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Tron Blockchain: Unveiling the Vision of Justin Sun

This article delves into the history of the Tron blockchain, exploring its origins, key features, and the role of Justin Sun in shaping its trajectory.

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Quantitative Trading: Unleashing the Power of Numbers in Financial Markets

Quantitative trading, also referred to as algorithmic trading or quant trading, is a type of trading strategy that makes trading decisions using automated systems, statistical analysis, and mathematical models. Trades are executed quickly and frequently in quantitative trading by traders using computer algorithms to spot patterns, trends, and opportunities in the financial markets.

Key aspects of quantitative trading include:

Data Analysis: Quantitative traders use historical and real-time market data to identify patterns and relationships that could indicate profitable trading opportunities.

Model Development: Traders create mathematical models and algorithms based on their analysis to predict future market movements and identify potential trades.

Automated Execution: Quantitative trading strategies are executed automatically by computer programs, eliminating the need for manual intervention and enabling rapid execution of trades.

Risk Management: Quantitative trading strategies often incorporate risk management techniques to control the size of trades, set stop-loss levels, and protect against significant losses.

High-Frequency Trading (HFT): Some quantitative trading strategies focus on executing a large number of trades at very high speeds, taking advantage of small price discrepancies in the market.

Arbitrage Opportunities: Quantitative trading can exploit arbitrage opportunities, where price discrepancies exist between different assets or markets, allowing traders to profit from price differences.

Statistical Arbitrage: Traders use statistical models to identify pairs of securities that tend to move together or apart, allowing them to profit from relative price movements.

Quantitative trading has become increasingly popular in financial markets due to its ability to process vast amounts of data quickly, make data-driven decisions, and execute trades with precision and efficiency. It is commonly used by hedge funds, proprietary trading firms, and large financial institutions to gain a competitive edge and generate consistent returns in the ever-evolving financial landscape.

There are various learning methods available for learners to understand these categories of Quantitative trading. Different universities offer Post Graduate Diploma in Management (PGDM) on quantitative trading.

JAGSoM, Bangalore is one of the universities that provide this course and they have a great record of creating CEOs and Founders. You will be getting a Dual EPAT certification once you successfully complete this program.

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Research develops a model of trust in Ethereum token ‘Ether’ payments (TRUSTEP)

My new research developed a model of trust in making payments with the Ethereum (Zarifis, 2023). I published the first peer reviewed research on trust in payments with Bitcoin in 2014 (Zarifis et al. 2014), and I wanted to apply my experience from that to understanding the consumer’s perspective to making Ethereum payments.

Ethereum is being utilised in various ways, including smart contracts and payments. Despite some similarities with Bitcoin, Ethereum is a different technology, with different governance and support.

Ethereum payments require digital wallets and the process is different to paying in traditional fiat currencies like the Euro. When a person wants to take an action without controlling all the parameters, and some risk is unavoidable, trust is necessary.

Figure 1. Model of trust in making Ethereum payments, TRUSTEP

The model demystifies how trust is built in consumer payments with Ethereum. The model starts with the individual’s predisposition and then covers the factors from the specific context of Ethereum payments. From the person’s individual characteristics, their willingness to innovate in finance and technology have a role. There are then five variables from the contexts: Adoption and reputation, stable value and low transaction fees, effective regulation, payment intermediaries and trust in the seller. The personal and contextual factors together influence trust in the Ethereum payment process and making a payment with Ether.

While the model has similarities to previous models of trust, such as the role of each individual’s psychological predisposition and the role of reputation, the role of institutions such as regulators and the importance of trust in the retailer, the distinct characteristics of Ethereum also play a role. In fact, the factors related to the distinct characteristics of Ethereum have the strongest support based on the average of the responses. This research can be added to a growing body of research in trust that illustrates how users’ beliefs in each cryptocurrency need to be explored separately.

Furthermore, the role of the organizations involved in the payment process are shown. While trust in the retailer is usually a factor in retail payments, the regulators and payment intermediaries are not always a significant factor, so it is a useful contribution to show that this is the case here.

That is what I want to share with you here. If you have experiences related to what I am talking about, please let me know, I would love to hear from you.

Reference

Zarifis A. (2023) ‘A Model of Trust in Ethereum Token ‘Ether’ Payments, TRUSTEP’ Businesses, vol.3, no. 4: pp.534-547. Available from (open access): https://doi.org/10.3390/businesses3040033

Zarifis A., Efthymiou L., Cheng X. & Demetriou S. (2014) ‘Consumer trust in digital currency enabled transactions’, Lecture Notes in Business Information Processing-Springer, vol.183, pp.241-254. Available from: http://link.springer.com/chapter/10.1007/978-3-319-11460-6_21#

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