Zonthur

In volatile markets, asset managers often face key challenges: - What’s driving the price movement? - Can I trust this signal and understand its foundation? Traditional trading signals can sometimes leave you with more questions than answers, especially when they are built on black-box models or superficial correlations. We focus on tackling these challenges with a different approach: - Identifying Key Price Drivers Our system analyzes the evolving relationships between assets to highlight the main drivers behind price changes. Instead of relying on loose correlations, we map out the interconnected factors impacting asset prices, helping you see?which assets are influencing others?and how. This gives you clearer context to act with more confidence. - Clarity and Transparency in Signal Performance We believe that understanding a signal’s?evolution and performance?is key to making informed decisions. You can track how asset relationships have shifted over time and see the historical performance of each signal. This level of transparency allows you to validate signals based on?clear, evolving dynamics?and adjust your strategies with greater precision. - Adaptive Alignment with Your Strategy We categorize our signals by short, medium, and long-term horizons, enabling you to align them with your current strategies. Whether you’re focused on short-term trades reacting to immediate events or longer-term shifts driven by structural changes, our approach helps you stay proactive in adapting to market changes. In a market where certainty is hard to come by, our platform aims to offer actionable clarity and a deeper understanding of the forces driving change. Reach out via PM to see our system in action. #tradingsignals #uncertainty #networkanalytics #riskmanagement #assetdrivers #complexsystems #graphtheory #cas #financialmarkets #financialanalysis #zonthur

One of the key challenges in financial markets is making causal inferences. We spend substantial effort on estimating correlations and tracking their changes over time, but often this doesn't provide the necessary insights into causality. Why? Because correlation is bidirectional—it shows how two variables move together, but not the why behind that movement. While economic theory can be used to suggest or infer causality, this approach often falls short when navigating short-term market behavior. Econometric tools such as Granger causality assess causal links using past data, but they don’t account for the changing strength and direction of causality over time, nor do they capture the complexities of global markets where feedback loops and third assets play a role. Simply relying on theory is not enough for managing fast-moving markets. Inferences on causality are inherently complex and require a deep use of data science and machine learning. The strength and direction of causal relationships are constantly evolving, and without the right tools, it’s nearly impossible to keep up. To address these challenges, we need to rethink how we approach markets. By treating them as Complex Adaptive Systems (CAS)—networks of interconnected assets that evolve based on factors like returns and volatility—we can better understand causality. Graphical analysis and network models allow us to visualize these dynamic relationships and uncover deeper insights that traditional methods overlook. At Zonthur, we’ve accomplished significant results in forecasting returns through advanced network models that adapt to the dynamic nature of causality in the markets. It’s a complex problem, but with the right tools it becomes more manageable. #causality #correlation #complexsystems #networkanalysis #graphtheory #machinelearning #datascience #financialmarkets #finance #economics #zonthur

We’re thrilled to have a CEO with such an interesting background leading Zonthur. His journey started in cybersecurity, where he became skilled at uncovering hidden patterns in complex and adversarial environments. Now, he’s bringing that expertise to finance, working alongside our team to apply innovative methods like graph theory, complex adaptive systems and agent-based modeling to improve trading in financial markets. We’re proud of the direction he’s taking us, and we’re excited about what the future holds for Zonthur. #zonthur #finance #graphanalytics #cas #team #leadership #innovation?

Think about complex systems and the three C’s of systemic risk? ? Hal Scott, a professor at the Harvard Law School coined a phrase the “three C’s of systemic risk” -?connectedness, correlation, and contagion.[1] Scott developed a framework for understanding?regulatory issues in the post-GFC environment and focused on oversight issues with banks to?prevent financial crises.[2] ? ? Zonthur also looks at the three C’s of systemic risk but from an asset price perspective using? graphical networks and complex system modeling. First, markets should be looked at as a?complex system which has different levels of connectedness and clustering. Second,?connectedness leads to correlation and more importantly, casual relationships. Third, changes in?connectedness and correlation will display possible contagion. Knowing how markets form a?complex system will help allow investors to make more informed decisions on what markets may?do in the future.? ? Markets will not usually face contagion risks, but investors should want a framework to understand how shocks move through a market system and how these perturbations can be exploited for?potential profit. It is well-known that if there is a crisis, correlations for many assets will move?higher - a pull to one. A common factor will cause contagion across markets with concentrated?selling or buying by many institutions at the same time. Unfortunately, focusing just on correlation?matrices is a simplistic approach that does not tell us how a system of markets may behave. A better approach is to think of markets as a complex system where asset prices have dynamic?connections across time. Market clusters have linkages with some being dominant leaders and?others being followers. These linkages and correlations will change based on different shocks or?stimuli. Correlations are just the manifestation of connections. These connections have time?series properties whether some markets change cause or precede the move in others within a?system. ? ? Zonthur models the system of markets through time rather than just presenting correlations at a given point in time. This allows us to better understand contagions, identify deviations from normal behavior across markets and their return to normal in the future. By employing a systematic approach, Zonthur better models market relationships and forms superior predictions.? ? Zontur focuses on all three C’s of systemic risk, connectedness, correlation, and contagion?through network structures to make better judgements on market system behavior. ? Notes in the comment section below ? #networkanalytics #graphtheory #complexadaptivesystems #cas #causality #correlation #connectedness #diversification #financialmarkets #globalmacro #zonthur

Can Graph Theory and Complex Adaptive Systems revolutionize capital allocation? Capital allocation traditionally relies on static models built from historical performance, which often fails to capture the fluid and interconnected nature of financial markets. As a result we can have a limited understanding of how different strategies interact within a portfolio, leading to suboptimal resource distribution. Let’s look into how Graph Theory and CAS offer a dynamic alternative. - Graph Theory?allows capital allocators to model the relationships between strategies as a network of interconnected nodes (strategies) and edges (correlations or dependencies). This helps identify key nodes (strategies or assets) that influence others, and also reveals clusters—groups of strategies that behave similarly due to common market forces. - Complex Adaptive Systems?(CAS) take this a step further by recognizing that strategies within a portfolio adapt and evolve based on market feedback and each other’s performance. Rather than viewing strategies as isolated entities, CAS treats them as part of a constantly changing system. This dynamic interaction helps portfolio managers anticipate how strategies will react under varying market conditions, leading to more informed capital allocation decisions. For example, imagine a Hedge Fund managing a portfolio that includes both equity and commodity strategies. Traditional allocation might treat these as independent entities, assigning capital based on historical returns. However, using a CAS and Graph Theory approach, the allocator can identify that during periods of high volatility, the commodity strategy's performance helps stabilize the portfolio, while during trending markets, the equity strategy drives gains. By understanding these interdependencies, capital can be allocated more dynamically, optimizing risk and return across different market conditions. If you are curious about how these methodologies can enhance your approach to capital allocation, have a look at what we do:?https://lnkd.in/eUQGkzAX #graphtheory #graphnetworks #complexadaptivesystems #capitalallocation #financialmarkets #marketanalysis #portfoliooptimization #portfoliomanagement #hedgefundstrategies #investmentstrategies #riskmanagement #systemdynamics #zonthur

If you want to understand how graph theory can be applied to analyzing the interconnections between financial assets and how this can be used to evaluate the market context and improve forecasts of market movements, here's a quick breakdown: Interconnections Between Assets - Nodes and Edges: In a financial graph, assets can be represented as nodes, and the relationships (correlations, causal links) between them are represented as edges. - Correlation Networks: Correlation between asset prices can be mapped as edges connecting nodes (assets) in a network. Strong correlations indicate closer connections, which might represent sectoral dependencies, market sentiment alignment, or exposure to common economic factors. - Causal Networks: With causality analysis, edges in a graph can also represent directional influence from one asset to another, helping to understand how changes in one asset might cause changes in another. Analyzing Market Context - Contextual Analysis: Analyzing the structure of the network (e.g., clusters, central nodes, etc.), gives you insights into how assets are related within the broader market context. For example, central nodes in the network may represent key assets that influence a large number of other assets, while clusters might indicate groups of assets that behave similarly due to common drivers. If you want to learn more about how graph network analytics can help you, check out this page about identifying macroeconomic trends with our platform: https://lnkd.in/ejsgFwXE #graphtheory #graphnetworks #financialmarkets #causalanalysis #marketanalysis #networkanalytics #macroeconomictrends #forecasting? #financialanalysis #investmentstrategies #riskmanagement #hedgefunds #investing #globalmacro

"People think that a thing called correlation exists. That’s wrong." ~ Ray Dalio. Correlation disguises the shifting logic of markets. Beyond them lies the market's deeper logic. Where many see point to point correlations, we see broader structures emerging, expanding, transforming or collapsing. At Zonthur we take this to heart and built a platform for those who think non-linearly and are focused in understanding these broader, more complex, trends that underly individual price movements. The link to our product video is in the comments?below ? #correlation #marketstructures #networkanalytics #graph #markets #hedgefunds #globalmacro #finance #zonthur

We are thrilled to see Zonthur’s networking graph enter the market. Through modeling global financial markets as a networking graph and implementing complex systems theory we enable quants and research analysts to: -> identify market structures impossible to observe before -> monitor how events propagate through assets -> take risk management to a whole new level. If you are interested in knowing more about our product, what we are building, and discover the potential of networking analytics for financial markets, follow us for updates! #globalmacro #trading #quantitative #quant #complexsystems #networking #graph #analytics #zonthur