Day traders, hedge fund managers, commodity brokers, and energy traders routinely take advantage of market price differences to turn immediate profits. Maximizing an arbitrage opportunity requires precise timing and confidence in your market data, otherwise what looks to be a sure profit could turn into a net loss. That’s why more financial, energy, and commodity traders are relying on prescriptive analytics to ensure their arbitrage profits.
Before delving deeper into how prescriptive analytics strategies create arbitrage opportunities, let’s be clear about what we mean by arbitrage. In brief, arbitrage is the practice of buying goods or commodities in one market, while simultaneously selling them in another. Profit is generated by the difference between the purchase price and the sales price. For example, you can buy stock on the New York Stock Exchange (NYSE) for a purchase price of $5.00 per share, and then sell it on the London Stock Exchange (LSE) for $5.05. The practice works because of market inefficiencies and differences in the timing of market updates and pricing. The same types of arbitrage opportunities can be created in commodities or any trading market.
Because arbitrage transactions are made quickly, reliable real-time analytics are important. Most professionals use some form of analytics to guide financial decision-making, most commonly predictive analytics. By analyzing what has happened in the past, models can be created to predict future behaviors. Using predictive analytics, you can identify various types of opportunities, such as:
- Cutting cost of goods by identifying areas to reduce spending without cutting quality
- Building revenue by identifying areas where you can raise prices or charge more for services
- Increasing return on investment (ROI) or return on assets (ROA) by shortening the time to return, such as by predicting slow-paying customers and obviating the impact of slow payments
- Managing risk by reducing the impact of potential actions or investments
For everyday business operations, these types of models can be extremely valuable and profitable, but for arbitrage, you really need prescriptive analytics to show you the best possible outcome based on various choices and known parameters. Prescriptive data modeling provides a new level of accuracy and insight and can reveal previously unseen opportunities.
The Power of Prescriptive Analytics
In order to take advantage of an arbitrage opportunity, you need to do more than predict trends—you have to balance a variety of moving parts. To make arbitrage trading decisions, you need to be able to see and act on the interplay of market demand, capacity, product availability, and a company’s existing commitments. You also have to be able to assess the trade-offs across an entire portfolio, such as sell versus buy.
In the case of energy trading, for example, prescriptive analytics can assess the company’s entire portfolio of contracts and energy commitments. It can then model scenarios for energy trade-offs, such as converting to liquified natural gas (LNG) or not. It also shows the impact of current logistics commitments and constraints, and the financial implications of each of these scenarios/changes.
In identifying arbitrage opportunities, prescriptive analytics can inform a variety of important factors that can affect profit:
- When assessing cost of goods for energy or commodities trading, predictive analytics will provide real-time insight based on current conditions. You need prescriptive analytics to understand the impact of variables, such as storage, shipping, and cost/benefit ratios. In arbitrage trading, these variables can affect profit and loss.
- Creating a more accurate forecast of goods requires prescriptive analytics. Predictive analytics can give you a projection based on past performance, but to factor in current market conditions, you need a more prescriptive data model.
- Production projections are also a prescriptive data modeling problem. You can’t rely on history alone to predict demand; you need prescriptive analytics to include other market factors that will impact demand.
One of the advantages of prescriptive analysis for arbitrage trading is that it can power operational machine learning to suggest actions that positively affect outcomes. As more prescriptive data flows into the system, models can offer updated outcomes and improve the accuracy of the arbitrage model.
Creating Real-World Arbitrage Opportunities
Let’s consider four real-world applications for prescriptive analytics in creating arbitrage opportunities:
1. Natural gas is increasingly becoming a source of energy, as well as a global commodity, especially with the expansion of liquified natural gas (LNG). Global gas markets are expanding as a result of LNG trading, including the development of gas hubs. Prescriptive analytics modeling can be invaluable in arbitrage pricing for global LNG trading because it’s the best way to incorporate variables such as the high price of gas storage and inventory levels.
2. Commodity markets are becoming increasingly important as part of tactical asset allocation. Feedback trading uses past performance (predictive analytics) to identify opportunities. With prescriptive modeling, commodity trading can become even more accurate, identifying spot prices and futures that promise a higher return.
3. In oil and gas, equipment costs are a substantial part of operating overhead. In order to accurately predict profit and loss, maintenance and depreciation of vital equipment has to be calculated into operating costs—something that can be best achieved using prescriptive analysis.
4. Staffing is another area where arbitrage can come into play with the help of prescriptive analysis. Predictive analytics can help project employee attrition and staffing needs, but by adding prescriptive analytics, you can also apply arbitrage to personnel and assess employees’ experience, education, and work profiles to create engagement plans that can deepen employee commitment, increase productivity, and create more realistic staffing profiles.
Creating successful arbitrage opportunities is a matter of assessing the appropriate variables that will affect pricing and being able to act quickly to optimize margins. The only means to accurately model these types of opportunities is using prescriptive analytics that goes beyond the historical data to offer outcomes based on all of the variables. When coupled with machine learning tools, prescriptive arbitrage models can yield impressive returns.