263 research outputs found
Deep learning for trading and hedging in financial markets
Deep learning has achieved remarkable results in many areas, from image classification, language translation to question answering. Deep neural network models have proved to be good at processing large amounts of data and capturing complex relationships embedded in the data. In this thesis, we use deep learning methods to solve trading and hedging problems in the financial markets. We show that our solutions, which consist of various deep neural network models, could achieve better accuracies and efficiencies than many conventional mathematical-based methods.
We use Technical Analysis Neural Network (TANN) to process high-frequency tick data from the foreign exchange market. Various technical indicators are calculated from the market data and fed into the neural network model. The model generates a classification label, which indicates the future movement direction of the FX rate in the short term. Our solution can surpass many well-known machine learning algorithms on classification accuracies.
Deep Hedging models the relationship between the underlying asset and the prices of option contracts. We upgrade the pipeline by removing the restriction on trading frequency. With different levels of risk tolerances, the modified deep hedging model can propose various hedging solutions. These solutions form the Efficient Hedging Frontier (EHF), where their associated risk levels and returns are directly observable. We also show that combining a Deep Hedging model with a prediction algorithm ultimately increases the hedging performances.
Implied volatility is the critical parameter for evaluating many financial derivatives. We propose a novel PCA Variational Auto-Enocder model to encode three independent features of implied volatility surfaces from the European stock markets. This novel encoding brings various benefits to generating and extrapolating implied volatility surfaces. It also enables the transformation of implied volatility surfaces from a stock index to a single stock, significantly improving the efficiency of derivatives pricing
Three essays on credit supply
This thesis consists of three independent essays on credit supply, each addressing different components, including the different impact of credit supply shocks financed through different supply channels, how different credit constraints impact debt structure and productivity, and how it affects their individual and collective exposure over time.
Chapter 1: Its conceptual appeal has made the Conditional Value at Risk (CoVaR) one of the most influential systemic risk indicators. Despite its popularity, an outstanding methodological challenge may hamper the CoVaRs’ accuracy in measuring the time-series dimension of systemic risk. The dynamics of the CoVaR are entirely due to the behaviour of the state variables and therefore without their inclusion, the CoVaR would be constant over time. The key contribution of this chapter is to relax the assumption of time-invariant tail dependence between the financial system and each institution’s losses, by allowing the estimated parameters of the model to change over time, in addition to changing over quantiles and different financial institutions. We find that the dynamic component that we introduce does not affect the estimations for the risk of individual financial institutions, but it largely affects estimations of systemic risk which exhibits more procyclicality than the one implied by the standard CoVaR. As expected, larger financial institutions have a higher effect on systemic risk, although they are also shown to be individually more robust. When adding balance sheet data, it introduces additional volatility into our model relative to the standard one. In terms of forecasting, the results depend on the horizon used or the variables included. There is no clear outperformance between either model when we add the balance sheet data, or in the short term (less than 12 weeks). However, our model outperforms the standard one for medium (between 15 and 25 weeks) to long term horizons (between 30 and 40 weeks).
Chapter 2: We seek to evaluate the impact of the different segments within the lending sector to the private non-financial sector can have on subsequent GDP growth. We isolate the bank lending channel as one of the main components, and group the remaining ones into a second segment which we classify as market based finance (MBF). We also include the 2 different segments of the borrowing sector, household debt and non-financial firm debt, to compare with the results obtained by the standard model. We debate the main source of these effects, and focus on either credit demand or credit supply shocks, in addition to other alternatives. We find that a rise in bank credit and/or household debt to GDP ratio lowers subsequent GDP growth. The predictive power is large in magnitude and robust across time and space. The bank credit booms and household debt booms are connected to lower interest rate spread environments, as well as periods with better financial conditions. And although the overall impact on subsequent GDP growth is negative, we found contrasting evidence when using the Financial Conditions Index (FCI) as an instrument. This would point to the potential different effects that bank credit and household debt could have on future economic growth (good booms vs bad booms), depending on the underlying cause of the boom. The results and the evidence that we found are more consistent with models where the fundamental source of the changes in household debt or bank credit lie in changes in the credit supply (credit supply shocks), rather than credit demand or other possibilities. This would likely be connected to incorrect expectations formation by lenders and investors (what many authors classify as “credit market sentiment” in the literature), which is an important element in explaining shifts in credit supply. Although credit demand shocks could play an important role in prolonging or amplifying the effects of the booms, it is unlikely that they are the source, as it would lead to results that conflict with empirical evidence. Finally, we find some differences in terms of statistical significance and magnitude in the different scenarios, where the bank credit shows more robustness to different specifications than the household debt. This would imply that there is a significance of the bank credit that goes well beyond the household debt. It would also mean that the main component that generates the boom bust cycle in GDP would be the bank credit, independent of its destination, rather than household debt, independent of its financing.
Chapter 3: We construct a dataset at the firm-year level by merging the syndicated loan data, provided by Refinitiv LPC DealScan ("DealScan"), with the firm level data, provided by Center for Research in Security Prices (CRSP)/Compustat Merged Database ("CCM"). We conduct an analysis on firms subjected to different covenants, and find that firms with earnings-based constraints have lower levels of TFP (Total Factor Productivity), and short-term debt, when compared to firms with asset-based constraints. The data also shows that this is connected to an additional negative impact that short-term debt has on the productivity for the firms with earnings based constraints, which does not verify in the firms with asset-based constraints. Both these characteristics are robust to the use of 3 different TFP estimation methods, different subsamples, and additional controls, including age and size of the firm. Thus, we consider a quantitative dynamic stochastic partial equilibrium model, with three main types of firms, distinguished by their constraints, which explores the impact of short-term and long term borrowing on firm’s balance sheets, on the different variables. We construct replications for this theoretical model, and assess the how well it fits our actual data. Our findings show that constraints exert an impact on short-term borrowing, but not on the remaining variables. More specifically, firms that face an earnings-based constraint show lower levels of short-term borrowing, compared with firms that are either unconstrained, or asset-based constraint. The adjustment is made through lower dividend distribution, as can be seen by the lower values of the value function. They also point to the impact being larger for firms with lower productivity shocks, which is in accordance withour empirical findings. Even though that our data shows
differences in some of this variables (for example, on long-term debt), these were not robust to some of the controls, including the size of the firm
COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory
Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination
International Academic Symposium of Social Science 2022
This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate
Modern trends in digital transformation of marketing & management
The monograph examines the current trends in the development of digital technologies in marketing, management and business administration. The prospects for the development of digital technologies in various sectors of the economy of Ukraine and the trends of the influence of digital technologies on global shifts in the systems of marketing management and business administration are determined. The transformations of business models in the conditions of the digital economy are analyzed, the impact of blockchain technologies on the development of promising areas of the marketing management system and business administration is analyzed. Reasonable impact of digital technologies on the transformation of management systems in social, public, legal and administrative spheres and various sectors of the economy. The contours of the formation of the digital economy in the sectors of economic activity and the social sphere have been developed
Analytics and Intuition in the Process of Selecting Talent
In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions
Firm‐level climate change exposure
We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
- …