经济学中的数据科学：机器学习与深度学习方法

in data science in emerging economic applications. The analysis is performed on the novel data

science methods in four individual classes of deep learning models, hybrid deep learning models,

hybrid machine learning, and ensemble models. Application domains include a broad and diverse

range of economics research from the stock market, marketing, and e-commerce to corporate banking

and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the

quality of the survey. The findings reveal that the trends follow the advancement of hybrid models,

which outperform other learning algorithms. It is further expected that the trends will converge

toward the evolution of sophisticated hybrid deep learning models.

machine learning; deep reinforcement learning; big data; bitcoin; time series; network science;

economics, has been increasing exponentially [

1

economics applications have been progressive with promising results [

,

data science applications in economics can be categorized and studied in various popular technologies,

such as deep learning, hybrid learning models, and ensemble algorithms [

4

]. Machine learning

(ML) algorithms provide the ability to learn from data and deliver in-depth insight into problems [5].

Researchers use machine learning models to solve various problems associated with economics. Notable

applications of data science in economics are presented in Table 1. Deep learning (DL), as an emerging

field of machine learning, is currently applied in many aspects of today’s society, from self-driving cars to

image recognition, hazard prediction, health informatics, and bioinformatics [

5

,

6

]. Several comparative

method to maximize the prediction function [

8

]. It has been demonstrated that the hybrid machine

accuracy [

power generation forecasting [

30

]. Exploring the scientific databases such as Thomson Reuters

Web-of-Science (WoS) shows an exponential rise in using both DL and ML in economics. The results of

an inquiry of essential ML and DL in the emerging applications to economics over the past decade is

illustrated in Figure 1. Even though many researchers have applied DS methods to address different

problems in the field of economics, these studies are scattered. At the same time, no single study

provides a comprehensive overview of the contributions of DS in economic-related fields. Therefore,

study is to investigate the advancement of DS in three parts: deep learning methods, hybrid deep

learning methods, and ensemble machine learning techniques in economics-related fields. The present

work aims to answer the following research questions. (1) what are the emerging economics domains

with the involvement of data science technologies? (2) what are the popular data science models and

applications in these domains?

3.1.1. Stock Market

Applying deep learning in the stock market has become more common than in other economics

category (37 out of 57). Table 2 summarizes the articles that employed predictive models in stock market

the stock market is profitable, while the higher the profit, the higher the risk. Therefore, investors always

to be the objective of 29 out of 37 articles. Other studies aimed at applying DS in sentiment analysis,

or the analysis of the context of texts to extracts subjective information, to identify future trends in the

stock market. In addition, portfolio management, algorithmic trading (i.e., using a pre-programmed

automated system for trading), automated stock trading, socially responsible investment portfolios,

the S&P 500 index trend prediction, and exchange-trade-fund (EFT) options prices prediction were

the objectives of other articles that projected to employ DS methods. Financial time series served as

the data source of all these studies, except for the studies aimed at sentiment analysis, which used

different data sources, such as social media and financial news.

LSTM

Long short-term memory (LSTM) networks are a special kind of recurrent neural network (RNN)

state are updated. The computations involved are described below: