Artificial Intelligence⁕ FULL Text ⁕Alternative Data
I recently spoke with an elderly gentleman – a trader and fund owner. This conversation inspired me to write an article about Artificial Intelligence tools that are used in trading today. His fund employs over a dozen traders investing in various markets, and he is a veteran of oil trading. His trading style is conservative – after finding a signal, he opens a trade holding a single position, sometimes for several weeks.
I want to show you where and how you can use the most modern solutions in this example. The largest funds use such solutions daily.
As we know, a trader’s performance depends on his decision-making process, signal quality, available liquidity and what happens during position management. New Artificial Intelligence tools will improve each stage of this process.
The solutions and ideas can be applied to any discrete trading system.
Alternative data means more accurate price forecasts and better, less risky signals
Many years ago, to find out the financial results of a chain of stores, one had to wait for their earnings announcement. Today, the analysis of satellite images of parking lots makes it possible to estimate whether there are more or fewer customers and, on this basis, to forecast the results.
Data from credit card transactions also allow estimating whether there will be an increase or a decrease in sales in the chain.
By analyzing card transactions and various data from the company’s environment, we can estimate the financial results that are yet to be published by this company. If we have access to relevant data, we can make such estimates ongoing and make investment decisions.
For the oil market, some companies forecast price movements based on inventories and demand. Today, you can count the amount of oil in tanks in remote areas of China by measuring the length of the shadow they cast (using satellite images). To this estimate, the amount of oil stored in tankers and on railway sidings is added. Thanks to this, we know the size of stocks in virtually every country “right now”.
Today’s “forecasting” goes to the real-time estimation of the selected quantity. It is one of the strong trends changing investing. As a result, traders and investors have a better picture of the current market situation. Consequently, entries are more precise and less risky. And funds that build this kind of competency at home are winning in the markets.
Supertrend: Moving from forecasting to nowcasting.
Traders who traded moves after the news are now seeing that these moves are getting weaker. On the other hand, traders in the largest mutual funds are rarely surprised when publishing data because they can estimate it in advance. It is also one of the reasons for the disappearance of some systems.
I read an article a few months ago by an economics professor arguing that alternative data is unnecessary because large funds react to earnings releases anyway. Of course, there is some point in this. However, suppose someone can assess the data before publication. In that case, he has a chance to enter the market faster – even before the others. Or take advantage of the entry of other large investors to exit the market with profits at the right time.
Market behaviour is changing because major investors have changed their behaviour. Those who fail to recognize this in time will be left with old signals and many stocks that are no longer as attractive as before.
Entry and exit - minimizing costs and adverse moves
One of the main problems that most institutional traders complain about today is the difficulty in finding liquidity.
The automated tool can analyze the available liquidity with different brokers, plan and execute market entry. Their use is to take advantage of available liquidity with minimal cost and minimal adverse move.
Virtually every large fund has this type of tool. For example, quants analyze order structures using Machine Learning methods and, on this basis, program various entry strategies. As a result, the program will execute the entry faster and better than a large team of traders.
Automation gives the trader more time for other tasks – more important than position building.
Trend: Entry automation: The program will execute an entry faster and better than a large trading team. The trader has more time for other tasks – more important than position building.
Entry when the signal is very good
Low risk attracts significant capital. Moreover, new signals are getting better and less risky, so the automated trading solutions get new tasks: increasing positions to the maximum for the best signals.
This change makes it difficult for other traders to find liquidity. As a result, they cannot enter the market as before, and the adverse movement is more substantial than previously.
However, this is not a problem for smaller traders. Some consider the lack of liquidity to confirm high signal quality. To quote:
“Lack of liquidity in good companies means that the biggest ones are already there.”
This situation is a problem for large traders who do not use the latest tools. The signals are still good and followed by the expected moves. However, there are no longer opportunities to build large positions.
The struggle for liquidity, especially in the best market situations, will only increase. Therefore, the focus will be on getting a good signal as early as possible to enter with significant capital quickly. It is with alternative data that this becomes possible.
The next step will be to exploit weak signals (I will describe what they are and how to exploit them in a future article).
Better signals and lower risk attract more capital. Automation of entries and exits becomes necessary; the program can utilize the available liquidity to the maximum. The competitive battle focuses on having a good signal as soon as possible.
Better assessment (view) of the situation while taking positions
While taking positions, a better view of the situation comes with new tools. However, this improvement does not end with market entry. Social media sentiment analysis and automated news analysis gives you a picture of market interest in a particular instrument.
Example:
One of the funds uses the mean reversion system. If the price of a company after good news has moved too far away from the average, then the program places a sell order.
The basis of this system is pure statistics. The program counts the main moving average the deviation from it. The system monitors the price, and if the deviation is “too high”, it places a sell order back to the average.
However, when good news keeps coming, the market often continues moving up.
To cope with this situation, they added a module for analyzing news and sentiment in social media. Suppose the information about the company is still good (or there are many positive posts on social media). In that case, the sell position is automatically closed.
Profit increase achieved.
Discretionary traders can use the same scheme. However, while it is still possible to follow the news for a few positions, it is difficult to follow and create any meaningful picture of social media sentiment. The Machine Learning tools for analyzing Twitter, Reddit, Finwiz, FB or any media where potential traders can undoubtedly help here.
Knowing what others think about a stock or commodity can help you manage your position better. If the sentiment gets bad, we can exit the market earlier, and a good social mood can make us hold the position.
Of course, these tools should be approached with caution – the market is dominated by the most significant players, and they do not publicly announce their intentions. But they have these tools, and with their aid, the smaller players can see what the big ones see and thus better understand their intentions and decisions.
Today, you can shorten or lengthen a position with news and social moods analysis because you have a better picture of what others are seeing, reading, and thinking.
Availability of the new solutions
The tools described here are available to the largest funds. However, their development is expensive and requires teams of good specialists (machine learning, social media analysis, liquidity analysis, microstructure of markets and its evolution over time).
As time goes on, these solutions will become more widespread, available from larger brokers or offered as stand-alone solutions.
Conclusion:
Thanks to alternative data, we see a shift from forecasting to nowcasting. We also have better forecasts of supply, demand and price behaviour. As a result, trading signals are better and less risky.
Current tools allow us to make better use of available liquidity, minimize entry costs and get a better average price. Over time, these two goals will be joined by a third: an emphasis on exploiting the best signals to the maximum.
Less risk will attract more capital, and liquidity will be depleted in many excellent current oves over time. The struggle for liquidity will intensify further. As a result, many sound systems will continue to yield high-quality signals. However, there will be no way to build a bigger position. Systems will still be good, but they will no longer be as profitable as before.
There will be a struggle to improve signal quality in the long run, mainly to make the signal appear faster.
These changes may, in some time, hit the best traders, like my interlocutor – a trader with enormous market experience. Why exactly them? People in his group have the most experience and the best signals. These are the signals that the biggest traders/funds will hunt for and try to improve.
This evolution does not mean that new tools are readily available or cheap. On the contrary, creating them requires a team of good Machine Learning specialists in finance. It also entails gathering much knowledge about the microstructure of markets.
In the next issue:
– The fastest path to cash in a fund – what to support with new tools first – some practical tips.
– AI and entry and exit hiding.
– Evolution of tools towards dynamic MM.
– Position and environment analysis.
– New and improved efficiency analysis of market paths.
– Automatic portfolio optimization (including new optimization models).
– New models for managing the size of positions on the horizon.
Full version of the article is available for you in New City Trader magazine (now free of charge):
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