scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

A Study on Investment Decision of Investors With Reference to Penetration of Mutual Funds in India

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Ratish C Gupta ◽  
Dr. Manish Mittal
Keyword(s):  
Decision Making ◽  
Real Estate ◽  
Mutual Funds ◽  
Capital Markets ◽  
Mutual Fund ◽  
Financial Services ◽  
Investment Decision ◽  
Fund Industry ◽  
Mutual Fund Industry ◽  
The Impact

The Indian mutual fund industry is one of the fastest growing and most competitive segments of the financial sector. The extent of under-penetration in the market is a sore point with the financial services industry, with a large amount of savings being channelized into fixed deposits, gold and real estate rather than the capital markets. The mutual fund industry is yet to spread its reach beyond Tier I cities. The top fifteen cities contribute to 85% of the pie, with the remaining 15% distributed among other cities. The study seeks to determine the impact of decision making of investors on current situation of mutual fund industry.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

scholarly journals Impact of Heuristics on Investment Decisions: The Moderating Role of Locus of Control

2020 ◽  
Vol 5 (1) ◽  
pp. 1-14
Author(s):  
Jeetendra Dangol ◽  
Rashmita Manandhar
Keyword(s):  
Decision Making ◽  
Locus Of Control ◽  
Investment Decision ◽  
Investment Decisions ◽  
Sampling Procedure ◽  
Moderating Effect ◽  
Moderation Effect ◽  
Investment Decision Making ◽  
Overconfidence Bias ◽  
The Impact

This paper aims to assess the impact of heuristics on the investment decision by analysing the effect of four heuristic biases, i.e., representativeness, availability, anchoring and adjustment, and overconfidence bias on rationality of Nepalese investor's investment decision-making and also examines the moderating effect of the internal locus of control in between. The study used 391 respondents based on a convenient sampling procedure, and structured questionnaire survey. The study result indicates that there is a significant relationship between irrationality in investment decision-making and all four heuristic biases. In addition, the study also concludes that locus of control has significant moderating effect in the relationship between investment decisions and three heuristic biases, i.e., availability, representative and anchoring bias. However, the study documents no moderation effect in case of relationship with overconfidence bias.

Analytical redefinition of DOL and managerial investment decisions

2014 ◽  
Vol 40 (7) ◽  
pp. 734-754 ◽  
Author(s):  
Yoram Kroll ◽  
David Yechiam Aharon
Keyword(s):  
Large Scale ◽  
Investment Decision ◽  
Production Level ◽  
Fixed Cost ◽  
Theoretical Formulation ◽  
Optimal Production ◽  
Demand Shocks ◽  
Content Type ◽  
Operating Leverage ◽  
The Impact

Purpose – The purpose of this paper is to develop alternative analytical measures for the degree of operating leverage (DOL) that reflect the impact of uncertain demand shocks in the product's market on optimal production levels, sales and profits of the firm. Design/methodology/approach – The elasticity measures are constructed according to a theoretical formulation of optimal production level that corresponds to demand shocks for given predetermined levels of fixed cost. Findings – The paper suggests two main findings. First, the analytical marginal DOL is at least twice the traditional DOL depending on the structure of the shock, the production function and demand's elasticity. The traditional DOL is equal to the measure only when large-scale negative demand prompts the firm to abandon production. Second, the paper also provides an analytical measure of DOL in terms of elasticity of profit to sales rather than to production level. Both theoretically and empirically elasticity of profit to sales can be better measured and better reflects risk. Research limitations/implications – This paper should be extended to encompass multiple shocks on demand and supply while investigating the empirical multi variants distribution of the shocks. Practical implications – The model can be used by managers who are well informed about the fixed and variable costs of their firm. The model determines the mean profit- risk trade off which is an important factor in all investment decision problems. Originality/value – Surprisingly and according to the best knowledge, this paper is the first attempt in the literature for alternative analytical DOLs’ formulations that is coherent with basic economic theories of optimal production level under risk.

The “Surge Phenomenon” of Macro Economy in Developing Countries from the Perspective of Tobacco Economy

2021 ◽  
Vol 7 (4) ◽  
pp. 663-673
Author(s):  
Lulu Liu
Keyword(s):  
Developing Countries ◽  
Tobacco Industry ◽  
Investment Decision ◽  
Relevant Literature ◽  
Economic Benefits ◽  
Social Costs ◽  
Advantages And Disadvantages ◽  
The Social ◽  
The Government ◽  
The Impact

Objectives: Starting from the tobacco economy, this paper studies the “surge phenomenon” of macro-economy in developing countries. Methods: This paper studies the impact of tobacco industry on Anhui economy by using the relevant theories of industrial economics, econometrics and regulatory economics, combined with the actual situation of tobacco industry. Based on the analysis of the overall development of tobacco industry, this paper empirically analyzes the relationship between tobacco industry and Anhui economic growth. This paper combs the relevant literature of the existing research results of this theory. Combined with the special fact that government investment accounts for a large proportion in China’s current economic construction, this paper redefines the hypothesis of the investor in the theory of principles. On this basis, the expected equilibrium results of enterprise investment decision-making under government led and market led modes are compared and analyzed by using incomplete information static game model. Results: When the output value of tobacco industry increases by 1%, it will drive the GDP to increase by 0.373%. Secondly, by comparing the economic benefits of tobacco with the social costs of tobacco, it is found that with the economic development, the social costs caused by tobacco increase year by year, but the economic benefits are slightly greater than the social costs. The difference between the two is also increasing year by year. Conclusion: In the context of tobacco control, we should fully consider the advantages and disadvantages of developing the tobacco industry. Under the excessive intervention of the government, the manifestation of the surge phenomenon is more intense, and the final consequence of overcapacity is more serious than that under the market-oriented mode..

Insider Trading Laws and Corporate Governance Impact on Investment Decision

2021 ◽  
Vol 3 (2) ◽  
pp. 126-137
Author(s):  
Sadaf Khan ◽  
Ubaid Ur Rehman
Keyword(s):  
Corporate Governance ◽  
Insider Trading ◽  
Positive Impact ◽  
Investment Decision ◽  
Investment Decisions ◽  
Future Research ◽  
Good Corporate Governance ◽  
Governance Practices ◽  
The Government ◽  
The Impact

This research aims to analyze the impact of insider trading laws and corporate governance on investment decisions. For this purpose, the data of 400 potential and actual investors employed who provided their feedback on a structured questionnaire. When the data is collected, it was cleaned. The normality of data and reliability of items were also checked and within limits. Simple Regression was applied to test hypotheses. It was concluded that the perception of insider trading laws and corporate governance have a positive impact on investment decisions. The study has wide implications and the government and corporation both can be beneficial from its insight and findings, and exercise good corporate governance practices and follow stringent insider trading laws. The study also paves the way for future research.

Does salience matter in investment decision?

Kybernetes ◽  
2019 ◽  
Vol 48 (8) ◽  
pp. 1894-1912
Author(s):  
Samra Chaudary
Keyword(s):  
Investment Decision ◽  
Investment Decisions ◽  
Equation Modeling ◽  
Individual Investors ◽  
Short Term ◽  
Content Type ◽  
Multigroup Analysis ◽  
Money Managers ◽  
The Impact

Purpose The paper takes a behavioral approach by making use of the prospect theory to unveil the impact of salience on short-term and long-term investment decisions. This paper aims to investigate the group differences for two types of investors’ groups, i.e. individual investors and professional investors. Design/methodology/approach The study uses partial least square-based structural equation modeling technique, measurement invariance test and multigroup analysis test on a unique data set of 277 active equity traders which included professional money managers and individual investors. Findings Results showed that salience has a significant positive impact on both short-term and long-term investment decisions. The impact was almost 1.5 times higher for long-term investment decision as compared to short-term decision. Furthermore, multigroup analysis revealed that the two groups (individual investors and professional investors) were statistically significantly different from each other. Research limitations/implications The study has implications for financial regulators, money managers and individual investors as it was found that individual investors suffer more with salience heuristic and may end up with sub-optimal portfolios due to inefficient diversification. Thus, investors should be cautious in fully relying on salience and avoid such bias to improve investment returns. Practical implications The study concludes with a discussion of policy and regulatory implications on how to minimize salience bias to achieve optimum and diversified portfolios. Originality/value The study has significantly contributed to the growing body of applied behavioral research in the discipline of finance.

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