Is it possible to build a highly effective forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science analytics, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies?
Is it possible to build a highly effective, multi-faceted, intelligent forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science analytics, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies as part of a forecasting system for complex, multi-faceted economic processes in such a way as to reduce the scale of the impact of the paradox of a self-fulfilling prediction and to increase the scale of the paradox of not allowing a predicted crisis to occur due to pre-emptive anti-crisis measures applied?
What do you think about the involvement of artificial intelligence in combination with Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies for the development of sophisticated, complex predictive models for estimating current and forward-looking levels of systemic financial, economic risks, debt of the state's public finance system, systemic credit risks of commercially operating financial institutions and economic entities, forecasting trends in economic developments and predicting future financial and economic crises?
Research and development work is already underway to teach artificial intelligence to 'think', i.e. the conscious thought process realised in the human brain. The aforementioned thinking process, awareness of one's own existence, the ability to think abstractly and critically, and to separate knowledge acquired in the learning process from its processing in the abstract thinking process in the conscious thinking process are just some of the abilities attributed exclusively to humans. However, as part of technological progress and improvements in artificial intelligence technology, attempts are being made to create "thinking" computers or androids, and in the future there may be attempts to create an artificial consciousness that is a digital creation, but which functions in a similar way to human consciousness. At the same time, as part of improving artificial intelligence technology, creating its next generation, teaching artificial intelligence to perform work requiring creativity, systems are being developed to process the ever-increasing amount of data and information stored on Big Data Analytics platform servers and taken, for example, from selected websites. In this way, it may be possible in the future to create "thinking" computers, which, based on online access to the Internet and data downloaded according to the needs of the tasks performed and processing downloaded data and information in real time, will be able to develop predictive models and specific forecasts of future processes and phenomena based on developed models composed of algorithms resulting from previously applied machine learning processes. When such technological solutions become possible, the following question arises, i.e. the question of taking into account in the built intelligent, multifaceted forecasting models known for years paradoxes concerning forecasted phenomena, which are to appear only in the future and there is no 100% certainty that they will appear. Well, among the various paradoxes of this kind, two particular ones can be pointed out. One is the paradox of a self-fulfilling prophecy and the other is the paradox of not allowing a predicted crisis to occur due to pre-emptive anti-crisis measures applied. If these two paradoxes were taken into account within the framework of the intelligent, multi-faceted forecasting models being built, their effect could be correlated asymmetrically and inversely proportional. In view of the above, in the future, once artificial intelligence has been appropriately improved by teaching it to "think" and to process huge amounts of data and information in real time in a multi-criteria, creative manner, it may be possible to build a highly effective, multi-faceted, intelligent forecasting system for future financial and economic crises based on artificial intelligence technology, a system for forecasting complex, multi-faceted economic processes in such a way as to reduce the scale of the impact of the paradox of a self-fulfilling prophecy and increase the scale of the paradox of not allowing a predicted crisis to occur due to pre-emptive anti-crisis measures applied. In terms of multi-criteria processing of large data sets conducted with the involvement of artificial intelligence, Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4. 0 technologies, which make it possible to effectively and increasingly automatically operate on large sets of data and information, thus increasing the possibility of developing advanced, complex forecasting models for estimating current and future levels of systemic financial and economic risks, indebtedness of the state's public finance system, systemic credit risks of commercially operating financial institutions and economic entities, forecasting economic trends and predicting future financial and economic crises.
In view of the above, I address the following questions to the esteemed community of scientists and researchers:
Is it possible to build a highly effective, multi-faceted, intelligent forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies in a forecasting system for complex, multi-faceted economic processes in such a way as to reduce the scale of the impact of the paradox of the self-fulfilling prophecy and to increase the scale of the paradox of not allowing a forecasted crisis to occur due to pre-emptive anti-crisis measures applied?
What do you think about the involvement of artificial intelligence in combination with Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies to develop advanced, complex predictive models for estimating current and forward-looking levels of systemic financial risks, economic risks, debt of the state's public finance system, systemic credit risks of commercially operating financial institutions and economic entities, forecasting trends in economic developments and predicting future financial and economic crises?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
Dear Dariusz
when you faced with big data problems and you want to forecast or predict the future of your problem you have many choice and methods. But first you should decrease your data so you need dimensionality reduction methods like t-SNE, PCA, LSA, SDV, LDA which all of them categorized as unsupervised learning methods and then you should use neural networks to predict the future of your data sets therefor you should use supervise learning algorithms that contain regression layer in their structure some of these networks are LSTM, GRU, BiLSTN or if you use Python you have XGBoost network to predict you future data . note that it's not important what is your data may be bitcoin price or wind speed data or specific illness data .
you can do a search in google, github, etc to find practical problems such as yours with their scripts and Python or MATLAB codes
Best regards;
Did you solicit ChatGPT to write this for you?
I see a few problems with this approach. 1) We have well enough understanding of what causes bubbles (malinvestments due to central planning incentives), and that they eventually pop (corrections to malinvestments), and if big enough leads to a financial crisis (we dug a pretty big hole before correcting)... however, we're terrible at predicting when and what will be the catalyst that breaks the preverbal camel's back; therefore, if we lack this understanding to accurately predict with causal confidence, then dumb luck is required for us to somehow "train" a computer to "understand" the causes involved and for us to blindly follow. 2) If you could somehow predict financial crisis accurately enough, then you could use that to become extremely wealthy--by betting against the market. This is what happened for some who thought the housing market was a bubble bound to bust in the 2000s... they even made a movie about it (the big short).
Why settle for predicting financial crises, when you could get rid of them entirely? Really if you want to avoid depressions and financial crisis, then your best bet is to free up the economy from dumb regulations that incentivizes malinvestments (e.g., that impel banks to find ways for everyone to buy a home even if it makes no financial sense for them, and simultaneously backing the banks for making bad loans, or , incentivizing youngsters to go to college by providing easy to access loans increasing the demand, and price, for degrees that can't possibly return on the investment). If you address the root cause, then the desire for predicting non-existing crises will evaporate. In the US before the FED became the pseudo central bank, you had the natural boom (good investments) and bust (reallocation of small malinvestments) cycle of the market, and they were nowhere near the extremes you see today--the first ever modern depression came after the establishment of the FED (which was supposed to put a stop to this "bad" cycle not make it worse). The natural boom/bust cycle is a product of the fact that humans are not omniscient... we don't necessarily know every bad productive idea is bad before we try it. Someone needs to pay for those bad investments... back in the day it was those who invested in them (they also reaped the rewards if it ended up paying off)... today the cost of bad investments is collectively shared via gov bailouts. Food for thought.
It's very simple. If sanctions are imposed and gas pipelines explode, it disrupts logistics, disrupts the balance of supply and demand, leads to higher prices, inflation and provokes a crisis. Even the natural intelligence of any sane person is enough to understand this. But, unfortunately, it turned out that the leading politicians did not have enough natural intelligence to understand this. And if it is not enough, then artificial intelligence can no longer help (even if it operates with big data), as glasses cannot help a blind person.
In my opinion, in order to determine the question of the possibility of building a highly effective forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science analytics, Big Data Analytics, Business Intelligence and/or other Industry 4.0/5.0 technologies, it is first necessary to precisely define the essence of forecasting specific risk factors, i.e. factors that in the past were the sources of the occurrence of certain types of economic, financial and other crises and that may be such factors in the future. But will such a structured forecasting system based on a combination of Big Data Analytics and Artificial Intelligence be able to forecast events that appear as unusual, generating new types of risks, referred to as so-called "black swans", such as forecasting the appearance of another but generated by a difficult to predict new type of risk, an unusual event leading to the occurrence of another e.g. something similar to the 2008 global financial crisis, the 2020 pandemic, or something completely new that has not yet appeared.
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
I spent years, trying to forecast technology evolution. Today I conclude «The history of forecasting is the history of error».
Financial crises are made by people – most often knowledgeable, selfish and greedy people. Those people create new systems that realign existing revenue flows, benefiting them. Such creators are surrounded by supporters to protect the new system.
Humans or artificial intelligence can’t foresee people’s behaviour, actions and consequences of their actions. We can only punish the involved people's, after the damage is done. If we delegate responsibility to a computer program the supporters have a “scapegoat” and rational reason for the results.
To stop and refund the losers we would have to start with a law/regulation that lists creators and supporters of such an algorithm. The system beneficiaries would have to pay a tax to reimburse losers for the damage caused.
By the way, similar structures were also put in place for uranium miners.
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