Tag: risk
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Titanic Benchmark Hypothesis Testing in Disaster Risk Management: (Auto)EDA, ML, HPO & SHAP
This project aims to apply the Titanic benchmark to hypothesis testing in disaster risk management. Using the Titanic dataset on Kaggle, a Machine Learning (ML) analysis was performed to determine the statistical significance relation between a person’s death and their passenger class, age, sex, and port of embarkation. The project involved comprehensive ML pipeline implementation…
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A Market-Neutral Strategy
The work aims to solve the problem of Markowitz portfolio optimization for a one-year investment horizon through the pairs trading cointegrated strategy. Market-neutral trading strategies seek to generate returns independent of market swings to achieve a zero beta against its relevant market index. Statistical arbitrage (SA), pairs trading, and APO signals are analyzed. The study…
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A Comprehensive Analysis of Best Trading Technical Indicators w/ TA-Lib – Tesla ’23
This study presents a comprehensive stock technical analysis guide for Tesla (TSLA) using the TA-Lib Python library. It explores the use of over 200 technical indicators, analyses historical data, and offers insight for both swing traders and long-term holders. The content includes detailed explanations and plots for various momentum, volume, volatility, and trend indicators, providing…
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Weather Forecasting & Flood De-Risking using Machine Learning, Markov Chain & Geospatial Plotly EDA
Foto door Pok Rie Scope: Business Value: Table of Contents U.S.A. Weather Forecast Australian Rainfall Prediction Kerala Flood Prediction Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is asymmetrical, (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). • Circles are the symmetrical numerical…
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Real-Time Anomaly Detection of NAB Ambient Temperature Readings using the TensorFlow/Keras Autoencoder
The content covers a detailed guide on implementing anomaly detection in time series data using autoencoders. The tutorial utilizes Python and real-world temperature dataset from Numenta Anomaly Benchmark (NAB). Following the Python workflow, the algorithm imports required libraries, performs anomaly detection, and visualizes anomalies. A trained autoencoder model identifies anomalies, with Precision, Recall, and F1…
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NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading
The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…
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NVIDIA Rolling Volatility: GARCH & XGBoost
This post examines the prediction of NVIDIA stock volatility using two models: the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and the Extreme Gradient Boosting (XGBoost). Both models are compared in terms of MSE and MAPE. The post discovers that the machine learning-based XGBoost model outperforms the GARCH model in NVDA volatility forecasting, showing the effectiveness of…
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IQR-Based Log Price Volatility Ranking of Top 19 Blue Chips
The focus is on risk assessment of top blue chips. We determine market regimes using standard deviation (STD) of log-domain stock prices.
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Image Based Fast Forest Fire Detection with TensorFlow
A recent study showcases the use of artificial intelligence (AI) and deep learning (DL) for efficient wildfire prediction and management. Utilizing a fast DL approach based on the TensorFlow Convolution Neural Network (CNN) algorithm, researchers trained models to distinguish between fire and non-fire images using a public-domain dataset. The implemented system predicted fires accurately and…
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Morocco Earthquake EDA
Featured design via Canva. Clickable Table of Contents Basic Installations and Imports Let’s set the working directory YOURPATH Let’s install and import the following libraries Download Earthquake Input Data For this project, we’ll use a dataset that contains all seismic events over the last seven days, which have a magnitude of 1.0 or greater: Output:…
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Risk-Aware Strategies for DCA Investors
Dollar-Cost Averaging (DCA) is an investment approach that involves investing a fixed amount regularly, regardless of market price. It offers benefits such as risk reduction and market downturn resilience. It’s useful for beginners and can be combined with other strategies for a disciplined investment approach. References include Investopedia and Yahoo Finance.
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GPT & DeepLake NLP: Amazon Financial Statements
The post outlines the implementation of an AI-powered chatbot using NLP to process and analyze financial data from Amazon’s financial statements. The tool employs LlamaIndex and DeepLake to answer queries, summarize financial information, and analyze trends. This approach enhances the efficiency of data analysis, making it a valuable resource for finance and banking professionals.
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Joint Analysis of Bitcoin, Gold and Crude Oil Prices
The content discusses a comprehensive analysis on a joint time-series analysis of Bitcoin, Gold and Crude Oil prices from 2021 to 2023. It explores data processing, exploratory data analysis before running a range of statistical tests, ARIMA models fitting, and finally, using the Markowitz portfolio optimization method. It then presents a detailed analysis, including data…
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Top 6 Reliability/Risk Engineering Learnings
The content provides a review of Eric Marsden’s e-learning Python courseware on risk engineering, loss prevention and safety management. It includes discussions of various topics such as the failure of light bulbs, electronic components, large computing facility maintenance, and oil field pumps. The content also delves into stock market risk analysis like Value at Risk…
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Gold Price Linear Regression
This content focuses on predicting gold prices using machine learning algorithms in Python. With an 80% R2-score and a Sharpe ratio of 2.33, it suggests a potential 8% revenue from an investment starting in December 2022. The forecasted next-day price for SPDR Gold Trust Shares is $185.136, aligning with Barchart’s “100% BUY” signal.
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Applying a Risk-Aware Portfolio Rebalancing Strategy to ETF, Energy, Pharma, and Aerospace/Defense Stocks in 2023
The post discusses applying Guillen’s algorithm for risk-aware portfolio rebalancing, using Python. It incorporates five different stocks with specific weight allocations within an initial portfolio of $1,000,000. The post demonstrates setting the parameters for portfolio, importing required libraries, downloading input data, setting algorithmic rules for rebalancing, calculation of shares and portfolio values, and plotting visualizations.…
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Post-SVB Risk Aware Investing
The recent collapse of Silicon Valley Bank and its repercussions have prompted a reevaluation of risk-aware investing in the US financial sector. The crisis has exposed the vulnerability of banks invested in long-term fixed income assets, highlighting the importance of diversification and risk management. Market indicators suggest continued volatility and uncertainty, urging investors to exercise…
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LSTM Price Predictions of 4 Tech Stocks
The given content explains the process of using Exploratory Data Analysis (EDA) and Long Short-Term Memory (LSTM) Sequential model for comparing the risk/return of four major tech stocks: Apple, Google, Microsoft, and Amazon, considering the tech scenario in 2023. The analysis involves examining stock price patterns, their correlations, risk-return assessment, and predicting stock prices using…
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Portfolio Optimization of 20 Dividend Growth Stocks
The post discusses implementing a stochastic optimization algorithm to create a balanced portfolio of 20 dividend growth stocks for maximum return within defined risk tolerance. By analyzing daily stock and benchmark data, the algorithm optimizes the portfolio to outperform the benchmark index and achieve desired risk-reward outcomes. The results facilitate spreading investment capital across diverse…
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Towards Max(ROI/Risk) Trading
This post compares 1-year ROI/Risk of selected stocks vs ETF using stock analyzer functions. It includes comparing prices, visualizing annual risk and return, and examining correlation matrix of stock returns. It provides insights for selecting CPB stock for trading based on low correlation with ^GSPC, high return (~20%), and low risk (~23%).
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