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Despite extremely high rates, the population's lending activity is at a record level.

Despite extremely high rates, the population's lending activity is at a record level.

Over the past 12 months, the net increase in lending to individuals amounted to almost 7 trillion rubles (total issuance minus repayments), compared with a peak rate of 5 trillion by February 2022 and about 3.3 trillion in 2019.

Before the start of the monetary policy tightening cycle in August 2023, the annual growth rate of ruble lending was “only” 5 trillion.

In addition to the message about lending ( according to updated data (all data in rubles):

• Mortgage loans – 18.9 trillion volume of debt, growth for the year – 3.9 trillion, the maximum rate of increase in lending over 12 months was by January 24 – 4.2 trillion, the peak growth rate was previously by March 22 – 2.7 trillion.

• Consumer loans – 14.6 trillion, annual growth – 1.96 trillion, which is almost comparable to the historical maximum of credit activity for 12 months by January 22 at the level of 2 trillion.

• Car loans – 2 trillion, annual growth – 0.7 trillion, which is an absolute record, because before the SVO, the maximum growth rate was only 0.25 trillion per year.

• Other loans – 1.1 trillion, annual growth – 0.26 trillion, analysis of this segment is not of interest.

As you can see, the rate of increase in lending is breaking all records, and this is in an environment of high rates.

In the analysis, it is important to compare the three-year period before the coronavirus, the credit boom in 2021, the growth in lending in the year before the tightening (Aug. 22-Jul. 23) and from Aug. 23 to Apr. 24. Further comparison will be the rate of change in the amount of debt in annual terms (%) in the above sequence.

• Total lending – 18.1, 22.3, 18.3, 21.9%
• Mortgage loans – 20.1, 26.6, 22.8, 26.6%
• Consumer loans - 17, 19.4, 13, 13.2%
• Car loans – 15.4, 22.1, 18.9, 54.9%

The growth rate of loans has increased for all types of lending, comparing the two periods after the CBO, and is in the region of a credit boom, even in percentage terms, and for auto lending - unimaginable madness.

Cost of gold

Cost of gold
The Ministry of Finance proposes to introduce a surcharge to the mineral extraction tax on gold starting from 2025 in the amount of 10% of the excess of the world price above $1,900 per ounce.

Negative for gold mining stocks. Today the market is already declining by 3.8% 📉

🔥 The Ministry of Finance plans to increase the mineral extraction tax on iron ore from 4.8% to 6.7% from 2025.

🔥 The Ministry of Finance proposes to introduce a surcharge on the mineral extraction tax on coal in the amount of 10% of the excess price in the ports of the Far Eastern Federal District over the threshold value.

On the applied use of large language models...

On the applied use of large language models...

Straight to the point - is there any positive experience of integration into research projects in the field of economics and finance? No, not a single model is functional, nothing works.

There are two critical and so far unsolvable problems at the architectural level of the GII models themselves.

First, there is no built-in control for verification of output data and correctness of interpretation. In other words, GII is not capable of assessing the correctness and adequacy of the generated content; there is no built-in criterion of truth.

Modern GII models do not have critical thinking and a verifier of results, which in the context of LLM work means: identifying logical connections and contradictions, evaluating arguments and evidence, analyzing data and sources, adapting the output result to the context of conditions.

LLMs available today:

• They do not check the reliability of information sources and do not distinguish reliable data from unreliable ones.
• Are not able to independently identify logical errors or contradictions in their answers.
• Cannot critically evaluate the arguments and evidence presented.
• Cannot adequately adapt their responses to the specific conditions or context of the task.

LLMs are trained on extremely large amounts of data, the initial reliability of which is in doubt, and in this set of information garbage, data compression and weights are determined.

The data on which LLMs were trained initially may contain errors, bias and unreliable information, and therefore training is often based on false information.

In a sense, weights in large language models (LLMs) define the hierarchy of information interpretation, allowing the model to recognize hierarchical and contextual dependencies in the data. In other words, weights determine the degree of connectivity of information blocks, how one piece of information affects another piece of information.

What does this mean in practice? LLMs are extremely ineffective at developing innovative meaning constructs and interpreting initially contradictory information, producing complex multi-level assessments of factors, circumstances and dependencies.

GIIs can be effective in interpreting the generally accepted most popular facts of a regular nature, but not methods for building a hierarchy of priorities and a multi-level composition of risk factors in an ambiguous and unstructured set of data, the distribution vector of which is not predictable.  

Consequently, complex analytics of processes and events is not subject to GII, therefore, in GII there is no intelligence in the broad sense. This is a highly erudite system that is quite stupid in understanding the connections and dependencies of complex systems, and sociology, psychology, political science, economics are precisely those areas where there is no strict structuring of data and no unambiguity in interpretation.  

You can formalize mathematics or physics (here GII can achieve success in the next 3-5 years), but it is impossible to formalize the motives and actions of society, therefore GII cannot manage business processes, cannot predict and evaluate all those areas where a person is involved (finance , economics, sociology, politics, etc.).

What does this lead to? GIIs generate a huge amount of content, which is almost impossible to apply to applied problems due to lack of reliability.

Ideally, the system should work like a low-level program in a processor, where repeating experiments always gives the same result - there is unambiguity and predictability. The GII has too wide a range of tolerances.

As a result, the time and resources to verify the results of the GII work exceed any potential benefits. Simply put, GIIs are too mesmerizingly fake to be used in serious research and business processes.

Low reliability of the output content is built into the LLM architecture level, so the problem is not correctable either now or in the near future.  

The second problem is the lack of learning and the limited length of the context window. This topic requires a separate review.

Austausch von Yandex-Papieren: das Wichtigste

Austausch von Yandex-Papieren: das Wichtigste

Die Sammlung der Bewerbungen begann am 16. Mai. Bei allen Brokern erfolgt die Antragstellung entweder in der Web-/Mobilversion oder über den Support-Service.

❗️ Aber Vorsicht: Das Ende der Antragssammlung nach einer Beschwerde von Anlegern der Moskauer Börse und der St. Petersburger Börse wurde bis zum 21. Juni um 15:00 Uhr verlängert, einige Broker schließen die Antragssammlung jedoch früher ab. Finam ist beispielsweise der 19. Juni und Sber der 20. Juni.

Für diejenigen, die nicht an der St. Petersburger Börse oder an der Moskauer Börse gekauft haben:

1️⃣ Wenn Sie Yandex-Papiere vor dem 7. September 2022 einschließlich an russische Depots übertragen haben, werden Sie 1:1 umgetauscht. Bewerbungen werden jedoch bis zum 11. Juni gesammelt (es ist jedoch auch hier besser, sich bei den Maklern nach deren Fristen zu erkundigen). Gleichzeitig können Eigentümer von Yandex N.V.-Aktien, die unter den Stichtag 7. September 2022 fallen, auch an einem außerbörslichen Rückkauf teilnehmen – sie können Wertpapiere zu 1.251,8 Rubel pro Yandex N.V.-Aktie verkaufen.

2️⃣ Wenn Sie die Papiere vor dem 30. November 2023 einschließlich übertragen haben, können Sie eine außerbörsliche Rücknahme zu 1251,8 Rubel pro Aktie von Yandex N.V. beantragen. Die Einreichung ist bis zum 11. Juni möglich, Bewerbungen werden bis zum 17. Juni entgegengenommen. Seien Sie vorsichtig: Ab dem Zeitpunkt der Annahme (die vom Makler gemeldet werden muss) haben Sie 10 Arbeitstage Zeit, um die Wertpapiere auf das Depotkonto des geschlossenen Investmentfonds-Konsortiums zu übertragen. Erste". Das Geld muss innerhalb von 15 Werktagen überwiesen werden.

3️⃣ Wenn sich Ihre Papiere in einer ausländischen Infrastruktur befinden, müssen Sie auf Neuigkeiten von Yandex N.V. warten. Sie versprachen, die Wertpapiere bis Ende des Jahres zurückzukaufen, Preis und Zeitpunkt sind jedoch noch unbekannt.