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The Trouble with Instruments: The Need for Pretreatment Balance in Shock-Based Instrumental Variable Designs
Credible causal inference in accounting and finance research often comes from natural experiments. These experiments can be exploited using several shock-based research designs, including difference in differences (DID), shock-based instrumental variable (shock-IV), and regression discontinuity. We study here shock-IV designs using panel data. We identify all shock-IV papers in two broad data sets and reexamine three of the apparently
strongest
papers—Desai and Dharmapala [Desai M, Dharmapala D (2009) Corporate tax avoidance and firm value.
Rev. Econom. Statist.
91:537–546.], Duchin et al. [Duchin R, Matsusaka J, Ozbas O (2010) When are outside directors effective?
J. Financial Econom.
95:195–214.], and Iliev [Iliev P (2010) The effect of SOX Section 404: Costs, earnings quality, and stock prices.
J. Finance
65:1163–1196.]. After we enforce covariate balance and common support for treated and control firms, the instruments in all three papers are unusable—they are no longer significant in the first stage. All three papers also show nonparallel pretreatment trends on outcomes or core covariates. The problems with these papers generalize to our full sample and to other papers exploiting the same shocks as Duchin et al. A core conclusion of our reexamination is that pretreatment balance (common support, covariate balance, and parallel pretreatment trends) is necessary for credible shock-IV designs. We provide a good-practice checklist for shock-IV design with panel data, much of which also applies to DID designs.
This paper was accepted by Shiva Rajgopal, accounting.
Journal Article
When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks
Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10-Ks during 1994 to 2008, almost three-fourths of the words indentified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10-K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.
Journal Article
Digital Transformation as a Driver of the Financial Sector Sustainable Development: An Impact on Financial Inclusion and Operational Efficiency
The increase in studies on how digital transformation based on the application of digital technologies affects the sustainable development of various sectors of the economy has been observed. Although digital transformation is important for the financial sector sustainable development, the drivers and links between them are weakly addressed by researchers. The study is aimed at exploring how digital transformation due to the application of innovative technologies and solutions, especially digital payments, is leading to the financial sector sustainable development through financial inclusion and operational efficiency. The current research presents the study of the financial sector digital transformation and its sustainable development based on a systematic literature review, a secondary data analysis, and expert interviews to provide further research directions and draw practical suggestions for professionals on the financial sector digital transformation toward sustainable development in the future. A systematic literature analysis is performed based on text analytics, a bibliometric analysis, and network maps aimed at acknowledging the existing research outcomes and identifying the research gaps on the digital transformation agenda in the financial sector. The collected data on the digital payments’ dynamic in the EU were analyzed with the use of statistical methods, including a correlation and regression analysis. Structured expert interviews were used to validate research findings and to highlight key issues of the digital transformation in the financial sector of Baltic countries. The authors have paid special attention to the sustainable development of the financial sector’s economic dimension and its efficiency indicators, such as financial inclusion and digital payments’ intensity. A social dimension is limited toward financial inclusion based on digital payments’ offering. The research results indicated recent trends in digital transformation and types of usage of digital technologies in the EU and Baltic countries to ensure the sustainable development of financial institutions. Furthermore, the results revealed a significant increase in the digital payments’ intensity during the last years in the EU, as well as a close relationship between digital payments with financial inclusion and operational efficiency of financial institutions.
Journal Article
The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis
In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.
Journal Article
Fintech, regtech, and financial development: evidence from China
This study investigates the influence of fintech on developments in China’s financial sector across 290 cities and 31 provinces between 2011 and 2018. Using a two-stage least squares instrumental variable regression approach and correcting for cross-sectional dependency, simultaneity, and endogeneity of regressors, the results establish a positive link between fintech and financial development. Our findings show that fintech supports financial sector development by enhancing access (loans), depth (deposits), and savings within China’s financial institutions. We also show that the emergence of fintech in the area of financial regulation (regulatory technology: regtech) can significantly improve financial development outcomes. Therefore, it is imperative for regulators to pursue policies that balance growth in the fintech sector while mitigating the associated risks. In addition, we use the difference-in-differences approach to show that policy measures such as interest rates liberalization also positively impacted financial development during the analysis period. In our conclusion, we suggest a policy framework for balanced fintech sector growth in developing countries.
Journal Article
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
Journal Article
Investigating the Impact of Financial Inclusion Drivers, Financial Literacy and Financial Initiatives in Fostering Sustainable Growth in North India
The present study examines how successful we are in achieving financial inclusiveness, investigating the influence of the drivers of financial inclusion (FI), financial literacy, and financial initiatives on sustainable growth. The drivers of FI considered are digitalization, technology, and usage. This study proceeds with a difference and investigates the impact of the drivers on sustainable growth through the mediation of financial literacy. The basic purpose is to understand whether mediation assists in enhancing the impact of the drivers of FI on sustainable growth. Sustainable growth is measured by knowing customers’ perceptions regarding FI success through the achievement of the SDGs, viz., SDGs 1, 3, 5, 8, 9, 10, 11, and 17, especially related to poverty alleviation; removing gender inequality; and promoting industrial growth. The study uses PLS-SEM modeling to investigate the impact of the drivers of FI, financial literacy, and financial initiatives on sustainable growth. The results highlight that usage, digitalization, and FinTech emerged as significant drivers of FI. The study assesses the direct impact of the drivers of FI on sustainable growth and the indirect effect through the mediation of financial literacy. This is indicative of the importance of financial literacy in accentuating the impact of the drivers on sustainable growth. However, financial initiatives positively impact sustainable growth in the northern region of India as well.
Journal Article