Kertos

Financial markets are chaotic. So chaotic, even, that many economists and investors believe market trends to be the product of ‘random walks’ and that prices cannot be predicted (see generally Malkiel). But randomness shouldn't be worrisome. In fact, random price movements can be good. Gaussian random walk, an assumption used by an options pricing model called Black-Scholes, treats intervals of an asset’s price over time as independent variables. By doing so, the changes in price over time, or the returns of an asset, are assumed to be normally distributed. Otherwise stated, “If transactions are fairly uniformly spread across time, and if the number of transactions per day, week, or month is very large, then the Central Limit Theorem leads us to expect that these price changes will have normal or Gaussian distributions” (Fama, 399). When an asset's returns are normally distributed, the probabilities of those returns are known. Knowing these probabilities can give investors a reliable framework accounting for the risk of holding said asset. When it comes to bitcoin, much has been said about how risky it is. The purpose of this article is to explore how to frame risk and to test how well traditional assumptions, implicit in derivatives pricing, apply to bitcoin. 

Kurtosis and Bitcoin: A Quantitative Analysis 

As AI floods the internet with text, the real question isn’t who wrote it—but whether it’s accurate, useful, and worth trusting.

Stop Asking If AI Wrote This. Start Asking If It’s Any Good

Salib Suci partners with Space and Time, Indomobil to verify credentials for 14,000 students across 70 schools using blockchain

Why blockchain verification could eliminate Indonesia's $22 billion fake degree problem

Airlines don’t just fly planes—they model your choices. This deep dive shows how layover times impact profits in codeshare agreements and passenger flows.

How Your Layover Time Can Make or Break an Airline’s Profit

Additional simulation results, including QQ plots, ACF graphs, and volatility analysis, revealing insights into agent performance and market dynamics.

Reinforcement Learning Simulation Metrics: QQ plots, ACF graphs, and Volatility Analysis

Analyzing RL and ZI agents shows RL agents closely mimic real market patterns, including volatility clustering and return autocorrelations, over time.

RL Agents Show Superior Realism and Adaptability Over Zero-Intelligence Agents

Explore how experiments validate RL agents' ability to replicate real-world market properties, from statistical behaviors to adaptability under external impacts

Realistic Market Simulations Are Essential for Testing Reinforcement Learning Behavior

This appendix explores the monotonicity of the sparsity objective function and provides a mathematical derivation to demonstrate its behavior

Understanding the Monotonicity of the Sparsity Objective Function

ClassBD combines time and frequency domain filters with deep learning classifiers for superior bearing fault diagnosis, excelling in noisy environments. 

ClassBD: A New Method for Enhanced Bearing Fault Diagnosis in Noisy Environments

Explore and compare various blind deconvolution methods for fault diagnosis and feature extraction, highlighting the superior performance of ClassBD.

New Study from JNU Researchers Shows ClassBD Outperforms Other Fault Diagnosis Methods

Researchers propose a time-domain quadratic convolutional filter (QCNN) with a novel initialization strategy. 

Quadratic Neural Networks Show Promise in Handling Noise and Data Imbalances

Researchers unveil optimized blind deconvolution techniques, enhancing machinery fault detection by leveraging kurtosis and advanced optimization methods.

KERTOS

kertos.io

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KERTOS

EVERGREEN INDEX #12406

Kertos's stories on HackerNoon

Kurtosis and Bitcoin: A Quantitative Analysis

Stop Asking If AI Wrote This. Start Asking If It’s Any Good

Why blockchain verification could eliminate Indonesia's $22 billion fake degree problem

How Your Layover Time Can Make or Break an Airline’s Profit

Reinforcement Learning Simulation Metrics: QQ plots, ACF graphs, and Volatility Analysis

RL Agents Show Superior Realism and Adaptability Over Zero-Intelligence Agents

Realistic Market Simulations Are Essential for Testing Reinforcement Learning Behavior

Understanding the Monotonicity of the Sparsity Objective Function

ClassBD: A New Method for Enhanced Bearing Fault Diagnosis in Noisy Environments

New Study from JNU Researchers Shows ClassBD Outperforms Other Fault Diagnosis Methods

Quadratic Neural Networks Show Promise in Handling Noise and Data Imbalances

Researchers Develop Advanced Methods for Fault Diagnosis Using Blind Deconvolution

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KERTOS

kertos.io

51-200 emps

Since 2021

KERTOS

EVERGREEN INDEX #12406

Kertos's stories on HackerNoon

Kurtosis and Bitcoin: A Quantitative Analysis

Stop Asking If AI Wrote This. Start Asking If It’s Any Good

Why blockchain verification could eliminate Indonesia's $22 billion fake degree problem

How Your Layover Time Can Make or Break an Airline’s Profit

Reinforcement Learning Simulation Metrics: QQ plots, ACF graphs, and Volatility Analysis

RL Agents Show Superior Realism and Adaptability Over Zero-Intelligence Agents

Realistic Market Simulations Are Essential for Testing Reinforcement Learning Behavior

Understanding the Monotonicity of the Sparsity Objective Function

ClassBD: A New Method for Enhanced Bearing Fault Diagnosis in Noisy Environments

New Study from JNU Researchers Shows ClassBD Outperforms Other Fault Diagnosis Methods

Quadratic Neural Networks Show Promise in Handling Noise and Data Imbalances

Researchers Develop Advanced Methods for Fault Diagnosis Using Blind Deconvolution

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