Decoding currency dynamics: A multiscale machine learning approach integrating economic indicators, ESG, and investor sentiment

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.

Prior research has shown that a firm’s tendency to meet or beat earning targets is greater during bad economic times than good times. The paper extends this line of research by investigating which means of earnings management is used in different states of economy. A sample of non-financial companies listed on Korea Securities Market from 2003 to 2011 is used for empirical tests. The findings of this study are summarized as follows. The magnitude of discretionary accruals is negatively related to investment sentiment, indicating that firms tend to use positive discretionary accruals to manipulate reported income upward when the sentiment is pessimistic. However, the real activity based earnings management is not significantly associated with the state of economy. Collectively, this study contributes to behavioral finance and accounting literature by suggesting that managers use discretionary portion of accruals, but do not change their real operating activities, in order to meet or beat earnings targets in economic downturn.