Intelligent Systems in Accounting, Finance and Management最新文献

The attempt to measure investors’ mood to find an early indicator of financial markets has evolved and developed with the advancement of technology over the years. The first attempts were based on surveys, a long and expensive process. Nowadays, big data has made it possible to measure the investor’s mood accurately and almost entirely online. This paper analyzes the explanatory and predictive capacity of Wikipedia pageviews for the Nasdaq index. For this purpose, two econometric models have been developed. In both models, the explanatory variable is the number of Wikipedia visits, and the endogenous variable is Nasdaq index return. As an alternative to this approach, an algorithmic trading system has been developed. It uses Wikipedia visits as investment signals for long and short positions to check the predictability power of this indicator. It is determined that the volume of queries about Nasdaq companies is a statistically significant variable for expressing the evolution of this index. However, it has no predictive capacity. Keeping in mind the capacity of Wikipedia to exemplify Nasdaq trends, further studies should be conducted to determine how to make this indicator profitable.

We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time-varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.

Dark pools are financial trading venues where orders are entered and matched in secret so that no order information is leaked. By preventing information leakage, dark pools offer the opportunity for large volume block traders to avoid the costly effects of market impact. However, dark pool operators have been known to abuse their privileged access to order information. To address this issue, we introduce a provably secure multi-party computation mechanism that prevents an operator from accessing and misusing order information. Specifically, we implement a secure emulation of Turquoise Plato Uncross, Europe's largest dark pool trading mechanism, and demonstrate that it can handle real world trading throughput, with guaranteed information integrity.

This paper uses an agent-based multi-asset model to examine the effect of risk preferences and optimal rebalancing frequency on performance measures while tracking profit and risk-adjusted return. We focus on the evolution of portfolios managed by heterogeneous mean-variance optimizers with a quadratic utility function under different market conditions. We show that patient and risk-averse agents are able to outperform aggressive risk-takers in the long-run. Our findings also suggest that the trading frequency determined by the optimal tolerance for the deviation from portfolio targets should be derived from a tradeoff between rebalancing benefits and rebalancing costs. In a relatively calm market, the absolute range of 6% to 8% and the complete-way back rebalancing technique outperforms others. During particular turbulent periods, however, none of the existing rebalancing techniques improves tax-adjusted profits and risk-adjusted returns simultaneously.

In an era of a continuous quest for business growth and sustainability it has been shown that synergies and growth-driven mergers and acquisitions (M&As) are an integral part of institutional strategy. In order to endure in the face of fierce competition M&As have become a very important channel of obtaining technology, increasing competitiveness and market share (Carbone & Stone, 2005; Christensen et al., 2011). During the post-2000 era, this is also a point where more than half of the said available growth and synergies in M&As are strongly related to information technology (IT) and its disruptive synergistic potential, as the first decade of the 2000s has shown (Sarrazin & West, 2011). Such business growth materializes at the intersection of internalizing, integrating, and applying the latest data management technology with M&As where there are vast opportunities to develop (a) new technologies, (b) new target screening and valuation methodologies, (c) new products, (d) new services, and (e) new business models (Hacklin et al., 2013; Lee & Lee, 2017). However, while technology and its disruptive capabilities have received considerable attention from the business community in general, studies regarding the examination of technology convergence, integration dynamics, and success of M&As from a market screening and valuation perspective are relatively scarce (Lee & Cho, 2015; Song et al., 2017). Furthermore, little attention has been devoted to investigating the evolutionary path of technology-assisted, target screening methods and understanding their potential for effective target acquisition in the future (Aaldering et al., 2019). We contribute to this by examining the application of neural network (NN) methodology in successful target screening in the US M&As IT sector.

In addition, while there are recognized idiosyncratic motivations for pursuing M&A-centered strategies for growth, there are also considerable system-wide issues that introduce waves of global M&A deals. Examples include reactions to globalization dynamics, changes in competition, tax reforms (such as the recent US tax reform indicating tax benefits for investors), deregulation, economic reforms and liberalization, block or regional economic integration (i.e., the Gulf Cooperation Council and the EU). Hence, effective target-firm identification is an important research topic to business leaders and academics from both management and economic perspectives.

Technology firms in particular often exhibit unconventional growth patterns, and this also makes firm valuation problematic as it can drive their stocks being hugely misvalued (i.e., overvalued) therefore increasing M&A activity (Rhodes-Kropf & Viswanathan, 2004). Betton et al. (2008) claimed that predicting targets with any degr