data set) but similar or worse accuracy in out-of-sample testing (74.0–77.4% vs. 74.1–79.1% in MSU data set; 76.1–77.0% vs. 75.5–77.3% in BSU data set). All machine learning models outperformed the euclidean norm minus one/GGIR method in out-of-sample testing (69.5–78.5% vs. 53.6–70.6%). From these results, we recommend out-of-sample testing to confirm generalizability of machine learning models. Additionally, random forest models and feature sets with only time-domain features provided the best accuracy for activity intensity prediction from a wrist-worn accelerometer. NEW & NOTEWORTHY This study includes in-sample and out-of-sample cross-validation of an alternate method for deriving meaningful physical activity outcomes from accelerometer data collected with a wrist-worn accelerometer. This method uses machine learning to directly predict activity intensity. By so doing, this study provides a classification model that may avoid high errors present with energy expenditure prediction while still allowing researchers to assess adherence to physical activity guidelines.

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High performance logistic regression for privacy-preserving genome analysis

BMC Medical Genomics ◽

10.1186/s12920-020-00869-9 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Martine De Cock ◽

Rafael Dowsley ◽

Anderson C. A. Nascimento ◽

Davis Railsback ◽

Jianwei Shen ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Genome Analysis ◽

Local Area Network ◽

Local Area ◽

Activation Function ◽

Area Network ◽

Learning Models ◽

Data Set ◽

Machine Learning Models

Abstract Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.

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Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Science ◽

10.1126/science.aat8763 ◽

2018 ◽

Vol 362 (6416) ◽

pp. eaat8763 ◽

Cited By ~ 13

Author(s):

Jesús G. Estrada ◽

Derek T. Ahneman ◽

Robert P. Sheridan ◽

Spencer D. Dreher ◽

Abigail G. Doyle

Keyword(s):

Machine Learning ◽

Cross Coupling ◽

Feature Model ◽

Learning Models ◽

Chemical Feature ◽

Out Of Sample ◽

Reaction Performance ◽

Machine Learning Models

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

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Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

10.20944/preprints202103.0284.v2 ◽

2021 ◽

Author(s):

Maicon Herverton Lino Ferreira da Silva Barros ◽

Geovanne Oliveira Alves ◽

Lubnnia Morais Florêncio Souza ◽

Élisson da Silva Rocha ◽

João Fausto Lorenzato de Oliveira ◽

...

Keyword(s):

Machine Learning ◽

Clinical Symptoms ◽

Treatment Decision ◽

Gradient Boosting ◽

Original Form ◽

Learning Models ◽

Data Set ◽

Risk Of Death ◽

Increased Risk ◽

Machine Learning Models

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

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A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment

Journal of Risk and Financial Management ◽

10.3390/jrfm13070155 ◽

2020 ◽

Vol 13 (7) ◽

pp. 155

Author(s):

Zhenlong Jiang ◽

Ran Ji ◽

Kuo-Chu Chang

Keyword(s):

Machine Learning ◽

Risk Aversion ◽

Financial Data ◽

Rolling Horizon ◽

Learning Models ◽

Market Index ◽

Out Of Sample ◽

The Mean ◽

Mean Risk ◽

Machine Learning Models

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

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Learning to Identify At-Risk Students in Distance Education Using Interaction Counts

Revista de Informática Teórica e Aplicada ◽

10.22456/2175-2745.62211 ◽

2016 ◽

Vol 23 (2) ◽

pp. 124 ◽

Cited By ~ 2

Author(s):

Douglas Detoni ◽

Cristian Cechinel ◽

Ricardo Araujo Matsumura ◽

Daniela Francisco Brauner

Keyword(s):

Machine Learning ◽

At Risk ◽

At Risk Students ◽

Drop Out ◽

Support Vector ◽

Learning Models ◽

Data Set ◽

Student Dropout ◽

Vector Machines ◽

Machine Learning Models

Student dropout is one of the main problems faced by distance learning courses. One of the major challenges for researchers is to develop methods to predict the behavior of students so that teachers and tutors are able to identify at-risk students as early as possible and provide assistance before they drop out or fail in their courses. Machine Learning models have been used to predict or classify students in these settings. However, while these models have shown promising results in several settings, they usually attain these results using attributes that are not immediately transferable to other courses or platforms. In this paper, we provide a methodology to classify students using only interaction counts from each student. We evaluate this methodology on a data set from two majors based on the Moodle platform. We run experiments consisting of training and evaluating three machine learning models (Support Vector Machines, Naive Bayes and Adaboost decision trees) under different scenarios. We provide evidences that patterns from interaction counts can provide useful information for classifying at-risk students. This classification allows the customization of the activities presented to at-risk students (automatically or through tutors) as an attempt to avoid students drop out.

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A publicly available crystallisation data set and its application in machine learning

CrystEngComm ◽

10.1039/c7ce00738h ◽

2017 ◽

Vol 19 (27) ◽

pp. 3737-3745 ◽

Cited By ~ 8

Author(s):

Max Pillong ◽

Corinne Marx ◽

Philippe Piechon ◽

Jerome G. P. Wicker ◽

Richard I. Cooper ◽

...

Keyword(s):

Machine Learning ◽

Learning Models ◽

Data Set ◽

Machine Learning Models

A publicly available crystallisation database for clusters of highly similar compounds is used to build machine learning models.

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Statistical Arbitrage in Cryptocurrency Markets

Journal of Risk and Financial Management ◽

10.3390/jrfm12010031 ◽

2019 ◽

Vol 12 (1) ◽

pp. 31 ◽

Cited By ~ 11

Author(s):

Thomas Fischer ◽

Christopher Krauss ◽

Alexander Deinert

Keyword(s):

Machine Learning ◽

Cross Sectional ◽

Academic Literature ◽

Limits To Arbitrage ◽

Statistical Arbitrage ◽

Out Of Sample ◽

Learning Research ◽

Short Sell ◽

Half Turn ◽

Binned Data

Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy.

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On the Influence of Contextual Features for the Identification of Complex Words

International Journal of Semantic Computing ◽

10.1142/s1793351x17400207 ◽

2017 ◽

Vol 11 (04) ◽

pp. 497-511

Author(s):

Elnaz Davoodi ◽

Leila Kosseim ◽

Matthew Mongrain

Keyword(s):

Machine Learning ◽

Natural Language ◽

Target Word ◽

Supervised Machine Learning ◽

Learning Models ◽

Data Set ◽

Contextual Features ◽

Complex Words ◽

Machine Learning Models

This paper evaluates the effect of the context of a target word on the identification of complex words in natural language texts. The approach automatically tags words as either complex or not, based on two sets of features: base features that only pertain to the target word, and contextual features that take the context of the target word into account. We experimented with several supervised machine learning models, and trained and tested the approach with the 2016 SemEval Word Complexity Data Set. Results show that when discriminating base features are used, the words around the target word can supplement those features and improve the recognition of complex words.

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Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

10.26434/chemrxiv.5883157.v2 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daniel Elton ◽

Zois Boukouvalas ◽

Mark S. Butrico ◽

Mark D. Fuge ◽

Peter W. Chung

Keyword(s):

Machine Learning ◽

Energetic Materials ◽

Molecular Structures ◽

Machine Learning Techniques ◽

Small Data ◽

Detonation Pressure ◽

Learning Models ◽

Data Set ◽

Learning Techniques ◽

Machine Learning Models

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, bag of bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

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