In today’s world, the investment and trading landscape changes quickly, involves an increasing number of competitive players, and requires more accurate insight and analysis than ever before. Yet, at the same time, regulation constraints can often limit the ability of market players to respond aggressively, and margin compression is constantly driving investors to do more with less. Organizations are also faced with finding a middle ground between risk and return on their investments, a gap that is incredibly narrow and complex to navigate.
These are just a few of the many challenges that face investment and trading desks, and as these hurdles become increasingly difficult to conquer, the need for sophisticated technical capabilities becomes increasingly critical.
Investing has always been an ultra-competitive market with tiny windows of opportunity, and each day it becomes more onerous to leverage optimal hedging strategies while also maximizing margin retention. In the past, banks have used traditional methods — such as statistical analysis, reversion analysis, mean variance reduction, statistical arbitrage, among others — to tackle these types of complex investment strategies. But the very popularity of these approaches, however, has diminished their power to create a lasting competitive advantage and has often exposed their limitations under market pressure. Additionally, statistical-based strategies suffer from a relative lack of agility due to the lag time between arriving at new insight and acting on that insight.
Since organizations are looking to create and benefit from a sustainable competitive advantage, many investors are increasingly augmenting their approach, or in some cases moving away from, purely statistical strategies. Instead, they are turning to sophisticated AI-powered capabilities.
Artificial intelligence is the most promising and capable solution class available to respond to the challenges previously mentioned. Through AI, organizations can improve analytical accuracy, hedge more effectively to minimize risk, accelerate reactions to fluctuating market conditions, and create — and sustain — the differentiation required to stand out in a growing and crowded field.
“Investing has always been an ultra-competitive market with tiny windows of opportunity, and each day it becomes more onerous to leverage optimal hedging strategies.”
Cluster detection, reinforcement learning-based automation, adaptive model modification, and dislocation detection are just some of the AI-based approaches coming to market. All of them can play a key role in the process of adjusting the investment equation, in or close to real time, to deliver competitive advantage while also lowering costs and risks as much as possible.
For example, SparkCognition’s AI-powered financial services solutions address these goals, regardless of whether an organization’s focus is on the buy side, sell side, or both. These end-toend market making trading platforms augment existing trading ecosystems, deliver industry-leading return on investment, and increase the ability to manage risk.
When it comes to investments, there is no time to waste on traditional methods. Optimizing trading and investment strategies to maintain a competitive advantage is critical, and in this complex, challenging landscape, that requires innovative approaches toward marketing making activity. With AI, this becomes possible.