Complete Intelligence


Elon Musk’s Tweet Saga: Impact on CEO Communications and Investor Relations

This video is a segment from a BBC TV interview. The full show is not yet available online as of the time of this publication.

The video discusses the recent lawsuit against Elon Musk regarding a tweet he made regarding taking Tesla company private. Tony Nash is being interviewed by the BBC to discuss the impact of this lawsuit on CEO communications and investor relations.

Tony mentions that many legal experts believed that Elon Musk was going to be found guilty for his tweet. He considers the tweet to be stupid, but not intentionally misleading. However, the impact of the tweet was damaging to shareholders.

Tony says that the share price of the company has risen over the last four years, despite the lawsuit, and the plaintiffs in the lawsuit were not able to prove that they lost billions of dollars.

Tony also mentions that the SEC has fined both Twitter and Elon Musk for the tweet. He says that this enforcement of regulatory laws will change the way that CEOs communicate with the public, especially in tech companies. CEOs may become more informal in their statements, even if their claims may not be true. This will make it more difficult for investors, who will have to be more cautious and do their research before making any decisions.

Tony mentions that the outcome of the lawsuit shows that executives can be more careless in their communications going forward. He says that while $40 million fine is not a significant amount, it may still lead to a reprimand from the SEC. He advises investors to be careful and do their research before taking positions in the market.



I know a lot of legal experts thought that he was going to be found guilty. It was a stupid tweet. I think he would take it back. He wouldn’t admit that, but I think he probably would if he could do it over again. But I think it does make the landscape pretty crazy going forward where executives can say things publicly that they would be very careful of saying before. So if you’re an investor, I think you now have to look at the formal declarations, regulatory filings to find out real information. Musk has just made this an incredibly informal way of talking with people.


So you think stupid, not intentionally misleading, but it proved very damaging, of course, to the shareholders?


It did. The share price has done very well since 2018. So I don’t know what these guys were investing in to lose billions of dollars, unless it was some sort of exotic maybe option or something like that. The shares have risen over the last four years. If those plaintiffs, some of the statements that they made said they were trying to look out for their well being, these sorts of things, well, the share price has delivered over the long term. So the SEC has fined Twitter and Musk. So in terms of a regulatory environment, that’s been enforced. But did they prove that they lost billions of dollars? Obviously they didn’t because this was a jury trial.


Do you think this will change the way that Elon Musk tweets, or will it change the way that other CEOs tweet in future?


Sure, I think the voice of the CEO now can get more informal and I think especially in tech companies, I think you’ll see CEOs becoming very informal and making claims, even if they can justifiably say, “well, I thought that this was going to happen.” Right. And this is what Musk defense as well. “I reasonably thought this was going to happen, but it didn’t happen, so oh, well.” I think especially in tech, you’ll see CEOs make claims that may or may not be true, but they’re leading in that direction. So investors are just going to have to be much more wary and they’re going to have to do their research before they make take positions in markets.


But surely now that Elon Musk has done this and he’s had this, I mean, it’s a reprimand, isn’t it? It’s a public reprimand. Even if he was found not guilty, people will have to be very careful. They won’t have the same defense because of what has happened to Elon Musk here.


Right, they won’t. And again, I think it’s from a regulatory perspective and from, let’s say in investor relations or communications perspective, it all of a sudden it’s the Wild West kind of you can kind of say what you want. You can kind of say, I think we’re going to go private at a certain price, and if it doesn’t happen, you just kind of shrug your shoulders and go, well, it didn’t work out. And again, you may get a slap on the wrist. $40 million isn’t really a slap on the wrist, but you can get a slap on the wrist from the SEC. But investors again, investors have to be very careful, and investors have to do their research before taking positions. And this is really what the basis of the lawsuit was about. So those executives, I guess, can be a bit more careless in their communications. I guess that’s what it tells me with the finding of the suit, is executives can be a bit more careless with their communications.

News Articles

How AI-based ”nowcasts“ try to parse economic uncertainty

This post was published originally at

This month, the S&P 500 officially hit bear-market territory—meaning a fall of 20+ percent from recent highs—and investors everywhere are looking for some way to predict how long the pain could last.

Machine learning startups specializing in “nowcasting” attempt to do just that, by analyzing up-to-the-minute data on everything from shipping costs to the prices of different cuts of beef. In times of economic volatility, investors and executives have often turned to market forecasts, and ML models can offer a way to absorb more information than ever into these analyses.

One example: Complete Intelligence is a ML startup based outside Houston, Texas, that specializes in nowcasting for clients in finance, healthcare, natural resources, and more. We spoke with its founder and CEO, Tony Nash, to get a read on how its ML works and how the startup had to adjust its algorithms due to market uncertainty.

This interview has been edited for length and clarity.

Can you put the idea of nowcasting in your own words—how it’s different from forecasting and the nature of what you do at Complete Intelligence?

So Complete Intelligence is a globally integrated machine learning platform for market finance and planning automation. In short, we’re a machine learning platform for time series data. And nowcasting is using data up to the immediate time period to get a quick snapshot on what the near-term future holds. You can do a nowcast weekly, daily, hourly, or minutely, and the purpose is really just to understand what’s happening in markets or in a company or whatever your outlook is right now

And what sort of data do you use to fuel these predictions?

We use largely publicly available datasets. And we’re using billions of data items in our platform to understand how the world works…Macroeconomic data is probably the least reliable data that we use, so we use it for maybe a directional look, at best, at what’s happening. Currencies data is probably the most accurate data that we use, because currencies trade in such narrow bands. We use commodities data, from widely traded ones like oil and gold, to more obscure ones like molybdenum and some industrial metals. We’re also looking at individual equities and equity industries, and we track things like shipping times for goods—shipping times…are usually pretty good indicators of price rises.

Who are your clients, and how are the nowcasts used in practice?

Our clients range from investors and portfolio managers, to healthcare firms and manufacturing firms, to mining and natural resources firms. So they want to understand what the environment looks like for their, say, investment or even procurement—for example, how the current inflation environment affects the procurement of some part of their supply chain.

In fact, we’re talking to a healthcare company right now, and they want to nowcast over the weekend for some of their key materials. In an investment environment, of course, people would want to understand how, say, expectations and other variables impact the outlook for the near-term future, like, days or a week. People are also using us for continuous budgeting—so revenue, budgeting, expenses, CFOs, and heads of financial planning are using us…to understand the 12- to 18-month outlook of their business, [so they don’t have to have an annual budgeting cycle].

Tell me about how the AI works—which kinds of models you’re using, whether you’re using deep learning, etc.

There are basically three phases to our AI. During the pre-process phase, we collect data and look for anomalies, understand data gaps and how data behaves, classify data, and those sorts of things.

Then we go into a forecasting phase, where we use what’s called an ensemble approach: multiple algorithmic approaches to understand the future scenarios for whatever we’re forecasting. Some of those algorithms are longer-term and fundamentals-based, some of them are shorter-term and technical-based, and some of them are medium-term. And we’re testing every forecast item on every algorithm individually and in a common combinatorial sense. For example, we may forecast an asset like gold using three or four different forecast approaches this month, and then using two forecast approaches next month, depending on how the environment changes

And then we have a post-process that really looks at what we’ve forecasted: Does it look weird? Are there obvious errors in it—for example, negative numbers or that sort of thing? We then circle back if there are issues…We’re retesting and re-weighting the methodologies and algorithms with every forecast that we do.

We’ve had very unique market conditions over the past two years. Since AI is trained on data from the past, how have these conditions affected the technology?

You know, there’s a lag. I would say that in 2020, we lagged the market changes by about six weeks. It took that amount of time for our platform to catch up with the magnitude of change that had happened in the markets. Now, back then, we were not iterating our forecasts more than twice a month. Since then, we’ve started to reiterate our forecasting much more frequently, so that the learning aspect of machine learning can really take place. But we’ve also added daily interval forecasts, so it’s a much higher frequency of forecasting and in smaller intervals, because we can’t rely on, say, monthly intervals as a good input in an environment this volatile.


What is the Future of Investing?

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8 July  2019 | Money FM 89.3

Tony Nash, CEO of Complete Intelligence shares what is the future of investing and how investors can deal with recent economic concerns including the US-China trade war, and how they can position themselves to come out on top.