Analyzing the Path from Rate Hikes to Recession: A Look at Inverted Yield Curves and Economic Slowdowns
MacroAlf @ twitter
orig
The Central Bank has aggressively raised rates, leading to an inverted yield curve indicating overly tight conditions. Historically, this inversion persists for 12-27 months, and we’re currently at month 24 post-inversion with the economy already slowing. Now, we’re seeing a rapid bull steepening of the yield curve, hinting at late-cycle dynamics before the inevitable recession. This pattern highlights the complex interplay between monetary policy and economic cycles.
@quant_feed
MacroAlf @ twitter
orig
The Central Bank has aggressively raised rates, leading to an inverted yield curve indicating overly tight conditions. Historically, this inversion persists for 12-27 months, and we’re currently at month 24 post-inversion with the economy already slowing. Now, we’re seeing a rapid bull steepening of the yield curve, hinting at late-cycle dynamics before the inevitable recession. This pattern highlights the complex interplay between monetary policy and economic cycles.
@quant_feed
Understanding Daily Variance in Performance: Lessons from Professional Poker to Quant Finance
gametheorizing @ twitter
orig
I observed significant daily variance in my poker performance due to factors like sleep, mood, and diet. The difference between my A-game and C-game could cost me dearly in buy-ins. Poker’s luck component made it difficult to gauge my mental sharpness until it was too late into a session, especially against skilled opponents who would exploit any errors.
To address this, I incorporated a daily routine of playing 5-6 blitz chess games each morning as a self-assessment tool. If I could compete well against high-rated players, I felt confident heading into high-stakes games. If I struggled, I would opt for more relaxed tournaments or take a day off. I also see value in simpler online games for assessing mental clarity through quick arithmetic calculations.
@quant_feed
gametheorizing @ twitter
orig
I observed significant daily variance in my poker performance due to factors like sleep, mood, and diet. The difference between my A-game and C-game could cost me dearly in buy-ins. Poker’s luck component made it difficult to gauge my mental sharpness until it was too late into a session, especially against skilled opponents who would exploit any errors.
To address this, I incorporated a daily routine of playing 5-6 blitz chess games each morning as a self-assessment tool. If I could compete well against high-rated players, I felt confident heading into high-stakes games. If I struggled, I would opt for more relaxed tournaments or take a day off. I also see value in simpler online games for assessing mental clarity through quick arithmetic calculations.
@quant_feed
Reflecting on Two Months of High Volatility: A Journey of 1699.9% Returns and a Sharpe Ratio of 4.246
BeatzXBT @ twitter
orig
The past two months have been a rollercoaster; December was particularly challenging, but I've recovered with impressive results—1699.9% returns and a Sharpe ratio of 4.246, alongside a trading volume of $9.67M. My primary successes came from BONK and MOVR, where I strategically oversized my positions due to their favorable conditions for stink bidding, benefiting from thin books and high retail flow.
However, the journey wasn't without significant drawdowns. There were three major setbacks:
1) An uncaught exception led to stale bids being filled, almost causing liquidation.
2) I quoted a faulty symbol, resulting in a quick -20% drop twice and liquidating a small sub-account.
3) The Bybit API failure, which caused massive drawdowns across multiple sub-accounts—this one stung the most as it was beyond my control.
These experiences highlighted the importance of risk management; I'm shifting my focus to retaining capital and reducing leverage, aiming for a conservative grind to low five figures while removing profits to cover development costs. It’s essential to avoid greed, especially since my strategy involves tail risk.
I'll be making my project open source once we finish the cleanup, which should lead to smoother operations. Thanks to everyone who reached out with feedback—it's been invaluable! Lastly, my trading strategy involves high turnover (over 75x) and a concentrated asset approach with a maximum of three assets. A backtester is on my to-do list, but studies are the current priority.
@quant_feed
BeatzXBT @ twitter
orig
The past two months have been a rollercoaster; December was particularly challenging, but I've recovered with impressive results—1699.9% returns and a Sharpe ratio of 4.246, alongside a trading volume of $9.67M. My primary successes came from BONK and MOVR, where I strategically oversized my positions due to their favorable conditions for stink bidding, benefiting from thin books and high retail flow.
However, the journey wasn't without significant drawdowns. There were three major setbacks:
1) An uncaught exception led to stale bids being filled, almost causing liquidation.
2) I quoted a faulty symbol, resulting in a quick -20% drop twice and liquidating a small sub-account.
3) The Bybit API failure, which caused massive drawdowns across multiple sub-accounts—this one stung the most as it was beyond my control.
These experiences highlighted the importance of risk management; I'm shifting my focus to retaining capital and reducing leverage, aiming for a conservative grind to low five figures while removing profits to cover development costs. It’s essential to avoid greed, especially since my strategy involves tail risk.
I'll be making my project open source once we finish the cleanup, which should lead to smoother operations. Thanks to everyone who reached out with feedback—it's been invaluable! Lastly, my trading strategy involves high turnover (over 75x) and a concentrated asset approach with a maximum of three assets. A backtester is on my to-do list, but studies are the current priority.
@quant_feed
Exploring the Dual Nature of Mean-Reversion in Futures Spreads
oxbquant @ twitter
orig
Mean reversion in futures spreads is quite an intriguing concept; I'm exploring whether the mean reversion time itself can be mean-reverting. I'm experimenting with a simple EMA combined with +1/-1 standard deviations to identify optimal trading points. The goal is to determine when spreads are "cheap" or "expensive," particularly if I'm looking to hedge delta within a 5-day window.
Interestingly, I've found that the mean reversion time is actually the duration required for the spread to revert from upper or lower bounds to 0.5 standard deviations of the EMA. When testing this with a basic RV score, results differ significantly. This disparity seems to stem from the RV score's ability to make larger jumps between points, leading to less fine granularity, which complicates the overall modeling.
On a more mathematical note, I'm considering modeling these reversions using gamma or exponential distributions. Then, at each new reversion event, I could initialize a Bayesian model using the prior of the gamma distribution and continuously update it with new data to refine trading decisions. Curious for thoughts on this approach!
@quant_feed
oxbquant @ twitter
orig
Mean reversion in futures spreads is quite an intriguing concept; I'm exploring whether the mean reversion time itself can be mean-reverting. I'm experimenting with a simple EMA combined with +1/-1 standard deviations to identify optimal trading points. The goal is to determine when spreads are "cheap" or "expensive," particularly if I'm looking to hedge delta within a 5-day window.
Interestingly, I've found that the mean reversion time is actually the duration required for the spread to revert from upper or lower bounds to 0.5 standard deviations of the EMA. When testing this with a basic RV score, results differ significantly. This disparity seems to stem from the RV score's ability to make larger jumps between points, leading to less fine granularity, which complicates the overall modeling.
On a more mathematical note, I'm considering modeling these reversions using gamma or exponential distributions. Then, at each new reversion event, I could initialize a Bayesian model using the prior of the gamma distribution and continuously update it with new data to refine trading decisions. Curious for thoughts on this approach!
@quant_feed
Challenging Overconfidence in FinTwit: Empowering Individuals to Conduct Independent Analysis
therobotjames @ twitter
orig
Never accept blanket statements or overconfident claims without challenging them. It's crucial to develop your analytical skills—simple analyses are accessible to everyone, and they provide clarity in a sea of noise. Anyone can learn to scrutinize data critically. Don't let the bravado of others drown out your own insights.
@quant_feed
therobotjames @ twitter
orig
Never accept blanket statements or overconfident claims without challenging them. It's crucial to develop your analytical skills—simple analyses are accessible to everyone, and they provide clarity in a sea of noise. Anyone can learn to scrutinize data critically. Don't let the bravado of others drown out your own insights.
@quant_feed
Exploring Intuition Through Simulation: Insights from LASSO vs Ridge Analysis
choffstein @ twitter
orig
LASSO and Ridge regression serve different purposes in regularization, but the real takeaway here is the importance of simulation for building intuition in quantitative analysis. It's an underrated method that can unlock deeper understanding, especially for those of us who aren't innate analytical thinkers. While some can quickly grasp concepts from first principles, experimenting and observing how variables react provides valuable insights. Being hands-on with simulations allows us to poke at complex models and see their dynamics, ultimately enhancing our comprehension. Great discussions like those from @quantymacro and insights from @Bubamara420 highlight the power of practical exploration in data science.
@quant_feed
choffstein @ twitter
orig
LASSO and Ridge regression serve different purposes in regularization, but the real takeaway here is the importance of simulation for building intuition in quantitative analysis. It's an underrated method that can unlock deeper understanding, especially for those of us who aren't innate analytical thinkers. While some can quickly grasp concepts from first principles, experimenting and observing how variables react provides valuable insights. Being hands-on with simulations allows us to poke at complex models and see their dynamics, ultimately enhancing our comprehension. Great discussions like those from @quantymacro and insights from @Bubamara420 highlight the power of practical exploration in data science.
@quant_feed
Exploring the Ehlers Coefficient Filter: A Regime Filter for Trend and Cycle Detection in Market Strategies
GoshawkTrades @ twitter
orig
The Ehlers Coefficient Filter is a valuable tool for identifying market regimes by assessing price movement trends and cyclicality. It possesses both advantages and disadvantages, which traders should consider thoroughly. Over 200 traders have leveraged our expertise to evolve their strategies into systematic approaches. For those facing challenges with conviction, execution, or requiring quant development, there are resources available to help automate your trading effectively.
@quant_feed
GoshawkTrades @ twitter
orig
The Ehlers Coefficient Filter is a valuable tool for identifying market regimes by assessing price movement trends and cyclicality. It possesses both advantages and disadvantages, which traders should consider thoroughly. Over 200 traders have leveraged our expertise to evolve their strategies into systematic approaches. For those facing challenges with conviction, execution, or requiring quant development, there are resources available to help automate your trading effectively.
@quant_feed
Enhancing Factor Timing Predictions: The Role of Economic Structure and Deep Learning
quantseeker @ twitter
orig
A new paper emphasizes the importance of incorporating economic structure and time series dynamics in factor timing models, significantly enhancing predictive accuracy. Critical variables identified for effective factor timing include tail risk, price trends, and leverage. Additionally, I've launched a newsletter aimed at providing weekly insights into quant research focused on investing, macroeconomic trends, and trading strategies.
@quant_feed
quantseeker @ twitter
orig
A new paper emphasizes the importance of incorporating economic structure and time series dynamics in factor timing models, significantly enhancing predictive accuracy. Critical variables identified for effective factor timing include tail risk, price trends, and leverage. Additionally, I've launched a newsletter aimed at providing weekly insights into quant research focused on investing, macroeconomic trends, and trading strategies.
@quant_feed
Navigating Low Liquidity: Understanding Market Vulnerabilities During Seasonal Calm
Ksidiii @ twitter
orig
We're entering a quiet seasonal period in the market, which heightens susceptibility to irrational headlines and narratives amid light liquidity. This phenomenon is a recurring theme each summer. While volatility may seem clustered at low levels, don't underestimate the potential for sudden spikes in volatility, as seen in past summers.
@quant_feed
Ksidiii @ twitter
orig
We're entering a quiet seasonal period in the market, which heightens susceptibility to irrational headlines and narratives amid light liquidity. This phenomenon is a recurring theme each summer. While volatility may seem clustered at low levels, don't underestimate the potential for sudden spikes in volatility, as seen in past summers.
@quant_feed
Rethinking Predictive Models: Why Past Performance Isn't an Indicator of Future Success
Citrini7 @ twitter
orig
Leaders who drive progress do not limit their vision based on past performance; this narrow perspective stifles innovation. It's amusing how articles elevate firms like Goldman Sachs for their focus on profitability while ignoring their true aim of attracting trading volume. Claims about the necessity of infrastructure often get debunked by innovation, showcasing the dynamic nature of industry. The presence of a Fed put illustrates how markets can remain buoyant despite skepticism. Amy embodies the spirit of resilience and adaptability we need to embrace.
@quant_feed
Citrini7 @ twitter
orig
Leaders who drive progress do not limit their vision based on past performance; this narrow perspective stifles innovation. It's amusing how articles elevate firms like Goldman Sachs for their focus on profitability while ignoring their true aim of attracting trading volume. Claims about the necessity of infrastructure often get debunked by innovation, showcasing the dynamic nature of industry. The presence of a Fed put illustrates how markets can remain buoyant despite skepticism. Amy embodies the spirit of resilience and adaptability we need to embrace.
@quant_feed
Exploring the Nature of Equity Factors: Mispricing vs. Risk in Quantitative Finance
quantseeker @ twitter
orig
Hundreds of equity factors exist, but the debate remains whether they indicate mispricing or just risk. Frey’s analysis suggests that at least 40% of factors signal mispricing, emphasizing that many factors are essentially the mechanisms through which prices adjust back to fundamental values. This highlights the crucial interplay between price dynamics and fundamental analysis in the investment landscape. Staying updated on emerging research in investing, macroeconomic trends, and trading can illuminate these concepts further, enhancing our understanding of market behavior.
@quant_feed
quantseeker @ twitter
orig
Hundreds of equity factors exist, but the debate remains whether they indicate mispricing or just risk. Frey’s analysis suggests that at least 40% of factors signal mispricing, emphasizing that many factors are essentially the mechanisms through which prices adjust back to fundamental values. This highlights the crucial interplay between price dynamics and fundamental analysis in the investment landscape. Staying updated on emerging research in investing, macroeconomic trends, and trading can illuminate these concepts further, enhancing our understanding of market behavior.
@quant_feed
Developing Predictive Models for Unusual Travel Patterns in Influencers
PtrPomorski @ twitter
orig
When choosing a model for predictive analysis in finance, it's critical to rethink the conventional preference for LSTMs often suggested by platforms like Kaggle. I advocate starting with regularized linear models and Generalized Additive Models (GAMs), such as ridge, lasso, and elastic net. These models are straightforward and have effective applications in high-frequency trading, despite the underlying assumption of independence being questionable in financial contexts.
Transitioning to non-linear methods, tree-based algorithms like LGBM often outperform others in this arena. They fit the interconnected nature of financial data well but come with complexities that can lead to overfitting and calibration challenges, particularly in calculating confidence intervals. Despite these drawbacks, tree models are generally more suited for lower frequency data and require less preprocessing compared to their linear counterparts.
Lastly, while neural networks and their combinations with GAMs (NAMs) are gaining traction, their utility is mostly seen in deep hedging scenarios. They offer significant out-of-sample accuracy given sufficient data and compute resources, but their complexity can hinder analysis and feature importance evaluation. Remember, starting with simple linear models not only provides a good foundation but also a clear benchmark for assessing the additional insights drawn from more intricate algorithms.
@quant_feed
PtrPomorski @ twitter
orig
When choosing a model for predictive analysis in finance, it's critical to rethink the conventional preference for LSTMs often suggested by platforms like Kaggle. I advocate starting with regularized linear models and Generalized Additive Models (GAMs), such as ridge, lasso, and elastic net. These models are straightforward and have effective applications in high-frequency trading, despite the underlying assumption of independence being questionable in financial contexts.
Transitioning to non-linear methods, tree-based algorithms like LGBM often outperform others in this arena. They fit the interconnected nature of financial data well but come with complexities that can lead to overfitting and calibration challenges, particularly in calculating confidence intervals. Despite these drawbacks, tree models are generally more suited for lower frequency data and require less preprocessing compared to their linear counterparts.
Lastly, while neural networks and their combinations with GAMs (NAMs) are gaining traction, their utility is mostly seen in deep hedging scenarios. They offer significant out-of-sample accuracy given sufficient data and compute resources, but their complexity can hinder analysis and feature importance evaluation. Remember, starting with simple linear models not only provides a good foundation but also a clear benchmark for assessing the additional insights drawn from more intricate algorithms.
@quant_feed
Optimizing Fill Performance While Addressing Drawdown Challenges
BeatzXBT @ twitter
orig
Progress in reducing drawdown spikes is evident, but there's still room for improvement in fill quality. The PnL graph remains relatively stable; I've focused on enhancing its smoothness following recent changes. Exploring Grafana and TimescaleDB for live monitoring is on my radar, though Streamlit is also an option, I’m leaning towards mastering TimescaleDB for efficiency. I've made some tweaks to the quoting strategy, but the core approach remains simple. While I could dig deeper into data trends, my current bandwidth is limited.
@quant_feed
BeatzXBT @ twitter
orig
Progress in reducing drawdown spikes is evident, but there's still room for improvement in fill quality. The PnL graph remains relatively stable; I've focused on enhancing its smoothness following recent changes. Exploring Grafana and TimescaleDB for live monitoring is on my radar, though Streamlit is also an option, I’m leaning towards mastering TimescaleDB for efficiency. I've made some tweaks to the quoting strategy, but the core approach remains simple. While I could dig deeper into data trends, my current bandwidth is limited.
@quant_feed
Navigating Career Pathways in Top Quant Funds: A Reality Check for Aspiring Candidates
PtrPomorski @ twitter
orig
It appears that top funds like Jane Street and Citadel are currently the gold standard for financial careers. However, accessing these opportunities is extremely challenging if you’re not from a target school—effectively, you’re out of the running. For those from target schools, the interview process is a brutal filter where the overwhelming majority of candidates, about 99%, may not succeed.
Interestingly, starting your career elsewhere as a junior and then aiming to transition back into these elite firms may be a more viable strategy. There's been a lot of chatter about the effectiveness of platforms like LinkedIn for job hunting, with mixed reviews on whether anyone has actually secured positions through it. This recruitment trend seems to perpetuate an elitist atmosphere, creating barriers that favor a select few.
@quant_feed
PtrPomorski @ twitter
orig
It appears that top funds like Jane Street and Citadel are currently the gold standard for financial careers. However, accessing these opportunities is extremely challenging if you’re not from a target school—effectively, you’re out of the running. For those from target schools, the interview process is a brutal filter where the overwhelming majority of candidates, about 99%, may not succeed.
Interestingly, starting your career elsewhere as a junior and then aiming to transition back into these elite firms may be a more viable strategy. There's been a lot of chatter about the effectiveness of platforms like LinkedIn for job hunting, with mixed reviews on whether anyone has actually secured positions through it. This recruitment trend seems to perpetuate an elitist atmosphere, creating barriers that favor a select few.
@quant_feed
Exploring Performance: Hyper vs. Reqwest in Rust Development
Dub0x3A @ twitter
orig
Hyper outperforms Reqwest in Rust, showing an ~18% speed advantage in a test with a simple local HTTP/1 server. If you're aiming for micro-optimizations, Hyper's lower-level client is worth considering. Kudos to @seanmonstar and contributors for their solid work on these libraries, and shoutout to @fasterthanlime for the helpful article. While Hyper is efficient and adopted by major players like AWS, Discord, and Cloudflare, I recommend most users stick with Reqwest for its ease of use and comprehensive handling of complexities like TCP screams, ALPN negotiation, and TLS.
@quant_feed
Dub0x3A @ twitter
orig
Hyper outperforms Reqwest in Rust, showing an ~18% speed advantage in a test with a simple local HTTP/1 server. If you're aiming for micro-optimizations, Hyper's lower-level client is worth considering. Kudos to @seanmonstar and contributors for their solid work on these libraries, and shoutout to @fasterthanlime for the helpful article. While Hyper is efficient and adopted by major players like AWS, Discord, and Cloudflare, I recommend most users stick with Reqwest for its ease of use and comprehensive handling of complexities like TCP screams, ALPN negotiation, and TLS.
@quant_feed
Choosing Quality Over Quantity: The Value of Experienced Insights in Quantitative Finance Literature
macrocephalopod @ twitter
orig
Investing in quality educational resources is crucial. Instead of wasting $300 a year on a subpar quant trading substack, consider allocating that budget to 4-6 professional books rich with practical insights gained from decades of industry experience. While some subscriptions might promise to elevate your trading skills, the harsh reality is that the majority won’t translate to actual profits. There’s a fine line between creating content and recommending it; if it lacks depth, I wouldn’t promote it authentically. Always prioritize resources that deliver substantial, actionable knowledge grounded in real-world experience.
@quant_feed
macrocephalopod @ twitter
orig
Investing in quality educational resources is crucial. Instead of wasting $300 a year on a subpar quant trading substack, consider allocating that budget to 4-6 professional books rich with practical insights gained from decades of industry experience. While some subscriptions might promise to elevate your trading skills, the harsh reality is that the majority won’t translate to actual profits. There’s a fine line between creating content and recommending it; if it lacks depth, I wouldn’t promote it authentically. Always prioritize resources that deliver substantial, actionable knowledge grounded in real-world experience.
@quant_feed
Exploring the Concept of Portable Alpha in Today's Investment Strategies
macrocephalopod @ twitter
orig
When engaging in fast trading, it's crucial to retain some fluff to maintain your edge, rather than completely hedging it out. You can often treat trades independently, evaluating whether the delta is increasing or decreasing. For very short-term trades (under five minutes), your size will typically be constrained by the available liquidity on the bid/offer, so maximize your position within that limit.
@quant_feed
macrocephalopod @ twitter
orig
When engaging in fast trading, it's crucial to retain some fluff to maintain your edge, rather than completely hedging it out. You can often treat trades independently, evaluating whether the delta is increasing or decreasing. For very short-term trades (under five minutes), your size will typically be constrained by the available liquidity on the bid/offer, so maximize your position within that limit.
@quant_feed
Exploring the Disparities Between Trading Fees and Tick Sizes in Market Microstructure
ltrd_ @ twitter
orig
I delved into market microstructure by analyzing BTCUSDT and CHZUSDT spots on Binance and BTCUSD perpetuals on ByBit. The study revealed significant differences in taker fees and tick sizes across these instruments. Notably, ByBit imposes a much higher taker fee, while CHZUSDT has a larger tick size compared to BTCUSDT on Binance.
Instantaneous market impact was assessed by observing how many levels in the order book vanished following market orders. For Binance's BTCUSDT, nearly 500 levels disappeared after a trade worth $3.6M, demonstrating substantial market impact. Conversely, CHZUSDT showed a maximum of only 8 levels disappearing, with negligible occurrences of high impact trades.
These findings highlight the critical role of optimal order placement for market makers. There's a vital trade-off between profit per trade and fill probability, complicating the strategy, especially when large market orders can consume numerous orders and affect trade execution. Furthermore, trading in markets with thin order books can introduce significant noise, rendering volume imbalance indicators unreliable and challenging to compare across exchanges.
It’s essential for traders, particularly market makers, to denoise such data to enhance strategy accuracy and to adapt their order placement logic depending on tick sizes. This nuanced understanding can lead to more informed trading decisions in varied market environments.
@quant_feed
ltrd_ @ twitter
orig
I delved into market microstructure by analyzing BTCUSDT and CHZUSDT spots on Binance and BTCUSD perpetuals on ByBit. The study revealed significant differences in taker fees and tick sizes across these instruments. Notably, ByBit imposes a much higher taker fee, while CHZUSDT has a larger tick size compared to BTCUSDT on Binance.
Instantaneous market impact was assessed by observing how many levels in the order book vanished following market orders. For Binance's BTCUSDT, nearly 500 levels disappeared after a trade worth $3.6M, demonstrating substantial market impact. Conversely, CHZUSDT showed a maximum of only 8 levels disappearing, with negligible occurrences of high impact trades.
These findings highlight the critical role of optimal order placement for market makers. There's a vital trade-off between profit per trade and fill probability, complicating the strategy, especially when large market orders can consume numerous orders and affect trade execution. Furthermore, trading in markets with thin order books can introduce significant noise, rendering volume imbalance indicators unreliable and challenging to compare across exchanges.
It’s essential for traders, particularly market makers, to denoise such data to enhance strategy accuracy and to adapt their order placement logic depending on tick sizes. This nuanced understanding can lead to more informed trading decisions in varied market environments.
@quant_feed
👍3
Exploring Optimal Trading Strategies and the Costs of Suboptimal Decisions in Market Impact Analysis
0xfdf @ twitter
orig
I recently reviewed two companion papers advancing our understanding of market impact: "The Cost of Misspecifying Market Impact" and "Trading with Concave Price Impact and Impact Decay." The key contributions are explicit optimal trading rules that incorporate nonlinear impact and decay, along with quantifying the "costs" of misestimating market impact.
There's an intrinsic conflict between expected returns (alpha signals) and implementation costs arising from market impact. The literature usually focuses on portfolio degradation from alpha estimation errors but overlooks the consequences of misspecifying market impact. Market impact is modeled using two critical parameters: concavity and decay, where concavity indicates that larger orders are proportionally cheaper, and decay captures the time taken for price reversion.
The classical square root model supports linear impact but lacks decay. The new model integrates the impact dynamics with a martingale approach, allowing for closed-form solutions, even with time-varying parameters. Optimal trading strategies derived from this model indicate that underestimating impact risks aggressive trading, which can erode profits, while overestimating leads to overly timid trades without major detriment.
A pivotal aspect is the model's immunity to price manipulation, establishing a no-arbitrage condition that confirms its robustness against inconsistencies. Empirical fitting of the model with a substantial dataset supports a fitted concavity around 0.48 and an optimal decay horizon near 0.2 days, challenging traditional models like the square root law, especially outside typical trading volumes.
While I prefer the Almgren model for its simplicity and empirical clarity in portfolio optimization, the insights and methodology emerging from this current research in stochastic control are promising and could drive future advancements in algorithmic trading strategies.
@quant_feed
0xfdf @ twitter
orig
I recently reviewed two companion papers advancing our understanding of market impact: "The Cost of Misspecifying Market Impact" and "Trading with Concave Price Impact and Impact Decay." The key contributions are explicit optimal trading rules that incorporate nonlinear impact and decay, along with quantifying the "costs" of misestimating market impact.
There's an intrinsic conflict between expected returns (alpha signals) and implementation costs arising from market impact. The literature usually focuses on portfolio degradation from alpha estimation errors but overlooks the consequences of misspecifying market impact. Market impact is modeled using two critical parameters: concavity and decay, where concavity indicates that larger orders are proportionally cheaper, and decay captures the time taken for price reversion.
The classical square root model supports linear impact but lacks decay. The new model integrates the impact dynamics with a martingale approach, allowing for closed-form solutions, even with time-varying parameters. Optimal trading strategies derived from this model indicate that underestimating impact risks aggressive trading, which can erode profits, while overestimating leads to overly timid trades without major detriment.
A pivotal aspect is the model's immunity to price manipulation, establishing a no-arbitrage condition that confirms its robustness against inconsistencies. Empirical fitting of the model with a substantial dataset supports a fitted concavity around 0.48 and an optimal decay horizon near 0.2 days, challenging traditional models like the square root law, especially outside typical trading volumes.
While I prefer the Almgren model for its simplicity and empirical clarity in portfolio optimization, the insights and methodology emerging from this current research in stochastic control are promising and could drive future advancements in algorithmic trading strategies.
@quant_feed
Understanding the Recent Surge in Japanese Yields and Yen Strength
MacroAlf @ twitter
orig
The Bank of Japan has raised interest rates again, establishing a short-term policy range around 0.25%, while also significantly reducing its government bond purchases over the next two years. Governor Ueda is optimistic about achieving a stable 2% inflation target, bolstered by significant wage increases secured by major labor unions. Although real rates remain negative, the BoJ's hawkish stance indicates further hikes could follow if core inflation approaches the target.
The market response has been sharp, with rising Japanese yields and a strengthening yen. Japanese investors, pivotal as global capital exporters, control over $1 trillion in US Treasuries and $0.5 trillion in EUR bonds. However, the current expense of FX hedging and the inverted US yield curve are making US Treasuries less attractive relative to Japanese government bonds, leading to potential capital allocation shifts.
As these changes manifest, the implications for global markets are significant, particularly in bond and stock allocations influenced by Japanese capital flows. Japan's monetary policy adjustments are not just local phenomena; they resonate throughout the global financial landscape.
@quant_feed
MacroAlf @ twitter
orig
The Bank of Japan has raised interest rates again, establishing a short-term policy range around 0.25%, while also significantly reducing its government bond purchases over the next two years. Governor Ueda is optimistic about achieving a stable 2% inflation target, bolstered by significant wage increases secured by major labor unions. Although real rates remain negative, the BoJ's hawkish stance indicates further hikes could follow if core inflation approaches the target.
The market response has been sharp, with rising Japanese yields and a strengthening yen. Japanese investors, pivotal as global capital exporters, control over $1 trillion in US Treasuries and $0.5 trillion in EUR bonds. However, the current expense of FX hedging and the inverted US yield curve are making US Treasuries less attractive relative to Japanese government bonds, leading to potential capital allocation shifts.
As these changes manifest, the implications for global markets are significant, particularly in bond and stock allocations influenced by Japanese capital flows. Japan's monetary policy adjustments are not just local phenomena; they resonate throughout the global financial landscape.
@quant_feed
Understanding Correlation in Quant Trading: Defining a "Good" Signal-Return Relationship
macrocephalopod @ twitter
orig
Correlation between your signal and future returns is crucial in quant trading. A key metric to consider is establishing what constitutes a "good" correlation. Using a simple model where future returns are normally distributed, we can derive beta based on correlation, volatility, and forecast horizon. This understanding implies that if trading costs are considered, identifying unprofitable signals becomes much simpler.
For instance, considering a stock with 3% daily volatility and 5 bps trading costs, we can expect to easily find signals with a correlation of 0.5%. If we engage factor hedging, we could halve the volatility but double our costs, aiming for a minimum correlation of 2% with idiosyncratic returns. In a different scenario, like predicting a 1-minute FX return with 0.2 bps trading costs and 0.3% daily volatility, we anticipate alphas with a much higher correlation of 8.5%.
These values represent baseline correlations; profitable trading requires exceeding these thresholds. A rule of thumb suggests that a correlation of 1.5 times the minimum allows for trades in about 5% of periods, while 2 times the minimum is considered very good. This framework is a practical way to extend the law of active management, emphasizing that realistic parameters will generally yield correlations less than 1.
@quant_feed
macrocephalopod @ twitter
orig
Correlation between your signal and future returns is crucial in quant trading. A key metric to consider is establishing what constitutes a "good" correlation. Using a simple model where future returns are normally distributed, we can derive beta based on correlation, volatility, and forecast horizon. This understanding implies that if trading costs are considered, identifying unprofitable signals becomes much simpler.
For instance, considering a stock with 3% daily volatility and 5 bps trading costs, we can expect to easily find signals with a correlation of 0.5%. If we engage factor hedging, we could halve the volatility but double our costs, aiming for a minimum correlation of 2% with idiosyncratic returns. In a different scenario, like predicting a 1-minute FX return with 0.2 bps trading costs and 0.3% daily volatility, we anticipate alphas with a much higher correlation of 8.5%.
These values represent baseline correlations; profitable trading requires exceeding these thresholds. A rule of thumb suggests that a correlation of 1.5 times the minimum allows for trades in about 5% of periods, while 2 times the minimum is considered very good. This framework is a practical way to extend the law of active management, emphasizing that realistic parameters will generally yield correlations less than 1.
@quant_feed