Charles has a clear philosophy for the shares he buys and sells. His track record is strong but he’s under increasing short-term pressure. Consultants and clients are demanding to know how he tracks market sentiment. Reading the FT is no longer enough.

Charles and Peter pitched for a new client. They presented their thesis and results but the client wanted more. How do they investigate funds flows, option markets and short selling? Do they use GPT market analysis? Why not?

Charles has seen market fads come and go. But he knows that technology is changing market structure and the way that money is managed. After all, he was early into Nvidia.

Peter has an idea.

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“We already present longer term trends to clients using AI-powered sentiment analysis. If we hook up more data then we can shorten the time frame.”

“But how’s it going to help my investment decision making?”

“You’re thinking about when to sell Nvidia, right? Do you know if a price move is significant for a particular time of day? No one does without financial data analysis.”

“The big moves are driven by results and occasional news from Taiwan”

“What if sellers started in the option market, or borrowing more stock? Wouldn’t you like to know if that mattered before it hit share prices?”

“How would I track that?”

 “We’ll add a chart to your screen and send an alert when outsized moves occur.”

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Signal and Noise

There is a lot of noise around stock trading. Brokers and bankers want you to use them to interpret big data for investors. But you don’t outsource financial risk management.

We started working with a data and analytics company in 2009. It approached big fund managers to offer a new service. The investors asked for sentiment analysis across the full range of asset markets.

Famous money managers at these firms had written books about their investment process. The data they analyzed to gain an advantage was digitized now. Machine learning for finance would tell them when trends and turning points mattered.

Our client excelled at this analysis. But it needed to scale its business to cover the high costs of data. This meant processes for cleaning it, identifying signals and visualizing the results. This is not work for a quant team.

The client outsourced the business processes to us. We integrated data, handled millions of calculations a day and showed results at human speed. The principles of quantitative investing and algorithmic trading informed fund managers who maintain discretion over investing.

The platform we created was a success and our client was sold several years later. We still work with the buying company.

Cross asset analysis is in reach of all money managers today thanks to natural language processing (NLP) in finance. But you must know how to interrogate data and when the results matter. The signal must be separate from the noise.

Investing and Winning Clients

Charles has a new screen on his monitor. The system sends alerts that mean he does not spend all day watching markets. If something matters, a quick click takes him to all the analysis he needs.

Peter has the software on his laptop. It’s impressive and the consultants love it as much as the clients. Pitches run smoothly, allowing Charles time to highlight his long-term trends. Investment performance is what matters and what he is paid for.

Now, what to do with those Nvidia shares?

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Charles is on a journey to utilize the power of AI for investing. His understanding is accelerating. Follow more of his adventures here:

• AI and Investing for the Security Conscious
• An Introduction to AI for Hesitant Fund Manager