Abstract: Periodicals are one of the most important sources of stock market history. By studying them, we can gain valuable information. from newspaper issues about the dynamics of stock prices, the agendas of shareholder meetings, and the balance sheets of commercial banks. Periodicals formed the information field of bidders, often setting the mood of the market. In this article, we would like to outline in detail how discussions about state regulation of the Russian Empire's stock market and securities speculation unfolded on the pages of one of the main stock market newspapers, Birzhevye Vedomosti, at the beginning of the 20th century. It seems productive to try to analyze which issues from the life of the stock market were of concern to readers in principle and, accordingly, were reflected in the texts of relevant newspapers. From the point of view of institutional analysis, it is extremely interesting to clarify the expectations of bidders from the regulatory authorities represented by the Ministry of Finance and exchange committees. In the presented article, the forms of displaying stock market life on the pages of the newspaper "Birzhevye Vedomosti" in the early 20th century were considered in detail. To summarize the results of the study briefly, it can be said unequivocally that the average bidder perceived the process of stock trading mainly through the prism of periodicals. Shareholders were informed about the results of the meetings through newspapers; the stock exchange chronicler briefly recounted the results of the trading day; reports to the State Duma on stock market regulation were published in the relevant columns. By studying the financial history at the micro level of individual stock market agents, we must strive to reliably restore the information field in which the bidders were. And as our analysis has shown, in any such study it is impossible to do without referring to the materials of the periodical press.
Periodicals represent one of the most significant sources for reconstructing the history of stock markets. Through their study, valuable insights can be gleaned regarding the dynamics of stock prices, the proceedings of shareholder meetings, and the financial statements of commercial banks. Periodicals played a pivotal role in shaping the informational landscape for market participants, often influencing the overall sentiment of the market. This article aims to provide a detailed examination of how discussions concerning state regulation of the Russian Empire’s stock market and securities speculation were articulated in one of the leading financial newspapers of the time, Birzhevye Vedomosti, during the early 20th century. It is particularly instructive to analyze which aspects of stock market activity captured the attention of readers and, consequently, were prominently featured in the pages of such newspapers.
From the perspective of institutional analysis, it is of considerable interest to explore the expectations of market participants regarding regulatory authorities, particularly the Ministry of Finance and exchange committees. This article delves into the ways in which the life of the stock market was portrayed in Birzhevye Vedomosti during the early 20th century. To summarize the findings briefly, it is evident that the average market participant primarily perceived the stock trading process through the lens of periodicals. Shareholders relied on newspapers to stay informed about the outcomes of meetings, while stock exchange chroniclers provided concise summaries of daily trading activities. Additionally, reports on stock market regulation submitted to the State Duma were published in dedicated columns.

Abstract: The key task of the presented article is to test how we can analyze the information potential of a historical sources collection by using thematic modeling. Some modern collections of digitized historical materials number tens of thousands of documents, and at the level of an individual researcher, it is difficult to cover available funds. Following a number of researchers, we suggest that thematic modeling can become a convenient tool for preliminary assessment of the content of a collection of historical documents; can become a tool for selecting only those documents that contain information relevant to the research tasks. In our case, the Birzhevye Vedomosti newspaper was chosen as one of the main collection of historical documents. At this stage, we can confirm that in our study, the use of topic modeling proved to be a productive solution for optimizing the process of searching for historical documents in a large collection of digitized historical materials. At the same time, it should be emphasized that in our work topic modeling was used exclusively as an applied tool for primary assessment of the information potential of a documents collection through the analysis of selected topics. Our experience has shown that, at least for Birzhevye Vedomosti, topic modeling with LDA does not allow us to draw conclusions from the standpoint of our content analysis methodology. The data of our models are too fragmentary, it can only be used for the initial assessment of the topics describing the information contained in the source.

Abstract: Our article is presenting an attempt to apply NLP methods to optimize the process of text recognition (in case of historical sources). Any researcher who decides to use scanned text recognition tools will face a number of limitations of the pipeline (sequence of recognition operations) accuracy. Even the most qualitatively trained models can give a significant error due to the unsatisfactory state of the source that has come down to us: cuts, bends, blots, erased letters - all these interfere with high-quality recognition. Our assumption is to use a predetermined set of words marking the presence of a study topic with Fuzzy sets module from the SpaCy to restore words that were recognized with mistakes. To check the quality of the text recovery procedure on a sample of 50 issues of the newspaper, we calculated estimates of the number of words that would not be included in the semantic analysis due to incorrect recognition. All metrics were also calculated using fuzzy set patterns. It turned out that approximately 119.6 words (mean for 50 issues) contain misprints associated with incorrect recognition. Using fuzzy set algorithms, we managed to restore these words and include them in semantic analysis.