Return Predictability from Industry Network Effects: Evidence from Rolling Window Adaptive Lasso

2020 ◽  
Author(s):  
Wentao Li
Keyword(s):  
Network Effects ◽  
Return Predictability ◽  
Adaptive Lasso ◽  
Rolling Window ◽  
Industry Network

Adaptive calendar effects and volume of extra returns in the cryptocurrency market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sashikanta Khuntia ◽  
J.K. Pattanayak
Keyword(s):  
Return Predictability ◽  
Significant Implication ◽  
Content Type ◽  
Calendar Effects ◽  
Year Effect ◽  
Rolling Window ◽  
Window Approach ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Practical Implications ◽  
Better Than

PurposeThis study broadly attempts to explore adaptive or dynamics patterns of calendar effects existed in the cryptocurrency market as per the adaptive market hypothesis (AMH) framework. Another agendum of this study is to investigate the quantum of extra returns which may result from the presence of calendar effects.Design/methodology/approachThe present study considers both parametric and non-parametric approaches to verify calendar effects empirically. Specifically, this study has implemented Generalised Autoregressive Conditional Heteroscedasticity (1, 1) and Kruskal–Wallis tests in the rolling window approach to reveal adaptive patterns of calendar effects. Additionally, the present study has used the implied trading strategy to evaluate the volume of excess returns resulted from calendar effects than buy-and-hold (BH) strategy.FindingsThe overall results of the current study exhibit that calendar effect in the cryptocurrency market is dynamic rather than static which indicates the calendar effect is a time-varying phenomenon. Moreover, this study also confirmed that ITS is not suitable to obtain extra returns despite the existence of calendar effects.Research limitations/implicationsThe present study has covered some broad aspects of calendar anomalies in the cryptocurrency market, keeping aside certain other limitations which need to be addressed in the following dimensions. Future studies may aim at addressing issues like, Turn-of-the-Year effect, Halloween effect, weather effect, and Month-of-the-Year effects, and try to explore the reasons of presence of dynamic patterns of calendar effects.Practical implicationsThe significant implication of this study is that it alerts investors about market return predictability due to calendar patterns or effects in different periods. It also suggests the period in which the ITS can perform better than the BH strategy.Originality/valueIt is the first study in the cryptocurrency literature which has adopted the AMH framework to verify adaptive calendar effects or anomalies. Furthermore, this study, instead of a mere examination of the presence of calendar effects, has evaluated the potential of calendar effects to produce extra returns through trading strategies.

scholarly journals African Stock Markets and Return Predictability

2016 ◽  
Vol 8 (5(J)) ◽  
pp. 91-99
Author(s):  
Gyamfi NE ◽  
Kyei KA ◽  
Gill R
Keyword(s):  
Time Series ◽  
Stock Markets ◽  
Weak Form ◽  
Return Predictability ◽  
Stationary Time Series ◽  
Conditional Heteroscedasticity ◽  
Rolling Window ◽  
Linear And Nonlinear ◽  
Window Approach ◽  
Over Time

This article re-examines the return predictability of eight African stock markets. When returns of stocks are predictable, arbitrageurs make abnormal gains from analyzing prices. The study uses a non-parametric Generalised Spectral (GS) test in a rolling window approach. The rolling window approach tracts the periods of efficiency over time. The GS test is robust to conditional heteroscedasticity and it detects the presence of linear and nonlinear dependencies in a stationary time series. Our results support the Adaptive Market Hypothesis (AMH). This is because, indices whose returns were observed to be predictable by analyzing them in absolute form and therefore weak - form inefficient showed trends of unpredictability in a rolling window.

scholarly journals Likelihood theory for the graph Ornstein-Uhlenbeck process

2021 ◽  
Author(s):  
Valentin Courgeau ◽  
Almut E. D. Veraart
Keyword(s):  
Lévy Process ◽  
Network Effects ◽  
Asymptotic Theory ◽  
Maximum Likelihood Estimators ◽  
Levy Process ◽  
Adaptive Lasso ◽  
Graph Structure ◽  
Uhlenbeck Process ◽  
Penalised Likelihood ◽  
The Impact

AbstractWe consider the problem of modelling restricted interactions between continuously-observed time series as given by a known static graph (or network) structure. For this purpose, we define a parametric multivariate Graph Ornstein-Uhlenbeck (GrOU) process driven by a general Lévy process to study the momentum and network effects amongst nodes, effects that quantify the impact of a node on itself and that of its neighbours, respectively. We derive the maximum likelihood estimators (MLEs) and their usual properties (existence, uniqueness and efficiency) along with their asymptotic normality and consistency. Additionally, an Adaptive Lasso approach, or a penalised likelihood scheme, infers both the graph structure along with the GrOU parameters concurrently and is shown to satisfy similar properties. Finally, we show that the asymptotic theory extends to the case when stochastic volatility modulation of the driving Lévy process is considered.

scholarly journals Adaptive Market Hypothesis and Artificial Neural Networks: Evidence from Pakistan

2019 ◽  
Vol IV (II) ◽  
pp. 190-203 ◽  
Author(s):  
Sehrish Kayani ◽  
Usman Ayub ◽  
Imran Abbas Jadoon
Keyword(s):  
Neural Network ◽  
Stock Return ◽  
Equity Market ◽  
Return Predictability ◽  
Stock Return Predictability ◽  
Rolling Window ◽  
Changing Patterns ◽  
Nonlinear Autoregressive ◽  
Repeating Patterns ◽  
Autoregressive Neural Network

The debate covering stock return predictability is now shifted towards the investigation of changing patterns of return predictability as suggested by the adaptive market hypothesis (AMH). The present article inspects the varying return predictability pertaining to the equity market in Pakistan under AMH framework. A nonlinear autoregressive neural network (NARNN) model is employed to investigate the nonlinear dependency of returns over a period of eighteen years. NARNN is a robust and flexible technique that is free from any restrictive assumptions. Under a rolling window framework, the repeating patterns of predictability and unpredictability are observed. This finding confirms the idea of AMH.

Transformation of education as a metatrend of the digital economy

2020 ◽  
Vol 16 (10) ◽  
pp. 1960-1979
Author(s):  
N.A. Egina ◽  
E.S. Zemskova
Keyword(s):  
Human Capital ◽  
Social Transformation ◽  
Network Effects ◽  
Digital Economy ◽  
Network Education ◽  
Economic Progress ◽  
Positive Externalities ◽  
Communication Processes ◽  
Education Network ◽  
The Impact

Subject. The study focuses on the impact of the digital economy determinants of the education transformation. Objectives. The article provides our own approach treating the education capital as a specific asset of the digital economy, which has an acceleration effect and sets up new trends in education through integrative networks. Methods. The study is based on principles of the systems integration, cross-disciplinary and multidisciplinary approaches. Results. The socio-economic progress was found to be determined with properties of human capital, which are solely specific to the digital economy. In new circumstances, it gets more important for actors of global, national, corporate and social networks to more actively cooperate within distributed networks in order to train high professionals, who would have skills in information networks. Thus, they would raise a new form of human capital – the capital of network education (network-based education capital). We describe positive externalities that arise when the educational sector joins communication processes. We illustrate how educational forms evolves, which are typical of a certain phase of the socio-economic development. The education capital was discovered to grow into a specific asset generating the quasi-rent and working as a social ladder only provided more actors are involved into the network. Conclusions and Relevance. Studying the evolution of educational forms through the cross-disciplinary method, we discovered the need for a system approach, which would help substantiate its transformation in the time of the digital economy, and the emergence of network-based education. These are technologies and tools of the digital economy that become unique factors generating the acceleration effect of the educational capital and ensuring the use of diverse network effects for the formation of intellectual capital and their social transformation.

Principles for sharing economy formation in the digital society

2020 ◽  
Vol 16 (5) ◽  
pp. 800-821
Author(s):  
E.V. Popov ◽  
K.A. Semyachkov
Keyword(s):  
Network Effects ◽  
Sharing Economy ◽  
Network Effect ◽  
Industrial Society ◽  
Economic Activities ◽  
Digital Platforms ◽  
Digital Society ◽  
Modern Economy ◽  
Characteristic Features ◽  
The Impact

Subject. The article addresses economic relations that are formed in various areas of economic application of digital platforms. The target of the research is the modern economy of digital platforms across different economic activities. Objectives. The aim is to systematize principles for share economy formation in the context of the digital society development. Methods. We employ general scientific methods of research. Results. The study shows that the development of digital platforms is one of the most important trends in the development of the modern economy. We classified certain characteristic features of modern digital platforms, analyzed principles for their creation. The paper emphasizes that the network effects achieved through the use of digital platforms are an important factor in the development of the share economy. The network effect describes the impact of the number of the platform users on the value created for each of them. The paper also considers differences in the organization of traditional economy companies and companies that are based on the digital platform model, reveals specifics of changes in socio-economic systems caused by the development of digital platforms, systematizes principles of the sharing economy formation in the context of the digital society development. Conclusions. The analyzed principles for sharing economy development on the basis of digital platforms can be applied to create models for the purpose of forecasting the transformation of economic activity in the post-industrial society.

Economic Institutions and Growth Nexus: The Role of Governance and Democratic Institutions—Evidence from Time Varying Parameters’ (TVPs) Models

2016 ◽  
Vol 55 (4I-II) ◽  
pp. 675-688
Author(s):  
Ghulam Murtaza ◽  
Muhammad Zahir Faridi
Keyword(s):  
Structural Changes ◽  
Jel Classification ◽  
Economic Institutions ◽  
Time Varying ◽  
Democratic Institutions ◽  
Main Hypothesis ◽  
Dynamic Relationship ◽  
Rolling Window

The present study has investigated the channels through which the linkage between economic institutions and growth is gauged, by addressing the main hypothesis of the study that whether quality of governance and democratic institutions set a stage for economic institutions to promote the long-term growth process in Pakistan. To test the hypothesis empirically, our study models the dynamic relationship between growth and economic institutions in a time varying framework in order to capture institutional developments and structural changes occurred in the economy of Pakistan over the years. Study articulates that, along with some customary specifics, the quality of government and democracy are the substantial factors that affect institutional quality and ultimately cause to promote growth in Pakistan. JEL Classification: O40; P16; C14; H10 Keywords: Economic Institutions, Growth, Governance and Democracy, Rolling Window Two-stage Least Squares, Pakistan

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