Our Services
Moneda systematically manages volatility of a currency basket and generates revenues outperforming inflation.
Our expertise:
Financial Quantitative Analysis
Financial quantitative analysis is a research method that uses numerical data and statistical analysis techniques to evaluate financial data and make investment decisions. It involves using mathematical models and algorithms to analyze financial data, such as stock prices, interest rates, and economic indicators. Some common quantitative analysis techniques used in finance include regression analysis, time series analysis, Monte Carlo simulation, and machine learning algorithms.
Financial quantitative analysis can be used for a variety of purposes, including:
- 1. Portfolio management: Quantitative analysts use statistical models to manage investment portfolios and maximize returns while minimizing risk.
- 2. Risk management: Quantitative analysis is used to identify and manage risks associated with financial investments.
- 3. Trading strategies: Quantitative analysis can be used to develop and test trading strategies based on historical market data.
- 4. Financial modeling: Financial analysts use quantitative analysis to build financial models that can be used to forecast future financial performance.
- 5. Genetic optimization is a technique used in finance to optimize investment portfolios using genetic algorithms. Genetic algorithms are computational techniques that use natural selection, crossover, and mutation to search for the best solution to a problem. Moneda uses genetic optimization to search for the best combination of investments to maximize returns while minimizing risk.
- 6. We analyze past performance of our models with a technique called Walk forward optimization. It consists in a method used in finance and investment strategies to evaluate the performance of a trading system over time. It involves splitting a historical dataset into segments, where each segment is used to train a trading system, and then the system is tested on a subsequent out-of-sample dataset.
- 7. Moneda uses AI and Deep learning to develop predictive models that analyze market trends and help generating better investment returns. This includes developing algorithms
Currency Risk Management
Currency risk management is a critical aspect of international trade and investing, as fluctuations in exchange rates can significantly impact the value of investments and transactions. There is no single “best” currency risk management strategy, as the most effective approach will depend on the client’s specific goals, risk tolerance, and market conditions. However, here are a few commonly used strategies:
- Hedging: Hedging involves using financial instruments such as forward contracts, futures, options, and swaps to lock in a specific exchange rate and protect against potential losses due to currency fluctuations.
- Diversification: Diversification involves spreading investments across multiple currencies, asset classes, and geographies to reduce exposure to currency risk.
- Monitoring: Regularly monitoring exchange rates and market conditions can help identify potential risks and opportunities, allowing for timely adjustments to portfolios and trading strategies.
Currency risk management is a complex and constantly evolving field, and effective risk management requires ongoing monitoring, analysis, and revision of strategies.
Model Portfolio Indexing and Volatility Control
Moneda partners with Azzilon Canada Inc to manage some of its model portfolios volatility and to index them with regulated index providers such as the Singapore Exchange “SGX”. These indices are calculated and published on Bloomberg and Refinitiv.
Financial Engineering
The field of financial engineering has grown in importance over the last few decades as financial markets have become increasingly complex and interconnected. It has played a key role in the development of new financial products and technologies.
Moneda’s founders and partners have a great deal of experience in financial engineering. This expertise can be applied in various areas such as investment banking, asset management, risk management, and insurance. This allow Moneda to provide ways to obtain exposure to its model portfolios, and to add, when so require, substantial degree of capital protection to the investors provided by partnering brokers and bank.
Trading as a Service
When one of our strategies can be used to sub-advice asset managers, Moneda delegate the task to our Trading as a Service (“TaaS”) department. TaaS refers to the provision of trading services, tools, and infrastructure to businesses and individuals who wish to participate in financial markets. This can include access to trading platforms, data analytics tools, market research, and execution services.
Proprietary Trading
Proprietary trading refers to a practice where a financial institution, such as a bank or a hedge fund, uses its own money to make trades in various financial markets, rather than trading on behalf of clients or customers.
Moneda believes in its ability to generate good risk-returns ratios in its portfolio. It therefore became logic to trade some of its own money which not only generate positive cash-flows for its treasury but also entitle our R&D department to constantly monitor and adjust the behavior of our models.
Systematic Market Maker
Systematic market makers are used by a variety of financial institutions, including hedge funds, investment banks, and proprietary trading firms. They are particularly prevalent in electronic markets for stocks, options, and other derivatives, where they play a crucial role in ensuring that these markets remain efficient and liquid.
Moneda acts, for some of its clients, as a systematic market maker using a type of algorithmic trading system that uses mathematical models and automated trading rules to provide liquidity to financial markets.
Moneda uses advanced statistical models to analyze market data and identify patterns that can be used to make predictions about future price movements. We also use sophisticated algorithms to execute trades automatically, in order to capitalize on these predictions and maintain a balanced book of orders.
One of the key advantages of systematic market making is that it can operate around the clock, without the need for human intervention as far as decisions are concern. This allows us to provide continuous liquidity to financial markets, even during periods of high volatility or low trading volumes.
Systematic Layering Exposure
Systematic Layering Exposure (“SLE”) is a strategy used to take advantage of market trends and price cycles. This approach involves buying or selling a single investment in stages, based on the direction of the trend or cycle, to increase or decrease exposure to the investment over time. Moneda applies SLE to enhance returns and reduce risk on typical Buy & Hold portfolios when Long & Short or Long & Cash strategies are not applicable or allowed.
The first step in the SLE strategy is to identify the direction of the momentum or price cycle of the underlying investment. If the momentum or price cycle is positive, then the initial exposure is taken. This position will be kept until the end of the investment.
If the momentum or price cycle confirms that the trend is continuing, then subsequent investment will be made adding to the initial position. The strategy will continue to add to the position as long as the momentum or price cycle remains positive and will liquidate all the subsequent investments as the momentum or price cycle begins to turn negative.
Overtime, SLE greatly enhance the return on investment which contributes to an overall portfolio risk reduction.