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Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing.
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J. Risk Financial Manag. 2021, 14, 302. https://doi.org/10.3390/jrfm14070302 www.mdpi.com/journal/jrfm
Article
Machine Learning in Finance: A Metadata-Based Systematic
Review of the Literature
Thierry Warin
1
and Aleksandar Stojkov
2,
*
1
HEC Montreal, Montréal, QC H3T 2A7, Canada; thierry.warin@hec.ca
2
Iustinianus Primus Law Faculty, Ss. Cyril and Methodius University in Skopje, 1000 Skopje,
North Macedonia
* Correspondence: a.stojkov@pf.ukim.edu.mk
Abstract: Machine learning in finance has been on the rise in the past decade. The applications of
machine learning have become a promising methodological advancement. The paper’s central goal
is to use a metadata-based systematic literature review to map the current state of neural networks
and machine learning in the finance field. After collecting a large dataset comprised of 5053
documents, we conducted a computational systematic review of the academic finance literature
intersected with neural network methodologies, with a limited focus on the documents’ metadata.
The output is a meta-analysis of the two-decade evolution and the current state of academic
inquiries into financial concepts. Researchers will benefit from a mapping resulting from
computational-based methods such as graph theory and natural language processing.
Keywords: efficient market hypothesis; machine learning; network analysis; sentiment analysis
1. Introduction
The theory and practice of finance have undergone a remarkable evolution in the
past five decades. The emergence and acceptance of the Efficient Market Hypothesis
(EMH), its subsequent mixed empirical record, the rise of pragmatically driven
‘Chartism’, and the present co-evolution of quantitative and behavioral finance represent
some exciting significant developments in the financial domain.
The vibrancy of finance can also be observed by two methodological revolutions
bringing sophisticated technical analysis of financial phenomena. Machine Learning
Algorithms (MLAs) application in explaining and forecasting financial market trends has
been a significant methodological advancement in the past three decades. Another critical
research direction has been the rise of sentiment analysis of unstructured data relating to
relevant news for financial markets.
In this article, we propose to take a comprehensive look at machine learning in
finance. For that, we will use neural network as a keyword in our data collection. Using
neural network as a keyword does not limit us to just neural networks approaches,
because the source data will also contain other terms such as machine learning, deep
learning, etc. The rationale behind using neural network as a core keyword is that the
most influential papers introducing machine learning in finance used neural networks as
a methodology of choice (i.e., Gencay and Stengos 1998).
Conventional systematic literature reviews (SLR) are a process that enables the
collection of relevant evidence on a given topic that meets predefined eligibility criteria
and provides an answer to the research questions formulated. A meta-analysis
necessitates descriptive and/or inferential statistical methods to synthesize data from
multiple studies on a particular subject. The techniques facilitate the generation of
knowledge from a variety of studies, both qualitative and quantitative. The conventional
Citation: Warin, Thierry and
Aleksandar Stojkov. 2021. Machine
Learning in Finance: A
Metadata-Based Systematic Review
of the Literature. Journal of Risk and
Financial Management 14: 302.
https://doi.org/10.3390/jrfm14070302
Academic Editor: Reza Tajaddini
Received: 25 April 2021
Accepted: 21 June 2021
Published: 2 July 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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(http://creativecommons.org/licenses
/by/4.0/).
J. Risk Financial Manag. 2021, 14, 302 2 of 33
method consists of four fundamental steps: search (define the search string and database
types), appraisal (pre-defined literature inclusion and exclusion criteria, and quality
assessment criteria), synthesis (extract and categorize the data), and analysis (narrate the
results and finally reach a conclusion) (SALSA) (Mengist et al. 2020). SLR is defined as a
“systematic, explicit, and reproducible method for identifying, evaluating, and
synthesizing the existing body of completed and recorded work” (del Amo et al. 2018).
According to Grant and Booth (2009), the SALSA framework is a methodology for
determining the search protocols that the SLR should follow. This ensures methodological
precision, standardization, comprehensiveness, and reproducibility. The majority of
scientific work employed this methodological approach to mitigate the risk of publication
bias and increase the work's acceptability (del Amo et al. 2018; Grant and Booth 2009;
Malinauskaite et al. 2019; Perevochtchikova et al. 2019). Thus, most review articles
followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Protocol and the Search, Appraisal, Synthesis, and Analysis (SALSA) framework (Grant
and Booth 2009).
From SALSA, this article adds a pre-processing step to reduce potential human biases
and highlights new results based on text-based analyses of the data collected.
Indeed, our main contribution is a computational systematic literature review of
machine learning (and neural networks in particular) in finance between 1990 and 2021.
We believe it is crucial to map the evolution of these new technologies and methodologies
in our field. When scholars in the computer science field essentially develop the Artificial
Intelligence (AI) sub-domain and machine learning techniques, including deep learning
and reinforcement learning, it is interesting to look at the bridges between these
developments and the ones in finance.
A second contribution is methodological. We indeed perform a metadata-based
systematic review of the relevant literature. In the methodology section, we will provide
a precise definition of the approach. We believe it is an essential methodological
complement to conventional qualitative reviews and econometric-based meta-analyses. A
metadata analysis means we will collect more articles than in a traditional systematic
literature review and use algorithms to filter and sort the initial dataset. The
methodological approach will be twofold: (1) we will use Natural Language Processing
(NLP) techniques to extract text-as-data information, and (2) we will use graph theory to
visualize potential collaboration networks. These two methodological approaches
combined will provide us a different analysis than a formal systematic review. It is not to
be seen as a substitute, but instead as a complement to the more conventional approach.
As an aside, and although we will not spend time on this aspect, a third contribution
could be an epistemological one in nature and leverages our first contribution on the
mapping of machine learning in finance to reflect on the implications of its significance
on the old debate between theorists and chartists in finance. Markowitz (1952); Sharpe
(1963, 1964), EMH emerged as a dominant paradigm providing a formal explanation of
financial markets' behavior. Empirical approaches emerged under the umbrella of
“Chartism” (e.g., Berardi 2011). Chartists-or empirically minded technical analysts-have
used extrapolative rules to discover statistical regularities in the time series for prices (e.g.,
Hsieh 1989; Frankel and Froot 1990; Taylor and Allen 1992; Menkhoff 1997, 2010; Lo 2004;
Neely et al. 2009; Kaucic 2010; Gradojevic and Gencay 2013; Neely et al. 2014; Gerritsen et
al. 2020). Additionally, a burgeoning literature on agent-based financial market models
emerged, allowing various interactions between chartists and fundamentalists (e.g., Day
and Huang 1990). Thanks to ML techniques, induction generates causal relationships
based on information at the moment of estimation (Popper 1962; Warin 2005). These
causal relationships are at the root of the predictive power of ML models. In the ML
context, causality and prediction seem to get theorists and technical analyses closer.
The structure of the paper is as follows. In the next section, we provide a metadata-
based systematic review of the academic literature on finance, published between January
1990 and May 2021. The third section elaborates the conceptual structures behind the
J. Risk Financial Manag. 2021, 14, 302 3 of 33
relevant literature by exploring the keywords, keywords co-occurrences, and the topics’
evolution based on a topic modeling technique. In the next section, we examine the
intellectual structures behind the evolution of analytical thinking on finance by focusing
on what vehicles and which organizations are the main engines in this topic dynamics.
The fifth section critically examines the social structures of our sample, encompassing
different measures to capture the social connections of authors, co-citations, and
collaborations across institutions. The concluding remarks summarize the potential of
machine learning, neural networks, and in general, the augmented technical analysis in
analyzing financial markets.
2. Materials and Methods
A standard introduction to financial theory would often distinguish several
valuation models that might be useful for analyzing securities and managing portfolios
(see Lee and Lee 2010). Since the 1970s, the evolution of financial theory has been greatly
influenced and informed by the emergence and acceptance of the EMH and the Modern
Portfolio Theory (MPT) (Prasch and Warin 2016). Given the vast literature on financial
analytics models, we confine our critical review only to the main strands of the relevant
academic literature.
To illustrate the development of neural networks in finance, we conduct a
scientometric study of the academic literature on finance, published between January 1990
and May 2021
2.1. Methodology
The methodology used here is a systematic literature review with a different
approach to more conventional reviews. In usual literature reviews, the author selects the
relevant literature based on her domain or methodological expertise. Then, the analysis is
based on the content found in the sample that has been created in the initial stage. The
primary characteristics of SLR and its associated procedure, meta-analysis, are the
following: (1) a clearly stated research question that the study will address; (2) explicit and
reproducible objectives; (3) search strings that include all related studies that meet the
eligibility criteria; and (4) an assessment of the quality/validity of the selected studies.
To have a comprehensive look, conventional systematic literature might not be the
best choice. Considering the pace of the new developments in the artificial intelligence
field, we propose here to map the extent of the usage of these new technologies and
methodologies in finance. Systematic literature is a mapping exercise of a knowledge area,
and it is also really focused, with between 50 to 200 papers being analyzed. Here, we also
want to map the machine learning knowledge area while collecting a significant number
of documents. The large dataset size will allow us to build an analysis based on the
documents’ metadata, such as authors’ affiliations, universities, etc. This research protocol
built around a metadata-based systematic literature review could be considered the first
phase in a systematic literature review.
In contrast to more conventional methods, we have two phases: First, similar to a
traditional systematic review, the selection of the relevant articles is performed via a
search engine, except the expert does not select the relevant articles from the results
presented to her. Here, the expert chooses the keywords and creates a comprehensive
dataset of all the documents matching the keywords in the title, abstract, keyword, and
keyword + section. The first phase, being automated, allows the utilization of quantitative
criteria to filter down the dataset. Then, in the second phase, a dataset reduction to 50–200
documents is made by an expert.
To summarize, one of the critical contributions of a metadata-based systematic
literature review is to reduce—though not wholly—potential human biases. Another
significant contribution of this new methodology based on these two phases is that it
allows us to consider the documents’ metadata in a text format. By adding a
computational treatment based on Natural Language Processing (NLP) techniques to
J. Risk Financial Manag. 2021, 14, 302 4 of 33
transform the text into data, we can then provide analyses that would not be possible
otherwise, leveraging analytical approaches such as graph theory. It is particularly
relevant to discover research patterns, research history, the actual research vehicles, or to
be able to associate discoveries with institutions, to name a few examples. These
sophisticated techniques allow us to perform a literature mapping thanks to this
computational approach.
Another critical point is the large size of the dataset, which has a lot of favorable
statistical properties. We will also use algorithms to help us analyze quantities of papers
that we would not be able to do otherwise due to the sheer quantity of information
analyzed by a human.
Finally, another important dimension is using each document’s reference section to
perform metrics that allow researchers to understand the knowledge transmission
patterns.
Beyond the computational treatment and to leverage the results obtained from these
computations, we use the following theoretical framework. Aria et al. (2017) propose to
look at three different structures: the conceptual, intellectual, and social structures. The
conceptual structures are about leveraging the metadata to help us understand which
concepts and topics are used in the academic conversation and how they have evolved
through time. The intellectual structure will help us understand who produced these
concepts, which journals played a pivot role in this nascent literature, and which articles
were among the most referenced that fueled this literature. Lastly, the social structure will
allow us to look at authors’ collaborations and the knowledge support from universities
and countries through their collaborations.
The data collection will be conducted using a “human-in-the-loop” (HIL) approach.
It consists of proceeding to a purely automated data collection with an ex-post validation
based on the field expertise.
First, we use an automated process in two phases as described earlier. The search was
performed on the publisher-independent citation database “Web of Science” (WoS),
Clarivate Analytics, by using combinations of keywords (and simultaneously removing the
duplicates): “neural network*” AND “finance*”.
These keywords allow us to build our sample. This sample does not aim at being
representative of the domain. Instead, it intends to analyze the dynamics of the
conversation about neural networks in finance. By building a sample about a modeling
technique, we risk overfitting the true representativity of neural networks in finance if
someone is interested in generalizing; this is not our intent.
We then use human-based field expertise to review the references anyway while
adding some potential missing references based on the domain expertise (see Appendix
A for a list of the added references). HIL allows us to have a combined qualitative
assessment with pure automatic data collection. This second step is marginal in terms of
added articles, but it is crucial for quality control.
Our approach differs at these two levels: in the sample creation, we try to be as
comprehensive as possible on a particular topic, here “neural network*” AND “financ*”.
The stars mean that we collect any occurrence with a declination of the word’s root. We
use neural networks as a proxy for machine learning techniques as authors who use neural
networks also reference machine learning in their keywords (among 10,160 used
keywords and 3606 keywords Plus, see Table 1). So, the sample includes papers on
machine learning as well. The sample is likely not comprehensive, as in any systematic
literature review, but it is larger than conventional methods. The sample is collected by
finding matches in the text title, the abstract, the keywords, and the keywords + in Web of
Science. It helps us create a 5053 rich sample, a larger sample than regular, systematic
reviews. We can deal with a larger sample thanks to the second differentiation point of
our methodology: leveraging the sample metadata through computational techniques.
The dataset can be found on the following webpage, including a search engine:
https://warin.ca/posts/article-machine-learning-finance/
(accessed
date: 29 June 2021).
J. Risk Financial Manag. 2021, 14, 302 5 of 33
Table 1. Preliminary information about data, overall period, and per year.
Description Overall Time
Period (1990–2021)
2017 2018 2019 2020 2021
Sources (Journals, Books, etc.) 2533 265 329 374 333 107
Documents
5053
355
436
578
592
Average years from publication 7.74 4 3 2 1 0
Average citations per documents 14.66 10.9 8.278 5.005 2.255 0.465
Average citations per year per document 1.699 2.18 2.069 1.668 1.128 0.465
References
105,684
10,844
13,281
18,239
22,817
In this second level of differentiation, we create and use the metadata from the title,
the abstract, the keywords, and the keywords +. The creation of metadata is conducted via
Natural Language Processing (NLP) techniques. We prepare the dataset by selecting
tokens, n-grams, etc. (Aria and Cuccurullo 2017).
These metadata are helpful to provide quantitative analysis to the sample. Using
these machine learning tools allows us to have a research synthesis that can be leveraged
with other techniques such as social network analysis. We can also look at the dynamics
of the research contributions, the collaborations, the idea generation, and propagation.
Let us first look at the descriptive statistics before studying the dynamics of the
research in this sample. We present the main descriptive statistics and empirical findings
from the systematic literature review in the next step.
2.2. Descriptive Statistics
The relevant ‘universe’ of the literature consists of references identified in the HIL-
Web of Science citation database (see Table 1) totaling 5053 documents, most of which are
published in refereed journals (see Table 2). The literature review covers the period
between 1 January 1990, and 10 May 2021 (see Figure 1).
Figure 1. Article count through time.
J. Risk Financial Manag. 2021, 14, 302 6 of 33
The overall number of documents in our sample is 5053 (see Table 1). This number is
the cumulative result of each year, and we can observe a significant rise in the number of
documents per year. The average citations per document are 14.66 but have evolved
through time to numbers ranging between 1 and 2. As a reference point, the total citations
per paper in economics and business for the highly cited papers were 3.04 for the 2011–
2015 period and 3.91 for the 2017–2021 period. In Social Sciences in general, the total
citations per paper for the highly cited papers were 2.89 for the 2011–2015 period and 3.30
for the 2017–2021 period. These results show the normalization of machine learning in
finance-related documents.
The number of articles dominates the sample for the overall period (see Table 2) with
2719 occurrences, followed by 1974 proceedings papers. So, short contributions (articles
and proceedings papers) represent the actual output in this sample. Authors indeed tend
to produce the knowledge body about machine learning in finance through short
contributions (e.g., Gu et al. 2020).
Table 2. Document type, overall period, and per year.
Description Overall Time Period (1990–
2021) 2017 2018 2019 2020 2021
Article
2719
196
222
339
484
Article; easy access 67 0 0 0 0 0
Article; proceedings paper 143 1 4 2 0 1
Article; retracted publication 1 0 1 0 0 0
Bibliography
1
0
0
0
0
Biographical item 1 0 0 0 0 0
Book review 6 0 0 0 0 0
Correction 3 0 0 1 0 1
Editorial material
9
0
2
1
0
Letter 3 0 0 0 0 0
Meeting abstract 3 0 0 0 1 0
Proceedings paper 1974 150 194 216 79 0
Review
120
8
13
19
28
Review; early access 3 0 0 0 0 0
Our database of references covers 308 keywords and 946 author appearances (see
Table 3). Most of the publications are multi-authored documents, indicating the
increasingly collaborative nature of research in the finance domain.
Table 3. Document content and authors, overall period, and per year.
Description Overall Time Period
2017 2018 2019 2020 2021
Keyword Plus (ID) 3607 604 693 849 950 234
Author’s Keywords (DE) 10164 1251 1429 1804 2044 688
Authors 9648 939 1210 1655 1651 492
Author Appearances 14628 1056 1350 1972 1985 519
Authors of single-
authored documents 520 44 40 37 47 8
Authors of multi-
authored documents 9128 895 1170 1618 1604 484
The descriptive statistical analysis also reveals that, on average, there are 2.32 authors
per publication and 2.72 co-authors per publication (see Table 4). Most of the documents
are collectively written. Only 661 documents have a single author.
J. Risk Financial Manag. 2021, 14, 302 7 of 33
Table 4. Authors’ collaboration, overall period, and per year. Note: The Collaboration Index (CI) is
calculated as total authors of multi-authored articles/total multi-authored articles.
Description Overall Time Period 2017
2018 2019
2020 2021
Single-authored documents 661 46 42 37 49 9
Documents per Author 0.524 0.378
0.360 0.349
0.359 0.319
Authors per Document 1.91 2.65 2.78 2.86 2.79 3.13
Co-Authors per Documents 2.89 2.97 3.10 3.41 3.35 3.31
Collaboration Index 2.08 2.90 2.97 2.99 2.95 3.27
To conclude this descriptive statistics section, we observed a similar trend in the
academic production about machine learning in finance based on short documents and
co-authorship. Let us now analyze the three different structures: conceptual, intellectual,
and social.
3. Conceptual Structures of Our Sample
The application of AI in the domain of finance is not a recent phenomenon in the
academic literature (e.g., Hutchinson et al. 1994; Lo et al. 2000; Gavrishchaka and Banerjee
2006; De Spiegeleer et al. 2018; Huang et al. 2020). However, the last decade witnessed
empirical studies using Machine Learning Algorithms (MLAs) to examine credit risk
analysis and forecasting stock returns. As Dixon et al. (2020, p. vii) highlight, “ML in
finance sits at the intersection of several emergent disciplines, including pattern
recognition, financial econometrics, statistical computing, probabilistic programming,
and dynamic programming”. One of the main competitive advantages of ML is that
computers have an outstanding ability to process large amounts of financial information.
From a methodological perspective, the empirical studies rely not only on
conventional MLAs such as support vector machine (SVM) and k-nearest neighbors
(kNN) but also on Deep Learning (DL) (e.g., Krauss et al. 2017; Fischer and Krauss 2018;
Huang et al. 2020), an advanced technique based on artificial neural network algorithms
(e.g., Chung-Ming and White 1994; Donaldson and Kamstra 1997; Hans and van
Griensven 1998; Gencay and Stengos 1998; Blake and Kapetanios 2000; Garcia and Gencay
2000; Fernandez-Rodrıguez et al. 2000; Bekiros and Georgoutsos 2008; Kristjanpoller and
Minutolo 2018; Atsalakis et al. 2019). Some DL models were also used to predict stock
prices (e.g., Kraus and Feuerriegel 2017; Minh et al. 2017; Jiang et al. 2018; Matsubara et
al. 2018). For instance, Schumaker and Chen (2010) make a stock market forecasting based
on financial news articles using a text classification approach. Glasserman et al. (2020)
study using the supervised Latent Dirichlet Allocation (sLDA) framework to select news
articles topics to explain stock returns.
The network analysis has been used more in the context of financial stability
analysis and financial linkages. Another strand of the literature examines the
impact of views and opinions of investors-also known as investor sentiment-on
stock price movements. The sentiment analysis aims to capture news by
traditional and/or social media and assess the investors’ views and market mood
(e.g., Mitra and Mitra 2011; Mitra and Yu 2016). The assessment of market
sentiment-often captured by market indices-can be strengthened by sentiment
analysis of the market mood or investors’ emotions. A popular approach is to
extract relevant news articles, preprocess the text, and assign a sentiment score to
each article. The sentiment score is then commonly calculated as the difference
between the number of positive and negative words in the article divided by the
total number of words. The studies use a reputable lexicon of financial terms-such
as Loughran and McDonald (2011) lexicon-to determine positive and negative
words.
J. Risk Financial Manag. 2021, 14, 302 8 of 33
In the following sub-sections, we will consider the conceptual structures of our
sample by looking at the keywords, the keywords co-occurrences, and the evolution of
the topics based on a topic modeling technique.
3.1. Keywords Analyses
We consider here the entire words that we find in the keyword section of every
document. Remember that the sample was created using “neural network*” AND
“finance*” (see Figure 2). It is thus expected that authors would again put neural networks
as keywords in the keyword section. They will also associate other keywords such as
prediction, forecasting, or machine learning, including deep learning. This is evidence that
our sample goes beyond just neural networks but also covers other related topics.
Figure 2. Keywords count through time.
It is interesting to see that deep learning is a very recent addition to the fintech field,
as approximated by our sample. It is also interesting to notice that it is recently that the
reasons why we would use the new techniques in finance have appeared, for instance, the
role of these new methodologies in prediction. Machine learning techniques are indeed a
paradigm shift when it comes to their predictive power.
Table 5 represents the top keywords in the overall sample and the top keywords per
year. It is interesting to see keywords ranking through time and how the literature has
evolved in machine learning ownership and maturity, with deep learning papers moving
up the ladder.
J. Risk Financial Manag. 2021, 14, 302 9 of 33
Table 5. Top keywords, overall period, and per year.
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
Overall Time Period
Neural Network 867 Neural Networks 800
Artificial Neural Network
423
Prediction
482
Forecasting 277 Model 402
Machine Learning 274 Neural Network 340
Deep Learning 257 Classification 305
2021
Neural Network
26
Neural Networks
13
Artificial Neural Network 22 Model 12
Forecasting
21 Prediction 10
Machine Learning 15 Market 8
Deep Learning 10 Classification 7
2020
Deep Learning 87 Neural Networks 81
Neural Network
85
Prediction
66
Machine Learning
79
Model
63
Artificial Neural Network 49 Neural Network 50
Forecasting 42 Models 40
2019
Neural Network 80 Neural Networks 96
Deep Learning
72
Prediction
51
Machine Learning 58 Model 49
Artificial Neural Network 43 Neural Network 38
Forecasting 35 Classification 36
2018
Neural Network
51
Neural Networks
83
Deep Learning 48 Prediction 44
Artificial Neural Network 45 Model 42
Machine Learning 35 Classification 26
Forecasting 25 Neural Network 25
2017
Neural Network
51
Neural Networks
68
Artificial Neural Network 39 Prediction 38
Forecasting 21 Model 34
Prediction 20 Neural Network 31
Machine Learning
18
Classification
30
To go beyond a single-dimensional perspective of the keywords, let us look now at
the co-occurrences matrix.
3.2. Keywords Co-Occurrences Network Analyses
Now, we are interested in looking at the keywords co-occurrences. When a keyword
is used, it is possible to build a count matrix and compute its relationships with other
keywords. From there, we can compute some relevant network indicators (centrality,
density, etc.). Several figures will plot the relevance degree (centrality, or notions of
‘importance’) against the development degree (density). Degree centrality counts the
number of links held by each node and points at themes that can easily connect with the
broader network. The density of a network is the frequency of realized edges relative to
potential edges.
In Figure 3, we represent the graphs based on the network indicators. The first figure
is the network of keywords for the entire sample, while each other graph represents a
network for 2021, 2020, 2019, 2018, and 2017, respectively.
When we consider the co-occurrences networks, particularly the years 2021 and 2017,
we observe that most of the conversations are organized around two groups, representing
both computer techniques and mathematical approaches. Only recently, applications in
finance are starting to appear, such as the prediction of bankruptcies.
J. Risk Financial Manag. 2021, 14, 302 10 of 33
Figure 3. Network of authors’ keywords, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 11 of 33
In Table 6, we compute the mathematical features of the networks. We
observe that the size of the networks has been on the rise in the past years,
showing an increase in the spread of the concepts. It is accompanied by a decrease
in density through time with a slight increase in the average path length,
confirming potentially that the literature opens up to applications.
Table 6. Graph indicators, overall period, and per year.
Statistics Overall
Time Period
2021 2020 2019 2018 2017
Size 3607.000 234.000 950.000 849.000 693.000 604.000
Density 0.005 0.036 0.014 0.016 0.018 0.021
Transitivity
0.128
0.538
0.238
0.232
0.266
0.269
Diameter 6.000 6.000 6.000 6.000 6.000 6.000
Degree Centralization 0.298 0.188 0.229 0.303 0.317 0.333
Average path length 2.752 3.067 2.792 2.716 2.732 2.682
3.3. Topic Modeling-Based Analyses
In the following analysis, we will add a new dimension based on structural topic
modeling. The goal here is to complement the information we obtained from the
keywords co-occurrences. A structural topic modeling first means that we will leverage
words including the keywords section and beyond: the title section, the abstract, and the
keyword + section.
We tokenize all the words, and we compute the latent variables to identify potential
topics.
In the following figures, we represent this analysis. The top-left figure covers the
whole period, while the other figures represent each year, 2021, 2020, 2019, 2018, and 2017,
respectively.
We found the topics mapped in four dimensions: basic themes, emerging or
declining, niche themes, and motor themes.
Interestingly, data mining and neural networks were part of the fundamental themes
in 2017 (see Figure 4). Since we consider mostly finished documents in our sample, it
means the work from the researchers has started a bit earlier, likely one or two years
before.
In 2017, a generic algorithm was an emerging theme as well as network theory. We
see here a burgeoning reflection about what will become the contribution from data
science in finance. Comparing 2017 and 2020, and 2021, it is interesting to see that the
motor themes are about the predictive capacity of machine learning-based models. We
can also observe the emerging sub-field of deep learning in finance. We can easily
extrapolate and imagine that deep learning in finance will have a prominent future in the
field.
We want to insist on the inductive nature of machine learning: it is inductive by
nature but does not come with the former empirical baggage of being potentially biased
and lacking theoretical grounds (the falsification potential, etc.). Inductive in the context
of ML implies finding causal patterns in empirical data.
J. Risk Financial Manag. 2021, 14, 302 12 of 33
Figure 4. Topic modeling, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 13 of 33
4. Intellectual Structures of Our Sample
An interesting analysis stems from the investigation of which authors and
organizations are driving the dynamics of this topic.
4.1. Authors
In the intellectual structure, authors are interesting to consider. We can see that the
top authors have published more than 30 papers on this topic in our sample (Figure 5).
Figure 5. Top authors in terms of production, overall period, and per year.
We can go a little deeper and look at the average productivity of all the authors (see
Figure 6). It has not evolved much through time, and on average, every author produces
two articles a year on this topic.
We can also look at the authors’ dominance ranking through time (see Figure 7). The
authors’ dominance is computed by looking at how many times an author is a first author
in a multi-authored paper. It can be a weak indicator as the alphabetical order is respected
most of the time, irrespective of the marginal contributions, as assumed by this indicator.
Interestingly, it is interesting to see that authors unfavored by the alphabetical order,
such as Zhang or Wang, are still making the top 10 of this ranking.
J. Risk Financial Manag. 2021, 14, 302 14 of 33
Figure 6. Scientific productivity, overall period, and per year.
Figure 7. Author dominance ranking, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 15 of 33
4.2. Articles
Table 7 illustrates the citations of the articles in our sample.
Table 7. Most cited manuscripts, overall period, and per year.
Article Total
Citations
Total
Citations
per Year NTC
Overall Time Period
Schaap Mg., 2001, J Hydrol 1361 64.8 20.06
Jordan Mi, 2015, Science
1189
169.9
78.27
Kim Kj, 2003, Neurocompeting 748 39.4 18.34
Pan Wt, 2012, Knowledge-Based Syst 725 72.5 33.93
Tay Feh, 2001, Omega-Int H Manage Sci 596 28.4 8.79
2017
Wei, Y, 2017, Ieee Trans Pattern Anal Mach Intell 199 39.8 18.25
Bao W, 2017, Plos One 198 39.6 18.16
Deng Y, 2017, Ieee Trans Neural Netw Learn Syst 142 28.4 13.03
Barboza F, 2017, Expert Syst Appl
135
27.0
12.38
Krauss C, 2017, Eur J Oper Res 115 23.0 10.55
2018
Fischer T, 2018, Eur J Oper Res 258 64.5 31.17
Termeh Svr, 2018, Sci Total Environ
144
36.0
17.40
Han J, 2018, Proc Natl Acad Sci USA 129 32.2 15.58
Kim Hy, 2018, Expert Syst Appl 108 27.0 12.38
Cai Y, 2018, Remote Sens Environ 102 25.5 12.32
2019
Altan A, 2019, Chaos Solitons Fractals 90 30.0 17.98
Cao J, 2019, Physica A 60 20.0 11.99
Long W, 2019, Knowledge-Based Syst 55 18.3 10.99
Strubell E, 2019, 57th Annual Meeting of the
Association for Computational Linguistics (ACl 2019) 48 16.0 9.59
Plawiak P, 2019, Appl Soft Comput 43 14.3 8.59
2020
Pang X, 2020, J Supercomput
44
22.0
19.51
Akhtar Ms, 2020, Ieee Comput Intell Mag 41 20.5 18.18
Ahmed R, 2020, Renew Sust Energ Rev 38 19.0 16.85
Sezer Ob, 2020, Appl Soft Comput 32 16.0 14.19
Gu S, 2020, Rev Financ Stud
29
14.5
12.86
2021
Marcelino P, 2021, Int J Pavement Eng 12 12 25.81
Talwar M, 2021, J Retail Consum Serv 8 8 17.21
Carta S, 2021, Expert Syst Appl 6 6 12.90
Brodny J, 2021, J Clean Prod 5 5 10.75
Hu Z, 2021, Appl Syst Innov 4 4 8.60
We can go a little further and look now at the articles that authors in our sample
include in their references. As such, those references are the foundations of this nascent
literature in machine learning in finance. Let us look at the top authors in the references
of each paper (see Figure 8).
J. Risk Financial Manag. 2021, 14, 302 16 of 33
Figure 8. Analysis of cited references, overall period, and per year.
We can also look at the most cited references in terms of journals beyond their
authors. The most cited authors and the most cited references will match, but it is
interesting to see the nuances (see Figure 9).
It is interesting to note that the literature has not moved too much from the top papers
from 2017 to 2021.
J. Risk Financial Manag. 2021, 14, 302 17 of 33
Figure 9. Most cited manuscripts, overall period, and per year.
5. Social Structures of Our Sample
In this section, we will spend time on different measures to capture the social
connections: the co-citations of authors, the co-citations of articles, the co-citations of
journals, and the collaborations across institutions.
5.1. Co-Citations of Authors
Figure 10 highlights the evolution of authors’ collaborations. We can observe that it
is still a narrow network of collaborators. We are showing the nascent nature of the field.
We represent here the network of the top authors.
As we can see in the previous figure, the top authors are still working nearby within
their groups of collaborators. The next question is to know whether it is still the case for
co-citations of articles.
J. Risk Financial Manag. 2021, 14, 302 18 of 33
Figure 10. Authors’ collaboration networks, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 19 of 33
5.2. Co-Citations of Articles
When a reference was addressed by two articles published in the same journal, this
reference was included in the co-citation network of references (see Figure 11). Therefore,
the co-citation network addressed the expected references to the concept of uncertainty in
articles published by a journal.
Figure 11. Co-citations of articles, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 20 of 33
In our sample, most of the authors in finance are residents of the People’s Republic
of China, the United States, the United Kingdom, and India (see Table 8). While the
dominant presence of authors from the advanced economies is undisputed, it is also
noticeable that the law of large numbers ensures the participation of authors from several
Emerging Market Economies (EMEs).
Table 8. Corresponding authors’ countries, overall period, and per year.
Country Articles Frequency SCP MCP MCP_Ratio
Overall Time Period
China 1438 0.2885 1253 185 0.1287
United States
476
0.0955
389
87
0.1828
India 293 0.0588 268 25 0.0853
United Kingdom 256 0.0514 195 61 0.2383
Brazil 147 0.0295 138 9 0.0612
2017
China 90 0.2535 74 16 0.1778
India 36 0.1014 33 3 0.0833
United States 28 0.0789 20 8 0.2857
Iran
18
0.0507
16
2
0.1111
Brazil 12 0.0338 11 1 0.0833
2018
China 106 0.2437 89 17 0.1604
India
35
0.0805
32
3
0.0857
United States 34 0.0782 22 12 0.3529
Iran 18 0.0414 15 3 0.1667
Turkey 16 0.0368 15 1 0.0625
2019
China 172 0.2976 136 36 0.2093
United States 55 0.0952 48 7 0.1273
India 36 0.0623 33 3 0.0833
Russia
23
0.0398
22
1
0.0435
Spain 19 0.0329 9 10 0.5263
2020
China 177 0.2990 147 30 0.169
India
44
0.0743
35
9
0.205
United States 43 0.0726 34 9 0.209
United Kingdom 29 0.0490 20 9 0.310
Iran 21 0.0355 18 3 0.143
2021
China 53 0.3397 42 11 0.208
India 13 0.0833 13 0 0.000
United States 9 0.0577 6 3 0.333
Italy 7 0.0449 7 0 0.000
Turkey 7 0.0449 6 1 0.143
Note: SCP = single country publications; MCP = multiple country publications; MCP_Ratio = share
of multiple country publications in the total number of publications.
J. Risk Financial Manag. 2021, 14, 302 21 of 33
Table 9 provides Supplementary Materials on the total citations per country. Asia
and China, in particular, dominate the ranking.
Figure 12 shows an apparent increase in the contributions coming from Asia: China
and India being at the forefront of academic production.
Table 9. Total citations per country, overall period, and per year.
Country
Total Citations
Average Article Citations
Overall Time Period
China 17154 11.929
United States 16876 35.454
United Kingdom 4691 18.324
South Korea 4482 32.715
India 2999 10.235
2017
China 1413 15.70
United States
463
16.54
India 404 11.22
Brazil 260 21.67
Germany 207 34.50
2018
United States 555 16.324
China 511 4.821
Iran 285 15.833
Germany
270
54.000
India 232 6.629
2019
China 607 3.529
United States
421
7.655
Brazil 165 9.706
Iran 132 9.429
South Korea 126 7.875
2020
China 352 1.989
United States 127 2.953
India 107 2.432
United Kingdom
72
2.483
Australia 63 5.727
2021
China 13 0.245
Portugal
12
12.000
Norway 9 3.000
India 7 0.538
Italy 6 0.857
Note: SCP = single country publications; MCP = multiple country publications; MCP_Ratio = share
of multiple country publications in the total number of publications.
J. Risk Financial Manag. 2021, 14, 302 22 of 33
Figure 12. The most productive countries (according to authors’ residence).
Starting from a bibliographic matrix, two groups of descriptive measures are
computed: (1) the summary statistics of the network and (2) the leading indices of
centrality and prestige of vertices.
This group of statistics presented in Table 8 allows us to describe the structural
properties of a network: (1) ‘size’: is the number of vertices composing the network; (i2)
‘density’: is the proportion of present edges from all possible edges in the network; (3)
‘transitivity’ is the ratio of triangles to connected triples; (4) ‘diameter’ is the longest
geodesic distance (length of the shortest path between two nodes) in the network; (5)
‘degree distribution’ is the cumulative distribution of vertex degrees, and (6) ‘degree
centralization’ is the normalized degree of the overall network.
When it comes to countries’ collaborations, China and the USA are at the center of
the graph (see Figure 13). Most of the international collaborations are between China and
the USA. There seems to be a slight regionalization of collaborations, China with Asian
countries, though it is much less apparent in the case of the USA, which seems to be a bit
more eclectic in terms of collaborations.
Considering the results mentioned above, it confirms that Asia and China are
somehow at the forefront of the academic production on neural networks and the larger
machine learning domain in finance. It is interesting to the connections with other
countries, notably in Europe. Below, we will also investigate the connections at the
institutional level.
J. Risk Financial Manag. 2021, 14, 302 23 of 33
Figure 13. Country collaboration networks, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 24 of 33
5.3. Co-Citations of Journals
We will look at which journals have contributed to developing the field’s
methodological transformation in what follows. Through time (see Table 10), we will see
that it mostly started in more engineering journals to penetrate the finance field. Still,
nowadays, the ranking is dominated by more engineering-oriented journals.
Table 10. Top journals, overall period, and per year.
Sources
Articles
Overall Time Period
Expert Systems with Applications 305
Applied Soft Computing 75
Ieee Access 74
Neurocomputing
71
Neural Computing & Applications 56
2017
Expert Systems with Applications 12
Applied Soft Computing
6
Physica a-Statistical Mechanics and Its Applications 5
2017 Ieee International Conference on Big Data (Big Data) 4
Agro Food Industry High-tech 4
2018
Expert Systems with Applications 12
Applied Soft Computing 9
Neurocomputing 8
2018 26th Signal Processing and Communications Applications Conference
(Sui) 7
2018 International Joint Conference on Neural Networks (ijcnn) 7
2019
Ieee Access
24
Expert Systems with Applications 19
Physica a-Statistical Mechanics and Its Applications 11
Sustainability 11
Applied Soft Computing
9
2020
Ieee Access 37
Expert Systems with Applications 17
2020 International Joint Conference on Neural Networks (ijcnn)
13
Soft Computing 13
Neural Computing & Applications 11
2021
Ieee Access
10
Expert Systems with Applications 8
Computational Economics 5
Annals of Operational Research 4
Complexity 4
Figure 14 is an excellent illustration of the evolution of the knowledge map seen
through journal co-citations. It is interesting to see the origin of the transformation and
the pace of the penetration of machine learning in finance journals and through which
channels. It is worth noticing the pivotal role played by the Expert Systems with
Applications” journal.
J. Risk Financial Manag. 2021, 14, 302 25 of 33
Figure 14. Journals source co-citation analysis, overall period, and per year.
5.4. Co-Citations of Institutions
Related to Figure 13, it is interesting to study the collaborations through a different
indicator: the co-citations of institutions.
J. Risk Financial Manag. 2021, 14, 302 26 of 33
The network of university collaboration is also well developed (see Figure 15),
indicating a strong presence of Chinese, U.S., and Indian universities. It is interesting to
notice a slight geographical concentration of China and Europe, the U.S. and Canada.
Geography seems to be a factor in the collaborations.
Figure 15. University collaboration networks, overall period, and per year.
J. Risk Financial Manag. 2021, 14, 302 27 of 33
To conclude, we visualize the main items of three fields (e.g., authors, keywords,
journals) and how they are related through a so-called Sankey diagram. The three fields
plot in Figure 16 also reveals the rising importance of deep learning and neural networks
in finance and its most robust channel for articulating academic contributions, the Experts
Systems with Applications Journal for the overall period, and IEEE Access for most of the
latest five years.
Overall
2021
J. Risk Financial Manag. 2021, 14, 302 28 of 33
2020
2019
2018
J. Risk Financial Manag. 2021, 14, 302 29 of 33
2017
Figure 16. Three fields plot, overall period and per year.
In the past five years, IEEE Access has been a prominent vehicle for developing the
academic conversation on neural networks in finance and, most importantly, deep
learning in finance.
6. Conclusions
Neural networks in finance are becoming increasingly popular tools to analyze
financial market trends based on preprocessing and transforming a large amount of
information into machine-readable data. It would be a mistake to attribute this
development solely to the outstanding computing power and storage capacity growth.
ML can make essential contributions to the technical analysis of financial market
trends. It has a wide variety of applications: supervised, unsupervised, and semi-
supervised learning; reinforcement learning; inverse reinforcement learning; imitation
learning; self-learning; feature learning; sparse dictionary learning; anomaly detection,
etc. A subfield at the intersection of linguistics, computer science, and artificial
intelligence—Natural Language Processing (NLP)—has found numerous applications in
finance.
This article demonstrated the basic steps required to conduct a metadata-based SLR
in the finance field.
The method can help generate topic-specific existing knowledge, trends, and gaps
observed and the derivation of a conclusion suitable for policymakers and the scientific
community.
Indeed, in this article, we conducted a metadata-based systematic review of the
academic contributions to finance between 1990 and 2021. A metadata-based systematic
literature review complements more conventional approaches to systematic literature
reviews. It allows to collect more significant amounts of documents and then analyze the
current dynamics within the collected documents. This article leverages the text
information found in this dataset. Titles, abstracts, keywords, authors’ names, institutions,
and references are transformed into quantitative indicators. From there, using text-as-data
techniques such as NLP as well as graph theory, we could provide a mapping capturing
multiple dimensions. In particular, we used a theoretical framework that organizes the
literature's mapping through three dimensions: conceptual, intellectual, and social.
Beyond this mapping, we also used two techniques to deal with the data: NLP and graph
theory.
J. Risk Financial Manag. 2021, 14, 302 30 of 33
The results are a mapping of the literature through these three dimensions.
Researchers can use this mapping to select a sub-sample to perform the systematic
literature review of their choice.
This mapping is helpful for researchers, university administrators willing to
understand the evolution of the finance field, and policymakers. Concerning the latter,
the conversation in academic circles about machine learning in finance finds its parallel in
the financial industry with the development of the so-called fintech. It is relevant to map
collaboration networks both at the authors’ level and the institutional level for
policymakers. It is also relevant to be able to visualize the knowledge maps.
For further research, the appearance of artificial intelligence and machine learning,
in particular in finance, is quite attractive in the context of the old-time debate between
the theorists and the chartists. While the opposing theorists and chartists debate is still
relevant, we conjecture that ML techniques could shed some new light on theoretical
advancement. MLAs are not an atheoretical approach, as it is premised on inductive
reasoning, which generates causal relationships based on the state of information at the
moment of estimation. The main advantage of ML is the ability to process vast
information, simultaneously ignoring ideological standpoints or inclinations to a
particular school of thought.
Supplementary Materials: The following are available online at
www.mdpi.com/article/10.3390/jrfm14070302/s1.
Author Contributions: Conceptualization, T.W. and A.S.; methodology, T.W.; software, T.W.;
validation, T.W. and A.S.; formal analysis, T.W. and A.S.; investigation, T.W.; resources, T.W.; data
curation, T.W.; writing—original draft preparation, A.S.; writing—review and editing, A.S. and
T.W.; visualization, T.W.; supervision, T.W.; project administration, A.S. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments: The authors express their deep gratitude to CIRANO (Montreal, Canada),
Martin Paquette (CIRANO), Marine Leroi (CIRANO), and Aïchata Kone (HEC Montréal) for their
excellent support. The usual caveats apply.
Conflicts of Interest: The authors declare no conflict of interest.
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This article presents a systematic review of scientific publications on the ecosystem services topic with an integrated approach in Latin American countries for the period 1992-2017. Ecosystem services were subdivided into functional (provisioning, regulating, supporting and cultural) and thematic (biodiversity, hydrological, carbon capture, landscape, soil) classifications to demonstrate their mutual interconnection. An integrated approach was assumed when ecological, social, economic, and political dimensions converged within studies. As a methodological procedure, the sequence of the PRISMA protocol and a semantic network analysis were conducted to select and review scientific articles from two international scientific databases. The articles were characterized according their evolution over time, geographical location and predetermined analysis variables. The results highlight that the most frequently analyzed services were provisioning and regulating ecosystem services related to hydrological and biodiversity. The timeline showed an increasing number of integrated studies since 2009, with most case studies developed at regional and local scales for forest and fishing socio-ecological systems in Brazil, Mexico and Costa Rica. The studies were predominantly multidisciplinary, with clear public policy demand and governmental funding. Transdisciplinary studies have the potential to build plural and inclusive knowledge to improve the decision-making process and offer support for solutions to complex socio-environmental problems.