Martin Davies

Most cited paper "Equity Portfolio Diversification" by William N. Goetzmann and Alok Kumar in Review of Finance, Volume 12, Issue 3, 2008, Pages 433–463, https://lnkd.in/dD64A-s Published: 28 March 2008 Abstract This study shows that U.S. individual investors hold under-diversified portfolios, where the level of under-diversification is greater among younger, low-income, less-educated, and less-sophisticated investors. The level of under-diversification is also correlated with investment choices that are consistent with over-confidence, trend-following behavior, and local bias. Furthermore, investors who over-weight stocks with higher volatility and higher skewness are less diversified. In contrast, there is little evidence that portfolio size or transaction costs constrains diversification. Under-diversification is costly to most investors, but a small subset of investors under-diversify because of superior information. JEL G11 - Portfolio Choice; Investment DecisionsG12 - Asset Pricing; Trading volume; Bond Interest Rates Oxford University Press OUP Academic Yale School of Management Texas McCombs School of Business European Finance Association

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'Common risk factors in cross-sectional FX options returns' by Xuanchen Zhang, Raymond H Y So and Tarik Driouchi is now available in Review of Finance, Volume 28, Issue 3. Abstract: We identify a comprehensive list of thirty-eight characteristics for predicting cross-sectional FX options returns. We find that three factors—long-term straddle momentum, implied volatility, and illiquidity—can generate economically and statistically significant risk premia not explained by other return predictors. Meanwhile, the predictability of the other characteristics becomes insignificant after accounting for the FX option three-factor model. The significance of the three factors is confirmed through a series of robustness tests covering different data sources, alternative options strategies, diversification effects, bootstrapping, and omitting crisis years. Read the article: https://lnkd.in/ethzW-HF #reviewoffinance #finance

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A detailed breakdown of Discrete Dynamic Delta Hedging with Python code implementation A risk management strategy where an options trader periodically adjusts their hedge position to maintain a delta-neutral portfolio, mitigating the risk from movements in the underlying asset's price. Key Concepts: Delta (Δ): Measures the sensitivity of an option's price to changes in the price of the underlying asset. Delta-Neutral Portfolio: A portfolio where the net delta is zero, meaning small movements in the underlying asset's price have minimal effect on the portfolio's value. Implementation: Periodic Adjustments: Traders rebalance their hedge at discrete time intervals (e.g., daily or weekly) rather than continuously. Hedging Actions: Involves buying or selling the underlying asset to offset changes in the option's delta. Practical Considerations: Discrete Intervals: Continuous hedging is impractical due to transaction costs and liquidity constraints, so adjustments are made at specific times. Hedging Errors: Price movements between hedging intervals can cause the hedge to be imperfect, leading to residual risk. Frequency Trade-off: More Frequent Hedging: Reduces hedging errors but increases transaction costs. Less Frequent Hedging: Lowers transaction costs but increases exposure to risk. Effectiveness: Risk Reduction: Improves with increased hedging frequency but cannot eliminate risk entirely. Option Characteristics: Out-of-the-Money Options: Hedging errors are more significant due to higher sensitivity to price movements. Near Expiration: Options approaching maturity may exhibit greater volatility, increasing hedging challenges. 100K Sale- 25% off on all our Products, Use the Coupon Code "100K25" to get 25% off ➡ Kickstart your Quant Interview Prep ↗ ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) ↗ Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) ↗ Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte and Project Handbook Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Machine Learning for Finance course- Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the ML for Finance course "𝐐𝐔𝐀𝐍𝐓𝐈𝐓𝐀𝐓𝐈𝐕𝐄 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐒𝐭𝐫𝐚𝐭 -𝐓𝐇𝐄 𝐏𝐑𝐎𝐅𝐄𝐒𝐒𝐈𝐎𝐍" Course, taught by Andrey Chirikhin - https://lnkd.in/gikuMSRg We have 𝐒𝐩𝐞𝐜𝐢𝐚𝐥 𝐩𝐫𝐢𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬- £𝟏𝟒𝟗, you just need to register with your University Email ID to receive the Coupon

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Here is some EV/GP analysis for the universe of stocks in which the NDI-FutureTech portfolio is constructed from (available on eToro and managed by NewDeal Invest) The chart shows where each stock's current EV/GP is relative to their upper and lower bounds, based on 2 standard deviations away from the their mean. This is based on 2 years of daily EV/GP multiples. The chart also shows the group's mean EV/GP and the mean upper and lower bounds (based on 2 standard deviations). As an initial screening for undervalued stocks, we can cross reference the stocks with current EV/GP multiples that are low compared to their 2-year history, with stocks that have current EV/GPs that are also low compared to whole group. Some stocks that stand out as potentially attractive include: OPEN, COUR, JAMF, FTNT, SE, ROKU, S, MELI, & FSLY. Some stocks that are potentially pricey include: DUOL, PLTR, AVGO, CRWD, IOT, & MVRL. Of course, any insights from this should be combined with qualitative analysis, as is done in the NDI-FutureTech portfolio. The reason for choosing only a 2-year period of EV/GP data is because I wanted to exclude the 2020/21 period which is regarded as abnormal. The choice of using a GP multiples was due to the fact that many of the stocks in the universe are FCF negative. Caveat: According to kurtosis and skewness metrics, most of these EV/GP distributions are non-normal, so be cautious in applying such analysis for any mean-reversion strategies.

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Gamma Exposure (GEX) is a critical concept in understanding how market makers and dealers hedge their positions in the options market and how these hedging activities influence market volatility and price movements Detailed Thread on Gamma Exposure (GEX) and Its Market Impact🧵 https://lnkd.in/gRbV7dc7

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Breaking down different Types of PCA with their Implementation in Quant Finance. ↔ Principal Component Analysis (PCA) A statistical technique for simplifying high-dimensional data. Transforms data into a new coordinate system. The greatest variance lies on the first principal component, the second greatest on the second, and so on. Application in Quant Finance: Reduces the dimensionality of financial data. Identifies key factors affecting asset returns. Enhances portfolio management and risk management. Decomposes the term structure of interest rates. Identifies common factors in stock returns for factor models. ↔ Kernel PCA Extension of PCA using kernel methods. Analyzes non-linear relationships by mapping data to a higher-dimensional space. Application in Quant Finance: Recognizes complex patterns in financial time series. Identifies non-linear factors influencing stock prices. Detects non-linear dependencies between financial instruments. Aids in constructing robust trading strategies and risk management. ↔ Incremental PCA Variation of PCA for large datasets. Processes data in mini-batches, updating principal components incrementally. Application in Quant Finance: Suitable for real-time financial applications with streaming data. Used in high-frequency trading for on-the-fly risk model updates. Maintains accurate and up-to-date models without reprocessing entire datasets. ↔ Sparse PCA Produces sparse principal components involving a subset of original variables. Imposes sparsity constraints during PCA process. Application in Quant Finance: Useful for feature selection in market movements. Improves interpretability of financial models. Enhances portfolio optimization by focusing on significant factors. ↔ Robust PCA Handles data with outliers. Separates data into a low-rank matrix and a sparse matrix. Application in Quant Finance: Valuable for financial time series with fat tails and volatility clustering. Cleans financial data by isolating extreme events or anomalies. Leads to more robust risk models and trading strategies. Independent Component Analysis (ICA) Separates multivariate signal into additive, independent components. Focuses on statistical independence, unlike PCA which focuses on variance. Application in Quant Finance: Separates mixed sources in financial data. Isolates noise from true market signals. Extracts independent factors from correlated financial time series. Improves accuracy of trading algorithms and hedging strategies. Kickstart your Quant Interview Prep with Quant Insider. Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte (1500+ Interview questions), and Quant Insider Project Handbook with Bonus Resources. ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) Quantopia Library is the goldmine for building your domain knowledge and technical skills. (https://lnkd.in/geThBB4d)

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NumPy provides comprehensive tools for working with various probability distributions through its random module. In this article, we will explore some of the best practices…  #statology

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Here is an in-depth presentation detailing my approach behind developing a systematic absolute return volatility portfolio using 0 DTE options. See the process I use to assess and evaluate trades in my 0 DTE portfolio. Say goodbye to fluff and dive straight into the essentials. Watch the full presentation here: https://lnkd.in/grt83Hua.

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15 Must Read Books for Quant Finance (Trading, Machine Learning, Portfolio Management, Statistical Modeling, Derivatives, Risk Management Management) Foundation Paul Wilmott Introduces Quantitative Finance is an accessible introduction to the world of quantitative finance, with no prior knowledge of the subject. Mathematics for Finance by Marek Capinski & Marco Frittelli Statistical Modeling: "Statistics and Data Analysis for Financial Engineering" by David Ruppert Focuses on statistical techniques specifically applied to financial engineering. Machine Learning: "Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python" by Stefan Jansen "Advances in Financial Machine Learning" by Marcos López de Prado Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python by Hariom Tatsat Portfolio Management Active Portfolio Management by Richard Grinold & Ronald Kahn Advances in Active Portfolio Management: New Developments in Quantitative Investing Trading Strategies and Markets: "Quantitative Financial Analytics: The Path to Investment Profits" by Edward E Williams "Quantitative Trading: How to Build Your Own Algorithmic Trading Business" by Ernest P. Chan "Algorithmic Trading: Winning Strategies and Their Rationale" by Ernie Chan Derivatives and Risk Management: "Options, Futures, and Other Derivatives" by John C. Hull "Risk Management and Financial Institutions" by John C. Hull Fixed Income Securities by Frank Fabozzi: Provides a thorough understanding of bond markets, pricing models, interest rate risk, and portfolio management for fixed income instruments. Algorithmic Trading and Implementation: "Building Winning Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading" by Kevin J. Davey Bonus: Fooled by Randomness by Nassim Nicholas Taleb Kickstart your Quant Interview Prep with Quant Insider. Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte, Quantopia Library, and Quant Insider Project Handbook with Bonus Resources. ‘Interview Byte’ contains 500+ Interview questions(https://lnkd.in/gkqcrrKf) Quantopia Library is the goldmine for building your domain knowledge and technical skills. (https://lnkd.in/geThBB4d) Quant Insider Project Handbook has 10 industry-oriented projects based on competitions conducted by Top HFTs and Hedge Funds. (https://lnkd.in/gWBEn78U) Checkout our Course on Machine Learning for Finance - https://lnkd.in/eyXnPRwz Use Coupon code - "EARLYBIRD20" for 20% off on the MLFin course

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"Quant Hedge Fund Primer: Demystifying Quantitative Strategies" provides an in-depth exploration of various quantitative strategies employed by hedge funds. Key Sections and Highlights: Introduction to Quantitative Strategies: Overview of the fundamental concepts and principles underlying quantitative strategies. Explanation of how quantitative approaches differ from traditional investment strategies. Common Quant Strategies: Detailed descriptions of various quant strategies such as equity statistical arbitrage, quantitative equity market neutral, managed futures/CTAs (Commodity Trading Advisors), quant macro and global asset allocation, and alternative risk premia. Each strategy section includes examples of sample trades, illustrating how these strategies are implemented in real-world scenarios. Risk/Return Profiles: Discussion on the risk and return profiles associated with each quantitative strategy. Analysis of how these strategies perform under different market conditions, including periods of market stress. Signal Types: Explanation of different signal types used in quantitative strategies, including value, momentum, carry, volatility, quality, and liquidity. Insight into how these signals are generated and utilized to make investment decisions. Performance Analysis: Examination of the historical performance of various quantitative strategies. Consideration of factors that influence the success and failure of these strategies over time. Quantitative Tools and Techniques: Overview of the tools and techniques used in quantitative analysis, including statistical methods, machine learning, and computational models. Discussion on the role of data in quant strategies and the importance of data quality and preprocessing. Kickstart your Quant Interview Prep Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte (500+ Interview questions) and Quant Insider Project Handbook ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) Quant Insider Project Handbook has 10 HFT/hedge fund industry-oriented projects(https://lnkd.in/gWBEn78U) Quant Insider Career Catalyst (https://lnkd.in/gVhA4tNG) Checkout our course Machine Learning for Finance - Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the ML for Finance course "𝐐𝐔𝐀𝐍𝐓𝐈𝐓𝐀𝐓𝐈𝐕𝐄 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐒𝐭𝐫𝐚𝐭 -𝐓𝐇𝐄 𝐏𝐑𝐎𝐅𝐄𝐒𝐒𝐈𝐎𝐍" Course, it is designed to take you from 𝐭𝐡𝐞𝐨𝐫𝐲 𝐭𝐨 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧, taught by Andrey Chirikhin 𝐂𝐥𝐢𝐜𝐤 𝐨𝐧 𝐭𝐡𝐞 𝐋𝐢𝐧𝐤 𝐭𝐨 𝐄𝐧𝐫𝐨𝐥𝐥 𝐢𝐧 𝐭𝐡𝐞 𝐂𝐨𝐮𝐫𝐬𝐞 𝐍𝐨𝐰 - https://lnkd.in/gikuMSRg We have 𝐒𝐩𝐞𝐜𝐢𝐚𝐥 𝐩𝐫𝐢𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬- £𝟏𝟒𝟗 To get the course at £𝟏𝟒𝟗, you just need to register on the Website with your University Email ID and you will receive the Coupon

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