Machine Learning-Driven Decision Making based on Financial Time Series

L'abstract è presente nell'allegato / the abstract is in the attachmen

Tendencias líderes de investigación sobre estrategias de trading

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted.[ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.Oliver-Muncharaz, J.; García García, F. (2020). Leading research trends on trading strategies. Finance, Markets and Valuation. 6(2):27-54. https://doi.org/10.46503/LHTP1113S27546

Explainable Machine Learning Exploiting News and Domain-Specific Lexicon for Stock Market Forecasting

In this manuscript, we propose a Machine Learning approach to tackle a binary classification problem whose goal is to predict the magnitude (high or low) of future stock price variations for individual companies of the SP 500 index. Sets of lexicons are generated from globally published articles with the goal of identifying the most impactful words on the market in a specific time interval and within a certain business sector. A feature engineering process is then performed out of the generated lexicons, and the obtained features are fed to a Decision Tree classifier. The predicted label (high or low) represents the underlying company's stock price variation on the next day, being either higher or lower than a certain threshold. The performance evaluation we have carried out through a walk-forward strategy, and against a set of solid baselines, shows that our approach clearly outperforms the competitors. Moreover, the devised Artificial Intelligence (AI) approach is explainable, in the sense that we analyze the white-box behind the classifier and provide a set of explanations on the obtained results

E-Fulfillment and Multi-Channel Distribution – A Review

This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.Distribution;E-fulfillment;Literature Review;Online Retailing

Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble

Network Momentum across Asset Classes

We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic and fundamental ties, such as the stock-bond connection of the same company and stocks linked through supply-demand chains, momentum spillover implies a propagation of momentum risk premium from one asset to another. The similarity of momentum risk premium, exemplified by co-movement patterns, has been spotted across multiple asset classes including commodities, equities, bonds and currencies. However, studying the network effect of momentum spillover across these classes has been challenging due to a lack of readily available common characteristics or economic ties beyond the company level. In this paper, we explore the interconnections of momentum features across a diverse range of 64 continuous future contracts spanning these four classes. We utilise a linear and interpretable graph learning model with minimal assumptions to reveal the intricacies of the momentum spillover network. By leveraging the learned networks, we construct a network momentum strategy that exhibits a Sharpe ratio of 1.5 and an annual return of 22%, after volatility scaling, from 2000 to 2022. This paper pioneers the examination of momentum spillover across multiple asset classes using only pricing data, presents a multi-asset investment strategy based on network momentum, and underscores the effectiveness of this strategy through robust empirical analysis.Comment: 27 page

Natural Language Financial Forecasting: The South African Context

The stock market plays a fundamental role in any country's economy as it efficiently directs the flow of savings and investments of an economy in ways that advances the accumulation of capital and the production of goods and services. Factors that affect the price movement of stocks include company news and performance, macroeconomic factors, market sentiment as well as unforeseeable events. The conventional prediction approach is based on historical numerical data such as price trends and trading volumes to name a few. This thesis reviews the literature of Natural Language Financial Forecasting (NLFF) and proposes novel implementation techniques with the use of Stock Exchange News Service (SENS) announcements to predict stock price trends with machine learning methods. Deep Learning has recently sparked interest in the data science communities, but the literature on the application of deep learning in stock prediction, especially in emerging markets like South Africa, is still limited. In this thesis, the process of labelling announcements, the use of a more statistically relevent technique called the event study was used. Classical textual preprocessing and representation techniques were replaced with state-of-the-art sentence embeddings. Deep learning models (Deep Neural Network (DNN)) were then compared to Classical Models (Logistic Regression (LR)). These models were trained, optimized and deployed using the Tensorflow Machine Learning (ML) framework on Google Cloud AI Platform. The comparison between the performance results of the models shows that both DNN and LR have potential operational capabilites to use information dissemination as a means to assist market participants with their trading decisions

Looking for heterogeneous firms : sources and implications for financial statement users

Cette thèse s’intéresse à la sélection de comparables dans le contexte de la comptabilité financière. Dans ce contexte, l’analyse de firmes se fait de façon relative, en comparaison avec d’autres firmes semblables — les « comparables ». Ainsi, il est nécessaire de former des groupes homogènes de firmes à ces fins. L’utilisation des classifications d’industries est la méthode privilégiée, car elle permet de grouper les firmes sur des critères objectifs et en lien avec le cœur de l’activité des firmes. Elles présentent l’avantage d’être très largement disponible, et très simples à utiliser. Dans cette thèse l’objectif principal est d’identifier des sources d’hétérogénéité intra-industrie, et d’examiner leurs conséquences à plusieurs niveaux. J’utilise trois approches différentes pour atteindre cet objectif. Dans un premier temps, l’objectif est de proposer une utilisation plus complète des classifications d’industries. Ainsi, j’utilise exclusivement les classifications d’industries pour identifier une source d’hétérogénéité : les industry classification misfits. La littérature précédente a pour habitude d’utiliser les différentes classifications comme des substituts l’une de l’autre, considérant qu’elles groupent les firmes sur la même dimension d’homogénéité. Ici, je prends une approche différente et considère ces classifications comme des compléments l’une de l’autre, en argumentant qu’elles possèdent le même objectif (former des groupes homogènes de firmes), mais qu’elles le font sur des dimensions différentes de l’homogénéité. Ainsi, en étudiant leur convergence j’identifie les industry classification misfits par opposition à celles appartenant au cœur de l’industrie (industry core firms). Ultimement, je montre les biais qu’engendre l’inclusion des industry classification misfits dans les groupes de comparables pour l’estimation des modèles d’accruals et la prédiction des misstatements. Dans un second temps, l’objectif est d’intégrer l’utilisation des ratios comptables et financiers pour identifier les firmes hétérogènes. Je pars de la classification qui offre la plus grande homogénéité pour développer une mesure continue d’homogénéité intra-industrie. J’utilise les ratios comptables et financiers qui sont régulièrement utilisés pour mesurer l’homogénéité d’un groupe de firmes. Contrairement aux études précédentes qui utilisent individuellement ces ratios, je propose une approche multidimensionnelle à l’homogénéité. Dans une première étape, je définis les ratios pertinents pour définir chacune des industries, puis j’utilise simultanément ces ratios pour construire ma mesure continue de distance intra-industrie entre chacune des firmes. Ainsi, je présente les firmes étant les plus éloignées du cœur de l’industrie comme des firmes différenciées (differentiated firms). Ensuite, j’étudie les conséquences sur les marchés financiers pour ces firmes. Je montre que les nouvelles d’industries sont incorporées dans les prix des firmes différenciées avec un retard. Aussi, je montre que les analystes couvrent moins ces firmes et commettent plus d’erreurs dans la prédiction des bénéfices de ces firmes. Enfin, je montre que les firmes différenciées souffrent d’une asymétrie de l’information plus importante sur les marchés, ce qui se matérialise par un plus grand écart bid-ask et une action moins liquide. Enfin, dans un troisième temps, l’objectif est d’utiliser les liens entre les industries pour mieux caractériser les firmes multisegments. Je m’intéresse à une source naturelle d’hétérogénéité intra-industrie — les conglomérats. Par définition, ces firmes opèrent dans plusieurs industries différentes, mais la construction des classifications d’industries restreint leur classification à une industrie. Ceci crée donc naturellement de l’hétérogénéité au sein des industries ce qui amène à les considérer comme complexes, notamment pour les analystes qui se spécialisent par industries. Habituellement, les études précédentes ont considéré que plus une compagnie possède de segments d’affaires différents, plus elle sera complexe. Dans ce chapitre, j’apporte une nuance sur leur complexité en prenant en compte le lien entre les différentes industries dans lesquelles opèrent les conglomérats. Je développe une mesure de distance entre les industries basée sur les ratios financiers. Ainsi, je considère les segments d’affaires comme complexes uniquement ceux qui sont éloignés du cœur d’activité de la firme. Par conséquent, deux conglomérats possédant le même nombre de segments d’affaires peuvent être complexes ou non, dépendamment si leurs activités secondaires sont dans une industrie proche de leur activité première. Ultimement, je montre les conséquences de ces firmes pour les analystes. Mes résultats dévoilent que les analystes ont plus de mal à prédire les bénéfices des conglomérats complexe.This thesis focuses on the selection of peer firms in the context of financial accounting. In this context, the analysis of firms is done cross-sectionally, in comparison with other similar firms – peer firms. Thus, it is necessary to form homogeneous groups of firms for these purposes. Industry classifications represent the most used method because it proposes an objective way to group firms based on their business activities. In addition, they present the advantage of being publicly available and easy to implement. In this thesis, the main objective is to identify sources of intra-industry heterogeneity, and to examine their consequences for several stakeholders. I provide three ways to fulfill this objective. First, I aim to provide a more complete exploitation of the information provided by industry classifications. Thus, I exclusively use them to identify a source of heterogeneity: industry classification misfits. Previous literature tends to consider industry classifications as substitutes for each other, assuming that they group firms on the same dimension of homogeneity. Here, I take a different approach and consider these classifications as complements arguing that they have the same objective (to form homogeneous groups of firms), but that they do it on different dimension of homogeneity. Thus, by studying their convergence I identify firms that are not systematically classified into the same peer group by industry classifications. I refer to them as industry classification misfits as opposed to those belonging to the heart of industry (industry core firms). Ultimately, I show the consequences of the inclusion of industry classification misfits in peer groups for the estimation of accrual models and the prediction of misstatements. Then, the main objective is to build on fundamental ratios to identify heterogeneous firms. I start from the classification which offers the greatest homogeneity (GICS) to develop a continuous measure of intra-industry homogeneity. I use accounting and financial ratios which are regularly utilized to measure the homogeneity of peer groups. Unlike previous studies which bring these ratios individually, I propose a multidimensional approach to homogeneity. In a first step, I select the relevant ratios that define each industry. These ratios are then used simultaneously to build my continuous measure of intra-industry distance between each firm belonging to the same industry. Ultimately, I present the firms that are furthest from the industry core as differentiated firms. Then, I study the consequences on financial markets for these firms. I show that industry news is incorporated into differentiated firms stock prices with a delay. Also, I show that analysts are less willing to cover these firms and make more mistakes in forecasting differentiated firms’ earnings. Finally, I show that differentiated firms suffer from asymmetric information on the stock market, which occurs as a larger bid-ask spreads and less liquid stocks. Finally, I aim to account for the industry relatedness to better characterize multi-segment firms. I focus on a natural source of intra-industry heterogeneity - conglomerates. These firms operate in several different industries through secondary business segments, but the construction of industry classifications restricts their classification to solely one industry. Therefore, it naturally creates heterogeneity within industries which leads to consider them as complex, especially for analysts who specialize in industries. Usually, previous studies have considered that the more business segments a company has, the more complex it will be. In this chapter, I add a nuance to this proxy for complexity by considering the relatedness between the industry membership of the secondary business segments in which conglomerates operate. I develop an inter-industry distance based on financial ratios to consider the relationship between industries. Thus, I regard business segments as complex only those that are unrelated to the conglomerate primary business segment. Therefore, two conglomerates sharing the same number of business segments are not systematically equally complex as it depends on whether their secondary activities are in an industry close to their primary activity. Ultimately, I show the consequences of complex business segments for financial analysts. My results show that conglomerates with complex business segments have harder earnings to predict

Recent Advances in Stock Market Prediction Using Text Mining: A Survey

Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study