Machine learning methods in finance: Recent applications and prospects

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Drivers of economic and financial integration: A machine learning approach

We propose a new approach to identifying drivers of economic and financial integration, separately, and across emerging and developed countries. Our advanced machine learning technique allows for nonlinear relationships, corrects for over-fitting, and is less prone to noise. It also can tackle a large number of highly correlated explanatory variables and controls for multicollinearity. Results suggest that general economic growth, increasing international trade, and contained population growth have helped emerging countries catch up to the level of the economic integration of developed countries. However, slow financial development and a high level of investment riskiness have hindered the speed of emerging countries’ financial integration. Furthermore, the results suggest that integration is a gradual process and is not driven by cyclical or transitory events.     

The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform

There have been concerns about the use of alternative data sources by fintech lenders. We compare loans made by LendingClub and similar loans that were originated by banks. The correlations between the rating grades (assigned by LendingClub) and the borrowers’ FICO scores declined from about 80% (for loans originated in 2007) to about 35% for recent vintages (originated in 2014–2015), indicating that nontraditional data (not already accounted for in the FICO scores) have been increasingly used by fintech lenders. The rating grades perform well in predicting loan default. The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into “better” loan grades, allowing them to obtain lower priced credit.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

大数据应用于信用评分模型的实践与启示

传统征信业务必因大数据而发生改变,大数据将为现有征信体系增加海量数据来源并推动普惠金融的发展。但是,由于存在个人隐私权保护、信贷风险控制及管理等限制因素,大数据技术最终如何实现与征信业务的完美结合以及究竟对传统征信业带来何种程度的影响,仍需要时间的检验。     

Internet Finance: Its Uncertain Legal Foundations and the Role of Big Data in Its Development

Internet finance has made significant progress in China. At the same time, it also suffers from legal gaps and inconsistencies. Traditionally, legislation regulates the emerging internet financial market by distinguishing between legal and illegal activities. Users of internet finance engage in regulatory arbitrage and pursue short-term profits, which distort the market. Regulations over internet finance should conform to market logic and utilize informational mechanisms and big data to reduce fraudulent information and market friction, ensuring market transparency, competition, and fair pricing.     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

基于大数据的餐饮业顾客体验与品牌引力耦合协调研究——以长沙市餐饮业为例

在分别构建餐饮业顾客体验和品牌引力评价指标体系的基础上,运用耦合协调度评价模型,以长沙市餐饮业为例,利用餐饮大数据进行实证分析。研究表明:长沙市餐饮业顾客体验与品牌引力整体上处于拮抗耦合、勉强耦合协调阶段;其九个行政区域的耦合协调度有所差异。鉴此,企业应改进关键因素,持续增强品牌引力;确保品牌质量,提升顾客消费体验;不同区域发展方向应各有侧重;减少虚假评论,抵制竞价排名,从而推动长沙市乃至全国餐饮业更高质量和更高水平发展。     

Trust in,trust out: a real cost of sudden and significant financial loss

This study examines the loss of trust that occurs when individuals suffer from sudden and significant financial loss. We use a qualitative case study to show that individuals lose trust in a range of parties, including financial advisors, banks, credit providers, government and perhaps most damagingly of all, oneself. Such outcomes are concerning as all financial services are based on trust between various parties, and trust is important in making financial decisions. A lack of trust can lead to poorer individual and societal outcomes. It also suggests that trends to financial self‐sufficiency have risks, which impact well beyond monetary losses.     

我国大数据征信行业的发展现状、问题与对策

近年来,我国大数据征信广泛应用于反欺诈策略、信用评估、授信策略与风险定价、贷后管理等领域。针对我国现有征信模式存在的问题,提出明确信息采集范围、加快行业整合、推进应用场景多元化、提升个人信用意识等相应对策。     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

Explaining the (non-) adoption of advanced data analytics in auditing: A process theory

Audit firms are increasingly engaging with advanced data analytics to improve the efficiency and effectiveness of external audits through the automation of audit work and obtaining a better understanding of the client’s business risk and thus their own audit risk. This paper examines the process by which audit firms adopt advanced data analytics, which has been left unaddressed by previous research. We derive a process theory from expert interviews which describes the activities within the process and the organizational units involved. It further describes how the adoption process is affected by technological, organizational and environmental contextual factors. Our work contributes to the extent body of research on technology adoption in auditing by using a previously unused theoretical perspective, and contextualizing known factors of technology adoption. The findings presented in this paper emphasize the importance of technological capabilities of audit firms for the adoption of advanced data analytics; technological capabilities within audit teams can be leveraged to support both the ideation of possible use cases for advanced data analytics, as well as the diffusion of solutions into practice.     

In search of meaning: Lessons,resources and next steps for computational analysis of financial discourse

We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of studies applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four tools are named entity recognition (NER), summarization, semantics and corpus linguistics.     

The choice of maturity and additional covenants in debt contracts: A panel data approach

This paper provides a comprehensive analysis for the choice of contract terms in UK Eurobonds. Typically, the theory associates the choice of debt contract terms to firm and market characteristics, arguing that an adequate choice of these terms allows for the reduction of debt contracting costs. We use a panel data approach to examine the validity of extant predictions concerning the choice of maturity, call options, convertible options and protective covenants. Findings provide support to the agency prediction that debt contract terms function as alternative control mechanisms. Additionally, complementary role is found for the use of convertible and call options. Evidence that managers follow a maturity-matching rule, favour capital structure's flexibility in high growth scenarios and use protective covenants when firm's credibility is low corroborates further agency predictions.     

Impact of the stock market capitalization and the banking spread in growth and development in Latin American: A panel data estimation with System GMM

This research is aimed at assessing the impact of the stock market capitalization and the banking spread in per capita economic growth (as a proxy of economic development) in the major Latin American economies during period 1994–2012. To do this, a panel data model is estimated with both system and difference Generalized Method of Moments. The main empirical findings are that economic growth in the countries under study is positively impacted by the stock market capitalization and negatively by the banking spread. Typical problems of multicollinearity and autocorrelation appearing in panel data analysis are corrected under the proposed methodology.