Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 Machine Learning

Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks Machine Learning

Abstract--Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding thebehavior of crude oil prices is important for economists, analysts, forecasters, and traders, to name a few. The price of crude oil has declined in the past decade and is seeing a phase of stability; but will this stability last? This work is an empirical study on how multivariate analysis may be employed to predict crude oil spot prices using neural networks. The concept of using neural networks showed promising potential. A very simple neural network model was able to perform on par with ARIMA models - the state-of-the-art model in time-series forecasting. Advanced neural network models using larger datasets may be used in the future to extend this proofof-concept toa full scale framework. I. INTRODUCTION Crude oil spot prices saw a tremendous uptick in the first decade of the 21 Since 2014, crude oil prices have fallen and may have stabilized now. However, there has always been a constant interest in accurately predicting crude oil prices; given that crude oil drives a major portion of the economy. Economists, scientists, data analysts, and traders are all interested in models that give them the best accuracy.

Gated Neural Networks for Option Pricing: Rationality by Design

AAAI Conferences

We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model's predictions, and provides econometrically useful byproduct such as risk neutral density.

Deep Smoothing of the Implied Volatility Surface Machine Learning

We present an artificial neural network (ANN) approach to value financial derivatives. Atypically to standard ANN applications, practitioners equally use option pricing models to validate market prices and to infer unobserved prices. Importantly, models need to generate realistic arbitrage-free prices, meaning that no option portfolio can lead to risk-free profits. The absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints on an extended grid of input values. ANNs can be pre-trained by first calibrating a standard option pricing model, and then training an ANN to a larger synthetic dataset generated from the calibrated model. The parameters transfer as well as the non-arbitrage constraints appear to be particularly useful when only sparse or erroneous data are available. We also explore how deeper ANNs improve over shallower ones, as well as other properties of the network architecture. We benchmark our method against standard option pricing models, such as Heston with and without jumps. We validate our method both on training sets, and testing sets, namely, highlighting both their capacity to reproduce observed prices and predict new ones.

Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data Machine Learning

Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field. Keywords: Machine Learning, limit order book, feature extraction, mid price forecasting 1. Introduction Forecasting of financial time series is a very challenging problem and has attracted scientific interest in the past few decades. Due to the inherently noisy and non-stationary nature of financial time series, statistical models are unsuitable for the task of modeling and forecasting such data. However, the lack of appropriate training and regularization algorithms for Neural Networks at the time, such as the dropout technique [6], rendered them susceptible to over fitting the training data. Support Vector Machines were deemed as better candidates for this task, as their solution implicitly involves the generalization error. The development of effective and efficient training algorithms for deeper architectures [7], in conjunction with the improved results such models presented, steered scientific interests towards Deep Learning techniques in many domains. Deep Learning methods are capable of modeling highly nonlinear, very complex data, making them suitable for application to financial data [8], as well as time series forecasting [9]. Furthermore, ML techniques which perform feature extraction may uncover robust features, better-suited to the specific task at hand. Autoencoders [10], are Neural Networks which learn new features extracted from the original input space, which can be used to enhance the performance of various tasks, such as classification or regression. Bag-of-Features (BoF) models comprise another feature extraction method that can be used to extract representations of objects described by multiple feature vectors, such as time-series [11].