Systematic Multi-Strategy
Investing
Investment Discipline
Systematic investment strategies provide a disciplined and objective approach to investing. At Leibniz Group, we utilize advanced technology alongside a team of experienced professionals to employ sophisticated algorithms and thorough data analysis. Our strategies aim to identify potential opportunities, striving to achieve steady risk-adjusted returns.
At a glance
Why us.
Systematic investment strategies utilize a structured and impartial approach to asset management. At Leibniz Group, we integrate modern technology with experienced professionals to develop algorithms and conduct detailed data analysis. Our methods are designed to identify potential investment opportunities, aiming to deliver consistent, risk-adjusted performance.
Furthermore, our application of advanced machine learning and AI technologies supports our investment processes. Our team, comprised of specialists in mathematics, computer science, data analysis, and finance, is committed to continuously improving our investment models. This ongoing enhancement helps us to respond effectively to changing market conditions and make decisions based on thorough analysis and the latest technology.
History
2021-today
Growth
Strategy Expansion
Recognizing the increasing demands of its clients, Leibniz expanded its range of strategies to cater to diverse investment objectives and risk profiles. Additionally, the company achieved a major milestone by signing institutional investor mandates, which not only reinforced its reputation but also attracted substantial capital inflows. As part of its strategic approach, Leibniz exclusively reserved certain strategy capacities for key investors, ensuring they received priority access to exclusive investment opportunities.
2019-2020
External Capital
Product Launches
During a transformative period, Leibniz embarked on a journey of opening up to external capital while commencing the buildout of a robust deal pipeline. This period marked a pivotal moment for Leibniz as it capitalized on its deep-rooted investor relationships, launched innovative products, and embraced external capital to propel its business forward. Simultaneously, the company introduced a range of new products, further diversifying its offerings and catering to the evolving needs of its clients.
2010-2019
Incubation
Strategy Accumulation
Leibniz has concentrated on developing a diverse array of strategies, with the goal of offering a broad multi-strategy offering. Understanding the importance of a thoroughly vetted approach, the company has methodically developed and refined its systematic strategies. Through extensive testing and analysis, Leibniz has worked to ensure that each strategy meets established standards of performance and reliability.
UNLEASHING THE POWER OF SYSTEMATIC INVESTING
Systematic Investing.
With our expertise in natural language processing (NLP), we are able to extract insights from unstructured data sources. Our NLP capabilities help us analyze market sentiment by reviewing news articles, social media sentiment, and other qualitative factors. By integrating sentiment analysis into our quantitative models, we strive to maintain a competitive stance in the industry.
At Leibniz Group, we employ higher frequency strategies and statistical arbitrage techniques. Our systematic approaches utilize real-time market data to execute trades quickly, aiming to take advantage of short-term market inefficiencies. We focus on capturing opportunities with speed and precision, reflecting practices common among industry leaders. Our commitment to agile execution and advanced strategies contributes to our standing in the field.
Leibniz Group strategies often have a history of trading at respected institutions, where they have aimed to deliver strong risk-adjusted returns, enhancing our reputation in the industry.
At Leibniz Group, we leverage advanced data analysis, machine learning, AI, and NLP. Our sophisticated technology infrastructure and robust quantitative models allow us to derive actionable insights and support data-driven decision-making. We continuously update and improve our models to respond to changes in market conditions, aiming to maintain a strong position in the industry.
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Risk Management.
Risk management is a central element of our investment philosophy at Leibniz Group. We maintain a structured risk management framework that includes portfolio diversification, risk assessment, and mitigation strategies. Our comprehensive risk controls are designed to protect capital and aim to achieve steady performance.
Discipline is a key component of our investment strategy at Leibniz Group. We follow predefined rules and algorithms to ensure disciplined execution and objective decision-making. This approach helps to minimize emotional biases and contributes to our goal of achieving long-term success.
We value transparent relationships with our clients. At Leibniz Group, open communication, regulatory compliance, and ethical conduct are priorities. We strive to provide clear insights into our investment processes, risk management techniques, and performance outcomes, fostering strong partnerships built on trust and integrity.
At Leibniz Group, we recognize that each investor has distinct goals and risk preferences. Our team is committed to working closely with clients to develop customized investment solutions tailored to individual needs. By adapting our strategies, we aim to provide a personalized service that distinguishes us in the market.
SEIZING OPPORTUNITIES
Strategies.
Consider partnering with Leibniz Group to explore the benefits of systematic investment strategies. Our expertise, advanced technology, and commitment to quality are integral to our approach.
Invest with an understanding that our strategies, listed below as examples, are developed with a focus on reliability. You can expect transparency, trustworthiness, and solutions that are tailored to your objectives and risk tolerance.
Strategies Overview
DISLOCATION
Inception: 2007
Type: Machine Learning Stat-Arb
The Leibniz DISLOCATION program is a set of machine learning and AI-based equity statistical arbitrage strategies that aim to profit from "dislocation", or disagreement among market participants about security values. It generates consistent, low-volatility returns that are not correlated to the broader market. The program uses diversified, liquid S&P1500 names, with an average of 1000 live positions and up to 2000 orders per day. The portfolio is constructed using a combination of optimization techniques, including closed-form, stochastically robust, and meta-heuristic methods.
EMOTION
Inception: 2011
Type: Multi-Strategy Stat-Arb
The Leibniz EMOTION program is a systematic investment strategy that has been utilized since 2011. The program trades globally diversified cash equities and ETFs, focusing on medium-term investor underreaction and short-term investor overreaction, operating with low frequency and capacity constraints. EMOTION aims to manage drawdowns and has been used to anticipate earnings surprises and identify ETF pairs with potential for price divergence.
SENTINEL
Inception: 2017
Type: AI/ML Multi-Strategy
SENTINEL is a deep learning-based, systematic trading program that utilizes daily sentiment data to predict price trends in a variety of global markets. The first sub- strategies went live in 2017. SENTINEL analyzes unstructured public data content from thousands of sources related to the economy and politics and uses proprietary tools to filter and map the data into price trend predictors. By doing this SENTINEL offers a unique perspective on potential market movements. SENTINEL uses supervised learning of classifiers across a broad spectrum of machine learning methodologies to improve prediction quality and to automatically create a diverse set of trading models and combines them into a multi-model, multi-scale strategy for high returns, maximized alpha/Sharpe and minimized drawdowns.
SWARM
Inception: 2015
Type: Machine Learning FX
The trading strategy uses a combination of neural networks and advanced statistical methods to trade a diverse range of financial products, primarily spot foreign exchange but also using futures market data. The sub-strategies are chosen using a Monte Carlo simulation and Markov Chain Monte Carlo process, and are combined into a single network with varying weightings. The strategy holds positions for varying lengths of time, typically 2 days, and has a high turnover. Since its inception in 2015, the strategy has evolved to become a system of non-optimized sub-strategies with unequal weightings, allowing for a more flexible and dynamic approach to the market.
REGIMENT
Inception: 2015
Type: Mid-Frequency FX
The REGIMENT investment program is a fully automated strategy that seeks to profit from volatility in the foreign exchange market using both directional and mean-reverting approaches. The program employs quantitative analysis, order routing algorithms, low latency technology and liquidity management to identify and exploit market inefficiencies. It is designed to adapt to different market conditions and risk regimes, using a diverse range of trading and hedging strategies. The program also includes a master risk layer that runs thousands of concurrent computations per second to manage risk.
P-REACTION
Inception: 2016
Type: NLP Stat-Arb
Leibniz P-REACTION rigorously applies a data-driven framework using machine learning methods and natural language processing to predict how investor behavior impacts asset prices and leads to short-term mispricing surrounding earnings related news and events in a medium-frequency approach. P-REACTION digests and pre-acts as as well as reacts to many thousand events via its news-driven strategies in a fully systematic manner. Many years of development work and attention to detail now allow the strategy a reliable approach and fast reaction time to market moving news by exploiting behavioral biases and market inefficiencies.
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