Optimal Machine Learning- and Deep Learning- driven algorithms for predicting the future value of investments: A systematic review and meta-analysis (preprint)

The COVID-19 pandemic and the increasing competitive landscape have led asset management companies to consider investing in applying Artificial Intelligence (AI)-driven technologies to minimise the risk and maximise the profitability of the investment funds they manage. Thus, a systematic review and a meta-analysis of the relevant literature were conducted to provide evidence-based informed recommendations on which AI-driven technologies could be leveraged for such purpose. Data on both Machine Learning (ML)- and Deep Learning (DL)-driven technologies applied to aid the management of investment funds in China and, specifically, in and around Shenzhen, were pooled from eleven eligible and recent studies (since 15 September 2017) and analysed accordingly. The key business-relevant and human-interpretable metrics representing their performance were identified in the root mean squared error (RMSE), in the same unit of currency of the investment funds, and the correlation strength between the predicted and actual values. One ML- and one DL-based algorithms were recommended to be used in the short and long terms respectively. In particular, the ML-based Gradient Boosting Decision Tree (GBDT) algorithm was found the most accurate in the relevant literature, e.g., 28.16% more accurate than the Support Vector Regressor (SVR), also having a highly competitive ability to capture trends in the actual values of investment funds (83.7% of correlation strength), whilst the Long-Short Term Memory (LSTM)-GBDT model was identified as the most accurate DL-based algorithm, 15.05% more accurate than the GBDT and with 13.2% higher ability to capture trends in the actual investment funds’ values than the GBDT.

Performance Management in the Virtual Workplace During the COVID-19 Pandemic and Beyond (preprint)

Previous research has either neglected to consider misbehaviour or misconduct in the virtual workplace during the current COVID-19 pandemic or highlighted some challenges encountered with remote working or work from home but in isolation, e.g., misbehaviours or ethical concerns associated with improper use of technology when accessing clients’ confidential information, without understanding their aetiology and context in a holistic manner. This has impacted the ability of existing research in providing recommendations organisations can practically implement to improve the sustainability and productivity of the virtual workplace, especially as they seek to navigate the current pandemic and prepare for the global business environment in the next decade. Instead, this study, for the first time, proposes a novel holistic methodology to identify the aetiologies of ethical and performance challenges with focus to the Information Technology consulting sector, as well as developing a set of recommendations companies can adopt to manage such behavioural and productivity challenges. Thus, this study illustrates the key issues associated with remote working implemented to respond to the current COVID-19 pandemic, which mine not only the individual performance in an organisation but also its overall productivity, as well as suggests the main performance and reward management strategies that can be leveraged to mitigate them. Tailored strategies are described and discussed as referred to this industry to help in navigating a more unstable yet competitive market to manage the virtual employees’ misbehaviours in the workplace and drive growth for individuals and organisations alike.

FMSI guidelines for return to physical activity after coronavirus pandemics lockdown

The lockdown period has certainly put a strain on our psychophysical health and for many it has represented a problem of functional efficiency of our body. In this document the Italian Sports Medicine Federation (FMSI) makes recommendations for the development and the performance of physical exercise sessions for the general population obliged to stay at home, due to the lockdown caused by the Coronavirus pandemics. Such recommendations are made in terms of intensity, frequency, volume and exercise modality, applying what has already been suggested by the Federation itself in the recent Italian Prescription Guidelines for Physical Activity for Healthy Adults. The proposed reconditioning program suggests the respect of a gradual return to physical activity, starting from at least 30-45 minutes every day up to a total of 150-300 minutes per week, combined with muscle strengthening and flexibility exercises. The intensity of the work must vary according to the individual's physical conditions and age, recommending activities of moderate intensity for most of the week, with a part of the individual sessions carried out at a higher intensity. If the period of isolation led to an increase in body weight, a return to normal weight is absolutely recommended, combining the returning to an active lifestyle with a controlled diet, moderately low in calories and rich in vitamins and with attention to appropriate hydration. Furthermore, an evaluation by a sports medicine specialist may be useful for a correct classification of health conditions and actual physical possibilities, especially for those with chronic diseases' risk factors or people over 50 years of age. For people who have been suffering from Covid-19, after the approval of the infectious disease specialist, but before returning to any kind of physical activity, an evaluation by the specialist is certainly advisable.